Package: a4 Version: 1.28.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: 44fdfaae597a79d4d57e833b0d9d045a NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Automated Affymetrix Array Analysis Umbrella Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/a4_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4_1.28.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R Package: a4Base Version: 1.28.0 Depends: methods, graphics, grid, Biobase, AnnotationDbi, annaffy, mpm, genefilter, limma, multtest, glmnet, a4Preproc, a4Core, gplots Suggests: Cairo, ALL Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 209744deedd17825697d04ccc29bc1ab NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Automated Affymetrix Array Analysis biocViews: Microarray Author: Willem Talloen, Tobias Verbeke, Tine Casneuf, An De Bondt, Steven Osselaer and Hinrich Goehlmann, Willem Ligtenberg Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Base_1.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4Base_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.28.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: 61aaea981902fe48d3ca2ece929c34f9 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Automated Affymetrix Array Analysis Classification Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Classif_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/a4Classif_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4Classif_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.28.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 0b6866839406b27cf62416f409f17bc0 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Automated Affymetrix Array Analysis Core Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Core_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/a4Core_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4Core_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.28.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: 192fad8d08bab5f1a6679ef0645825a3 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Automated Affymetrix Array Analysis Preprocessing Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Preproc_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/a4Preproc_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4Preproc_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite Package: a4Reporting Version: 1.28.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: 2682b0cbca7358bff87e9fa9ac8cb71a NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Automated Affymetrix Array Analysis Reporting Package biocViews: Microarray Author: Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Reporting_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/a4Reporting_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/a4Reporting_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: ABAEnrichment Version: 1.10.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0), ABAData (>= 0.99.2), data.table (>= 1.10.4), grDevices, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: c081777c3e430049b87936f37d35ba0d NeedsCompilation: yes Title: Gene expression enrichment in human brain regions Description: The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function 'aba_enrich' integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project. 'aba_enrich' interfaces the ontology enrichment software FUNC to perform the statistical analyses. Additional functions provided in this package like 'get_expression' and 'plot_expression' facilitate exploring the expression data. From version 1.3.5 onwards genomic regions can be provided as input, too; and from version 1.5.9 onwards the function 'get_annotated_genes' offers an easy way to obtain annotations of genes to enriched or user-defined brain regions. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr source.ver: src/contrib/ABAEnrichment_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ABAEnrichment_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ABAEnrichment_1.10.0.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html vignetteTitles: Introduction to ABAEnrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R Package: ABarray Version: 1.48.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: aa30d344e1bf457e39ca92640e7463ad NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun source.ver: src/contrib/ABarray_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ABarray_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ABarray_1.48.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ABSSeq Version: 1.34.1 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: f6367d5cbc54980805fd0990de5fe9e5 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_7 git_last_commit: 0c3a251 git_last_commit_date: 2018-06-28 Date/Publication: 2018-06-28 source.ver: src/contrib/ABSSeq_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ABSSeq_1.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ABSSeq_1.34.1.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R Package: acde Version: 1.10.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: f6784e3bbb8257147e38f1bc8406bea9 NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta source.ver: src/contrib/acde_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/acde_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/acde_1.10.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R importsMe: coexnet Package: aCGH Version: 1.58.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, cluster, grDevices, graphics, methods, multtest, stats, survival, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: 0490fca04fb72d7b02fe8d1e9da25d41 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data. Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov source.ver: src/contrib/aCGH_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/aCGH_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/aCGH_1.58.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP Package: ACME Version: 2.36.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: b0be80fd78754c79796d40a43d48cdf7 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis source.ver: src/contrib/ACME_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ACME_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ACME_2.36.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo Package: ADaCGH2 Version: 2.20.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 3da422e0804cac497a23677fdf197f7e NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 source.ver: src/contrib/ADaCGH2_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ADaCGH2_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ADaCGH2_2.20.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R Package: adaptest Version: 1.0.0 Depends: R (>= 3.5.0) Imports: origami (>= 1.0.0), tmle, calibrate, methods, graphics, stats, utils, SummarizedExperiment Suggests: testthat, Matrix, SuperLearner, earth, gam, nnls, airway, rmarkdown, knitr, BiocStyle License: GPL-2 MD5sum: 38dde7dc608cef9c63e9a941e8bdf58c NeedsCompilation: no Title: Data-Adaptive Statistics for High-Dimensional Multiple Testing Description: Data-adaptive test statistics represent a general methodology for performing multiple hypothesis testing on effects sizes while maintaining honest statistical inference when operating in high-dimensional settings (). The utilities provided here extend the use of this general methodology to many common data analytic challenges that arise in modern computational and genomic biology. biocViews: Genetics, GeneExpression, DifferentialExpression, Sequencing, Microarray, Regression, DimensionReduction, MultipleComparison Author: Weixin Cai [aut, cre, cph], Nima Hejazi [aut], Alan Hubbard [ctb, ths] Maintainer: Weixin Cai URL: https://github.com/wilsoncai1992/adaptest VignetteBuilder: knitr BugReports: https://github.com/wilsoncai1992/adaptest/issues source.ver: src/contrib/adaptest_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/adaptest_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/adaptest_1.0.0.tgz vignettes: vignettes/adaptest/inst/doc/differentialExpression.html vignetteTitles: Data-Mining Biomarkers and High-Dimensional Testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adaptest/inst/doc/differentialExpression.R Package: adSplit Version: 1.50.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGG.db (>= 1.8.1), methods, multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: i386, x64 MD5sum: 4402f844024b657a3ca5ed7f349dafa4 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml source.ver: src/contrib/adSplit_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/adSplit_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/adSplit_1.50.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R Package: affxparser Version: 1.52.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.20.0), R.utils (>= 2.4.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: d351e4aca053be59c85324fc2f4ae94b NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues source.ver: src/contrib/affxparser_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affxparser_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affxparser_1.52.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, rMAT, Starr importsMe: affyILM, cn.farms, crossmeta, EventPointer, GeneRegionScan, ITALICS, oligo, rMAT suggestsMe: TIN Package: affy Version: 1.58.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocInstaller, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 67e196736c98e42bd7b87761a988629b NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry source.ver: src/contrib/affy_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affy_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affy_1.58.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, AffyExpress, affyPara, affypdnn, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, LMGene, logitT, maskBAD, MLP, panp, plw, prebs, qpcrNorm, ReadqPCR, RefPlus, rHVDM, Risa, RPA, SCAN.UPC, simpleaffy, sscore, Starr, webbioc importsMe: affycoretools, affyILM, affylmGUI, affyQCReport, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, coexnet, Cormotif, crossmeta, Doscheda, EGAD, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, LVSmiRNA, makecdfenv, mimager, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, STATegRa, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, ArrayExpress, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, paxtoolsr, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks Package: affycomp Version: 1.56.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 0eedb54b9f7f0f454364a60a13861862 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry source.ver: src/contrib/affycomp_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affycomp_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affycomp_1.56.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R Package: AffyCompatible Version: 1.40.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 0e57bb4dafbe054450ae51697bf655cd NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan source.ver: src/contrib/AffyCompatible_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AffyCompatible_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AffyCompatible_1.40.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R importsMe: IdMappingRetrieval Package: affyContam Version: 1.38.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: cb7f6391e50253a1c82eee785b677759 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey source.ver: src/contrib/affyContam_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyContam_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyContam_1.38.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R Package: affycoretools Version: 1.52.2 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 11f656b2d9329400d15fee90f42aec0e NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_7 git_last_commit: 2f98c74 git_last_commit_date: 2018-07-26 Date/Publication: 2018-07-26 source.ver: src/contrib/affycoretools_1.52.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/affycoretools_1.52.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affycoretools_1.52.2.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R Package: AffyExpress Version: 1.46.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: 82963bc767c3b78aad306a2c339ce18a NeedsCompilation: no Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li source.ver: src/contrib/AffyExpress_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AffyExpress_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AffyExpress_1.46.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyExpress/inst/doc/AffyExpress.R Package: affyILM Version: 1.32.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 70234e8c014809fa24a104e0add91e5e NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger source.ver: src/contrib/affyILM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyILM_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyILM_1.32.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R Package: affyio Version: 1.50.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 1a3c1b47b9a375b9265085d0cc31f0e3 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio source.ver: src/contrib/affyio_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyio_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyio_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods Package: affylmGUI Version: 1.54.1 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, BiocInstaller, R2HTML, xtable License: GPL (>=2) MD5sum: 57637a7497cf1f4d617db9722eafcdae NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Ken Simpson [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.54.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/affylmGUI_1.54.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affylmGUI_1.54.1.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R Package: affyPara Version: 1.40.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: 5052fcf40cca2f9aefcbdec8830842fd NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de source.ver: src/contrib/affyPara_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyPara_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyPara_1.40.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R Package: affypdnn Version: 1.54.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 98e9c5269b271dba0d1b664f3547f5db NeedsCompilation: no Title: Probe Dependent Nearest Neighbours (PDNN) for the affy package Description: The package contains functions to perform the PDNN method described by Li Zhang et al. biocViews: OneChannel, Microarray, Preprocessing Author: H. Bjorn Nielsen and Laurent Gautier (Many thanks to Li Zhang early communications about the existence of the PDNN program and related publications). Maintainer: Laurent Gautier source.ver: src/contrib/affypdnn_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affypdnn_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affypdnn_1.54.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affypdnn/inst/doc/affypdnn.R Package: affyPLM Version: 1.56.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: ea84311abb10e677d086e7f1421b9a62 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM source.ver: src/contrib/affyPLM_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyPLM_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyPLM_1.56.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: RefPlus importsMe: affylmGUI, affyQCReport, arrayQualityMetrics, mimager suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, piano Package: affyQCReport Version: 1.58.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) MD5sum: 320d1ef30adc84febfbff1f3eb6a88af NeedsCompilation: no Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman source.ver: src/contrib/affyQCReport_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/affyQCReport_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/affyQCReport_1.58.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyQCReport/inst/doc/affyQCReport.R suggestsMe: BiocCaseStudies Package: AffyRNADegradation Version: 1.26.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: c2f379ef1101d2a3ca4652660558fa78 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold source.ver: src/contrib/AffyRNADegradation_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AffyRNADegradation_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AffyRNADegradation_1.26.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R Package: AGDEX Version: 1.28.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 455f9f2bafcd6f1e5ea7cb7197a219b7 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao source.ver: src/contrib/AGDEX_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AGDEX_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AGDEX_1.28.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R Package: agilp Version: 3.12.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 50cde21010e256b3e5a75732867c8fe9 NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain source.ver: src/contrib/agilp_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/agilp_3.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/agilp_3.12.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R Package: AgiMicroRna Version: 2.30.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 48ec78057707ccf8162a36a15cf2a9f6 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero source.ver: src/contrib/AgiMicroRna_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AgiMicroRna_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AgiMicroRna_2.30.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R Package: AIMS Version: 1.12.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 8ce6bd49b2e164c992e3eb0f054d3fcf NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS source.ver: src/contrib/AIMS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AIMS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AIMS_1.12.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu Package: ALDEx2 Version: 1.12.0 Depends: methods, stats Imports: BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat License: file LICENSE MD5sum: 42e2efff5381373bf6f014adedc27771 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcox rank test or Welch t-test (via aldex.ttest), or a glm and Kruskal-Wallis test (via aldex.glm). Reports p-values and Benjamini-Hochberg corrected p-values. biocViews: DifferentialExpression, RNASeq, DNASeq, ChIPSeq, GeneExpression, Bayesian, Sequencing, Software, Microbiome, Metagenomics Author: Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx2 BugReports: https://github.com/ggloor/ALDEx2/issues source.ver: src/contrib/ALDEx2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ALDEx2_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ALDEx2_1.12.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: An R Package for determining differential abundance in high throughput sequencing experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R importsMe: omicplotR Package: AllelicImbalance Version: 1.18.0 Depends: R (>= 3.2.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.31.2), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 3c26f525bb7c5d095c8c12f83f26954a NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues source.ver: src/contrib/AllelicImbalance_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AllelicImbalance_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AllelicImbalance_1.18.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R Package: alpine Version: 1.6.0 Depends: R (>= 3.3) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 5ab187df7167595a669f93fbfadeed3f NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/alpine_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/alpine_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/alpine_1.6.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R Package: alsace Version: 1.16.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice, knitr License: GPL (>= 2) MD5sum: 6e0514b61de2e762d33a7dfab0caf031 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens URL: https://github.com/rwehrens/alsace VignetteBuilder: knitr source.ver: src/contrib/alsace_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/alsace_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/alsace_1.16.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: altcdfenvs Version: 2.42.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: f865185127b0ae27b15e14d4a91077ca NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/altcdfenvs_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/altcdfenvs_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/altcdfenvs_2.42.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, affy primer, affy primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight Package: AMOUNTAIN Version: 1.6.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 808bfe93af4bf8e1891126ea2874305b NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr source.ver: src/contrib/AMOUNTAIN_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AMOUNTAIN_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AMOUNTAIN_1.6.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA Package: amplican Version: 1.2.1 Depends: R (>= 3.4.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), data.table (>= 1.10.4-3) Imports: utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggforce (>= 0.1.2), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>= 1.2.7) Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 MD5sum: 602ad88a15aa3c927647b6f056b39caa NeedsCompilation: no Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: RELEASE_3_7 git_last_commit: 54d0eff git_last_commit_date: 2018-06-14 Date/Publication: 2018-06-15 source.ver: src/contrib/amplican_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/amplican_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/amplican_1.2.1.tgz vignettes: vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R Package: ampliQueso Version: 1.18.0 Depends: R (>= 2.15.0), rnaSeqMap (>= 2.17.1), knitr, rgl, ggplot2, gplots, parallel, doParallel, foreach, VariantAnnotation,genefilter,statmod,xtable Imports: edgeR, DESeq, samr License: GPL-2 MD5sum: 5bae03871c90b6fa058ce0d836368bfe NeedsCompilation: no Title: Analysis of amplicon enrichment panels Description: The package provides tools and reports for the analysis of amplicon sequencing panels, such as AmpliSeq biocViews: ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Visualization Author: Alicja Szabelska ; Marek Wiewiorka ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/ampliQueso_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ampliQueso_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ampliQueso_1.18.0.tgz vignettes: vignettes/ampliQueso/inst/doc/ampliQueso.pdf vignetteTitles: ampliQueso primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ampliQueso/inst/doc/ampliQueso.R Package: AnalysisPageServer Version: 1.14.0 Imports: methods, log4r, tools, rjson, Biobase, graph Suggests: RUnit, XML, knitr Enhances: Rook (>= 1.1), fork, FastRWeb, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: d4387a45b6f9c791042e180be215875b NeedsCompilation: yes Title: A framework for sharing interactive data and plots from R through the web Description: AnalysisPageServer is a modular system that enables sharing of customizable R analyses via the web. biocViews: GUI, Visualization, DataRepresentation Author: Brad Friedman , Adrian Nowicki, Hunter Whitney , Matthew Brauer Maintainer: Brad Friedman VignetteBuilder: knitr source.ver: src/contrib/AnalysisPageServer_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnalysisPageServer_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnalysisPageServer_1.14.0.tgz vignettes: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.html, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.html, vignettes/AnalysisPageServer/inst/doc/embedding.html, vignettes/AnalysisPageServer/inst/doc/ExampleServers.html, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.html, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.html, vignettes/AnalysisPageServer/inst/doc/Interactivity.html, vignettes/AnalysisPageServer/inst/doc/Licenses.html, vignettes/AnalysisPageServer/inst/doc/StaticContent.html, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.html vignetteTitles: 0. AnalysisPageServer, 6. Apache Deployment, 2. Embedding APS datasets in other documents, 4. Non-interactive servers and Rook Deployment, 7. FastRWeb Deployment, 5. Interactive Apps AnalysisPageServer, 3. AnalysisPageServer Interactivity, 8. Licenses, 1. Making Static Content Interactive with AnalysisPageServer, 8. Condition Trapping hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.R, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.R, vignettes/AnalysisPageServer/inst/doc/embedding.R, vignettes/AnalysisPageServer/inst/doc/ExampleServers.R, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.R, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.R, vignettes/AnalysisPageServer/inst/doc/Interactivity.R, vignettes/AnalysisPageServer/inst/doc/StaticContent.R, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.R Package: anamiR Version: 1.8.0 Depends: R (>= 3.3.3), SummarizedExperiment(>= 1.1.6) Imports: stats, DBI, limma, lumi, agricolae, RMySQL, DESeq2, SummarizedExperiment, gplots, gage, S4Vectors Suggests: knitr, rmarkdown, data.table License: GPL-2 MD5sum: 8ec5293178e5a4199420dadf794bb72a NeedsCompilation: no Title: An integrated analysis package of miRNA and mRNA expression data Description: This package is intended to identify potential interactions of miRNA-target gene interactions from miRNA and mRNA expression data. It contains functions for statistical test, databases of miRNA-target gene interaction and functional analysis. biocViews: Software, AssayDomain, GeneExpression, BiologicalQuestion, GeneSetEnrichment, GeneTarget, Normalization, Pathways, DifferentialExpression, GeneRegulation, ResearchField, Genetics, Technology, Microarray, Sequencing, miRNA, WorkflowStep Author: Ti-Tai Wang [aut, cre], Tzu-Pin Lu [aut], Chien-Yueh Lee[ctb,] Eric Y. Chuang [aut] Maintainer: Ti-Tai Wang URL: https://github.com/AllenTiTaiWang/anamiR VignetteBuilder: knitr BugReports: https://github.com/AllenTiTaiWang/anamiR/issues source.ver: src/contrib/anamiR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/anamiR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/anamiR_1.8.0.tgz vignettes: vignettes/anamiR/inst/doc/IntroductionToanamiR.html vignetteTitles: Introduction to anamiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anamiR/inst/doc/IntroductionToanamiR.R Package: Anaquin Version: 2.4.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 43f1cc066668c363a3fbe0d3bc227d4b NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues source.ver: src/contrib/Anaquin_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Anaquin_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Anaquin_2.4.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R Package: AneuFinder Version: 1.8.0 Depends: R (>= 3.3), GenomicRanges, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, ggplot2, reshape2, ggdendro, ggrepel, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: i386, x64 MD5sum: c839deca225b152430a14ee388d4a867 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr source.ver: src/contrib/AneuFinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AneuFinder_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AneuFinder_1.8.0.tgz vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R Package: ANF Version: 1.2.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: a0cb25cf2b6009e6c822df771f23135c NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr source.ver: src/contrib/ANF_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ANF_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ANF_1.2.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R Package: annaffy Version: 1.52.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: ebd0ad3a6c63bed0091a68cdb899861f NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith source.ver: src/contrib/annaffy_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/annaffy_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/annaffy_1.52.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: a4Base, a4Reporting, PGSEA, webbioc suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies Package: annmap Version: 1.22.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: 358fe42224c120dda792696585ca9b58 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk source.ver: src/contrib/annmap_1.22.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/annmap_1.22.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: annotate Version: 1.58.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), RCurl Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: b3f18e88745f98425dbe8c124d5ce96c NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/annotate_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/annotate_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/annotate_1.58.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useHomology.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, sampleClassifier, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DOQTL, DrugVsDisease, facopy, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, MGFR, PathwaySplice, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, GenomicRanges, GlobalAncova, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pcxn, RnBeads, siggenes, SummarizedExperiment Package: AnnotationDbi Version: 1.42.1 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.23.1), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25) Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: 573f9e52352b0bbb54f5e3f67a8bd154 NeedsCompilation: no Title: Annotation Database Interface Description: Provides user interface and database connection code for annotation data packages using SQLite data storage. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik source.ver: src/contrib/AnnotationDbi_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationDbi_1.42.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationDbi_1.42.1.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, DEXSeq, EGSEA, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, GSReg, miRNAtap, MLP, OrganismDbi, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, ASpli, beadarray, biomaRt, BioNet, biovizBase, bumphunter, CancerMutationAnalysis, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, crisprseekplus, CrispRVariants, crossmeta, csaw, debrowser, derfinder, DominoEffect, DOSE, dSimer, EDASeq, eegc, EnrichmentBrowser, enrichplot, ensembldb, erma, esATAC, ExpressionView, GA4GHshiny, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, geneXtendeR, GenVisR, GGBase, ggbio, GGtools, GlobalAncova, globaltest, GOfuncR, GOFunction, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gQTLstats, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, HTSanalyzeR, ideal, IMAS, InPAS, interactiveDisplay, iSEE, isomiRs, IVAS, lumi, mAPKL, MCbiclust, mdgsa, MeSHDbi, meshes, MetaboSignal, methyAnalysis, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, multiMiR, NanoStringQCPro, Onassis, ontoProc, Organism.dplyr, PADOG, PAnnBuilder, pathview, PathwaySplice, pcaExplorer, pcaGoPromoter, PCpheno, PGA, phenoTest, pwOmics, qpgraph, RCAS, ReactomePA, REDseq, restfulSE, rgsepd, rTRM, ScISI, scPipe, SGSeq, singleCellTK, SLGI, SMITE, SpidermiR, StarBioTrek, SVM2CRM, tenXplore, TFutils, tigre, trackViewer, trena, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, DEGreport, edgeR, esetVis, FELLA, FGNet, fgsea, GA4GHclient, gCrisprTools, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, limma, miRLAB, MmPalateMiRNA, oligo, piano, Pigengene, pRoloc, qcmetrics, R3CPET, recount, RGalaxy, sigPathway, SummarizedExperiment, wiggleplotr Package: AnnotationFilter Version: 1.4.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db License: Artistic-2.0 MD5sum: c71083e11c9b5f177795a55e4a418e8e NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Maintainer [cre] Maintainer: Bioconductor Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues source.ver: src/contrib/AnnotationFilter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationFilter_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationFilter_1.4.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, ensembldb, Organism.dplyr importsMe: biovizBase, ggbio, Pbase, TFutils, TVTB suggestsMe: TxRegInfra, wiggleplotr Package: AnnotationForge Version: 1.22.2 Depends: R (>= 2.7.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.16.0), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 148fd5a6ab5dfe86dc3ae2cc1e9bd28c NeedsCompilation: no Title: Code for Building Annotation Database Packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_7 git_last_commit: 8eafb16 git_last_commit_date: 2018-08-03 Date/Publication: 2018-08-03 source.ver: src/contrib/AnnotationForge_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationForge_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationForge_1.22.2.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats suggestsMe: AnnotationDbi, AnnotationHub Package: AnnotationFuncs Version: 1.30.0 Depends: R (>= 2.7.0), AnnotationDbi Imports: DBI Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: b803dc3df6c20663608825bc96285ccc NeedsCompilation: no Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions source.ver: src/contrib/AnnotationFuncs_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationFuncs_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationFuncs_1.30.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.R importsMe: bioCancer Package: AnnotationHub Version: 2.12.1 Depends: BiocGenerics (>= 0.15.10) Imports: utils, methods, grDevices, RSQLite, BiocInstaller, curl, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 4c03d9abbd7c46eef1298fd81b7a5474 NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Martin Morgan [cre], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_7 git_last_commit: 471407b git_last_commit_date: 2018-09-05 Date/Publication: 2018-09-05 source.ver: src/contrib/AnnotationHub_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationHub_2.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationHub_2.12.1.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/CreateAnAnnotationPackage.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, AnnotationHub: Creating An AnnotationHub Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/CreateAnAnnotationPackage.R dependsOnMe: AnnotationHubData, ExperimentHub, hipathia, MetaGxOvarian, ProteomicsAnnotationHubData, RefNet importsMe: annotatr, dmrseq, GenomicScores, GSEABenchmarkeR, gwascat, PathwaySplice, psichomics, pwOmics, REMP, restfulSE, scmeth, TSRchitect suggestsMe: Chicago, CINdex, clusterProfiler, DNAshapeR, dupRadar, ensembldb, epiNEM, epivizrData, GenomicRanges, GOSemSim, MIRA, MSnbase, OrganismDbi, Pbase, VariantAnnotation Package: AnnotationHubData Version: 1.10.3 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.9.14) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocInstaller, biocViews, AnnotationDbi, Biobase, Biostrings, DBI, GEOquery, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, rBiopaxParser, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, BiocInstaller License: Artistic-2.0 MD5sum: 5991922f4d6062f4e8758b969d5413d2 NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_7 git_last_commit: fbc995e git_last_commit_date: 2018-10-12 Date/Publication: 2018-10-12 source.ver: src/contrib/AnnotationHubData_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/AnnotationHubData_1.10.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AnnotationHubData_1.10.3.tgz vignettes: vignettes/AnnotationHubData/inst/doc/CreateAnAnnotationPackage.html, vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: AnnotationHub: Creating An AnnotationHub Package, Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHubData/inst/doc/CreateAnAnnotationPackage.R dependsOnMe: ExperimentHubData Package: annotationTools Version: 1.54.0 Imports: Biobase, stats License: GPL MD5sum: 7d58a787cbe2e42dbca48571d05e47fe NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases. Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/annotationTools_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/annotationTools_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/annotationTools_1.54.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: DOQTL Package: annotatr Version: 1.6.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.17.5), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: 9048f14f66becf2f7304849fc52ec8d7 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues source.ver: src/contrib/annotatr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/annotatr_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/annotatr_1.6.0.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth Package: anota Version: 1.28.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 45c910172e65a2cf6bab606c00f5ea70 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson source.ver: src/contrib/anota_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/anota_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/anota_1.28.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome Package: anota2seq Version: 1.2.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: 2b8b00f60d73f7c78a278f8a3bbce05e NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Julie Lorent VignetteBuilder: knitr source.ver: src/contrib/anota2seq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/anota2seq_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/anota2seq_1.2.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R Package: antiProfiles Version: 1.20.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: fb298e9f4e7f59d93b33df0aaf3c0bc6 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles source.ver: src/contrib/antiProfiles_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/antiProfiles_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/antiProfiles_1.20.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R Package: apComplex Version: 2.46.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 44fda1c1fc79c44c4ce842b7727dcc0d NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/apComplex_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/apComplex_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/apComplex_2.46.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependsOnMe: ScISI suggestsMe: BiocCaseStudies Package: apeglm Version: 1.2.1 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: d402d9fefc5211473c27fe647cf64c0d NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu, Joseph G. Ibrahim, Michael I. Love Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_7 git_last_commit: b492032 git_last_commit_date: 2018-08-01 Date/Publication: 2018-08-01 source.ver: src/contrib/apeglm_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/apeglm_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/apeglm_1.2.1.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R suggestsMe: DESeq2 Package: aroma.light Version: 3.10.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.21.0), R.utils (>= 2.6.0), matrixStats (>= 0.52.2) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: ca3fee53f034a8819d9da08e856db4c4 NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, http://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues source.ver: src/contrib/aroma.light_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/aroma.light_3.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/aroma.light_3.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone suggestsMe: scran, TIN Package: ArrayExpress Version: 1.40.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: c6572fd09a1301fbccbf9805b9df489d NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Ugis Sarkans source.ver: src/contrib/ArrayExpress_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ArrayExpress_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ArrayExpress_1.40.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: gCMAPWeb Package: ArrayExpressHTS Version: 1.30.0 Depends: sampling, Rsamtools (>= 1.19.36), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rsamtools License: Artistic License 2.0 MD5sum: db5a857e79642e7f0dc84c84fc2bd4f3 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov source.ver: src/contrib/ArrayExpressHTS_1.30.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ArrayExpressHTS_1.30.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R Package: arrayMvout Version: 1.38.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy, lumi Imports: simpleaffy, mdqc, affyContam, Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: c8e06458b35bc306ed1b0dc7c65447fb NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey source.ver: src/contrib/arrayMvout_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/arrayMvout_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/arrayMvout_1.38.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R Package: arrayQuality Version: 1.58.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: e419c3b429ea8cdb4e8aafffdc5276a0 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ source.ver: src/contrib/arrayQuality_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/arrayQuality_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/arrayQuality_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.36.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, Cairo (>= 1.4-6), genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 723064b8c71e08bbd99ff7e0f34098a2 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues source.ver: src/contrib/arrayQualityMetrics_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/arrayQualityMetrics_3.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/arrayQualityMetrics_3.36.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R importsMe: EGAD Package: ArrayTools Version: 1.40.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) MD5sum: c8c36de3208d24ab983f71413deba6f6 NeedsCompilation: no Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li source.ver: src/contrib/ArrayTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ArrayTools_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ArrayTools_1.40.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTools/inst/doc/ArrayTools.R Package: ArrayTV Version: 1.18.0 Depends: R (>= 2.14) Imports: methods, foreach, S4Vectors (>= 0.9.25), IRanges (>= 2.13.24), DNAcopy, oligoClasses (>= 1.21.3) Suggests: RColorBrewer, crlmm, ff, BSgenome.Hsapiens.UCSC.hg18,BSgenome.Hsapiens.UCSC.hg19, lattice, latticeExtra, RUnit, BiocGenerics Enhances: doMC, doSNOW, doParallel License: GPL (>= 2) MD5sum: cb0b5820fce7b929b54ab413f0f1a51f NeedsCompilation: no Title: Implementation of wave correction for arrays Description: Wave correction for genotyping and copy number arrays biocViews: CopyNumberVariation Author: Eitan Halper-Stromberg Maintainer: Eitan Halper-Stromberg source.ver: src/contrib/ArrayTV_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ArrayTV_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ArrayTV_1.18.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTV/inst/doc/ArrayTV.R suggestsMe: VanillaICE Package: ARRmNormalization Version: 1.20.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 730a84b9e596fe76180a6bb37dba1c9d NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin source.ver: src/contrib/ARRmNormalization_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ARRmNormalization_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ARRmNormalization_1.20.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R Package: ASAFE Version: 1.6.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 596959f51c2731d304f2cc7f931733f8 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr source.ver: src/contrib/ASAFE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASAFE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASAFE_1.6.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R Package: ASEB Version: 1.24.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 41bcce165076fdd1c5dd51ba7b4fde51 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang source.ver: src/contrib/ASEB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASEB_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASEB_1.24.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R Package: ASGSCA Version: 1.14.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: e100b14fb14767a8788f6ca12da24124 NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani source.ver: src/contrib/ASGSCA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASGSCA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASGSCA_1.14.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R Package: ASICS Version: 1.0.1 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, grDevices, gridExtra, methods, plyr, quadprog, ropls, speaq, stats, SummarizedExperiment, utils, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 1469615965b67d0bc5fa2f939a3d9196 NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_7 git_last_commit: 4a113d6 git_last_commit_date: 2018-08-21 Date/Publication: 2018-08-21 source.ver: src/contrib/ASICS_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASICS_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASICS_1.0.1.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R Package: ASpli Version: 1.6.0 Depends: methods, grDevices, stats, utils, parallel, edgeR Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, AnnotationDbi, Rsamtools, BiocStyle License: GPL MD5sum: e61b5e3ee971a8cdc2dc5dac023fb905 NeedsCompilation: no Title: Analysis of alternative splicing using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini source.ver: src/contrib/ASpli_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASpli_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASpli_1.6.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: Analysis of alternative splicing using ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R Package: ASSET Version: 1.98.0 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 585cec88d13642f8ff509d35ee915212 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: Samsiddhi Bhattacharjee source.ver: src/contrib/ASSET_1.98.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASSET_1.98.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASSET_1.98.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R Package: ASSIGN Version: 1.16.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2 Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 532ae563715a264fd3578f9df1d0169e NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman VignetteBuilder: knitr source.ver: src/contrib/ASSIGN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ASSIGN_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ASSIGN_1.16.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R Package: ATACseqQC Version: 1.4.3 Depends: R (>= 3.4), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat License: GPL (>= 2) MD5sum: a5acf5256e3682d373a95cf47aaea76b NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning Author: Jianhong Ou, Haibo Liu, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_7 git_last_commit: 29b48ac git_last_commit_date: 2018-09-27 Date/Publication: 2018-09-27 source.ver: src/contrib/ATACseqQC_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/ATACseqQC_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ATACseqQC_1.4.3.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R Package: attract Version: 1.32.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: 733846bc959f934bee90503a6fdb764d NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman source.ver: src/contrib/attract_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/attract_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/attract_1.32.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R Package: AUCell Version: 1.2.4 Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, utils Suggests: Biobase, BiocStyle, devtools, DT, GEOquery, knitr, NMF, plotly, R2HTML, rbokeh, rmarkdown, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: ccee2ed38da1e1232b21efce9694169e NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_7 git_last_commit: be604c8 git_last_commit_date: 2018-06-15 Date/Publication: 2018-06-15 source.ver: src/contrib/AUCell_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/AUCell_1.2.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/AUCell_1.2.4.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R importsMe: RcisTarget Package: BaalChIP Version: 1.6.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 0db1fb4f76057d76e1e0d43a10b4307b NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr source.ver: src/contrib/BaalChIP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BaalChIP_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BaalChIP_1.6.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R Package: BAC Version: 1.40.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 8d8c7c6f3d8f8ef6e052d917bfc185cc NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/BAC_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BAC_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BAC_1.40.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R Package: bacon Version: 1.8.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 8510614dfad1fc7ea0f42b2e94ffe5b0 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr source.ver: src/contrib/bacon_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bacon_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bacon_1.8.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R Package: BADER Version: 1.18.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: d99949a043af6d8a326e66f2d4965b7b NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker source.ver: src/contrib/BADER_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BADER_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BADER_1.18.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R Package: BadRegionFinder Version: 1.8.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 79d609a64c6724b591245e3c5ebc7154 NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann source.ver: src/contrib/BadRegionFinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BadRegionFinder_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BadRegionFinder_1.8.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R Package: BAGS Version: 2.20.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: b9e40469e8606d5856cbc2ed0b9e236a NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate source.ver: src/contrib/BAGS_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BAGS_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BAGS_2.20.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R Package: ballgown Version: 2.12.0 Depends: R (>= 3.1.1), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: fbc59268a116576a01c64cc480c2aca9 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues source.ver: src/contrib/ballgown_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ballgown_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ballgown_2.12.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R suggestsMe: polyester, variancePartition Package: bamsignals Version: 1.12.1 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.12.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 3001e892b1c0ead3d7cc11a110780060 NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Alessandro Mammana VignetteBuilder: knitr source.ver: src/contrib/bamsignals_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/bamsignals_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bamsignals_1.12.1.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, chromstaR, normr Package: banocc Version: 1.4.0 Depends: R (>= 3.4), rstan (>= 2.10.1) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat License: MIT + file LICENSE MD5sum: bfa443482d5ceba4c6cfdd7f5535e2a1 NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre] Maintainer: Emma Schwager VignetteBuilder: knitr source.ver: src/contrib/banocc_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/banocc_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/banocc_1.4.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R Package: basecallQC Version: 1.4.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: 0c7cce8ff6a224835363bbaa085b46f3 NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr source.ver: src/contrib/basecallQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/basecallQC_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/basecallQC_1.4.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R Package: BaseSpaceR Version: 1.24.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 63157fcf8e3a1e2205bac8dddfa2b141 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell source.ver: src/contrib/BaseSpaceR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BaseSpaceR_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BaseSpaceR_1.24.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R Package: Basic4Cseq Version: 1.16.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 375a03834d84bc00e88f490f3c3a1f80 NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Basic4Cseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Basic4Cseq_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Basic4Cseq_1.16.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R Package: BASiCS Version: 1.2.1 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, methods, coda, data.table, ggplot2, graphics, grDevices, KernSmooth, MASS, matrixStats, Rcpp (>= 0.11.3), S4Vectors, scran, stats, SummarizedExperiment, testthat, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 79c5796d2e08baa3efb158a90b0e806d NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology Author: Catalina Vallejos [aut, cre], Nils Eling [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina A. Vallejos , Nils Eling URL: https://github.com/catavallejos/BASiCS VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues source.ver: src/contrib/BASiCS_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/BASiCS_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BASiCS_1.2.1.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R suggestsMe: splatter Package: BasicSTARRseq Version: 1.8.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 5aef3b1b662619b0cc5ad8d751b6e64b NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr source.ver: src/contrib/BasicSTARRseq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BasicSTARRseq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BasicSTARRseq_1.8.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R Package: BatchQC Version: 1.8.1 Depends: R (>= 3.3.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, d3heatmap, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: f45e1f9fb1c13cba299dc3c4a8a1107a NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_7 git_last_commit: cf39f9b git_last_commit_date: 2018-09-22 Date/Publication: 2018-09-23 source.ver: src/contrib/BatchQC_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/BatchQC_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BatchQC_1.8.1.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R Package: BayesKnockdown Version: 1.6.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 0426d4f9ada4d09b09c8297957280168 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young source.ver: src/contrib/BayesKnockdown_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BayesKnockdown_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BayesKnockdown_1.6.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R Package: BayesPeak Version: 1.32.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 636c4e69cbf11653dd98f67a7fb14135 NeedsCompilation: yes Title: Bayesian Analysis of ChIP-seq Data Description: This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data. biocViews: ChIPSeq Author: Christiana Spyrou, Jonathan Cairns, Rory Stark, Andy Lynch, Simon Tavar\\'{e}, Maintainer: Jonathan Cairns source.ver: src/contrib/BayesPeak_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BayesPeak_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BayesPeak_1.32.0.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesPeak/inst/doc/BayesPeak.R Package: baySeq Version: 2.14.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 42035558007917b3c5de18c11aeea330 NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/baySeq_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/baySeq_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/baySeq_2.14.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC importsMe: debrowser, EDDA, metaseqR, riboSeqR suggestsMe: compcodeR Package: BBCAnalyzer Version: 1.10.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 80f4916eb531eb9b1b2e5d74f1958325 NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann source.ver: src/contrib/BBCAnalyzer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BBCAnalyzer_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BBCAnalyzer_1.10.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R Package: BCRANK Version: 1.42.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: a4f7993c53111aeac55188300b0bc95a NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur source.ver: src/contrib/BCRANK_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BCRANK_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BCRANK_1.42.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R Package: bcSeq Version: 1.2.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 6b461ca0e96b8a60422549327c075e33 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org source.ver: src/contrib/bcSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bcSeq_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bcSeq_1.2.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R Package: beachmat Version: 1.2.1 Depends: R (>= 3.5) Imports: utils, methods, Rhdf5lib (>= 1.1.4), rhdf5, HDF5Array (>= 1.7.3), DelayedArray (>= 0.5.30), Rcpp (>= 0.12.14) LinkingTo: Rcpp, Rhdf5lib Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, devtools License: GPL-3 Archs: i386, x64 MD5sum: 75743ae51fcd0bcfe78a77ae37ddcc19 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for a variety of commonly used matrix types, including sparse and HDF5-backed matrices. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/beachmat_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/beachmat_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/beachmat_1.2.1.tgz vignettes: vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/miscellaneous.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: Using beachmat to read data from R matrices in C++, Linking to beachmat with C++ code from another package, Using beachmat's helper functions in R, Using beachmat to write data into R matrix objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/miscellaneous.R, vignettes/beachmat/inst/doc/output.R suggestsMe: DropletUtils, scater, scran linksToMe: DropletUtils, scater, scran Package: beadarray Version: 2.30.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, ggplot2 Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: GPL-2 Archs: i386, x64 MD5sum: 9f60efa8b3ed353bf84877d6a2782826 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr source.ver: src/contrib/beadarray_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/beadarray_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/beadarray_2.30.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi Package: beadarraySNP Version: 1.46.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: eab8bda2f811517420a4eae53f07385c NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting source.ver: src/contrib/beadarraySNP_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/beadarraySNP_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/beadarraySNP_1.46.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R Package: BeadDataPackR Version: 1.32.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 53b2c87d360a3979d5442021c33005d6 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/BeadDataPackR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BeadDataPackR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BeadDataPackR_1.32.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray Package: BEARscc Version: 1.0.0 Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: 9a2f9fb18d412aab8c21972b8a3a5134 NeedsCompilation: no Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr source.ver: src/contrib/BEARscc_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BEARscc_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BEARscc_1.0.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R Package: BEAT Version: 1.18.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: b5af15ea71b096c89eda79624c0d6a69 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman source.ver: src/contrib/BEAT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BEAT_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BEAT_1.18.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R Package: BEclear Version: 1.12.1 Depends: snowfall, Matrix License: GPL-2 MD5sum: 5e07f79aabfdf3d39bcc25c698071265 NeedsCompilation: no Title: Correct for batch effects in DNA methylation data Description: Provides some functions to detect and correct for batch effects in DNA methylation data. The core function "BEclear" is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software Author: Markus Merl, Ruslan Akulenko Maintainer: David Rasp git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_7 git_last_commit: 4ec4f42 git_last_commit_date: 2018-09-26 Date/Publication: 2018-09-26 source.ver: src/contrib/BEclear_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/BEclear_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BEclear_1.12.1.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.pdf vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEclear/inst/doc/BEclear.R Package: bgafun Version: 1.42.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: 1980cf97099dae5c8776137a7113cbe7 NeedsCompilation: no Title: BGAfun A method to identify specifity determining residues in protein families Description: A method to identify specifity determining residues in protein families using Between Group Analysis biocViews: Classification Author: Iain Wallace Maintainer: Iain Wallace source.ver: src/contrib/bgafun_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bgafun_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bgafun_1.42.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgafun/inst/doc/bgafun.R Package: BgeeDB Version: 2.6.2 Depends: R (>= 3.3.0), topGO, tidyr Imports: data.table, RCurl, digest, methods, stats, utils, dplyr, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown License: GPL-3 MD5sum: ee9ad351e62f8e26b121149718025211 NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_7 git_last_commit: c61de70 git_last_commit_date: 2018-06-15 Date/Publication: 2018-06-15 source.ver: src/contrib/BgeeDB_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/BgeeDB_2.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BgeeDB_2.6.2.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: psygenet2r, RITAN Package: BGmix Version: 1.40.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 2fbb40431a0a731d61b24ab97f99f237 NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin source.ver: src/contrib/BGmix_1.40.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BGmix_1.40.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R Package: bgx Version: 1.46.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: 3d9d4a7388ad724454966a5105153e92 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro source.ver: src/contrib/bgx_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bgx_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bgx_1.46.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R Package: BHC Version: 1.32.0 License: GPL-3 Archs: i386, x64 MD5sum: 95935f555a086a09e461d6d3c081eef7 NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage source.ver: src/contrib/BHC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BHC_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BHC_1.32.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R Package: BicARE Version: 1.38.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: f01f0967bb54a254c0bdf2055d44c56c NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr source.ver: src/contrib/BicARE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BicARE_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BicARE_1.38.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R Package: BiFET Version: 1.0.1 Imports: stats, poibin, GenomicRanges Suggests: testthat, knitr License: GPL-3 MD5sum: 22f693b3085d7cfbd89e5f6aec09233d NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_7 git_last_commit: b1cb4c5 git_last_commit_date: 2018-07-14 Date/Publication: 2018-07-15 source.ver: src/contrib/BiFET_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiFET_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiFET_1.0.1.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R Package: BiGGR Version: 1.16.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: bd946febe83d5d0e06a0bf331bead331 NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway, Network,GraphAndNetwork,Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ source.ver: src/contrib/BiGGR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiGGR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiGGR_1.16.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R Package: bigmelon Version: 1.6.0 Depends: R (>= 3.3), wateRmelon (>= 1.19.1), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics Suggests: BiocGenerics, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 License: GPL-3 MD5sum: 0050dcc6a7500c4df3c519b683e18ef4 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone source.ver: src/contrib/bigmelon_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bigmelon_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bigmelon_1.6.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R Package: bigmemoryExtras Version: 1.28.0 Depends: R (>= 2.12), bigmemory (>= 4.5.31) Imports: methods Suggests: testthat, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: 9e640a6c84898551efa486f1b13c0345 NeedsCompilation: no Title: An extension of the bigmemory package with added safety, convenience, and a factor class Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras VignetteBuilder: knitr source.ver: src/contrib/bigmemoryExtras_1.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bigmemoryExtras_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: gCMAP Package: bioassayR Version: 1.18.0 Depends: R (>= 3.1.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: da8c67f9698c0960a66bde9aaa2af280 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Tyler Backman URL: https://github.com/TylerBackman/bioassayR VignetteBuilder: knitr BugReports: https://github.com/TylerBackman/bioassayR/issues source.ver: src/contrib/bioassayR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bioassayR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bioassayR_1.18.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R Package: Biobase Version: 2.40.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: 8498549b10d94dbf6874d8137d7918a7 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Biobase_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Biobase_2.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Biobase_2.40.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: a4Base, a4Core, ACME, affy, affycomp, affyContam, affycoretools, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, ArrayTools, BAGS, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BiocCaseStudies, BioMVCClass, BioQC, birta, BLMA, BrainStars, CAMERA, cancerclass, Cardinal, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, charm, chimera, chroGPS, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DESeq, DEXSeq, DFP, diggit, doppelgangR, DSS, dualKS, dyebias, EBarrays, EDASeq, edge, EGSEA, eisa, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, gCMAPWeb, GeneAnswers, GeneExpressionSignature, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, GeneSelector, geNetClassifier, GEOquery, GOexpress, GOFunction, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, htSeqTools, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, IVAS, LMGene, lumi, macat, mAPKL, massiR, MEAL, MergeMaid, metagenomeFeatures, metagenomeSeq, MetaGxOvarian, metavizr, MethPed, methyAnalysis, methylumi, Mfuzz, MiChip, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, MmPalateMiRNA, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NOISeq, nondetects, normalize450K, NormqPCR, oligo, omicRexposome, OrderedList, OTUbase, OutlierD, pandaR, PAnnBuilder, panp, pcaMethods, pcot2, pdInfoBuilder, pepStat, PGSEA, phenoTest, PLPE, plrs, prada, PREDA, pRolocGUI, PROMISE, qpcrNorm, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, reb, RefPlus, rexposome, rHVDM, Ringo, Risa, Rmagpie, rMAT, RNAinteract, rnaSeqMap, Rnits, Roleswitch, RpsiXML, RTopper, RUVSeq, safe, SCAN.UPC, scater, SeqGSEA, sigaR, SigCheck, siggenes, simpleaffy, simulatorZ, singleCellTK, SpeCond, SPEM, spkTools, splicegear, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, variancePartition, VegaMC, viper, vsn, wateRmelon, waveTiling, webbioc, xcms, XDE, yarn importsMe: ABarray, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, AnalysisPageServer, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, ArrayTools, attract, ballgown, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, BioSeqClass, biosigner, biosvd, birte, BiSeq, blima, BrainStars, bsseq, BubbleTree, CAFE, canceR, CATALYST, CellScore, CGHnormaliter, charm, ChIPpeakAnno, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, clipper, cn.mops, coexnet, cogena, ConsensusClusterPlus, consensusOV, crlmm, crossmeta, cummeRbund, cycle, cydar, cytofkit, CytoML, ddCt, DEGreport, DESeq2, destiny, diffloop, discordant, DOQTL, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, FindMyFriends, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowStats, flowType, flowUtils, flowViz, flowWorkspace, FourCSeq, frma, frmaTools, FunciSNP, gCMAP, gCrisprTools, gcrma, genbankr, geneClassifiers, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GeneSelectMMD, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GEOsubmission, gep2pep, gespeR, GGBase, ggbio, GGtools, girafe, GISPA, globaltest, gmapR, GOFunction, GoogleGenomics, gQTLstats, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, IdMappingAnalysis, imageHTS, ImmuneSpaceR, ImpulseDE2, IsoGeneGUI, isomiRs, iterClust, JunctionSeq, kimod, kissDE, lapmix, LINC, LiquidAssociation, LVSmiRNA, maanova, makecdfenv, maSigPro, MAST, mBPCR, MCRestimate, MeSHDbi, metaArray, methyAnalysis, MethylAid, methylumi, mfa, MiChip, MIGSA, minfi, MinimumDistance, MiPP, MIRA, MLSeq, MmPalateMiRNA, mogsa, MoonlightR, MoPS, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, npGSEA, nucleR, OGSA, oligoClasses, ontoProc, oposSOM, oppar, OrderedList, OrganismDbi, PAnnBuilder, panp, PathwaySplice, Pbase, pbcmc, PCpheno, phantasus, PharmacoGx, phyloseq, piano, plateCore, plethy, plgem, plier, podkat, POST, PowerExplorer, ppiStats, prada, prebs, proFIA, progeny, pRoloc, PROMISE, PROPS, ProteomicsAnnotationHubData, PSEA, psygenet2r, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, qpgraph, quantro, QuasR, qusage, randPack, readat, ReadqPCR, RGalaxy, RIVER, Rmagpie, rMAT, rols, ropls, ROTS, rqubic, RTCGAToolbox, Rtreemix, RUVnormalize, SAGx, scmap, SeqVarTools, ShortRead, sigsquared, SimBindProfiles, simpleaffy, singscore, SLGI, SNPchip, SomaticSignatures, spkTools, splicegear, SPONGE, STATegRa, subSeq, synapter, TEQC, TFBSTools, TFutils, timecourse, TMixClust, TnT, topdownr, TSSi, TTMap, twilight, uSORT, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, XBSeq suggestsMe: AUCell, BiocCaseStudies, BiocCheck, BiocGenerics, BSgenome, CellMapper, cellTree, clustComp, coseq, CountClust, DAPAR, DART, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GlobalAncova, GSAR, Heatplus, interactiveDisplay, kebabs, les, limma, Logolas, mCSEA, messina, msa, multiClust, nem, OSAT, pkgDepTools, RcisTarget, ROC, RTCGA, scmeth, scran, SeqArray, stageR, survcomp, TargetScore, TCGAbiolinks, tkWidgets, TypeInfo, vbmp, widgetTools Package: biobroom Version: 1.12.1 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, SummarizedExperiment License: LGPL MD5sum: a9db4978f6125e6eb8612cc416df1fc8 NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_7 git_last_commit: 5a2a7a8 git_last_commit_date: 2018-08-09 Date/Publication: 2018-08-09 source.ver: src/contrib/biobroom_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/biobroom_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biobroom_1.12.1.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP Package: bioCancer Version: 1.8.0 Depends: R (>= 3.3.0), radiant.data (>= 0.8.1), cgdsr(>= 1.2.6), XML(>= 3.98) Imports: DT (>= 0.2), dplyr (>= 0.7.2), shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, shinythemes, Biobase, geNetClassifier, AnnotationFuncs, org.Hs.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(>= 0.7), visNetwork, htmlwidgets, plyr, tibble Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: acfa059e05c3126f381c1e72a11ee09d NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues source.ver: src/contrib/bioCancer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bioCancer_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bioCancer_1.8.0.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R Package: BiocCaseStudies Version: 1.42.0 Depends: tools, methods, utils, Biobase Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 MD5sum: a15dd5886dd96725e5dc046f10085585 NeedsCompilation: no Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocCaseStudies_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocCaseStudies_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocCaseStudies_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.16.0 Depends: R (>= 3.5.0) Imports: biocViews (>= 1.33.7), BiocInstaller, stringdist, graph, httr, tools, optparse, codetools, methods, utils Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, knitr, devtools (>= 1.4.1) Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 55dfbb31e66309931b2a6f8326d814a3 NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre], Daniel von Twisk [ctb], Lori Shepherd [ctb], Kevin Rue [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr source.ver: src/contrib/BiocCheck_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocCheck_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocCheck_1.16.0.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: ExperimentHubData suggestsMe: CNPBayes Package: BiocFileCache Version: 1.4.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: 1435fa6fbe5d878b6713dbf75cb45a2a NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues source.ver: src/contrib/BiocFileCache_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocFileCache_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocFileCache_1.4.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R importsMe: cbaf, CTDquerier, EnrichmentBrowser, GSEABenchmarkeR, IMMAN, Organism.dplyr, UniProt.ws suggestsMe: BiocOncoTK, progeny, seqsetvis Package: BiocGenerics Version: 0.26.0 Depends: methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, Rsamtools, AnnotationDbi, oligoClasses, oligo, affyPLM, flowClust, affy, DESeq2, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 92d87d4395138bfaf3d420a02e8a4d62 NeedsCompilation: no Title: S4 generic functions for Bioconductor Description: S4 generic functions needed by many Bioconductor packages. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocGenerics_0.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocGenerics_0.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocGenerics_0.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cleanUpdTSeq, codelink, consensusSeekeR, copynumber, CRISPRseek, cummeRbund, DelayedArray, DESeq, dexus, ensembldb, ensemblVEP, ExperimentHub, ExperimentHubData, flowQ, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, Genominator, genoset, ggbio, girafe, graph, GSEABase, GUIDEseq, HelloRanges, htSeqTools, interactiveDisplay, interactiveDisplayBase, IRanges, MBASED, MeSHDbi, meshr, methyAnalysis, MIGSA, MineICA, minfi, MLInterfaces, MotifDb, MotIV, mpra, MSnbase, multtest, NADfinder, oligo, OrganismDbi, Pbase, PICS, plethy, plyranges, PSICQUIC, PWMEnrich, RareVariantVis, REDseq, Repitools, rMAT, RNAprobR, RnBeads, RPA, rsbml, S4Vectors, scsR, shinyMethyl, ShortRead, simpleaffy, simulatorZ, soGGi, SummarizedBenchmark, TEQC, tigre, topdownr, topGO, UNDO, UniProt.ws, VanillaICE, VariantAnnotation, VariantFiltering, XVector, yamss importsMe: affycoretools, affylmGUI, AllelicImbalance, annmap, annotate, AnnotationHubData, ArrayExpressHTS, ASpli, bamsignals, BASiCS, bigmelon, biocGraph, biosvd, biovizBase, BiSeq, blima, BrowserViz, BrowserVizDemo, BSgenome, BubbleTree, bumphunter, CAGEfightR, CAGEr, casper, ccfindR, cellHTS2, cghMCR, ChemmineOB, ChemmineR, ChIC, chipenrich, ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, ChIPSeqSpike, chromVAR, clusterSeq, cn.mops, CNEr, CNPBayes, cobindR, compEpiTools, crlmm, crossmeta, cummeRbund, dada2, ddCt, DEGreport, derfinder, DEScan2, DESeq2, destiny, DEXSeq, diffcoexp, diffHic, DirichletMultinomial, DOQTL, DRIMSeq, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisa, epigenomix, epivizrStandalone, erma, esATAC, FamAgg, fastseg, ffpe, FindMyFriends, flowBin, flowClust, flowCore, flowFP, flowQ, FlowSOM, flowStats, flowWorkspace, fmcsR, frma, FunciSNP, GA4GHclient, GA4GHshiny, gcapc, gCMAPWeb, genbankr, geneAttribution, geneClassifiers, GENESIS, GenomicAlignments, GenomicInteractions, GenomicTuples, genotypeeval, GenVisR, GGBase, GGtools, gmapR, GOTHiC, gQTLBase, gQTLstats, GSVA, Gviz, gwascat, HDF5Array, heatmaps, hiReadsProcessor, hopach, HTSeqGenie, igvR, IHW, IMAS, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, isomiRs, IVAS, JunctionSeq, KCsmart, ldblock, LOLA, LVSmiRNA, M3D, MAST, matter, MEAL, metaMS, methInheritSim, MethylAid, methylPipe, methylumi, methyvim, mimager, MinimumDistance, MIRA, MiRaGE, mogsa, monocle, motifbreakR, msa, MultiAssayExperiment, MultiDataSet, multiMiR, MutationalPatterns, mzR, NarrowPeaks, npGSEA, nucleR, oligoClasses, openPrimeR, parglms, PathwaySplice, pbcmc, pcaMethods, pdInfoBuilder, phyloseq, piano, PING, plrs, podkat, prada, ProCoNA, profileScoreDist, pRoloc, PureCN, pwOmics, qsea, QuasR, R3CPET, R453Plus1Toolbox, RaggedExperiment, ramwas, Rariant, RCAS, RcisTarget, RCy3, RCyjs, recoup, REDseq, RefNet, REMP, ReportingTools, RGalaxy, RGMQL, RGSEA, RiboProfiling, Ringo, RJMCMCNucleosomes, rMAT, roar, rols, Rqc, rqubic, Rsamtools, rsbml, RTCGAToolbox, rtracklayer, SC3, scater, scmap, scPipe, scran, sevenC, SGSeq, signeR, simpleaffy, SingleCellExperiment, SLGI, SNPhood, snpStats, splatter, SplicingGraphs, sscu, STAN, Streamer, SummarizedExperiment, systemPipeR, TarSeqQC, TCGAutils, TCseq, TFBSTools, trackViewer, transcriptR, TransView, triform, tRNAscanImport, TSRchitect, TSSi, TVTB, unifiedWMWqPCR, uSORT, VariantTools, wavClusteR, xcms, XDE, XVector suggestsMe: acde, AIMS, ArrayTV, ASSET, BaalChIP, baySeq, bigmelon, bigmemoryExtras, BiocCheck, BiocInstaller, BiocParallel, BiocStyle, biocViews, biosigner, BiRewire, BLMA, CAFE, CAMERA, CancerSubtypes, CAnD, CausalR, ccrepe, CellNOptR, CexoR, ChIPanalyser, ChIPXpress, CHRONOS, CINdex, clipper, clonotypeR, clustComp, CNORfeeder, CNORfuzzy, CNVPanelizer, coexnet, coMET, cosmiq, COSNet, cpvSNP, cydar, cytofkit, DAPAR, DBChIP, DEsubs, DMRcaller, DMRcate, ENmix, epiNEM, EventPointer, fCCAC, FGNet, flowCL, flowQB, FlowRepositoryR, flowTime, focalCall, GateFinder, gCMAP, gCrisprTools, gdsfmt, GEM, GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster, geNetClassifier, genomation, GEOquery, GMRP, GOstats, GraphPAC, GreyListChIP, GWASTools, h5vc, Harman, hiAnnotator, hierGWAS, hypergraph, iCARE, iClusterPlus, illuminaio, InPAS, INPower, IPO, kebabs, KEGGREST, ldblock, LINC, mAPKL, massiR, MatrixRider, MBttest, mCSEA, mdgsa, Mergeomics, Metab, MetaboSignal, metagene, metagenomeSeq, metaseqR, MetCirc, methylInheritance, microbiome, miRBaseConverter, miRcomp, mirIntegrator, miRLAB, Mirsynergy, motifStack, MSnID, multiClust, MultiMed, multiOmicsViz, MWASTools, netbenchmark, NetSAM, nondetects, nucleoSim, OncoScore, PAA, panelcn.mops, Path2PPI, PathNet, pathview, pepXMLTab, PGA, PhenStat, powerTCR, Prize, proBAMr, proFIA, qpgraph, quantro, QuartPAC, RBGL, rBiopaxParser, Rcade, rcellminer, rCGH, Rcpi, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, RTN, RTNduals, RTNsurvival, rTRM, sangerseqR, SANTA, sapFinder, scmeth, segmentSeq, SeqArray, seqPattern, seqTools, SeqVarTools, SICtools, sigsquared, SIMAT, similaRpeak, SIMLR, SNPRelate, SpacePAC, sparseDOSSA, SparseSignatures, specL, STATegRa, STRINGdb, TCC, TFEA.ChIP, TIN, transcriptogramer, traseR, trena, TRONCO, Uniquorn, variancePartition Package: biocGraph Version: 1.42.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 487e7608aea92186840ee29488adb754 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne source.ver: src/contrib/biocGraph_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biocGraph_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biocGraph_1.42.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R importsMe: EnrichmentBrowser suggestsMe: BiocCaseStudies Package: BiocInstaller Version: 1.30.0 Depends: R (>= 3.5.0) Suggests: remotes, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ebe82b0023d78e9ac2d9bcbb4a3f6710 NeedsCompilation: no Title: Install/Update Bioconductor, CRAN, and github Packages Description: This package is used to install and update Bioconductor, CRAN, and (some) github packages. biocViews: Infrastructure Author: Dan Tenenbaum and Biocore Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocInstaller_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocInstaller_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocInstaller_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: affy, affylmGUI, AnnotationHub, AnnotationHubData, BiocCheck, ccmap, ChIPpeakAnno, crisprseekplus, crossmeta, esATAC, ExperimentHub, ExperimentHubData, gcrma, oligoClasses, OrganismDbi, ProteomicsAnnotationHubData, psygenet2r, QuasR, webbioc suggestsMe: AnnotationHubData, BSgenome, GOSemSim, KEGGlincs, metaseqR, pkgDepTools, recoup Package: BiocOncoTK Version: 1.0.3 Depends: R (>= 3.5.0), methods Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, httr, rjson, dplyr, magrittr, grid, utils, DT Suggests: knitr, dbplyr, DBI, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, ggplot2, GenomeInfoDb, restfulSE, BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene License: Artistic-2.0 MD5sum: e7ecfcfe006986ea5c00e7b071a91681 NeedsCompilation: no Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_7 git_last_commit: 5f05421 git_last_commit_date: 2018-08-24 Date/Publication: 2018-08-24 source.ver: src/contrib/BiocOncoTK_1.0.3.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocOncoTK_1.0.3.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R Package: BioCor Version: 1.4.0 Depends: R (>= 3.4.0) Imports: BiocParallel, Matrix, GSEABase Suggests: reactome.db, org.Hs.eg.db, WGCNA, methods, GOSemSim, testthat, knitr, rmarkdown, BiocStyle, airway, DESeq2, boot, targetscan.Hs.eg.db, Hmisc License: GPL-3 + file LICENSE MD5sum: 577ddeb3b05a2e1d3cff5dc9ac4e5e6e NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho Maintainer: Lluís Revilla Sancho URL: https://github.com/llrs/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues source.ver: src/contrib/BioCor_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioCor_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioCor_1.4.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R Package: BiocParallel Version: 1.14.2 Depends: methods Imports: stats, utils, futile.logger, parallel, snow LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 9ba7f6af10e264dab7a90e271c839169 NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_7 git_last_commit: 1d5a449 git_last_commit_date: 2018-07-08 Date/Publication: 2018-07-08 source.ver: src/contrib/BiocParallel_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocParallel_1.14.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocParallel_1.14.2.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. 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TPP, tracktables, trackViewer, transcriptogramer, transcriptR, Trendy, tRNAscanImport, TRONCO, TTMap, TurboNorm, TVTB, twoddpcr, variancePartition, VariantAnnotation, VariantFiltering, vidger, vsn, wavClusteR, XBSeq, xcms, yamss, YAPSA, zinbwave Package: BiocVersion Version: 3.7.4 Depends: R (>= 3.5.0), R (< 3.6.0) License: Artistic-2.0 MD5sum: e09edea231bfe3954f40054a9ae89b4d NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocVersion_3.7.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocVersion_3.7.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocVersion_3.7.4.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biocViews Version: 1.48.3 Depends: R (>= 2.4.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit Suggests: BiocGenerics, knitr License: Artistic-2.0 MD5sum: 4b8e818275ae94cdd8e3aa8c3790e4e9 NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support Bioconductor 'views' used to classify software packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org/packages/release/BiocViews.html git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_7 git_last_commit: e0ccc26 git_last_commit_date: 2018-08-28 Date/Publication: 2018-08-28 source.ver: src/contrib/biocViews_1.48.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/biocViews_1.48.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biocViews_1.48.3.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, monocle Package: BiocWorkflowTools Version: 1.6.2 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, devtools, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils License: MIT + file LICENSE MD5sum: 7d992001aae31933ba15bc77b57ed9c3 NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_7 git_last_commit: 142e12f git_last_commit_date: 2018-08-03 Date/Publication: 2018-08-03 source.ver: src/contrib/BiocWorkflowTools_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiocWorkflowTools_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiocWorkflowTools_1.6.2.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R Package: bioDist Version: 1.52.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 522be103fe4573e1db1fde5c0791b355 NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/bioDist_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bioDist_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bioDist_1.52.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R dependsOnMe: flowQ suggestsMe: BiocCaseStudies Package: biomaRt Version: 2.36.1 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi, progress, stringr, httr Suggests: annotate, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 70c339113aaf945a2f4608e4a352115f NeedsCompilation: no Title: Interface to BioMart databases (e.g. Ensembl, COSMIC, Wormbase and Gramene) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (http://www.biomart.org). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/biomaRt_2.36.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/biomaRt_2.36.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biomaRt_2.36.1.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.html vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: chromPlot, coMET, customProDB, dagLogo, DrugVsDisease, GenomeGraphs, MineICA, PPInfer, PSICQUIC, Roleswitch, VegaMC importsMe: ArrayExpressHTS, BadRegionFinder, branchpointer, ChIPpeakAnno, CHRONOS, cobindR, DEXSeq, diffloop, DominoEffect, DOQTL, easyRNASeq, EDASeq, ELMER, GDCRNATools, GenomicFeatures, GenVisR, gespeR, GOexpress, goSTAG, Gviz, HTSanalyzeR, IdMappingRetrieval, isobar, MAGeCKFlute, MEDIPS, MetaboSignal, metaseqR, methyAnalysis, MGFR, OncoScore, oposSOM, Pbase, pcaExplorer, PGA, phenoTest, pRoloc, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, RCAS, recoup, rgsepd, RNAither, scPipe, seq2pathway, SeqGSEA, SPLINTER, TCGAbiolinks, TFEA.ChIP, transcriptogramer, trena, yarn suggestsMe: AnnotationForge, bioassayR, BiocCaseStudies, cellTree, chromstaR, ClusterJudge, FELLA, GeneAnswers, Genominator, h5vc, LINC, martini, massiR, MineICA, MiRaGE, MutationalPatterns, netSmooth, oligo, OrganismDbi, paxtoolsr, piano, Pigengene, progeny, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, systemPipeR, trackViewer, wiggleplotr, zinbwave Package: biomformat Version: 1.8.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: a5a4de502922a7f157d3db4be3aee7ef NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues source.ver: src/contrib/biomformat_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biomformat_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biomformat_1.8.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: phyloseq suggestsMe: metagenomeSeq Package: BioMVCClass Version: 1.48.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 83c4f7d4e5bc80d565d6e15031b47e0e NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen source.ver: src/contrib/BioMVCClass_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioMVCClass_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioMVCClass_1.48.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biomvRCNS Version: 1.20.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: 9d2e7c569aba66d9394be8f8ebc893bd NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du source.ver: src/contrib/biomvRCNS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biomvRCNS_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biomvRCNS_1.20.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R Package: BioNet Version: 1.40.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 30a5d942e810758bf5d4d4c013dd4f17 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ source.ver: src/contrib/BioNet_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioNet_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioNet_1.40.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: HTSanalyzeR, SMITE suggestsMe: SANTA Package: BioNetStat Version: 1.0.4 Depends: R (>= 3.5), shiny, igraph, shinyBS, pathview Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr License: GPL (>= 3) MD5sum: b00d5692de1d10abb54e31c3a5e49af4 NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr BugReports: http://github.com/jardimViniciusC/BioNetStat/issues source.ver: src/contrib/BioNetStat_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioNetStat_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioNetStat_1.0.4.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 1. Line command tutorial, 2. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: BioQC Version: 1.8.0 Depends: utils, Rcpp, Biobase, methods, stats Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, hgu133plus2.db, ineq License: GPL (>=3) Archs: i386, x64 MD5sum: 0f1441f84bf62856e385ba336a2839fc NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod Author: Jitao David Zhang , Laura Badi, Gregor Sturm, Roland Ambs Maintainer: Jitao David Zhang VignetteBuilder: knitr source.ver: src/contrib/BioQC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioQC_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioQC_1.8.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc.html vignetteTitles: BioQC Alogrithm: Speeding up the Wilcoxon-Mann-Whitney Test, Using BioQC with signed genesets, BioQC: Detect tissue heterogeneity in gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc.R Package: BioSeqClass Version: 1.38.0 Depends: R (>= 2.10), scatterplot3d Imports: Biostrings, ipred, e1071, klaR, randomForest, class, tree, nnet, rpart, party, foreign, Biobase, utils, stats, grDevices Suggests: scatterplot3d License: LGPL (>= 2.0) MD5sum: ef5218aa476710b502c0f745eab8f2d2 NeedsCompilation: no Title: Classification for Biological Sequences Description: Extracting Features from Biological Sequences and Building Classification Model biocViews: Classification Author: Li Hong sysptm@gmail.com Maintainer: Li Hong source.ver: src/contrib/BioSeqClass_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BioSeqClass_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BioSeqClass_1.38.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioSeqClass/inst/doc/BioSeqClass.R Package: biosigner Version: 1.8.0 Imports: methods, e1071, randomForest, ropls, Biobase Suggests: BioMark, RUnit, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, rmarkdown License: CeCILL MD5sum: 4d0b658b2809e1fa55026bf79294bad7 NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics Author: Philippe Rinaudo , Etienne Thevenot Maintainer: Philippe Rinaudo , Etienne Thevenot VignetteBuilder: knitr source.ver: src/contrib/biosigner_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biosigner_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biosigner_1.8.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R Package: Biostrings Version: 2.48.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.24), XVector (>= 0.19.8) Imports: graphics, methods, stats, utils LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: ef9ea9e5a68e37cb6a8d3a913399cc07 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès source.ver: src/contrib/Biostrings_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Biostrings_2.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Biostrings_2.48.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPpeakAnno, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, genphen, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, methVisual, minfi, MotifDb, motifRG, motifStack, msa, muscle, oligo, pcaGoPromoter, PGA, pqsfinder, qrqc, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, SICtools, systemPipeR, topdownr, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, ArrayExpressHTS, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BioSeqClass, biovizBase, branchpointer, BSgenome, BUMHMM, charm, ChIPseqR, ChIPsim, chromVAR, CNEr, cobindR, compEpiTools, CrispRVariants, customProDB, dada2, dagLogo, diffHic, DNAshapeR, DominoEffect, easyRNASeq, EDASeq, ensembldb, ensemblVEP, esATAC, eudysbiome, FindMyFriends, FourCSeq, GA4GHclient, gcapc, gcrma, genbankr, GeneRegionScan, GenoGAM, genomation, GenomicAlignments, GenomicFeatures, GenomicScores, GenVisR, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiTC, HTSeqGenie, IMMAN, IntEREst, InterMineR, IONiseR, IsoformSwitchAnalyzeR, KEGGREST, Logolas, LowMACA, LymphoSeq, MACPET, MADSEQ, maftools, MatrixRider, MDTS, MEDIPS, MEDME, metagenomeFeatures, methimpute, methVisual, methylPipe, microRNA, MMDiff2, motifbreakR, motifcounter, motifmatchr, MotIV, MutationalPatterns, nucleR, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase, Pbase, pdInfoBuilder, phyloseq, podkat, polyester, proBAMr, procoil, ProteomicsAnnotationHubData, PureCN, Pviz, qrqc, qsea, QuasR, r3Cseq, ramwas, RCAS, Rcpi, REDseq, REMP, Repitools, rGADEM, RNAprobR, Rqc, rSFFreader, rtracklayer, scmeth, scoreInvHap, SeqArray, seqcombo, seqPattern, seqplots, SGSeq, signeR, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SPLINTER, sscu, synapter, TarSeqQC, TFBSTools, trena, tRNAscanImport, TVTB, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, AnnotationForge, AnnotationHub, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, GWASTools, methylumi, MiRaGE, rpx, rTRM, XVector linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, rSFFreader, ShortRead, triplex, VariantAnnotation, VariantFiltering Package: biosvd Version: 2.16.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: 288eec8219a7765876eea3eee7064a51 NeedsCompilation: no Title: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling Description: The biosvd package contains functions to reduce the input data set from the feature x assay space to the reduced diagonalized eigenfeature x eigenassay space, with the eigenfeatures and eigenassays unique orthonormal superpositions of the features and assays, respectively. Results of SVD applied to the data can subsequently be inspected based on generated graphs, such as a heatmap of the eigenfeature x assay matrix and a bar plot with the eigenexpression fractions of all eigenfeatures. These graphs aid in deciding which eigenfeatures and eigenassays to filter out (i.e., eigenfeatures representing steady state, noise, or experimental artifacts; or when applied to the variance in the data, eigenfeatures representing steady-scale variance). After possible removal of steady state expression, steady-scale variance, noise and experimental artifacts, and after re-applying SVD to the normalized data, a summary html report of the eigensystem is generated, containing among others polar plots of the assays and features, a table with the list of features sortable according to their coordinates, radius and phase in the polar plot, and a visualization of the data sorted according to the two selected eigenfeatures and eigenassays with colored feature/assay annotation information when provided. This gives a global picture of the dynamics of expression/intensity levels, in which individual features and assays are classified in groups of similar regulation and function or similar cellular state and biological phenotype. biocViews: TimeCourse, Visualization Author: Anneleen Daemen , Matthew Brauer Maintainer: Anneleen Daemen , Matthew Brauer source.ver: src/contrib/biosvd_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biosvd_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biosvd_2.16.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosvd/inst/doc/biosvd.R Package: biotmle Version: 1.4.0 Depends: R (>= 3.4) Imports: dplyr, ggplot2, ggsci, superheat, doFuture, future, stats, methods, limma, BiocParallel, SummarizedExperiment, tmle Suggests: testthat, knitr, rmarkdown, BiocStyle, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: file LICENSE MD5sum: ff42c6667e140960c0896cc73075e40a NeedsCompilation: no Title: Moderated and Targeted Statistical Learning for Biomarker Discovery Description: This package facilitates the discovery of biomarkers from biological sequencing data (e.g., microarrays, RNA-seq) based on the associations of potential biomarkers with exposure and outcome variables by implementing an estimation procedure that combines a generalization of moderated statistics with targeted minimum loss-based estimates (TMLE) of parameters defined via causal inference (e.g., Average Treatment Effect) whose estimators admit asymptotically linear representations. biocViews: GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Nima Hejazi [aut, cre, cph], Alan Hubbard [aut, ths], Weixin Cai [ctb] Maintainer: Nima Hejazi URL: https://github.com/nhejazi/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues source.ver: src/contrib/biotmle_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/biotmle_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biotmle_1.4.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html, vignettes/biotmle/inst/doc/rnaseqProcessing.html vignetteTitles: Identifying Biomarkers from an Exposure Variable, Processing and Analyzing RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R, vignettes/biotmle/inst/doc/rnaseqProcessing.R Package: biovizBase Version: 1.28.2 Depends: R (>= 2.10), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 9de8458d0f0de6fc472f1119840be70b NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_7 git_last_commit: 43d0906 git_last_commit_date: 2018-08-22 Date/Publication: 2018-08-23 source.ver: src/contrib/biovizBase_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/biovizBase_1.28.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/biovizBase_1.28.2.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinder, derfinderPlot, R3CPET, regionReport, TxRegInfra Package: BiRewire Version: 3.12.0 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 2242c756751bc025179493dfc5b15caf NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire source.ver: src/contrib/BiRewire_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiRewire_3.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiRewire_3.12.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R Package: birta Version: 1.24.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 2b7c165cfac665d156519f68f1b3c6bf NeedsCompilation: yes Title: Bayesian Inference of Regulation of Transcriptional Activity Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states. biocViews: Microarray, Sequencing, GeneExpression, Transcription, GraphAndNetwork Author: Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich Maintainer: Benedikt Zacher , Holger Froehlich source.ver: src/contrib/birta_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/birta_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/birta_1.24.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birta/inst/doc/birta.R Package: birte Version: 1.16.0 Depends: R(>= 3.0.0), RcppArmadillo (>= 0.3.6.1), Rcpp Imports: MASS, limma(>= 3.22.0), glmnet, Biobase, nem, graphics, stats, utils LinkingTo: RcppArmadillo, Rcpp Suggests: knitr Enhances: Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 50331914e40b5eb387dbe247edf6cc49 NeedsCompilation: yes Title: Bayesian Inference of Regulatory Influence on Expression (biRte) Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators. biocViews: Microarray, Sequencing, GeneExpression, Transcription, Network, Bayesian, Regression, NetworkInference Author: Holger Froehlich, contributions by Benedikt Zacher Maintainer: Holger Froehlich SystemRequirements: BLAS, LAPACK VignetteBuilder: knitr source.ver: src/contrib/birte_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/birte_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/birte_1.16.0.tgz vignettes: vignettes/birte/inst/doc/birte.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birte/inst/doc/birte.R Package: BiSeq Version: 1.20.0 Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 6cbcae92378d0c702c97767ad2cc94cb NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit source.ver: src/contrib/BiSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BiSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BiSeq_1.20.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R importsMe: M3D Package: BitSeq Version: 1.24.0 Depends: Rsamtools, zlibbioc Imports: S4Vectors, IRanges LinkingTo: Rsamtools (>= 1.19.38), zlibbioc Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: eb2b89c80a359feb7c830c149346ba2d NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis source.ver: src/contrib/BitSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BitSeq_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BitSeq_1.24.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R Package: blima Version: 1.14.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 Archs: i386, x64 MD5sum: 681fdd6ddb51693c96518232bfbc7203 NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr source.ver: src/contrib/blima_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/blima_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/blima_1.14.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R Package: BLMA Version: 1.4.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 661bdec5cf92f7acc3553aad7db7ac86 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen and Sorin Draghici Maintainer: Tin Nguyen source.ver: src/contrib/BLMA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BLMA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BLMA_1.4.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R Package: bnbc Version: 1.2.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 139ebbaa3f6e6527ee8fb2a750a7d0ca NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues source.ver: src/contrib/bnbc_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bnbc_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bnbc_1.2.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R Package: BPRMeth Version: 1.6.0 Depends: R (>= 3.4.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: a271b24a44d2e8c6fe9dd6b57c06cea0 NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr source.ver: src/contrib/BPRMeth_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BPRMeth_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BPRMeth_1.6.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R Package: BRAIN Version: 1.26.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 3c4c2a9e4feed227447f8d7a51220473 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald source.ver: src/contrib/BRAIN_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BRAIN_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BRAIN_1.26.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter Package: BrainStars Version: 1.24.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 9aeeb308fcd0eb20dcc224a281291974 NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO source.ver: src/contrib/BrainStars_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BrainStars_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BrainStars_1.24.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainStars/inst/doc/BrainStars.R Package: branchpointer Version: 1.6.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: 3b407e6a07f2b6ed8497371e0b391085 NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr source.ver: src/contrib/branchpointer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/branchpointer_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/branchpointer_1.6.0.tgz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R Package: bridge Version: 1.44.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: ca0541cf6ec4f629633da74995dd7649 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/bridge_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bridge_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bridge_1.44.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R Package: BridgeDbR Version: 1.14.0 Depends: R (>= 3.3.0), rJava Imports: RCurl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 48f4854749227fd0f7fff78c9e15d3d1 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Anwesha Bohler , Lars Eijssen Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/egonw/BridgeDbR/issues source.ver: src/contrib/BridgeDbR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BridgeDbR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BridgeDbR_1.14.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R Package: BrowserViz Version: 2.2.0 Depends: R (>= 3.4.0), jsonlite (>= 1.5), httpuv(>= 1.4.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: 27a84f396607a277dff023ca298a6002 NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/BrowserViz_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BrowserViz_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BrowserViz_2.2.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.pdf vignetteTitles: BrowserViz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: BrowserVizDemo, igvR, RCyjs Package: BrowserVizDemo Version: 1.11.0 Depends: R (>= 3.2.3), BrowserViz, Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: a83066d08b49ae10838900c3f960b201 NeedsCompilation: no Title: BrowserVizDemo: How to subclass BrowserViz Description: A BrowserViz subclassing example, xy plotting in the browser using d3. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/BrowserVizDemo_1.11.0.tar.gz vignettes: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.pdf vignetteTitles: BrowserVizDemo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.R Package: BSgenome Version: 1.48.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, XVector, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocInstaller, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: 546d8179e958c945e3ddf43e15b3f076 NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès Maintainer: H. Pagès source.ver: src/contrib/BSgenome_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BSgenome_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BSgenome_1.48.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: ChIPanalyser, cleanUpdTSeq, GOTHiC, HelloRanges, htSeqTools, MEDIPS, motifRG, REDseq, regioneR, rGADEM importsMe: AllelicImbalance, ATACseqQC, BEAT, CAGEr, charm, ChIPpeakAnno, chromVAR, cobindR, CRISPRseek, crisprseekplus, diffHic, esATAC, gcapc, genomation, GenomicScores, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR, MADSEQ, MethylSeekR, MMDiff2, motifbreakR, motifmatchr, msgbsR, PING, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, regioneR, REMP, Repitools, scmeth, seqplots, SparseSignatures, TFBSTools, trena, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, chipseq, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, metaseqR, MiRaGE, MutationalPatterns, ORFik, QDNAseq, recoup, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.16.1 Depends: R (>= 3.3), methods, BiocGenerics, GenomicRanges (>= 1.29.14), SummarizedExperiment (>= 1.9.18), parallel Imports: IRanges (>= 2.11.16), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table, S4Vectors, R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.1.12), permute, limma, DelayedArray (>= 0.5.34), HDF5Array Suggests: RUnit, bsseqData, BiocStyle, rmarkdown, knitr, License: Artistic-2.0 MD5sum: 45a686a2d4fe21bcbf81aa5853623b29 NeedsCompilation: no Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_7 git_last_commit: 8494740 git_last_commit_date: 2018-06-12 Date/Publication: 2018-06-14 source.ver: src/contrib/bsseq_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/bsseq_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bsseq_1.16.1.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: dmrseq, DSS importsMe: MIRA, scmeth Package: BubbleTree Version: 2.10.0 Depends: R (>= 3.3), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.7.5), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: f9865f9945e244571f5a55b66b050a20 NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr source.ver: src/contrib/BubbleTree_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BubbleTree_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BubbleTree_2.10.0.tgz vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R Package: BufferedMatrix Version: 1.44.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 9fc67206114d72b2d7e9b89e9f378e94 NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix source.ver: src/contrib/BufferedMatrix_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BufferedMatrix_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BufferedMatrix_1.44.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods Package: BufferedMatrixMethods Version: 1.44.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: f187d5d54a5a82c306ec819e87abf304 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods source.ver: src/contrib/BufferedMatrixMethods_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BufferedMatrixMethods_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BufferedMatrixMethods_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BUMHMM Version: 1.4.0 Depends: R (>= 3.4) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: 7b743f8e0bf1cce83f9dd11b7ccdccf0 NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr source.ver: src/contrib/BUMHMM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BUMHMM_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BUMHMM_1.4.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R Package: bumphunter Version: 1.22.0 Depends: R (>= 3.4), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 53297e9851af0781efa08092c0757c94 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison Author: Rafael A. Irizarry [cre, aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb] Maintainer: Rafael A. Irizarry URL: https://github.com/ririzarr/bumphunter source.ver: src/contrib/bumphunter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/bumphunter_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/bumphunter_1.22.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: derfinder, dmrseq, methyvim suggestsMe: derfinderPlot, epivizrData, regionReport Package: BUS Version: 1.36.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: 800968c1ceea4151f2f8b1623d2c148c NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu source.ver: src/contrib/BUS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/BUS_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/BUS_1.36.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R Package: CAFE Version: 1.16.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: b39d7cbafb4c113098c1861dc3c4ba4c NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen source.ver: src/contrib/CAFE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CAFE_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CAFE_1.16.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R Package: CAGEfightR Version: 1.0.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods, Matrix(>= 1.2-12), Matrix.utils(>= 0.9.6), grr, BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 7fcd2ab44263b091202b578fb4fdf9b8 NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common CAGE data analysis tasks. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, calculation of TSS shapes and quantification of CAGE expression as expression matrices. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues source.ver: src/contrib/CAGEfightR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CAGEfightR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CAGEfightR_1.0.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R Package: CAGEr Version: 1.22.3 Depends: methods, R (>= 2.15.0) Imports: beanplot, BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.23.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.5.27), KernSmooth, memoise, MultiAssayExperiment, plyr, Rsamtools, reshape, rtracklayer, S4Vectors, som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 31161365b0bd1d0bb87b1788b1b61f08 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle Maintainer: Vanja Haberle , Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_7 git_last_commit: 5a42f82 git_last_commit_date: 2018-06-25 Date/Publication: 2018-06-25 source.ver: src/contrib/CAGEr_1.22.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/CAGEr_1.22.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CAGEr_1.22.3.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern Package: CALIB Version: 1.46.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: 5d6237d80cc6a45b5f3bbc3a4875162d NeedsCompilation: yes Title: Calibration model for estimating absolute expression levels from microarray data Description: This package contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes. biocViews: Microarray,TwoChannel,Preprocessing Author: Hui Zhao, Kristof Engelen, Bart De Moor and Kathleen Marchal Maintainer: Hui Zhao source.ver: src/contrib/CALIB_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CALIB_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CALIB_1.46.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CALIB/inst/doc/quickstart.R Package: CAMERA Version: 1.36.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: i386, x64 MD5sum: cff7d2b5a033b5a6745c77c70b68edeb NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new source.ver: src/contrib/CAMERA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CAMERA_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CAMERA_1.36.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Isotope pattern validation with CAMERA, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS suggestsMe: msPurity, RMassBank, ropls Package: canceR Version: 1.14.0 Depends: R (>= 3.3), tcltk, tcltk2, cgdsr Imports: GSEABase, GSEAlm, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils Suggests: testthat (>= 0.10.0), R.rsp License: GPL-2 MD5sum: 47ef9294b46fc7b293f4a3d93a05dbb6 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC. Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Software Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud VignetteBuilder: R.rsp source.ver: src/contrib/canceR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/canceR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/canceR_1.14.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.pdf vignetteTitles: canceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cancerclass Version: 1.24.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: d5d761c027dfe5ea5828c62aa9cf5669 NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla source.ver: src/contrib/cancerclass_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cancerclass_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cancerclass_1.24.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R Package: CancerInSilico Version: 2.0.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl License: GPL-2 Archs: i386, x64 MD5sum: d16d1060963c5a0c1966c11bfe25bc87 NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr source.ver: src/contrib/CancerInSilico_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CancerInSilico_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CancerInSilico_2.0.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R Package: CancerMutationAnalysis Version: 1.22.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 2164a0cbc37a3c486ea8a95c3af7dc21 NeedsCompilation: yes Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca source.ver: src/contrib/CancerMutationAnalysis_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CancerMutationAnalysis_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CancerMutationAnalysis_1.22.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.R Package: CancerSubtypes Version: 1.6.0 Depends: R (>= 3.4), sigclust, NMF Imports: SNFtool, iCluster, cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, RUnit, knitr, RTCGA.mRNA, RTCGA.clinical License: GPL (>= 2) MD5sum: acd9bbc7361d700353c77805fab0694a NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues source.ver: src/contrib/CancerSubtypes_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CancerSubtypes_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CancerSubtypes_1.6.0.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R Package: CAnD Version: 1.12.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: d963351a7e1b00b6b17f43c95182c207 NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the CAnD test on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh, Timothy Thornton Maintainer: Caitlin McHugh source.ver: src/contrib/CAnD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CAnD_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CAnD_1.12.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R Package: caOmicsV Version: 1.10.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: 4ca0f8b913e1e845b48005a780b2d79e NeedsCompilation: no Title: Visualization of multi-dimentional cancer genomics data Description: caOmicsV package provides methods to visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout. biocViews: Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang source.ver: src/contrib/caOmicsV_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/caOmicsV_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/caOmicsV_1.10.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R Package: Cardinal Version: 1.12.1 Depends: BiocGenerics, Biobase, graphics, matter, methods, stats, ProtGenerics Imports: grDevices, grid, irlba, lattice, signal, sp, stats4, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 995b67e8a9568026b97ccd6e18c8f650 NeedsCompilation: yes Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, Normalization, MassSpectrometry, ImagingMassSpectrometry, Clustering, Classification Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_7 git_last_commit: 8885df8 git_last_commit_date: 2018-07-22 Date/Publication: 2018-07-23 source.ver: src/contrib/Cardinal_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/Cardinal_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Cardinal_1.12.1.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-development.pdf, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.pdf vignetteTitles: Cardinal design and development, Cardinal: Analytic tools for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-development.R, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.R Package: casper Version: 2.14.0 Depends: R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics, coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: acb818814aa3fed83c9f36a6989a545a NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell source.ver: src/contrib/casper_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/casper_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/casper_2.14.0.tgz vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R Package: CATALYST Version: 1.4.2 Depends: R (>= 3.4) Imports: Biobase, circlize, ComplexHeatmap, ConsensusClusterPlus, dplyr, drc, DT, flowCore, FlowSOM, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, htmltools, limma, magrittr, matrixStats, methods, nnls, plotly, RColorBrewer, reshape2, Rtsne, S4Vectors, scales, shiny, shinydashboard, shinyjs, shinyBS, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, diffcyt License: GPL (>=2) MD5sum: 96bc65f435307dcf2704fc617d7739ce NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: MassSpectrometry, Preprocessing, StatisticalMethod, SingleCell, Normalization Author: Helena L. Crowell [cre], Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues source.ver: src/contrib/CATALYST_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/CATALYST_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CATALYST_1.4.2.tgz vignettes: vignettes/CATALYST/inst/doc/differential_analysis.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "Differential analysis with CATALYST", "Preprocessing with CATALYST" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential_analysis.R, vignettes/CATALYST/inst/doc/preprocessing.R suggestsMe: diffcyt Package: Category Version: 2.46.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, SNPchip, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 4e914090e2589cc3cf544b99da04e0d7 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Category_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Category_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Category_2.46.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats, meshr, PCpheno importsMe: categoryCompare, cellHTS2, eisa, gCMAP, interactiveDisplay, MAGeCKFlute, PCpheno, phenoTest, ppiStats, RDAVIDWebService suggestsMe: BiocCaseStudies, miRLAB, MmPalateMiRNA, qpgraph, RnBeads Package: categoryCompare Version: 1.24.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: c13db92f27be15acc3e9533dd4a114b9 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues source.ver: src/contrib/categoryCompare_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/categoryCompare_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/categoryCompare_1.24.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R Package: CausalR Version: 1.12.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 8927fb88ad699084478abf7026335785 NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr source.ver: src/contrib/CausalR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CausalR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CausalR_1.12.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R Package: cbaf Version: 1.2.0 Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots, grDevices, stats, utils, xlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1a6d8ba8aee01466ed1591acb5fc9b74 NeedsCompilation: no Title: Multiple automated functions for cbioportal.org Description: This package contains functions that allow analysing and comparing various gene groups from different cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Technology Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr source.ver: src/contrib/cbaf_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cbaf_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cbaf_1.2.0.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R Package: ccfindR Version: 1.0.0 Depends: R (>= 3.5.0), Imports: stats, S4Vectors, BiocGenerics, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: eed109e62a02f888347d41791189a096 NeedsCompilation: no Title: Cancer Clone Finder Description: A collection of tools for cancer single cell RNA-seq analysis. Cell clustering and feature gene selection analysis employ maximum likelihood and Bayesian non-negative matrix factorization algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks, quality measures (maximum likelihood) or evidence (Bayesian) with respect to rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for cell clusters. biocViews: Transcriptomics, SingleCell, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo VignetteBuilder: knitr source.ver: src/contrib/ccfindR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ccfindR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ccfindR_1.0.0.tgz vignettes: vignettes/ccfindR/inst/doc/ccfindR.pdf vignetteTitles: ccfindR: single-cell RNA-seq clustering with non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R Package: ccmap Version: 1.6.0 Imports: AnnotationDbi (>= 1.36.2), BiocInstaller (>= 1.24.0), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE MD5sum: 1036a1cd798e8e48a68c576bac88ea2d NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr source.ver: src/contrib/ccmap_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ccmap_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ccmap_1.6.0.tgz vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R importsMe: crossmeta Package: CCPROMISE Version: 1.6.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: c99a924027619e2f471b88132ab81623 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao source.ver: src/contrib/CCPROMISE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CCPROMISE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CCPROMISE_1.6.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R Package: ccrepe Version: 1.16.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: b5d6092e512819f78fb3a14018bc9ba3 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr source.ver: src/contrib/ccrepe_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ccrepe_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ccrepe_1.16.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R Package: cellbaseR Version: 1.4.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: 4f86b2b65717f6d5526d86a384b1fe49 NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr source.ver: src/contrib/cellbaseR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellbaseR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellbaseR_1.4.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R Package: cellGrowth Version: 1.24.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: e506f24c0696f2f3fff15c586af7712e NeedsCompilation: no Title: Fitting cell population growth models Description: This package provides functionalities for the fitting of cell population growth models on experimental OD curves. biocViews: CellBasedAssays, MicrotitrePlateAssay, DataImport, Visualization, TimeCourse Author: Julien Gagneur , Andreas Neudecker Maintainer: Julien Gagneur source.ver: src/contrib/cellGrowth_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellGrowth_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellGrowth_1.24.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellGrowth/inst/doc/cellGrowth.R Package: cellHTS2 Version: 2.44.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: e13abcc605718e711adee32310aa282e NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber source.ver: src/contrib/cellHTS2_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellHTS2_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellHTS2_2.44.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR, prada Package: cellity Version: 1.8.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 38fd32bbf69052474b30b2ec8da2eaa2 NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr source.ver: src/contrib/cellity_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellity_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellity_1.8.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R Package: CellMapper Version: 1.6.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 3ea62e8e87866dc00c8b9206d7c3b042 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms source.ver: src/contrib/CellMapper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CellMapper_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CellMapper_1.6.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R Package: CellNOptR Version: 1.26.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, RCurl, Rgraphviz, XML, ggplot2 Suggests: RUnit, BiocGenerics, igraph, stringi, stringr License: GPL-3 Archs: i386, x64 MD5sum: 5c6168a5f93d74c14bda9a64312d001d NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: A.Gabor SystemRequirements: Graphviz version >= 2.2 source.ver: src/contrib/CellNOptR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CellNOptR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CellNOptR_1.26.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf, vignettes/CellNOptR/inst/doc/CellNOptR0_1flowchart.pdf, vignettes/CellNOptR/inst/doc/Fig2.pdf, vignettes/CellNOptR/inst/doc/Fig3.pdf, vignettes/CellNOptR/inst/doc/Fig4.pdf, vignettes/CellNOptR/inst/doc/Fig6.pdf, vignettes/CellNOptR/inst/doc/Fig7.pdf, vignettes/CellNOptR/inst/doc/Fig8.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR, CellNOptR0_1flowchart.pdf, Fig2.pdf, Fig3.pdf, Fig4.pdf, Fig6.pdf, Fig7.pdf, Fig8.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-examples.R, vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR Package: cellscape Version: 1.4.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 710d0416d99f27802975800fe0645ba6 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr source.ver: src/contrib/cellscape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellscape_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellscape_1.4.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R Package: CellScore Version: 1.0.0 Depends: R (>= 3.5.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: dc677d16fca356c6921226002a2d62f0 NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr source.ver: src/contrib/CellScore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CellScore_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CellScore_1.0.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R Package: cellTree Version: 1.10.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: 495047d64580c351c0ac7802fe9b367b NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr source.ver: src/contrib/cellTree_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cellTree_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cellTree_1.10.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R Package: CEMiTool Version: 1.4.2 Depends: R (>= 3.4) Imports: methods, scales, gRbase, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable Suggests: testthat License: GPL-3 MD5sum: 7ca081fadcc14b563346a4a59faaff25 NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_7 git_last_commit: 91964b6 git_last_commit_date: 2018-06-25 Date/Publication: 2018-06-25 source.ver: src/contrib/CEMiTool_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/CEMiTool_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CEMiTool_1.4.2.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R Package: CexoR Version: 1.18.0 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 28d135567b4ce268123094e4fe63ba6b NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/CexoR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CexoR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CexoR_1.18.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R Package: CFAssay Version: 1.14.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 0e85e76c377304ebcf01520d7c26d937 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann source.ver: src/contrib/CFAssay_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CFAssay_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CFAssay_1.14.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R Package: CGEN Version: 3.16.0 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: e0cf1e9fdddc482254f73d52981eb5e7 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: An R package for analysis of case-control studies in genetic epidemiology. biocViews: SNP, MultipleComparisons, Clustering Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee, Summer Han and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/CGEN_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CGEN_3.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CGEN_3.16.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R Package: CGHbase Version: 1.40.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 325c4e2ceba64127be38f9b662ca8aa5 NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues source.ver: src/contrib/CGHbase_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CGHbase_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CGHbase_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.42.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: ef37cb2b3920777a5659847faa4f0a35 NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel source.ver: src/contrib/CGHcall_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CGHcall_2.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CGHcall_2.42.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, focalCall, GeneBreak importsMe: CGHnormaliter, QDNAseq suggestsMe: sigaR Package: cghMCR Version: 1.38.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 74205a95d1647e154366ec45cf564049 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang source.ver: src/contrib/cghMCR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cghMCR_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cghMCR_1.38.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R Package: CGHnormaliter Version: 1.34.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 64c5b61a9278cdda21ed51b68b6c8d12 NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte source.ver: src/contrib/CGHnormaliter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CGHnormaliter_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CGHnormaliter_1.34.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R Package: CGHregions Version: 1.38.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 2d4b23b2294446ce4d070ad4c5e5053b NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse source.ver: src/contrib/CGHregions_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CGHregions_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CGHregions_1.38.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 Package: ChAMP Version: 2.10.2 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0), FEM (>= 3.1),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b2.hg19, limma,RPMM, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 36e71cc763a3ed5d29a907dccf2189df NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_7 git_last_commit: 8f14386 git_last_commit_date: 2018-09-13 Date/Publication: 2018-09-13 source.ver: src/contrib/ChAMP_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChAMP_2.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChAMP_2.10.2.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R Package: CHARGE Version: 1.0.0 Depends: R (>= 3.5), GenomicRanges, methods Imports: SummarizedExperiment, FactoMineR, factoextra, IRanges, graphics, modes, parallel, plyr, cluster, diptest, stats, matrixStats Suggests: roxygen2, EnsDb.Hsapiens.v86 License: GPL-2 MD5sum: f6a6579a7dc380dcd2c0be3f77221b43 NeedsCompilation: no Title: CHARGE: CHromosome Assessment in R from Gene Expression data Description: Identifies genomic duplications or deletions from gene expression data. biocViews: GeneExpression, Clustering Author: Benjamin Mayne Maintainer: Benjamin Mayne source.ver: src/contrib/CHARGE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CHARGE_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CHARGE_1.0.0.tgz vignettes: vignettes/CHARGE/inst/doc/CHARGE_Vignette.pdf vignetteTitles: CHARGE_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHARGE/inst/doc/CHARGE_Vignette.R Package: charm Version: 2.26.0 Depends: R (>= 2.14.0), Biobase, SQN, fields, RColorBrewer, genefilter Imports: BSgenome, Biobase, oligo (>= 1.11.31), oligoClasses(>= 1.17.39), ff, preprocessCore, methods, stats, Biostrings, IRanges, siggenes, nor1mix, gtools, grDevices, graphics, utils, limma, parallel, sva(>= 3.1.2) Suggests: charmData, BSgenome.Hsapiens.UCSC.hg18, corpcor License: LGPL (>= 2) MD5sum: a6c10d643277c2fae8187428b798909e NeedsCompilation: no Title: Analysis of DNA methylation data from CHARM microarrays Description: This package implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools. biocViews: Microarray, DNAMethylation Author: Martin Aryee, Peter Murakami, Harris Jaffee, Rafael Irizarry Maintainer: Peter Murakami source.ver: src/contrib/charm_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/charm_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/charm_2.26.0.tgz vignettes: vignettes/charm/inst/doc/charm.pdf vignetteTitles: charm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/charm/inst/doc/charm.R Package: ChemmineOB Version: 1.18.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitcitations, knitrBootstrap Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: a878588c43c8ecce8330b12c2ffbd10f NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 2.3.1) with headers (http://openbabel.org). VignetteBuilder: knitr source.ver: src/contrib/ChemmineOB_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChemmineOB_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChemmineOB_1.18.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R suggestsMe: ChemmineR Package: ChemmineR Version: 3.32.1 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineOB (>= 1.16.1), ChemmineDrugs, png,rmarkdown Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 06e2664522fbce6e44fb436112f5e626 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/ChemmineR_3.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChemmineR_3.32.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChemmineR_3.32.1.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR importsMe: bioassayR, eiR, fmcsR, Rchemcpp, Rcpi suggestsMe: ChemmineOB Package: ChIC Version: 1.0.0 Depends: spp, R (>= 3.5) Imports: ChIC.data (>= 0.99), caTools, methods,GenomicRanges, IRanges, parallel, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics License: GPL-2 MD5sum: 9936bd4a138d958dfbf66ace0da0f7f6 NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control pipeline for ChIP-seq data using a comprehensive set of quality control metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The framework allows assessing quality of samples with sharp or broad enrichment profiles, whereas previously proposed metrics were not taking this into account. CHIC provides a reference compendium of quality control metrics and trained machine learning models for scoring samples. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi source.ver: src/contrib/ChIC_1.0.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIC_1.0.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC_Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC_Vignette.R Package: Chicago Version: 1.8.0 Depends: R (>= 3.2), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: 4144ae6f936d3cc5ec609ac3461f895f NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr source.ver: src/contrib/Chicago_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Chicago_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Chicago_1.8.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R Package: chimera Version: 1.22.0 Depends: Biobase, GenomicRanges (>= 1.13.3), Rsamtools (>= 1.13.1), GenomicAlignments, methods, AnnotationDbi, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens Suggests: BiocParallel, geneplotter Enhances: Rsubread, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: b5a88934b7c53f6aea596ffefb17608d NeedsCompilation: yes Title: A package for secondary analysis of fusion products Description: This package facilitates the characterisation of fusion products events. It allows to import fusion data results from the following fusion finders: chimeraScan, bellerophontes, deFuse, FusionFinder, FusionHunter, mapSplice, tophat-fusion, FusionMap, STAR, Rsubread, fusionCatcher. biocViews: Infrastructure Author: Raffaele A Calogero, Matteo Carrara, Marco Beccuti, Francesca Cordero Maintainer: Raffaele A Calogero SystemRequirements: STAR, TopHat, bowtie and samtools are required for some functionalities source.ver: src/contrib/chimera_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chimera_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chimera_1.22.0.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimera/inst/doc/chimera.R Package: chimeraviz Version: 1.6.2 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, ArgumentCheck Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 5775575fa0a77b4ee423f613f63175b1 NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_7 git_last_commit: 7dc4fd3 git_last_commit_date: 2018-09-19 Date/Publication: 2018-09-19 source.ver: src/contrib/chimeraviz_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/chimeraviz_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chimeraviz_1.6.2.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R Package: ChIPanalyser Version: 1.2.0 Depends: R (>= 3.4.1),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: fccf3a10be6c30d91bd49216a15967a6 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr source.ver: src/contrib/ChIPanalyser_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPanalyser_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPanalyser_1.2.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R Package: ChIPComp Version: 1.10.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL Archs: i386, x64 MD5sum: 32c86a6a4cd192562a85f8b57d8b3965 NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen source.ver: src/contrib/ChIPComp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPComp_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPComp_1.10.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R Package: chipenrich Version: 2.4.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors, stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: b0f65d89f8ee3f2fdfe7c00fea819db3 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich performs gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut, cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr source.ver: src/contrib/chipenrich_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chipenrich_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chipenrich_2.4.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R Package: ChIPexoQual Version: 1.4.0 Depends: R (>= 3.4.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: c95dea93394c8445428692fca93ff173 NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues source.ver: src/contrib/ChIPexoQual_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPexoQual_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPexoQual_1.4.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R Package: ChIPpeakAnno Version: 3.14.2 Depends: R (>= 3.2), methods, grid, IRanges (>= 2.13.12), Biostrings (>= 2.47.6), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25), VennDiagram Imports: BiocGenerics (>= 0.1.0), GO.db, biomaRt, BSgenome, GenomicFeatures, GenomeInfoDb, matrixStats, AnnotationDbi, limma, multtest, RBGL, graph, BiocInstaller, stats, regioneR, DBI, ensembldb, Biobase, seqinr, idr, GenomicAlignments, DelayedArray, SummarizedExperiment, Rsamtools Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, gplots, BiocStyle, rtracklayer, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: 25fd41283ae4822e45b012d61498da4a NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu , Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_7 git_last_commit: d0826d4 git_last_commit_date: 2018-09-07 Date/Publication: 2018-09-07 source.ver: src/contrib/ChIPpeakAnno_3.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPpeakAnno_3.14.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPpeakAnno_3.14.2.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq importsMe: ATACseqQC, DChIPRep, DEScan2, FunciSNP, GUIDEseq, REDseq suggestsMe: R3CPET, RIPSeeker, seqsetvis Package: ChIPQC Version: 1.16.1 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, BiocParallel, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: 335dc2a4e634f4bb4033635081eb85e2 NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: RELEASE_3_7 git_last_commit: 79ddfdd git_last_commit_date: 2018-09-21 Date/Publication: 2018-09-21 source.ver: src/contrib/ChIPQC_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPQC_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPQC_1.16.1.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R Package: ChIPseeker Version: 1.16.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicFeatures (>= 1.31.3), ggplot2 (>= 2.2.0), gplots, graphics, grDevices, grid, gridBase, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors (>= 0.17.25), stats, TxDb.Hsapiens.UCSC.hg19.knownGene, UpSetR, utils Suggests: clusterProfiler, ReactomePA, org.Hs.eg.db, knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 9c0536d18687a6e4f3bcef86f36a5def NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (), Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_7 git_last_commit: 056ef7c git_last_commit_date: 2018-07-21 Date/Publication: 2018-07-21 source.ver: src/contrib/ChIPseeker_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPseeker_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPseeker_1.16.1.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: esATAC Package: chipseq Version: 1.30.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: d50a96f3ab5d164e0784ae0348c824a4 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/chipseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chipseq_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chipseq_1.30.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R dependsOnMe: PING importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR suggestsMe: GenoGAM, ggbio Package: ChIPseqR Version: 1.34.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 2b3711026698d6fed0e523f86a62c2e9 NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg source.ver: src/contrib/ChIPseqR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPseqR_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPseqR_1.34.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R Package: ChIPSeqSpike Version: 1.0.0 Depends: R (>= 3.5), rtracklayer (>= 1.37.6) Imports: tools, stringr, Rsamtools, GenomicRanges, IRanges, seqplots, ggplot2, LSD, corrplot, methods, stats, grDevices, graphics, utils, BiocGenerics, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 7fdde522a6ab8b14b3cd644eaa384368 NeedsCompilation: no Title: ChIP-Seq data scaling according to spike-in control Description: Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to determine the binding sites of any protein of interest, such as transcription factors or histones with or without a specific modification, at a genome scale. The many steps of the protocol can introduce biases that make ChIP-Seq more qualitative than quantitative. For instance, it was shown that global histone modification differences are not caught by traditional downstream data normalization techniques. A case study reported no differences in histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle this problem, external spike-in control were used to keep track of technical biases between conditions. Exogenous DNA from a different non-closely related species was inserted during the protocol to infer scaling factors that enabled an accurate normalization, thus revealing the inhibitor effect. ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions. biocViews: ChIPSeq, Sequencing, Normalization, Transcription, Coverage, DifferentialMethylation, Epigenetics, DataImport, HistoneModification Author: Nicolas Descostes Maintainer: Nicolas Descostes VignetteBuilder: knitr source.ver: src/contrib/ChIPSeqSpike_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPSeqSpike_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPSeqSpike_1.0.0.tgz vignettes: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.pdf vignetteTitles: ChIPSeqSpike hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.R Package: ChIPsim Version: 1.34.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 084292061359354de8fee1c71e55a8a2 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg source.ver: src/contrib/ChIPsim_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChIPsim_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPsim_1.34.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R Package: ChIPXpress Version: 1.24.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 7bc18d558d02d7ea98d2219b5b9d3165 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu source.ver: src/contrib/ChIPXpress_1.24.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChIPXpress_1.24.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R Package: chopsticks Version: 1.46.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 95ca9a03c8f614f07d895b30c7dae66a NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ source.ver: src/contrib/chopsticks_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chopsticks_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chopsticks_1.46.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R Package: chroGPS Version: 1.28.0 Depends: R (>= 3.1.0), GenomicRanges, methods, Biobase, MASS, graphics, stats, changepoint Imports: cluster, DPpackage, ICSNP Enhances: parallel, XML, rgl License: GPL (>=2.14) MD5sum: 505b30f436d61a34ea00ebc7b1bac1ed NeedsCompilation: no Title: Visualizing the epigenome Description: We provide intuitive maps to visualize the association between genetic elements, with emphasis on epigenetics. The approach is based on Multi-Dimensional Scaling. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds. biocViews: Visualization, Clustering Author: Oscar Reina, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/chroGPS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chroGPS_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chroGPS_1.28.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chroGPS/inst/doc/chroGPS.R Package: chromDraw Version: 2.10.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: eba703de74969568ee5f42183e5b45d1 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) source.ver: src/contrib/chromDraw_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chromDraw_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chromDraw_2.10.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R Package: ChromHeatMap Version: 1.34.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: c52e9c330e9493e9ec87a9e9892bdac5 NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner source.ver: src/contrib/ChromHeatMap_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ChromHeatMap_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ChromHeatMap_1.34.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R Package: chromPlot Version: 1.8.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 56d52edc705843b9ce895c730874ff24 NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica source.ver: src/contrib/chromPlot_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chromPlot_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chromPlot_1.8.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R Package: chromstaR Version: 1.6.2 Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: db54709fe1ff8778e653d5c03e7428e5 NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: RELEASE_3_7 git_last_commit: f692575 git_last_commit_date: 2018-07-19 Date/Publication: 2018-07-19 source.ver: src/contrib/chromstaR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/chromstaR_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chromstaR_1.6.2.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R Package: chromswitch Version: 1.2.1 Depends: R (>= 3.4), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.14.4), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE MD5sum: 0b795249a7b2a99aca412ac794eca1f0 NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues source.ver: src/contrib/chromswitch_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/chromswitch_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chromswitch_1.2.1.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R Package: chromVAR Version: 1.2.0 Depends: R (>= 3.4) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE Archs: i386, x64 MD5sum: b0acc9df2130c14f08277c5c4e2a2e34 NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/chromVAR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/chromVAR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/chromVAR_1.2.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R Package: CHRONOS Version: 1.8.1 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt Suggests: RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 6d3cbc4fb53e4dea3619f325c0b1d44a NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_7 git_last_commit: 9d7c8a8 git_last_commit_date: 2018-08-05 Date/Publication: 2018-08-06 source.ver: src/contrib/CHRONOS_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/CHRONOS_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CHRONOS_1.8.1.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R Package: CINdex Version: 1.8.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens License: GPL (>= 2) MD5sum: 86b6b1a77ad55959678c7bc130aa4b52 NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song, Krithika Bhuvaneshwar, Yue Wang, Yuanjian Feng, Ie-Ming Shih, Subha Madhavan, Yuriy Gusev Maintainer: Yuriy Gusev VignetteBuilder: knitr source.ver: src/contrib/CINdex_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CINdex_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CINdex_1.8.0.tgz vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/PrepareInputData.R Package: cisPath Version: 1.20.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: fbbce239d8ff990b8e729d125dfb58a1 NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang source.ver: src/contrib/cisPath_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cisPath_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cisPath_1.20.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R Package: ClassifyR Version: 2.0.10 Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel Imports: locfit, grid, utils, plyr Suggests: limma, edgeR, car, Rmixmod, ggplot2 (>= 2.2.0), gridExtra (>= 2.0.0), BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, randomForest, robustbase, mlogit, mnlogit, glmnet License: GPL-3 MD5sum: 7dec518f7df848b2c06147b7235c87b4 NeedsCompilation: no Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_7 git_last_commit: e553f79 git_last_commit_date: 2018-09-03 Date/Publication: 2018-09-04 source.ver: src/contrib/ClassifyR_2.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.5/ClassifyR_2.0.10.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ClassifyR_2.0.10.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html vignetteTitles: An Introduction to the ClassifyR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R Package: cleanUpdTSeq Version: 1.18.0 Depends: R (>= 2.15), BiocGenerics (>= 0.1.0), methods, BSgenome, BSgenome.Drerio.UCSC.danRer7, GenomicRanges, seqinr, e1071 Suggests: BiocStyle, knitr, RUnit License: GPL-2 MD5sum: 0c4149683076eb668bbb682a7d2b6e07 NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed Description: This package uses the Naive Bayes classifier (from e1071) to assign probability values to putative polyadenylation sites (pA sites) based on training data from zebrafish. This will allow the user to separate true, biologically relevant pA sites from false, oligodT primed pA sites. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Sarah Sheppard ; Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/cleanUpdTSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cleanUpdTSeq_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cleanUpdTSeq_1.18.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R importsMe: InPAS Package: cleaver Version: 1.18.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: 6a6d32f555e89e1e813527647d0cb516 NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ source.ver: src/contrib/cleaver_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cleaver_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cleaver_1.18.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.pdf vignetteTitles: in-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: Pbase, synapter Package: clippda Version: 1.30.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 0d0bfefecfa0f5da2fa9db1905d505c6 NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml source.ver: src/contrib/clippda_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clippda_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clippda_1.30.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R Package: clipper Version: 1.20.1 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: 53210de77beb815d02fe23e6bc74dd42 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini source.ver: src/contrib/clipper_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/clipper_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clipper_1.20.1.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite Package: Clomial Version: 1.16.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 076d5b25c7122da8e77b47bc6cb27801 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing Author: Habil Zare and Alex Hu Maintainer: Habil Zare source.ver: src/contrib/Clomial_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Clomial_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Clomial_1.16.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R Package: Clonality Version: 1.28.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: 3911a3f76ddf136af8ccb97eb23616a7 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: Microarray, CopyNumberVariation, Classification, aCGH Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya source.ver: src/contrib/Clonality_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Clonality_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Clonality_1.28.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R Package: clonotypeR Version: 1.18.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 33fb53552136ef2b3ad53b4dfaaeed27 NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy URL: http://clonotyper.branchable.com/ VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ source.ver: src/contrib/clonotypeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clonotypeR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clonotypeR_1.18.0.tgz vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html vignetteTitles: clonotypeR User's Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R Package: clst Version: 1.28.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: da4eaceb19993f64e0fd9e6dc215e980 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman source.ver: src/contrib/clst_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clst_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clst_1.28.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils Package: clstutils Version: 1.28.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 6b0ea500d5698e5212853bd616b25127 NeedsCompilation: no Title: Tools for performing taxonomic assignment. Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman source.ver: src/contrib/clstutils_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clstutils_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clstutils_1.28.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R Package: clustComp Version: 1.8.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: d2b403b88df844cdd3d00450979fc054 NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente source.ver: src/contrib/clustComp_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clustComp_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clustComp_1.8.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R Package: clusterExperiment Version: 2.0.2 Depends: R (>= 3.4.2), SingleCellExperiment, SummarizedExperiment Imports: methods, NMF, RColorBrewer, ape (>= 5.0), phylobase, cluster, stats, limma, dendextend, howmany, locfdr, matrixStats, graphics, parallel, RSpectra, kernlab, stringr, S4Vectors, grDevices, Rcpp, HDF5Array (>= 1.7.10), DelayedArray (>= 0.5.31) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, scRNAseq, MAST, Rtsne License: Artistic-2.0 Archs: i386, x64 MD5sum: d3b198b242fb0a2fc98e4139f29bde2f NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut], Marla Johnson [ctb] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues source.ver: src/contrib/clusterExperiment_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/clusterExperiment_2.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clusterExperiment_2.0.2.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html vignetteTitles: clusterExperiment Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R dependsOnMe: netSmooth Package: ClusterJudge Version: 1.2.0 Depends: R (>= 3.4), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 5a5579679ecc9e5412680e9489eb642e NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr source.ver: src/contrib/ClusterJudge_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ClusterJudge_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ClusterJudge_1.2.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R Package: clusterProfiler Version: 3.8.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot (>= 0.99.7), ggplot2, GO.db, GOSemSim, magrittr, methods, plyr, qvalue, rvcheck, stats, tidyr, utils Suggests: AnnotationHub, GSEABase, KEGG.db, knitr, org.Hs.eg.db, prettydoc, pathview, ReactomePA, testthat License: Artistic-2.0 MD5sum: b5149ff975cb1efeac28751e76537a48 NeedsCompilation: no Title: statistical analysis and visualization of functional profiles for genes and gene clusters Description: This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb] (formula interface of compareCluster) Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues source.ver: src/contrib/clusterProfiler_3.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/clusterProfiler_3.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clusterProfiler_3.8.1.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R importsMe: bioCancer, CEMiTool, DAPAR, debrowser, eegc, esATAC, GDCRNATools, LINC, MAGeCKFlute, miRsponge, MoonlightR, TCGAbiolinksGUI suggestsMe: ChIPseeker, DOSE, enrichplot, GOSemSim, isomiRs, ReactomePA, TCGAbiolinks Package: clusterSeq Version: 1.4.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 70fa257ede3f43bf87b45167903889f4 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle & Irene Papatheodorou Maintainer: Thomas J. Hardcastle source.ver: src/contrib/clusterSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clusterSeq_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clusterSeq_1.4.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R Package: ClusterSignificance Version: 1.8.2 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: d4a91c3ca51aa902ca74c92cf836ea9d NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_7 git_last_commit: a7ef7c6 git_last_commit_date: 2018-07-16 Date/Publication: 2018-07-16 source.ver: src/contrib/ClusterSignificance_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/ClusterSignificance_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ClusterSignificance_1.8.2.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R Package: clusterStab Version: 1.52.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 489d2b030e7cb387d7812dade4125c4f NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald source.ver: src/contrib/clusterStab_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/clusterStab_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/clusterStab_1.52.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R Package: CMA Version: 1.38.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 10a98c1d06648db3323a73d5ef8bf14c NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Christoph Bernau source.ver: src/contrib/CMA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CMA_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CMA_1.38.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R Package: cn.farms Version: 1.28.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 288cf118768ab5f7c5bf718208fe9293 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html source.ver: src/contrib/cn.farms_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cn.farms_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cn.farms_1.28.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R Package: cn.mops Version: 1.26.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: e95b8477a957fcf5547ffb936cbeda39 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html source.ver: src/contrib/cn.mops_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cn.mops_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cn.mops_1.26.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops Package: CNAnorm Version: 1.26.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: d4bf95c8d04639e162149d2c8aba2388 NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, source.ver: src/contrib/CNAnorm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNAnorm_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNAnorm_1.26.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R Package: CNEr Version: 1.16.1 Depends: R (>= 3.4) Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 413fb43a12a56d6b5211cfaa34d92997 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues source.ver: src/contrib/CNEr_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNEr_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNEr_1.16.1.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools Package: CNORdt Version: 1.22.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 7cac688f7f17fb884c8db1d719ef4e74 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara source.ver: src/contrib/CNORdt_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNORdt_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNORdt_1.22.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R Package: CNORfeeder Version: 1.20.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph License: GPL-3 MD5sum: 9d4a5db07fd2324a05f14f91ec3b0187 NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, NetworkInference Author: F.Eduati Maintainer: F.Eduati source.ver: src/contrib/CNORfeeder_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNORfeeder_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNORfeeder_1.20.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R Package: CNORfuzzy Version: 1.22.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: b893d81ee05a58e8b84cff7b1686397b NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer source.ver: src/contrib/CNORfuzzy_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNORfuzzy_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNORfuzzy_1.22.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R Package: CNORode Version: 1.22.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: d61f294cea8348fc40a9e4fd8859e881 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer Maintainer: David Henriques source.ver: src/contrib/CNORode_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNORode_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNORode_1.22.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR Package: CNPBayes Version: 1.10.0 Depends: R (>= 3.4.0), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.1), S4Vectors, matrixStats, RColorBrewer, gtools, combinat, GenomeInfoDb (>= 1.11.6), methods, BiocGenerics, graphics, stats, coda, SummarizedExperiment, mclust, reshape2, ggplot2, magrittr, purrr, tidyr, dplyr, tibble, scales LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, BiocCheck, MASS License: Artistic-2.0 Archs: i386, x64 MD5sum: 0149db1a75e9ac592165a0ab8d7bfc76 NeedsCompilation: yes Title: Bayesian mixture models for copy number polymorphisms Description: Bayesian hierarchical mixture models for batch effects and copy number. biocViews: CopyNumberVariation, Bayesian Author: Stephen Cristiano, Robert Scharpf, and Jacob Carey Maintainer: Jacob Carey URL: https://github.com/scristia/CNPBayes VignetteBuilder: knitr BugReports: https://github.com/scristia/CNPBayes/issues source.ver: src/contrib/CNPBayes_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNPBayes_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNPBayes_1.10.0.tgz vignettes: vignettes/CNPBayes/inst/doc/Convergence.pdf, vignettes/CNPBayes/inst/doc/FindCNPs.pdf, vignettes/CNPBayes/inst/doc/Implementation.pdf, vignettes/CNPBayes/inst/doc/Overview.pdf vignetteTitles: Overview of CNPBayes package, Identifying Copy Number Polymorphisms, Implementation of Bayesian mixture models for copy number estimation, Overview of CNPBayes package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNPBayes/inst/doc/Convergence.R, vignettes/CNPBayes/inst/doc/FindCNPs.R, vignettes/CNPBayes/inst/doc/Implementation.R, vignettes/CNPBayes/inst/doc/Overview.R Package: CNTools Version: 1.36.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: ce65198e71f91329b94e9615741b0fa1 NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang source.ver: src/contrib/CNTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNTools_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNTools_1.36.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR Package: cnvGSA Version: 1.24.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: dd92a3bffb622fc3a9a27906f6bf7009 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo source.ver: src/contrib/cnvGSA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cnvGSA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cnvGSA_1.24.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNVPanelizer Version: 1.12.0 Depends: R (>= 3.2.0), GenomicRanges Imports: S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 6bc9276c80957dc2335fc08c68659241 NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr source.ver: src/contrib/CNVPanelizer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNVPanelizer_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNVPanelizer_1.12.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R Package: CNVrd2 Version: 1.18.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: bf708c40273e4704df60e131f3621235 NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr source.ver: src/contrib/CNVrd2_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNVrd2_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNVrd2_1.18.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R Package: CNVtools Version: 1.74.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: 876e11141917857302ffae12662bb66e NeedsCompilation: yes Title: A package to test genetic association with CNV data Description: This package is meant to facilitate the testing of Copy Number Variant data for genetic association, typically in case-control studies. biocViews: GeneticVariability Author: Chris Barnes and Vincent Plagnol Maintainer: Chris Barnes source.ver: src/contrib/CNVtools_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CNVtools_1.74.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CNVtools_1.74.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVtools/inst/doc/CNVtools-vignette.R Package: cobindR Version: 1.18.0 Imports: methods, seqinr, yaml, rtfbs, gplots, mclust, gmp, BiocGenerics (>= 0.13.8), IRanges, Biostrings, BSgenome, biomaRt Suggests: RUnit Enhances: rGADEM, seqLogo, genoPlotR, parallel, VennDiagram, RColorBrewer, vcd, MotifDb, snowfall License: Artistic-2.0 MD5sum: 0d490375ababfafae5bb9671280586a1 NeedsCompilation: no Title: Finding Co-occuring motifs of transcription factor binding sites Description: Finding and analysing co-occuring motifs of transcription factor binding sites in groups of genes biocViews: ChIPSeq, CellBiology, MultipleComparison, SequenceMatching Author: Manuela Benary, Stefan Kroeger, Yuehien Lee, Robert Lehmann Maintainer: Manuela Benary source.ver: src/contrib/cobindR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cobindR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cobindR_1.18.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cobindR/inst/doc/cobindR.R Package: CoCiteStats Version: 1.52.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 0ba9fe89f4c5eef9b58c680c62ee60c9 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/CoCiteStats_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CoCiteStats_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CoCiteStats_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.48.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: f7e5bdf122cacc61eb534133d29aea1c NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues source.ver: src/contrib/codelink_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/codelink_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/codelink_1.48.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R Package: CODEX Version: 1.12.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 615a1a34aed1fca40a3fa66e2908a1c9 NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang source.ver: src/contrib/CODEX_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CODEX_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CODEX_1.12.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV Package: coexnet Version: 1.2.0 Depends: R (>= 3.4) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL MD5sum: 4c00dc47e7775eb816e9baf1f316173a NeedsCompilation: no Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr source.ver: src/contrib/coexnet_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/coexnet_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/coexnet_1.2.0.tgz vignettes: vignettes/coexnet/inst/doc/coexnet.pdf vignetteTitles: The title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coexnet/inst/doc/coexnet.R Package: CoGAPS Version: 3.0.2 Depends: R (>= 3.4.0), Rcpp (>= 0.11.0) Imports: RColorBrewer (>= 1.0.5), gplots (>= 2.8.0), graphics, grDevices, methods, cluster, shiny, stats, utils, doParallel, foreach, ggplot2, reshape2 LinkingTo: Rcpp, BH Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL (==2) Archs: i386, x64 MD5sum: 512ac58882729c1481d76f7e689f169b NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_7 git_last_commit: 9d82c1f git_last_commit_date: 2018-07-26 Date/Publication: 2018-07-26 source.ver: src/contrib/CoGAPS_3.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/CoGAPS_3.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CoGAPS_3.0.2.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf, vignettes/CoGAPS/inst/doc/GWCoGAPSvignette.html vignetteTitles: GAPS/CoGAPS Users Manual, GWCoGAPS and PatternMarkers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.R, vignettes/CoGAPS/inst/doc/GWCoGAPSvignette.R Package: cogena Version: 1.14.0 Depends: R (>= 3.2), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, dplyr, devtools Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: ac5f6cb2b46d452ebd845a8783dd9d47 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues source.ver: src/contrib/cogena_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cogena_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cogena_1.14.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: Vignette Title, Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R Package: coGPS Version: 1.24.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 935f00dd17bd27a66a59c08a370b7fc0 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei source.ver: src/contrib/coGPS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/coGPS_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/coGPS_1.24.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R Package: COHCAP Version: 1.26.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 Archs: i386, x64 MD5sum: 6c1ec460322d1a1095cff3d635786683 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: This package provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden Maintainer: Charles Warden SystemRequirements: Perl source.ver: src/contrib/COHCAP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/COHCAP_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/COHCAP_1.26.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R Package: coMET Version: 1.12.0 Depends: R (>= 3.4.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: c1fc7562dffdee22c1ad1483d5a3d514 NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr source.ver: src/contrib/coMET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/coMET_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/coMET_1.12.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R Package: COMPASS Version: 1.18.1 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.9.66), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData License: Artistic-2.0 Archs: i386, x64 MD5sum: 8e98e94409837c43c3f545ccfd505da3 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_7 git_last_commit: 578f2b6 git_last_commit_date: 2018-10-12 Date/Publication: 2018-10-12 source.ver: src/contrib/COMPASS_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/COMPASS_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/COMPASS_1.18.1.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R Package: compcodeR Version: 1.16.1 Depends: R (>= 3.0.2), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, gdata, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods Suggests: BiocStyle, EBSeq, DESeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0) Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: cb2d60fb94cdce40890e8b73bd1faa8e NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data and interfaces to several packages for performing the differential expression analysis. biocViews: RNASeq, DifferentialExpression Author: Charlotte Soneson Maintainer: Charlotte Soneson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_7 git_last_commit: 860f6dd git_last_commit_date: 2018-10-02 Date/Publication: 2018-10-02 source.ver: src/contrib/compcodeR_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/compcodeR_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/compcodeR_1.16.1.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R Package: compEpiTools Version: 1.14.1 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 06ff9d5b763aa20f129c78521a785697 NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/compEpiTools_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/compEpiTools_1.14.1.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R Package: CompGO Version: 1.16.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 01dda5359a3a18f1e38e5530dbea08e0 NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg source.ver: src/contrib/CompGO_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CompGO_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CompGO_1.16.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R Package: ComplexHeatmap Version: 1.18.1 Depends: R (>= 3.1.2), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.1), GetoptLong, colorspace, RColorBrewer, GlobalOptions (>= 0.0.10) Suggests: testthat (>= 0.3), knitr, markdown, cluster, MASS, pvclust, dendsort, HilbertCurve, Cairo, png, jpeg, tiff, fastcluster, dendextend (>= 1.0.1) License: MIT + file LICENSE MD5sum: ff3322b7ea128d775fa8cc353782b8d9 NeedsCompilation: no Title: Making Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential structures. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_7 git_last_commit: be0dd9d git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/ComplexHeatmap_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ComplexHeatmap_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ComplexHeatmap_1.18.1.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/s1.introduction.html, vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.html, vignettes/ComplexHeatmap/inst/doc/s3.a_list_of_heatmaps.html, vignettes/ComplexHeatmap/inst/doc/s4.heatmap_annotation.html, vignettes/ComplexHeatmap/inst/doc/s5.legend.html, vignettes/ComplexHeatmap/inst/doc/s6.heatmap_decoration.html, vignettes/ComplexHeatmap/inst/doc/s7.interactive.html, vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.html, vignettes/ComplexHeatmap/inst/doc/s9.examples.html vignetteTitles: 1. Introduction to ComplexHeatmap package, 2. Making a single heatmap, 3. Making a list of Heatmaps, 4. Heatmap Annotations, 5. Heatmap and Annotation Legends, 6. Heatmap Decoration, 7. Interactive with Heatmaps, 8. OncoPrint, 9. More Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/s1.introduction.R, vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.R, vignettes/ComplexHeatmap/inst/doc/s3.a_list_of_heatmaps.R, vignettes/ComplexHeatmap/inst/doc/s4.heatmap_annotation.R, vignettes/ComplexHeatmap/inst/doc/s5.legend.R, vignettes/ComplexHeatmap/inst/doc/s6.heatmap_decoration.R, vignettes/ComplexHeatmap/inst/doc/s7.interactive.R, vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.R, vignettes/ComplexHeatmap/inst/doc/s9.examples.R dependsOnMe: EnrichedHeatmap, recoup importsMe: BiocOncoTK, CATALYST, DEComplexDisease, DEGreport, DEP, diffcyt, ELMER, EnrichmentBrowser, fCCAC, ImpulseDE2, LineagePulse, maftools, MWASTools, PathoStat, singleCellTK, TCGAbiolinks, YAPSA suggestsMe: gtrellis, HilbertCurve Package: CONFESS Version: 1.8.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: 38e7728b0695864075b583fed8e0af44 NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr source.ver: src/contrib/CONFESS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CONFESS_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CONFESS_1.8.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R Package: ConsensusClusterPlus Version: 1.44.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 6d7bc0d6b6bca02b49a9b48c99861cfb NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson source.ver: src/contrib/ConsensusClusterPlus_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ConsensusClusterPlus_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ConsensusClusterPlus_1.44.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CancerSubtypes, CATALYST, CVE, DEGreport, FlowSOM, TCGAbiolinks Package: consensusOV Version: 1.2.0 Depends: R (>= 3.4) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils Suggests: knitr, ggplot2 License: Artistic-2.0 MD5sum: 68daf7cbe9be314bbdca45bef88e26e2 NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues source.ver: src/contrib/consensusOV_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/consensusOV_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/consensusOV_1.2.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R Package: consensusSeekeR Version: 1.8.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 3ee13137571f05a864a7aeecfa251308 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues source.ver: src/contrib/consensusSeekeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/consensusSeekeR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/consensusSeekeR_1.8.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes Package: contiBAIT Version: 1.8.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, IRanges, GenomicRanges, S4Vectors, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: f2b8a7763503fd4899468c6ee7430d2f NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill source.ver: src/contrib/contiBAIT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/contiBAIT_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/contiBAIT_1.8.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R Package: conumee Version: 1.14.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: 875c3101f15a109abc29b30396da08f5 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr source.ver: src/contrib/conumee_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/conumee_1.13.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/conumee_1.14.0.tgz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R Package: convert Version: 1.56.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 97630be0e7e9d9769afddb7b7c7ca928 NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html source.ver: src/contrib/convert_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/convert_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/convert_1.56.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: BiocCaseStudies, dyebias, OLIN Package: copa Version: 1.48.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 3d8e7a05a3662ec04a9972d6d0eb3e22 NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald source.ver: src/contrib/copa_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/copa_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/copa_1.48.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R Package: copynumber Version: 1.20.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 97d9be539499c63afc2ee91746473f59 NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen source.ver: src/contrib/copynumber_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/copynumber_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/copynumber_1.20.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R Package: CopywriteR Version: 2.12.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: d1a9333a383c7f2051173b68b3b38972 NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Thomas Kuilman URL: https://github.com/PeeperLab/CopywriteR source.ver: src/contrib/CopywriteR_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CopywriteR_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CopywriteR_2.12.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R Package: CoRegNet Version: 1.18.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 1f70d2e481bb7e1b5f7c2fcf3271a52a NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr source.ver: src/contrib/CoRegNet_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CoRegNet_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CoRegNet_1.18.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R Package: Cormotif Version: 1.26.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: d97774143216b5da8fe3cbff0d97f032 NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei source.ver: src/contrib/Cormotif_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Cormotif_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Cormotif_1.26.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R Package: CorMut Version: 1.22.0 Depends: methods,seqinr,igraph License: GPL-2 MD5sum: b47abf755c4865569b64e698604aa6a8 NeedsCompilation: no Title: Detect the correlated mutations based on selection pressure Description: CorMut provides functions for computing kaks for individual sites or specific amino acids and detecting correlated mutations among them. Three methods are provided for detecting correlated mutations ,including conditional selection pressure, mutual information and Jaccard index. The computation consists of two steps: First, the positive selection sites are detected; Second, the mutation correlations are computed among the positive selection sites. Note that the first step is optional. Meanwhile, CorMut facilitates the comparison of the correlated mutations between two conditions by the means of correlated mutation network. The reference sequence should be the first sequence of the sequence file, and does not allow the presence of gap. biocViews: Sequencing Author: Zhenpeng Li Maintainer: Zhenpeng Li source.ver: src/contrib/CorMut_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CorMut_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CorMut_1.22.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CorMut/inst/doc/CorMut.R Package: CORREP Version: 1.46.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 76a76f45985a665b47d53087e9fa8ce7 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu source.ver: src/contrib/CORREP_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CORREP_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CORREP_1.46.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R Package: coseq Version: 1.4.0 Depends: R (>= 3.4.1), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2 (>= 2.1.0), scales, HTSFilter, corrplot, HTSCluster (>= 2.0.8), gridExtra, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat License: GPL (>=3) MD5sum: dd9d8c9de1b6241957f41587785fed4a NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software Author: Andrea Rau, Cathy Maugis-Rabusseau, Antoine Godichon-Baggioni Maintainer: Andrea Rau VignetteBuilder: knitr source.ver: src/contrib/coseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/coseq_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/coseq_1.4.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R Package: cosmiq Version: 1.13.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 6fd67da773524aa0123052e29b04f5b8 NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: MassSpectrometry, Metabolomics Author: David Fischer , Christian Panse , Endre Laczko Maintainer: David Fischer , Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html source.ver: src/contrib/cosmiq_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cosmiq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cosmiq_1.13.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf, vignettes/cosmiq/inst/doc/poster.pdf vignetteTitles: cosmiq primer, 13th Annual Conference of the Metabolomics Society 2017 poster P-32 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R, vignettes/cosmiq/inst/doc/poster.R Package: COSNet Version: 1.14.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: d7f7be67f1db5e024f2af8b5922d0e07 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub source.ver: src/contrib/COSNet_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/COSNet_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/COSNet_1.14.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R Package: CountClust Version: 1.8.0 Depends: R (>= 3.4), ggplot2 (>= 2.1.0) Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) MD5sum: ecf2de1f9c02543715d19d452c9527f9 NeedsCompilation: no Title: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models Description: Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships. biocViews: RNASeq, GeneExpression, Clustering, Sequencing, StatisticalMethod, Software, Visualization Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/CountClust VignetteBuilder: knitr source.ver: src/contrib/CountClust_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CountClust_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CountClust_1.8.0.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R Package: covEB Version: 1.6.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: 2dec1729b3d69668777bc0926780f7b6 NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini source.ver: src/contrib/covEB_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/covEB_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/covEB_1.6.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R Package: CoverageView Version: 1.18.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 6d5b4af9ecd25e72abc8afd047e732ac NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy source.ver: src/contrib/CoverageView_1.18.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CoverageView_1.18.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R Package: covRNA Version: 1.6.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 6d3eb076b4248a2ba9925a5b8354ff00 NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr source.ver: src/contrib/covRNA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/covRNA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/covRNA_1.6.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R Package: cpvSNP Version: 1.12.0 Depends: R (>= 2.10), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: f9adc71d9ecbd4f228930715687c6bb6 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh source.ver: src/contrib/cpvSNP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cpvSNP_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cpvSNP_1.12.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R Package: cqn Version: 1.26.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 1cb39cc30a219c4d4f9b4feb7f1108ae NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen source.ver: src/contrib/cqn_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cqn_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cqn_1.26.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R importsMe: tweeDEseq Package: CRImage Version: 1.28.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 75e4c6a8197e10ec4a6249b30a402efa NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan source.ver: src/contrib/CRImage_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CRImage_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CRImage_1.28.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R Package: CRISPRseek Version: 1.20.0 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, BiocParallel, hash Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 7ee05ee150235a6601fd9cf5dd43542e NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. If GeneRfold and GeneR are installed (http://bioconductor.case.edu/bioconductor/2.8/bioc/html/GeneRfold.html, http://bioc.ism.ac.jp/packages/2.8/bioc/html/GeneR.html), then the minimum free energy and bracket notation of secondary structure of gRNA and gRNA backbone constant region will be included in the summary file. This package leverages Biostrings and BSgenome packages. biocViews: GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Benjamin R. Holmes, Hervé Pagès, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CRISPRseek_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CRISPRseek_1.20.0.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R dependsOnMe: crisprseekplus importsMe: GUIDEseq Package: crisprseekplus Version: 1.6.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocInstaller, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: f33ce9154794d961ca593ba9b4da5087 NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new source.ver: src/contrib/crisprseekplus_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/crisprseekplus_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/crisprseekplus_1.6.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R Package: CrispRVariants Version: 1.8.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 90cf919f7efb79bd7110f198a655b3f4 NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr source.ver: src/contrib/CrispRVariants_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CrispRVariants_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CrispRVariants_1.8.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R Package: crlmm Version: 1.38.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, SNPchip, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: abaf7504f64e3ca49980bde44e9b2ffd NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie source.ver: src/contrib/crlmm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/crlmm_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/crlmm_1.38.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R importsMe: VanillaICE suggestsMe: ArrayTV, oligoClasses, SNPchip Package: crossmeta Version: 1.6.0 Depends: R (>= 3.3) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocInstaller (>= 1.24.0), ccmap, DT (>= 0.2), data.table (>= 1.10.4), doParallel (>= 1.0.10), doRNG (>= 1.6), fdrtool (>= 1.2.15), foreach (>= 1.4.3), ggplot2 (>= 2.2.1), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), metap (>= 0.8), miniUI (>= 0.1.1), oligo (>= 1.38.0), pander (>= 0.6.0), plotly(>= 4.5.6), reshape (>= 0.8.6), RColorBrewer (>= 1.1.2), rdrop2 (>= 0.7.0), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), stats (>= 3.3.3) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat, ccdata License: MIT + file LICENSE MD5sum: 6bed54ef33a5912252418b3948c2ed3a NeedsCompilation: no Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression/pathway analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. Finally, effect size and pathway meta-analyses can proceed and the results graphically explored. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr source.ver: src/contrib/crossmeta_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/crossmeta_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/crossmeta_1.6.0.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap Package: CSAR Version: 1.32.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 771bab2beffbe2cd1620ba16835c7711 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino source.ver: src/contrib/CSAR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CSAR_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CSAR_1.32.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R importsMe: NarrowPeaks suggestsMe: NarrowPeaks Package: csaw Version: 1.14.1 Depends: R (>= 3.5), GenomicRanges, SummarizedExperiment, BiocParallel Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, S4Vectors, IRanges, GenomeInfoDb, Rhtslib, stats, Rcpp LinkingTo: Rhtslib (>= 1.12.1), zlibbioc, Rcpp Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: ea050f264cd943ee584882a385ecbb47 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun , Gordon Smyth Maintainer: Aaron Lun SystemRequirements: C++11 source.ver: src/contrib/csaw_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/csaw_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/csaw_1.14.1.tgz vignettes: vignettes/csaw/inst/doc/csaw.pdf, vignettes/csaw/inst/doc/csawUserGuide.pdf vignetteTitles: csaw Vignette, csawUserGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: diffHic, NADfinder, vulcan Package: CSSP Version: 1.18.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: c583b4da9d28e1720f5b4a34c0cfe067 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo source.ver: src/contrib/CSSP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CSSP_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CSSP_1.18.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R Package: ctc Version: 1.54.0 Depends: amap License: GPL-2 MD5sum: be117f460df80462dd59e2832480a0e2 NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc source.ver: src/contrib/ctc_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ctc_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ctc_1.54.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: multiClust Package: CTDquerier Version: 1.0.0 Depends: R (>= 3.4.0) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache, rappdirs Suggests: BiocStyle, knitr License: MIT + file LICENSE MD5sum: 50ff188d0082836f26c13b6506a62757 NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut, cre], Jaun R. Gonzalez [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr source.ver: src/contrib/CTDquerier_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CTDquerier_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CTDquerier_1.0.0.tgz vignettes: vignettes/CTDquerier/inst/doc/batch_query.html, vignettes/CTDquerier/inst/doc/case_study.html, vignettes/CTDquerier/inst/doc/vignette.html vignetteTitles: Simple comparison between CTDquerier R package and CTDbase Batch Query web tool, Case study on Environmental Chemicals and asthma-related genes, CTDquerier: A package to retrieve CTDbase data for downstream analysis and data visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CTDquerier/inst/doc/batch_query.R, vignettes/CTDquerier/inst/doc/case_study.R, vignettes/CTDquerier/inst/doc/vignette.R Package: ctsGE Version: 1.6.1 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 964f7e673fc002e9181979336f6d4360 NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues source.ver: src/contrib/ctsGE_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ctsGE_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ctsGE_1.6.1.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R Package: cummeRbund Version: 2.22.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 4a5a13c6654a95591386592e3e53376f NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff source.ver: src/contrib/cummeRbund_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cummeRbund_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cummeRbund_2.22.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependsOnMe: IsoformSwitchAnalyzeR, meshr, spliceR Package: customProDB Version: 1.20.2 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt(>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 571becb5d9b8baa860e998074ab941ac NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: RELEASE_3_7 git_last_commit: 7fb6349 git_last_commit_date: 2018-08-08 Date/Publication: 2018-08-09 source.ver: src/contrib/customProDB_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/customProDB_1.20.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/customProDB_1.20.2.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R importsMe: PGA Package: CVE Version: 1.6.0 Depends: R (>= 3.4.0) Imports: shiny, ConsensusClusterPlus, RColorBrewer, gplots, plyr, ggplot2, jsonlite, ape, WGCNA Suggests: knitr, rmarkdown, RTCGAToolbox, testthat, BiocStyle License: GPL-3 MD5sum: f8d0a2b07682f7ad315f45c7dff97932 NeedsCompilation: no Title: Cancer Variant Explorer Description: Shiny app for interactive variant prioritisation in precision oncology. The input file for CVE is the output file of the recently released Oncotator Variant Annotation tool summarising variant-centric information from 14 different publicly available resources relevant for cancer researches. Interactive priortisation in CVE is based on known germline and cancer variants, DNA repair genes and functional prediction scores. An optional feature of CVE is the exploration of the tumour-specific pathway context that is facilitated using co-expression modules generated from publicly available transcriptome data. Finally druggability of prioritised variants is assessed using the Drug Gene Interaction Database (DGIdb). biocViews: BiomedicalInformatics Author: Andreas Mock [aut, cre] Maintainer: Andreas Mock VignetteBuilder: knitr source.ver: src/contrib/CVE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/CVE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CVE_1.6.0.tgz vignettes: vignettes/CVE/inst/doc/CVE_tutorial.html, vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.html vignetteTitles: Cancer Variant Explorer (CVE) tutorial, Weighted gene co-expression network analysis with TCGA RNAseq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CVE/inst/doc/CVE_tutorial.R, vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.R Package: cycle Version: 1.34.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: e7735cb7d040e4904f00533180ba4a02 NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu source.ver: src/contrib/cycle_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cycle_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cycle_1.34.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R Package: cydar Version: 1.4.0 Depends: BiocParallel, SummarizedExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, S4Vectors, flowCore, Biobase, Rcpp LinkingTo: Rcpp Suggests: ncdfFlow, testthat, BiocGenerics, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 Archs: i386, x64 MD5sum: 848f5d2a8733a4bb28a3255ed491f572 NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/cydar_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cydar_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cydar_1.4.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differentially abundant subpopulations in mass cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R Package: CytoDx Version: 1.0.1 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr License: GPL-2 MD5sum: eb6c115268406c2eb912282548719d09 NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_7 git_last_commit: d966fb0 git_last_commit_date: 2018-07-31 Date/Publication: 2018-07-31 source.ver: src/contrib/CytoDx_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/CytoDx_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CytoDx_1.0.1.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R Package: cytofkit Version: 1.12.0 Depends: R (>= 3.4.0), ggplot2, plyr Imports: tcltk, grDevices, graphics, utils, stats, Rtsne, e1071, flowCore, gplots, colourpicker, VGAM, reshape2, ggrepel, shiny, shinyFiles, vegan, Biobase, doParallel, parallel, pdist, methods, destiny, FlowSOM(>= 1.4.0), igraph(>= 1.1.2), RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 7c5832c7e6c5466bb86353465cf52f95 NeedsCompilation: yes Title: cytofkit: an integrated mass cytometry data analysis pipeline Description: An integrated mass cytometry data analysis pipeline that enables simultaneous illustration of cellular diversity and progression. biocViews: FlowCytometry, GUI, CellBiology, Clustering, DimensionReduction, BiomedicalInformatics Author: Jinmiao Chen, Hao Chen, Matthew Myint Maintainer: Jinmiao Chen , Matthew Myint URL: https://github.com/JinmiaoChenLab/cytofkit VignetteBuilder: knitr BugReports: https://github.com/JinmiaoChenLab/cytofkit/issues source.ver: src/contrib/cytofkit_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cytofkit_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cytofkit_1.12.0.tgz vignettes: vignettes/cytofkit/inst/doc/cytofkit_example.html, vignettes/cytofkit/inst/doc/cytofkit_shinyAPP.html, vignettes/cytofkit/inst/doc/cytofkit_workflow.html vignetteTitles: Quick Start, ShinyAPP tutorial, Analysis Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofkit/inst/doc/cytofkit_example.R, vignettes/cytofkit/inst/doc/cytofkit_shinyAPP.R, vignettes/cytofkit/inst/doc/cytofkit_workflow.R Package: cytolib Version: 1.2.0 Depends: R (>= 3.4) LinkingTo: BH(>= 1.62.0-1), RProtoBufLib(>= 1.1.2) Suggests: knitr License: Artistic-2.0 MD5sum: e07510592cbe91e75dd53a170f8642f7 NeedsCompilation: no Title: C++ infrastructure for representing and interacting with the gated cytometry Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr source.ver: src/contrib/cytolib_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/cytolib_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/cytolib_1.2.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytolib/inst/doc/cytolib.R linksToMe: flowWorkspace Package: CytoML Version: 1.6.5 Imports: flowCore (>= 1.43.10), flowWorkspace (>= 3.28.1), openCyto (>= 1.11.3), XML, data.table, flowUtils (>= 1.35.7), jsonlite, RBGL, ncdfFlow, Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc, plyr, grDevices, methods, ggcyto (>= 1.8.2) Suggests: testthat, flowWorkspaceData (>= 2.11.1), knitr, parallel License: Artistic-2.0 MD5sum: 47c91235ac664464f37af482fff39d13 NeedsCompilation: no Title: GatingML interface for openCyto Description: This package is designed to use GatingML2.0 as the standard format to exchange the gated data with other software platform. biocViews: FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_7 git_last_commit: d484433 git_last_commit_date: 2018-07-17 Date/Publication: 2018-07-17 source.ver: src/contrib/CytoML_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/CytoML_1.6.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/CytoML_1.6.5.tgz vignettes: vignettes/CytoML/inst/doc/HowToExportGatingSet.html, vignettes/CytoML/inst/doc/HowToParseGatingML.html vignetteTitles: How to export a GatingSet to GatingML, How to parse gatingML into a GatingSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoML/inst/doc/HowToExportGatingSet.R, vignettes/CytoML/inst/doc/HowToParseGatingML.R Package: dada2 Version: 1.8.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.2), methods (>= 3.2.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), data.table (>= 1.9.4), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: 0237782cd8b059208745e92be56d2688 NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and genus-species assignment by exact matching. biocViews: Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues source.ver: src/contrib/dada2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dada2_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dada2_1.8.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R Package: dagLogo Version: 1.18.0 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, BiocStyle, knitr, rmarkdown, testthat, UniProt.ws License: GPL (>=2) MD5sum: 0865beff3e113d822ff6371add513201 NeedsCompilation: no Title: dagLogo Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/dagLogo_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dagLogo_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dagLogo_1.18.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R Package: daMA Version: 1.52.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 917470083a0a53f87b60c59721dda27c NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de source.ver: src/contrib/daMA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/daMA_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/daMA_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DaMiRseq Version: 1.4.2 Depends: R (>= 3.4), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 8dc959742f3298d7d0d89f3464ba0889 NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr source.ver: src/contrib/DaMiRseq_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/DaMiRseq_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DaMiRseq_1.4.2.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS Package: DAPAR Version: 1.12.11 Depends: R (>= 3.5) Imports: MSnbase, RColorBrewer,stats,preprocessCore,Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, doParallel, stringr, parallel, foreach,grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot, imp4p (>= 0.5), highcharter (>= 0.5.0), DAPARdata (>= 1.10.2), siggenes, graph, lme4, readxl, clusterProfiler, dplyr, tidyr,AnnotationDbi, tidyverse, imputeLCMD Suggests: BiocGenerics, Biobase, testthat, BiocStyle, Prostar License: Artistic-2.0 MD5sum: cacf7b6d0276a60419fa05e663b0441a NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: This package contains a collection of functions for the visualisation and the statistical analysis of proteomic data. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb] Maintainer: Samuel Wieczorek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_7 git_last_commit: 9f6641c git_last_commit_date: 2018-07-12 Date/Publication: 2018-07-12 source.ver: src/contrib/DAPAR_1.12.11.tar.gz win.binary.ver: bin/windows/contrib/3.5/DAPAR_1.12.11.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DAPAR_1.12.11.tgz vignettes: vignettes/DAPAR/inst/doc/intro.pdf, vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: DAPAR One Page Introduction, Prostar user manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar Package: DART Version: 1.28.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 7f1765cde9dbcf1c6104e84593b0783a NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng source.ver: src/contrib/DART_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DART_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DART_1.28.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R Package: DBChIP Version: 1.24.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 66eaf6af1df452512d31dff9fdd76631 NeedsCompilation: no Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang source.ver: src/contrib/DBChIP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DBChIP_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DBChIP_1.24.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DBChIP/inst/doc/DBChIP.R importsMe: metagene Package: dcGSA Version: 1.8.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 1752bb1801ddef70929909845e0294eb NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr source.ver: src/contrib/dcGSA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dcGSA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dcGSA_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DChIPRep Version: 1.10.0 Depends: R (>= 3.4), DESeq2 Imports: methods, stats, utils, ggplot2, fdrtool, reshape2, GenomicRanges, SummarizedExperiment, smoothmest, plyr, tidyr, assertthat, S4Vectors, purrr, soGGi, ChIPpeakAnno Suggests: mgcv, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENCE MD5sum: 230851b8625092ed5203ed096e465fc5 NeedsCompilation: no Title: DChIPRep - Analysis of chromatin modification ChIP-Seq data with replication Description: The DChIPRep package implements a methodology to assess differences between chromatin modification profiles in replicated ChIP-Seq studies as described in Chabbert et. al - http://www.dx.doi.org/10.15252/msb.20145776. A detailed description of the method is given in the software paper at https://doi.org/10.7717/peerj.1981 biocViews: Sequencing, ChIPSeq, WholeGenome Author: Bernd Klaus [aut, cre], Christophe Chabbert [aut], Sebastian Gibb [ctb] Maintainer: Bernd Klaus VignetteBuilder: knitr source.ver: src/contrib/DChIPRep_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DChIPRep_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DChIPRep_1.10.0.tgz vignettes: vignettes/DChIPRep/inst/doc/DChIPRepVignette.html vignetteTitles: DChIPRepVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DChIPRep/inst/doc/DChIPRepVignette.R Package: ddCt Version: 1.36.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 0b5d545dbf4a2327f231365a5aa8f2e1 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang source.ver: src/contrib/ddCt_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ddCt_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ddCt_1.36.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R Package: ddPCRclust Version: 1.0.1 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: d969de16d177f418644cbe53a41f1974 NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues source.ver: src/contrib/ddPCRclust_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ddPCRclust_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ddPCRclust_1.0.1.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R Package: debrowser Version: 1.8.5 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, pathview, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, baySeq, d3heatmap, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, V8, methods, sva, RCurl, googleAuthR, enrichplot, colourpicker, plotly, heatmaply, Harman Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: b160c6105f82eb76279fd747096aa577 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_7 git_last_commit: 15dc641 git_last_commit_date: 2018-09-26 Date/Publication: 2018-09-26 source.ver: src/contrib/debrowser_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/debrowser_1.8.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/debrowser_1.8.5.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R Package: DECIPHER Version: 2.8.1 Depends: R (>= 3.3.0), Biostrings (>= 2.35.12), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 768c0c20e853753683dc868ba046945f NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome Author: Erik Wright Maintainer: Erik Wright source.ver: src/contrib/DECIPHER_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/DECIPHER_2.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DECIPHER_2.8.1.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R importsMe: metagenomeFeatures, openPrimeR Package: DEComplexDisease Version: 1.0.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 3fdda07a48143c1b20edeb3024627909 NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr source.ver: src/contrib/DEComplexDisease_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEComplexDisease_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEComplexDisease_1.0.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R Package: DeconRNASeq Version: 1.22.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 4e43689ad4288fd9ee5e75ca8b7550f7 NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong source.ver: src/contrib/DeconRNASeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DeconRNASeq_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DeconRNASeq_1.22.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R Package: decontam Version: 1.0.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: d273c7ba53b90a21cdd3cc9e833219fc NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Nicole Marie Davis Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues source.ver: src/contrib/decontam_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/decontam_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/decontam_1.0.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R Package: DEDS Version: 1.54.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: fa007b278396e175b8d541ac6cb68033 NeedsCompilation: yes Title: Differential Expression via Distance Summary for Microarray Data Description: This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach. biocViews: Microarray, DifferentialExpression Author: Yuanyuan Xiao , Jean Yee Hwa Yang . Maintainer: Yuanyuan Xiao source.ver: src/contrib/DEDS_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEDS_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEDS_1.54.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEDS/inst/doc/DEDS.R Package: DeepBlueR Version: 1.6.0 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) MD5sum: 02d10f3abea9def708054945dad00c1a NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List VignetteBuilder: knitr source.ver: src/contrib/DeepBlueR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DeepBlueR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DeepBlueR_1.6.0.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R Package: deepSNV Version: 1.26.1 Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.12.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 8fdf5131a5a9fa1b6421aa10a14261d6 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung URL: http://github.com/gerstung-lab/deepSNV VignetteBuilder: knitr source.ver: src/contrib/deepSNV_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/deepSNV_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/deepSNV_1.26.1.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R suggestsMe: GenomicFiles Package: DEFormats Version: 1.8.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2bed67bb5735f4079125740a9e23df2c NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues source.ver: src/contrib/DEFormats_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEFormats_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEFormats_1.8.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal Package: DEGraph Version: 1.32.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: 3c0c62a176f68dc7df21d6a9444497e6 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob source.ver: src/contrib/DEGraph_1.32.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R Package: DEGreport Version: 1.16.0 Depends: R (>= 3.4.0), quantreg Imports: utils, methods, Biobase, BiocGenerics, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, grid, ggrepel, grDevices, knitr, logging, magrittr, Nozzle.R1, psych, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: cc27c8ed97451f83be2d60b9137910e8 NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb] Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/DEGreport_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEGreport_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEGreport_1.16.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R Package: DEGseq Version: 1.34.1 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 47cae96dc327ff6151fabdd5ca11b462 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression Author: Likun Wang and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_7 git_last_commit: 90cd7a6 git_last_commit_date: 2018-08-04 Date/Publication: 2018-08-04 source.ver: src/contrib/DEGseq_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEGseq_1.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEGseq_1.34.1.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R Package: DelayedArray Version: 0.6.6 Depends: R (>= 3.4), methods, stats4, matrixStats, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.43), IRanges (>= 2.11.17), BiocParallel Imports: stats Suggests: Matrix, HDF5Array, genefilter, SummarizedExperiment, airway, pryr, knitr, BiocStyle, RUnit License: Artistic-2.0 MD5sum: 1a99d1d760d400b3fb52f1cd24dd3941 NeedsCompilation: no Title: Delayed operations on array-like objects Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès Maintainer: Hervé Pagès VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_7 git_last_commit: bdb0ac0 git_last_commit_date: 2018-09-10 Date/Publication: 2018-09-11 source.ver: src/contrib/DelayedArray_0.6.6.tar.gz win.binary.ver: bin/windows/contrib/3.5/DelayedArray_0.6.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DelayedArray_0.6.6.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R dependsOnMe: DelayedMatrixStats, GDSArray, HDF5Array, rhdf5client, singleCellTK, SummarizedExperiment importsMe: beachmat, bsseq, CAGEr, ChIPpeakAnno, clusterExperiment, DEScan2, DSS, hipathia, minfi, scater, scmeth, scran suggestsMe: S4Vectors Package: DelayedMatrixStats Version: 1.2.0 Depends: DelayedArray (>= 0.5.27) Imports: methods, matrixStats (>= 0.53.1), Matrix, S4Vectors (>= 0.17.5), IRanges Suggests: testthat, HDF5Array (>= 1.7.10), knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem License: MIT + file LICENSE MD5sum: 71571312bfde9859706473d89b03b270 NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues source.ver: src/contrib/DelayedMatrixStats_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DelayedMatrixStats_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DelayedMatrixStats_1.2.0.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: bsseq, dmrseq, minfi, scater, scran Package: deltaGseg Version: 1.20.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 98aab0fb38ebb5038ea9ffc6d1021763 NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/deltaGseg_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/deltaGseg_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/deltaGseg_1.20.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R Package: DeMAND Version: 1.10.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: f1bd4e1209cffd6c29008893aa1b695a NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez source.ver: src/contrib/DeMAND_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DeMAND_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DeMAND_1.10.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R Package: DEP Version: 1.2.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment, MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 MD5sum: aec1d01b1226e024b2522858f3049231 NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr source.ver: src/contrib/DEP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEP_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEP_1.2.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R Package: derfinder Version: 1.14.0 Depends: R(>= 3.2) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel, bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.9.38) Suggests: BiocStyle (>= 2.5.19), biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 8d611a7224089f24485c9748c8d0fe05 NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software Author: Leonardo Collado-Torres [aut, cre], Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ source.ver: src/contrib/derfinder_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/derfinder_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/derfinder_1.14.0.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: derfinderPlot, recount, regionReport Package: derfinderHelper Version: 1.14.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle (>= 2.5.19), rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: 4c3405cf3491513668e00a1e1dafd9ed NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper source.ver: src/contrib/derfinderHelper_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/derfinderHelper_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/derfinderHelper_1.14.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder Package: derfinderPlot Version: 1.14.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, RefManageR, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: ed745d00989b3a363606f7b7fde4b881 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot source.ver: src/contrib/derfinderPlot_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/derfinderPlot_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/derfinderPlot_1.14.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R suggestsMe: derfinder, regionReport Package: DEScan2 Version: 1.0.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors, SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 Archs: i386, x64 MD5sum: 79e710a8d6ce7cab7da68af4d6171240 NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr source.ver: src/contrib/DEScan2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEScan2_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEScan2_1.0.0.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R Package: DESeq Version: 1.32.0 Depends: BiocGenerics (>= 0.7.5), Biobase (>= 2.21.7), locfit, lattice Imports: genefilter, geneplotter, methods, MASS, RColorBrewer Suggests: pasilla (>= 0.2.10), vsn, gplots License: GPL (>= 3) Archs: i386, x64 MD5sum: 03a53dcb13b33bb128f0ff71a4e6687e NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution biocViews: Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq source.ver: src/contrib/DESeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DESeq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DESeq_1.32.0.tgz vignettes: vignettes/DESeq/inst/doc/DESeq.pdf vignetteTitles: Analysing RNA-Seq data with the "DESeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq/inst/doc/DESeq.R dependsOnMe: DBChIP, metaseqR, Polyfit, SeqGSEA, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, DEsubs, easyRNASeq, EDASeq, EDDA, gCMAP, HTSFilter, rnaSeqMap, vulcan suggestsMe: BitSeq, compcodeR, dexus, DiffBind, ELBOW, gage, genefilter, regionReport, SSPA, XBSeq Package: DESeq2 Version: 1.20.0 Depends: S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, IHW, apeglm, ashr, tximport, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10) License: LGPL (>= 3) Archs: i386, x64 MD5sum: 304e489fa3cb41138dd2e9df2b62da32 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, Microbiome, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering Author: Michael Love, Simon Anders, Wolfgang Huber Maintainer: Michael Love URL: https://github.com/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown source.ver: src/contrib/DESeq2_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DESeq2_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DESeq2_1.20.0.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DChIPRep, DEXSeq, FourCSeq, rgsepd, TCC, XBSeq importsMe: anamiR, Anaquin, coseq, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, DiffBind, eegc, EnrichmentBrowser, FourCSeq, GDCRNATools, GenoGAM, HTSFilter, ideal, ImpulseDE2, IntEREst, isomiRs, JunctionSeq, kissDE, MLSeq, PathoStat, pcaExplorer, PowerExplorer, regionReport, ReportingTools, singleCellTK, SNPhood, srnadiff, systemPipeR, vidger suggestsMe: apeglm, biobroom, BiocGenerics, BioCor, CAGEr, compcodeR, derfinder, diffloop, gage, GenomicAlignments, GenomicRanges, Glimma, gsean, IHW, miRmine, OPWeight, phyloseq, progeny, recount, RUVSeq, scran, subSeq, SummarizedBenchmark, TFEA.ChIP, tximport, variancePartition, zinbwave Package: DEsingle Version: 1.0.5 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 4e30532eeb083290f728cab45be741e9 NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_7 git_last_commit: 3512fdd git_last_commit_date: 2018-09-21 Date/Publication: 2018-09-21 source.ver: src/contrib/DEsingle_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEsingle_1.0.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEsingle_1.0.5.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R Package: destiny Version: 2.10.2 Depends: R (>= 3.3.0) Imports: methods, graphics, grDevices, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, Biobase, BiocGenerics, SummarizedExperiment, ggplot2, ggthemes, VIM, proxy, igraph, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: nbconvertR, testthat, FNN, tidyr, SingleCellExperiment Enhances: rgl, SingleCellExperiment License: GPL Archs: i386, x64 MD5sum: ad5b5cccdb4dfea0caa5fafc3901fdf4 NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut], Laleh Haghverdi [ctb], Maren Büttner [ctb], Fabian Theis [ctb], Carsten Marr [ctb], Florian Büttner [ctb] Maintainer: Philipp Angerer URL: https://github.com/theislab/destiny SystemRequirements: C++11 VignetteBuilder: nbconvertR BugReports: https://github.com/theislab/destiny/issues source.ver: src/contrib/destiny_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/destiny_2.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/destiny_2.10.2.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.pdf, vignettes/destiny/inst/doc/Diffusion-Maps.pdf, vignettes/destiny/inst/doc/DPT.pdf, vignettes/destiny/inst/doc/Global-Sigma.pdf, vignettes/destiny/inst/doc/tidyverse.pdf vignetteTitles: Diffusion-Map-recap.pdf, Diffusion-Maps.pdf, DPT.pdf, Global-Sigma.pdf, tidyverse.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: cytofkit suggestsMe: monocle, scater Package: DEsubs Version: 1.6.3 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, DESeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: 7fca3df0c915e7eb3db1404b12ea893f NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_7 git_last_commit: 8217ad1 git_last_commit_date: 2018-08-31 Date/Publication: 2018-09-01 source.ver: src/contrib/DEsubs_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEsubs_1.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEsubs_1.6.3.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R Package: DEXSeq Version: 1.26.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr License: GPL (>= 3) MD5sum: c739c9b02dc599b34805c945c87b2513 NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr source.ver: src/contrib/DEXSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DEXSeq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DEXSeq_1.26.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.pdf vignetteTitles: Analyzing RNA-seq data for differential exon usage with the "DEXSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R importsMe: IntEREst suggestsMe: GenomicRanges, stageR, subSeq Package: dexus Version: 1.20.0 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 9d122bfbc450f1c36305dc42f6492a03 NeedsCompilation: yes Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer source.ver: src/contrib/dexus_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dexus_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dexus_1.20.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dexus/inst/doc/dexus.R Package: DFP Version: 1.38.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 0d28bb71635dd2d39850e5a409e5124c NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez source.ver: src/contrib/DFP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DFP_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DFP_1.38.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R Package: DiffBind Version: 2.8.0 Depends: R (>= 3.5), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, edgeR, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, zlibbioc, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools, DESeq2, methods, graphics, ggrepel LinkingTo: Rsamtools (>= 1.19.38), Rcpp Suggests: DESeq, BiocStyle, testthat Enhances: rgl, XLConnect License: Artistic-2.0 Archs: i386, x64 MD5sum: fc0285d1ceec8b3b0af6fb2ba98db404 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq, DifferentialPeakCalling Author: Rory Stark, Gord Brown Maintainer: Rory Stark source.ver: src/contrib/DiffBind_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DiffBind_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DiffBind_2.8.0.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan Package: diffcoexp Version: 1.0.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) MD5sum: 42e3c35cd22e9f9c2327367c98358eb7 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp source.ver: src/contrib/diffcoexp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffcoexp_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffcoexp_1.0.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R Package: diffcyt Version: 1.0.10 Depends: R (>= 3.5.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: 300d03478b4902ba68a96370f2c6e42e NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on (i) high-resolution clustering and (ii) empirical Bayes moderated tests adapted from transcriptomics. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_7 git_last_commit: 7792f0c git_last_commit_date: 2018-08-15 Date/Publication: 2018-08-15 source.ver: src/contrib/diffcyt_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffcyt_1.0.10.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffcyt_1.0.10.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R suggestsMe: CATALYST Package: diffGeneAnalysis Version: 1.62.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 8915ed2e6e8312f885323c6af3457315 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi source.ver: src/contrib/diffGeneAnalysis_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffGeneAnalysis_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffGeneAnalysis_1.62.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R Package: diffHic Version: 1.12.1 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp LinkingTo: Rhtslib (>= 1.12.1), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix License: GPL-3 Archs: i386, x64 MD5sum: 31aca451907aecf80ba85635e35f800c NeedsCompilation: yes Title: Differential Analyis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun Maintainer: Aaron Lun SystemRequirements: C++11 source.ver: src/contrib/diffHic_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffHic_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffHic_1.12.1.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DiffLogo Version: 2.4.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: 57ad5379233d89d6dcda003ec2dd6c3a NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ VignetteBuilder: knitr BugReports: https://github.com/mgledi/DiffLogo/issues source.ver: src/contrib/DiffLogo_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DiffLogo_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DiffLogo_2.4.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R Package: diffloop Version: 1.8.0 Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr, data.table, rtracklayer, pbapply, limma Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 6f3c6198a886338b4d7fc036549dc643 NeedsCompilation: no Title: Identifying differential DNA loops from chromatin topology data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau URL: https://github.com/aryeelab/diffloop VignetteBuilder: knitr BugReports: https://github.com/aryeelab/diffloop/issues source.ver: src/contrib/diffloop_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffloop_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffloop_1.8.0.tgz vignettes: vignettes/diffloop/inst/doc/diffloop.html vignetteTitles: diffloop: Identifying differential DNA loops from chromatin topology data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R Package: diffuStats Version: 0.104.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2 License: GPL-3 Archs: i386, x64 MD5sum: 0549cfc81fb4596efbd60ff5e8846075 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork Author: Sergio Picart-Armada and Alexandre Perera-Lluna Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/diffuStats_0.104.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/diffuStats_0.104.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diffuStats_0.104.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R Package: diggit Version: 1.12.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: e728146cfa19f8d9df57f23c43792ccd NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez source.ver: src/contrib/diggit_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/diggit_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/diggit_1.12.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R Package: Director Version: 1.6.0 Depends: R (>= 3.3) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: e3461790a2fbe5a30898375bf7a177d4 NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues source.ver: src/contrib/Director_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Director_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Director_1.6.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R Package: DirichletMultinomial Version: 1.22.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 9e80433171af2e53fc1b28e38eab257e NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl source.ver: src/contrib/DirichletMultinomial_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DirichletMultinomial_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DirichletMultinomial_1.22.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: TFBSTools Package: discordant Version: 1.4.0 Depends: R (>= 3.4) Imports: Biobase, stats, biwt, gtools, MASS, tools Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: f88bb42889ed807517a24837d2c590b5 NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al. biocViews: BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [cre,aut], Katerina Kechris [aut] Maintainer: Charlotte Siska URL: https://github.com/siskac/discordant VignetteBuilder: knitr source.ver: src/contrib/discordant_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/discordant_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/discordant_1.4.0.tgz vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf vignetteTitles: Discordant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R Package: dks Version: 1.26.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 2c03b4f5c794e0211a2edf7b5705df7b NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek source.ver: src/contrib/dks_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dks_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dks_1.26.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R Package: DMCHMM Version: 1.2.0 Depends: R (>= 3.4.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr License: GPL-3 MD5sum: 0c31fc366315b0c48d26dd9c4ea870d8 NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: DMCHMM is a novel profiling tool for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues source.ver: src/contrib/DMCHMM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DMCHMM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DMCHMM_1.2.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R Package: DMRcaller Version: 1.12.7 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 37b811cc0fa7dc94f5bd73e36ffa3c8c NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_7 git_last_commit: 474898b git_last_commit_date: 2018-09-12 Date/Publication: 2018-09-12 source.ver: src/contrib/DMRcaller_1.12.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/DMRcaller_1.12.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DMRcaller_1.12.7.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R Package: DMRcate Version: 1.16.0 Depends: R (>= 3.3.0), minfi, DSS, DMRcatedata Imports: limma, missMethyl, GenomicRanges, parallel, methods, graphics, plyr, Gviz, IRanges, stats, utils, S4Vectors Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 License: file LICENSE MD5sum: 1780bd7ec92c5df80a8b99a4bd911994 NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulphite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr source.ver: src/contrib/DMRcate_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DMRcate_1.16.0.zip vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R importsMe: MEAL Package: DMRforPairs Version: 1.16.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: fda421bd6cf2f96e68d721a8477b331b NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ source.ver: src/contrib/DMRforPairs_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DMRforPairs_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DMRforPairs_1.16.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R Package: DMRScan Version: 1.6.0 Depends: R (>= 3.4.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 0201a5e907c283faa7e4faca66acba42 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active source.ver: src/contrib/DMRScan_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DMRScan_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DMRScan_1.6.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.pdf vignetteTitles: DMRScan.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R Package: dmrseq Version: 1.0.14 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.12), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 96f6fffa2f2f86978555ec32fa42ff2e NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer , Sutirtha Chakraborty , Yuval Benjamini , Rafael Irizarry Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_7 git_last_commit: 59751a4 git_last_commit_date: 2018-08-07 Date/Publication: 2018-08-07 source.ver: src/contrib/dmrseq_1.0.14.tar.gz win.binary.ver: bin/windows/contrib/3.5/dmrseq_1.0.14.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dmrseq_1.0.14.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R Package: DNABarcodes Version: 1.10.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 957345b09b3035eb79823b3556a1e6fc NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr source.ver: src/contrib/DNABarcodes_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DNABarcodes_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DNABarcodes_1.10.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R Package: DNAcopy Version: 1.54.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: 0b7079fa0282cd22416ae62eb7930ccb NeedsCompilation: yes Title: DNA copy number data analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan source.ver: src/contrib/DNAcopy_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DNAcopy_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DNAcopy_1.54.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, snapCGH importsMe: ADaCGH2, AneuFinder, ArrayTV, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MDTS, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH suggestsMe: beadarraySNP, cn.mops, fastseg, genoset Package: DNAshapeR Version: 1.8.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 Archs: i386, x64 MD5sum: 9ad38f00e62f0e7044c182213e55514f NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr source.ver: src/contrib/DNAshapeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DNAshapeR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DNAshapeR_1.8.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R Package: DominoEffect Version: 1.0.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges Suggests: knitr, testthat License: GPL (>= 3) MD5sum: c310e666a0c33fba35502cd59c7c7e09 NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr source.ver: src/contrib/DominoEffect_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DominoEffect_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DominoEffect_1.0.0.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R Package: doppelgangR Version: 1.8.0 Depends: R (>= 3.3), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, ROCR, pROC, RUnit, simulatorZ, proxy License: GPL (>=2.0) MD5sum: 6f44a32618f56c50becdcef76a6237bb NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron, Markus Riester, Marcel Ramos Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues source.ver: src/contrib/doppelgangR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/doppelgangR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/doppelgangR_1.8.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R Package: DOQTL Version: 1.16.2 Depends: R (>= 3.0.0), BSgenome.Mmusculus.UCSC.mm10, GenomicRanges, VariantAnnotation Imports: annotate, annotationTools, biomaRt, Biobase, BiocGenerics, corpcor, doParallel, foreach, fpc, hwriter, IRanges, iterators, mclust, QTLRel, regress, rhdf5, Rsamtools, RUnit, XML Suggests: MUGAExampleData, doMPI License: GPL-3 Archs: i386, x64 MD5sum: 61e049ad70c756087f790d0866ed7703 NeedsCompilation: yes Title: Genotyping and QTL Mapping in DO Mice Description: DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel Author: Daniel Gatti, Karl Broman, Andrey Shabalin, Petr Simecek Maintainer: Daniel Gatti URL: http://do.jax.org git_url: https://git.bioconductor.org/packages/DOQTL git_branch: RELEASE_3_7 git_last_commit: 58a24f2 git_last_commit_date: 2018-07-09 Date/Publication: 2018-07-09 source.ver: src/contrib/DOQTL_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/DOQTL_1.16.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DOQTL_1.16.2.tgz vignettes: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.pdf vignetteTitles: QTL Mapping using Diversity Outbred Mice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.R Package: Doscheda Version: 1.2.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, d3heatmap, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e6c3a24c5c2001343768eb63f3e6956c NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr source.ver: src/contrib/Doscheda_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Doscheda_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Doscheda_1.2.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R Package: DOSE Version: 3.6.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), methods, qvalue, reshape2, S4Vectors, stats, utils Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: 40619a9505c52a3fdbf514c12f7b83d6 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_7 git_last_commit: f2967f0 git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-20 source.ver: src/contrib/DOSE_3.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/DOSE_3.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DOSE_3.6.1.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html, vignettes/DOSE/inst/doc/enrichmentAnalysis.html, vignettes/DOSE/inst/doc/GSEA.html, vignettes/DOSE/inst/doc/semanticAnalysis.html vignetteTitles: 00 DOSE introduction, 02 Disease enrichment analysis, 03 Disease GSEA, 01 DOSE semantic similarity analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R, vignettes/DOSE/inst/doc/enrichmentAnalysis.R, vignettes/DOSE/inst/doc/GSEA.R, vignettes/DOSE/inst/doc/semanticAnalysis.R importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot, facopy, GDCRNATools, LINC, MAGeCKFlute, meshes, miRsponge, MoonlightR, PathwaySplice, ReactomePA suggestsMe: GOSemSim Package: drawProteins Version: 1.0.0 Depends: R (>= 3.4) Imports: ggplot2, httr, dplyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 6be12bf6bc65d74651622f0ad9fd48cd NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new source.ver: src/contrib/drawProteins_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/drawProteins_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/drawProteins_1.0.0.tgz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R Package: DRIMSeq Version: 1.8.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: 95d49814a728d1b80be1237947d384ea NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr source.ver: src/contrib/DRIMSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DRIMSeq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DRIMSeq_1.8.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R importsMe: IsoformSwitchAnalyzeR Package: DriverNet Version: 1.20.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 2191d56cd8dcc78042f968b3775e73a9 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding source.ver: src/contrib/DriverNet_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DriverNet_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DriverNet_1.20.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R Package: DropletUtils Version: 1.0.3 Depends: R (>= 3.5), BiocParallel, SingleCellExperiment Imports: S4Vectors, Matrix, methods, utils, stats, edgeR, Rcpp, rhdf5 LinkingTo: Rcpp, beachmat, Rhdf5lib Suggests: testthat, beachmat, knitr, BiocStyle, rmarkdown, HDF5Array License: GPL-3 Archs: i386, x64 MD5sum: 4f1ec5d4c5990a15f274ed0efda2b87d NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut, cre], Jonathan Griffiths [ctb], Davis McCarthy [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_7 git_last_commit: 076f4fa git_last_commit_date: 2018-08-07 Date/Publication: 2018-08-07 source.ver: src/contrib/DropletUtils_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/DropletUtils_1.0.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DropletUtils_1.0.3.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R suggestsMe: scater Package: DrugVsDisease Version: 2.22.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: f13c41520f5d1f1324c4824d7b83242d NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez source.ver: src/contrib/DrugVsDisease_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DrugVsDisease_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DrugVsDisease_2.22.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R Package: dSimer Version: 1.6.0 Depends: R (>= 3.3.0), igraph (>= 1.0.1) Imports: stats, Rcpp (>= 0.11.3), ggplot2, reshape2, GO.db, org.Hs.eg.db, AnnotationDbi, graphics LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: cfeb9b17e5baaf4ab6a7fd6650c0ef0e NeedsCompilation: yes Title: Integration of Disease Similarity Methods Description: dSimer is an R package which provides computation of nine methods for measuring disease-disease similarity, including a standard cosine similarity measure and eight function-based methods. The disease similarity matrix obtained from these nine methods can be visualized through heatmap and network. Biological data widely used in disease-disease associations study are also provided by dSimer. biocViews: Software, Visualization, Network Author: Min Li , Peng Ni with contributions from Zhihui Fei and Ping Huang. Maintainer: Peng Ni VignetteBuilder: knitr source.ver: src/contrib/dSimer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dSimer_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dSimer_1.6.0.tgz vignettes: vignettes/dSimer/inst/doc/dSimer.html vignetteTitles: Integration of Disease Similarity Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dSimer/inst/doc/dSimer.R Package: DSS Version: 2.28.0 Depends: R (>= 3.3), Biobase, bsseq, splines, methods Imports: stats, graphics, DelayedArray Suggests: BiocStyle License: GPL Archs: i386, x64 MD5sum: dd2f1d1139793d0a312d2cda0bf2fa3f NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, ChIPSeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu Maintainer: Hao Wu source.ver: src/contrib/DSS_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DSS_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DSS_2.28.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.pdf vignetteTitles: Differential expression for RNA-seq data with dispersion shrinkage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R dependsOnMe: DMRcate importsMe: kissDE Package: DTA Version: 2.26.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: b4cfca31df258d43fc1063be34a98507 NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb source.ver: src/contrib/DTA_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DTA_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DTA_2.26.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R Package: dualKS Version: 1.40.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: 31c54afb79c400f82f9f4a12e7ae09a5 NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang source.ver: src/contrib/dualKS_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dualKS_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dualKS_1.40.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R Package: DupChecker Version: 1.18.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: d76dd473cbcac63bc4ee1c8b76709bdc NeedsCompilation: no Title: a package for checking high-throughput genomic data redundancy in meta-analysis Description: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. biocViews: Preprocessing Author: Quanhu Sheng, Yu Shyr, Xi Chen Maintainer: "Quanhu SHENG" VignetteBuilder: knitr source.ver: src/contrib/DupChecker_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DupChecker_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DupChecker_1.18.0.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DupChecker/inst/doc/DupChecker.R Package: dupRadar Version: 1.10.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 68f6447b16abcabc6168f1e093723bf7 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein VignetteBuilder: knitr source.ver: src/contrib/dupRadar_1.10.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dupRadar_1.10.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R Package: dyebias Version: 1.40.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 74648af31f030fd76babb95ba20cf4f4 NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad source.ver: src/contrib/dyebias_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/dyebias_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/dyebias_1.40.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R Package: DynDoc Version: 1.58.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: f091be3a53421bbd58c2080c66a7c4ce NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/DynDoc_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/DynDoc_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/DynDoc_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.21.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: 2c43b17e9dc46cedf574f1570c03a603 NeedsCompilation: no Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain source.ver: src/contrib/EasyqpcR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EasyqpcR_1.21.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EasyqpcR_1.21.0.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.R Package: easyRNASeq Version: 2.16.0 Imports: Biobase (>= 2.39.1), BiocGenerics (>= 0.25.1), BiocParallel (>= 1.13.1), biomaRt (>= 2.35.8), Biostrings (>= 2.47.6), DESeq (>= 1.31.0), edgeR (>= 3.21.6), GenomeInfoDb (>= 1.15.2), genomeIntervals (>= 1.35.1), GenomicAlignments (>= 1.15.6), GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 1.9.9), graphics, IRanges (>= 2.13.13), LSD (>= 3.0), locfit, methods, parallel, Rsamtools (>= 1.31.2), S4Vectors (>= 0.17.25), ShortRead (>= 1.37.1), utils Suggests: BiocStyle (>= 2.7.8), BSgenome (>= 1.39.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.31) License: Artistic-2.0 MD5sum: f0cfa4159afe50bc1e36292a0181e691 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr source.ver: src/contrib/easyRNASeq_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/easyRNASeq_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/easyRNASeq_2.16.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR suggestsMe: SeqGSEA Package: EBarrays Version: 2.44.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 3723cfda3f388abe18d6c4ff75479390 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan source.ver: src/contrib/EBarrays_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBarrays_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBarrays_2.44.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category Package: EBcoexpress Version: 1.24.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: 8036b363617f8ae704232b871782048b NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson source.ver: src/contrib/EBcoexpress_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBcoexpress_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBcoexpress_1.24.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R dependsOnMe: SRGnet Package: EBImage Version: 4.22.1 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL Archs: i386, x64 MD5sum: 4325ca122d55e5db75637acf93b3ba38 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_7 git_last_commit: e7c6f12 git_last_commit_date: 2018-08-03 Date/Publication: 2018-08-04 source.ver: src/contrib/EBImage_4.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBImage_4.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBImage_4.22.1.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: bnbc, flowCHIC, heatmaps, MaxContrastProjection, yamss suggestsMe: HilbertVis, tofsims Package: EBSEA Version: 1.8.0 Imports: edgeR, limma, graphics, stats, plyr License: GPL-2 MD5sum: f6f52c6fe9de44388637aa82431a2f1a NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood source.ver: src/contrib/EBSEA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBSEA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBSEA_1.8.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.pdf vignetteTitles: EBSEA: Exon Based Strategy for Expression Analysis of genes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R Package: EBSeq Version: 1.20.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 522be9a8378d46946e586b1feb1060dd NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBSeq_1.20.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR Package: EBSeqHMM Version: 1.14.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 8840ca8211017b31809b3d653b90c822 NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeqHMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EBSeqHMM_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EBSeqHMM_1.14.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R Package: ecolitk Version: 1.52.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 0df8868a36b891ecd2049474af02b999 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/ecolitk_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ecolitk_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ecolitk_1.52.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R Package: EDASeq Version: 2.14.1 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), DESeq, aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth License: Artistic-2.0 MD5sum: a131f3875a4448f840ef2885e8847e5e NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_7 git_last_commit: e2c7e38 git_last_commit_date: 2018-06-21 Date/Publication: 2018-06-22 source.ver: src/contrib/EDASeq_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/EDASeq_2.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EDASeq_2.14.1.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.pdf vignetteTitles: EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq importsMe: DaMiRseq, EnrichmentBrowser, TCGAbiolinks suggestsMe: DEScan2, HTSFilter Package: EDDA Version: 1.18.0 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 278c4c0a0ad4341330d48fe16c369ec7 NeedsCompilation: yes Title: Experimental Design in Differential Abundance analysis Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527). biocViews: Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527 source.ver: src/contrib/EDDA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EDDA_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EDDA_1.18.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: edge Version: 2.12.2 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: i386, x64 MD5sum: 832b99d3acc58495078251dd64d68192 NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_7 git_last_commit: dfdcbff git_last_commit_date: 2018-08-30 Date/Publication: 2018-08-30 source.ver: src/contrib/edge_2.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/edge_2.12.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/edge_2.12.2.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R Package: edgeR Version: 3.22.5 Depends: R (>= 2.15.0), limma (>= 3.34.5) Imports: graphics, stats, utils, methods, locfit, Rcpp LinkingTo: Rcpp Suggests: AnnotationDbi, org.Hs.eg.db, readr, splines License: GPL (>=2) Archs: i386, x64 MD5sum: b60126e1fa13c19411beda9856862353 NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison Author: Yunshun Chen , Aaron Lun , Davis McCarthy , Xiaobei Zhou , Mark Robinson , Gordon Smyth Maintainer: Yunshun Chen , Aaron Lun , Mark Robinson , Davis McCarthy , Gordon Smyth URL: http://bioinf.wehi.edu.au/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_7 git_last_commit: 44461aa git_last_commit_date: 2018-09-26 Date/Publication: 2018-10-02 source.ver: src/contrib/edgeR_3.22.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/edgeR_3.22.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/edgeR_3.22.5.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, DBChIP, IntEREst, manta, methylMnM, RUVSeq, TCC, tRanslatome importsMe: affycoretools, ampliQueso, ArrayExpressHTS, baySeq, compcodeR, coseq, csaw, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, DiffBind, diffcyt, diffHic, diffloop, DRIMSeq, DropletUtils, easyRNASeq, EBSEA, EDDA, eegc, EGSEA, EnrichmentBrowser, erccdashboard, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, IsoformSwitchAnalyzeR, MEDIPS, metaseqR, MIGSA, MLSeq, msgbsR, msmsTests, PathoStat, PROPER, psichomics, regsplice, Repitools, rnaSeqMap, scater, scde, scone, scran, singscore, splatter, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, TCseq, tweeDEseq, vidger, yarn, zinbwave suggestsMe: ABSSeq, biobroom, BitSeq, ClassifyR, clonotypeR, cqn, cydar, DEScan2, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, ideal, missMethyl, multiMiR, regionReport, SSPA, stageR, subSeq, SummarizedBenchmark, tximport, variancePartition, zFPKM Package: eegc Version: 1.6.1 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 MD5sum: deb9e9396cadbf5d1d086f6cd34185d9 NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr source.ver: src/contrib/eegc_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/eegc_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/eegc_1.6.1.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R Package: EGAD Version: 1.8.0 Depends: R(>= 3.3) Imports: gplots, Biobase, GEOquery, limma, arrayQualityMetrics, impute, RColorBrewer, zoo, igraph, plyr, Matrix, MASS, RCurl, affy Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 32f65b5dd911655980350f45fe55ebe2 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: knitr source.ver: src/contrib/EGAD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EGAD_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EGAD_1.8.0.tgz vignettes: vignettes/EGAD/inst/doc/EGAD.pdf vignetteTitles: "EGAD user guide" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGAD/inst/doc/EGAD.R Package: EGSEA Version: 1.8.0 Depends: R (>= 3.4), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), Glimma (>= 1.4.0), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: a6a77d638dff49bbd1df54e51514f12d NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. biocViews: DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr source.ver: src/contrib/EGSEA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EGSEA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EGSEA_1.8.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R Package: eiR Version: 1.20.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, gespeR,RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap License: Artistic-2.0 Archs: x64 MD5sum: fb8b0a509d682a9a059a38fdc6809408 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr source.ver: src/contrib/eiR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/eiR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/eiR_1.20.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R Package: eisa Version: 1.32.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) MD5sum: ea69e8cf0afdac458c0499f51814f3f4 NeedsCompilation: no Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi source.ver: src/contrib/eisa_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/eisa_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/eisa_1.32.0.tgz vignettes: vignettes/eisa/inst/doc/EISA_tutorial.pdf vignetteTitles: The Iterative Signature Algorithm for Gene Expression Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisa/inst/doc/EISA_tutorial.R dependsOnMe: ExpressionView importsMe: ExpressionView Package: ELBOW Version: 1.16.0 Depends: R (>= 2.15.0) Imports: graphics, stats, utils Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer, hgu133plus2cdf, hgu133plus2probe License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: 6bde74f4292b457c9265d8c5be819210 NeedsCompilation: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang source.ver: src/contrib/ELBOW_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ELBOW_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ELBOW_1.16.0.tgz vignettes: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.pdf vignetteTitles: Using ELBOW --- the definitive ELBOW tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.R Package: ELMER Version: 2.4.4 Depends: R (>= 3.4.0), ELMER.data Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.5.5), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, rvest, xml2, plotly, gridExtra, rmarkdown, stringr Suggests: BiocStyle, knitr, testthat, DT, GenomicInteractions, webshot, rtracklayer, R.utils, covr License: GPL-3 MD5sum: 34bcd764bcf85d4a739c3b9fb3484557 NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: RELEASE_3_7 git_last_commit: a667145 git_last_commit_date: 2018-06-26 Date/Publication: 2018-06-26 source.ver: src/contrib/ELMER_2.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/ELMER_2.4.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ELMER_2.4.4.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAbiolinksGUI Package: EMDomics Version: 2.10.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: a03f51af3fc277e48ec1b01c7a69d318 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr source.ver: src/contrib/EMDomics_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EMDomics_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EMDomics_2.10.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R Package: EmpiricalBrownsMethod Version: 1.8.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 368f11dff05a15a90c2c6406c5089eb6 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr source.ver: src/contrib/EmpiricalBrownsMethod_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EmpiricalBrownsMethod_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EmpiricalBrownsMethod_1.8.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R Package: ENCODExplorer Version: 2.6.0 Depends: R (>= 3.3), shiny, DT, shinythemes Imports: tools, jsonlite, parallel, RCurl, tidyr, data.table, dplyr, stringr, stringi Suggests: RUnit,BiocGenerics,knitr, curl, httr License: Artistic-2.0 MD5sum: b9694d7442033b583376a4b3e02d0a90 NeedsCompilation: no Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant , Audrey Lemacon , Louis Gendron Astrid-Louise Deschenes, and Arnaud Droit Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues source.ver: src/contrib/ENCODExplorer_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ENCODExplorer_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ENCODExplorer_2.6.0.tgz vignettes: vignettes/ENCODExplorer/inst/doc/DataUpdate.html, vignettes/ENCODExplorer/inst/doc/DBmodel.html, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html vignetteTitles: Data update, Database model, Introduction to ENCODExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENCODExplorer/inst/doc/DataUpdate.R, vignettes/ENCODExplorer/inst/doc/DBmodel.R, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.R suggestsMe: TSRchitect Package: ENmix Version: 1.16.0 Depends: parallel,doParallel,foreach, SummarizedExperiment (>= 1.1.6),minfi (>= 1.22.0) Imports: MASS,preprocessCore,wateRmelon,sva,geneplotter,impute,grDevices,graphics,stats Suggests: minfiData (>= 0.4.1), RPMM, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 3d6c179fff2cb67344918e6dd44e9bdf NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip Description: The ENmix package provides a set of quality control and data pre-processing tools for Illumina HumanMethylation450 and MethylationEPIC Beadchips. It includes ENmix background correction, RELIC dye bias correction, RCP probe-type bias adjustment, along with a number of additional tools. These functions can be used to remove unwanted experimental noise and thus to improve accuracy and reproducibility of methylation measures. ENmix functions are flexible and transparent. Users have option to choose a single pipeline command to finish all data pre-processing steps (including background correction, dye-bias adjustment, inter-array normalization and probe-type bias correction) or to use individual functions sequentially to perform data pre-processing in a more customized manner. In addition the ENmix package has selectable complementary functions for efficient data visualization (such as data distribution plots); quality control (identifing and filtering low quality data points, samples, probes, and outliers, along with imputation of missing values); identification of probes with multimodal distributions due to SNPs or other factors; exploration of data variance structure using principal component regression analysis plot; preparation of experimental factors related surrogate control variables to be adjusted in downstream statistical analysis; and an efficient algorithm oxBS-MLE to estimate 5-methylcytosine and 5-hydroxymethylcytosine level. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu source.ver: src/contrib/ENmix_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ENmix_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ENmix_1.16.0.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R Package: EnrichedHeatmap Version: 1.10.0 Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 1.15.2), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.1), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, genefilter, RColorBrewer License: MIT + file LICENSE Archs: i386, x64 MD5sum: 027b8cdf133f4338fb42988c185f6b06 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr Date/Publication: 2018-4-6 00:00:00 source.ver: src/contrib/EnrichedHeatmap_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EnrichedHeatmap_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EnrichedHeatmap_1.10.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: Make Enriched Heatmaps, Visualize Comprehensive Associations in Roadmap dataset, Compare row ordering methods, Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R Package: EnrichmentBrowser Version: 2.10.11 Depends: R(>= 3.4.0), SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, ComplexHeatmap, DESeq2, EDASeq, GSEABase, GO.db, KEGGREST, KEGGgraph, MASS, ReportingTools, Rgraphviz, S4Vectors, SPIA, biocGraph, edgeR, geneplotter, graphite, hwriter, limma, methods, pathview, rappdirs, safe, topGO Suggests: ALL, BiocStyle, airway, hgu95av2.db, knitr License: Artistic-2.0 MD5sum: 7536b3e80eb9a67e168d206a2fb12d32 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_7 git_last_commit: 96bfeea git_last_commit_date: 2018-08-26 Date/Publication: 2018-08-27 source.ver: src/contrib/EnrichmentBrowser_2.10.11.tar.gz win.binary.ver: bin/windows/contrib/3.5/EnrichmentBrowser_2.10.11.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EnrichmentBrowser_2.10.11.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR, PathwaySplice suggestsMe: ToPASeq Package: enrichplot Version: 1.0.2 Depends: R (>= 3.4.0) Imports: AnnotationDbi, cowplot, DOSE (>= 3.5.1), ggplot2, ggraph, ggridges, GOSemSim, graphics, grDevices, grid, igraph, methods, reshape2, UpSetR, utils Suggests: clusterProfiler, knitr, org.Hs.eg.db, prettydoc License: Artistic-2.0 MD5sum: d703058fb06ced3efa6aefdfd99e6e7e NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] () Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/enrichplot VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues source.ver: src/contrib/enrichplot_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/enrichplot_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/enrichplot_1.0.2.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichplot/inst/doc/enrichplot.R importsMe: ChIPseeker, clusterProfiler, debrowser, meshes, ReactomePA Package: ensembldb Version: 2.4.1 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.1.9) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr, AnnotationHub Enhances: RMySQL, shiny License: LGPL MD5sum: 5f4b27e52da258c4500ed0224d947b10 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jotsetung/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jotsetung/ensembldb/issues source.ver: src/contrib/ensembldb_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ensembldb_2.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ensembldb_2.4.1.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz importsMe: biovizBase, ChIPpeakAnno, epivizrData, ggbio, metagene, PathwaySplice, Pbase, TVTB suggestsMe: alpine, GenomicFeatures, TFutils, TxRegInfra, wiggleplotr Package: ensemblVEP Version: 1.22.1 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: a239f68854e1cd32039f7e3ebd958665 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 94) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_7 git_last_commit: 7ea50fe git_last_commit_date: 2018-10-10 Date/Publication: 2018-10-10 source.ver: src/contrib/ensemblVEP_1.22.1.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ensemblVEP_1.22.1.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: TVTB Package: ENVISIONQuery Version: 1.28.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 47a66ddfba41b205f9dee4b389e82e63 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/ENVISIONQuery_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ENVISIONQuery_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ENVISIONQuery_1.28.0.tgz vignettes: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.pdf vignetteTitles: An R Package for retrieving data from EnVision into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.R importsMe: IdMappingRetrieval Package: EpiDISH Version: 1.2.0 Depends: R (>= 3.4) Imports: MASS, e1071, quadprog Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: 38b489fb55eb98626640350881d8cdca NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell subtypes present in a sample representing a mixture of such cell-types. Inference proceeds via one of 3 methods (Robust Partial Correlations-RPC, Cibersort (CBS), Constrained Projection (CP)), as determined by user. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation Author: Andrew E. Teschendorff , Shijie C. Zheng Maintainer: Shijie Charles Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues source.ver: src/contrib/EpiDISH_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EpiDISH_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EpiDISH_1.2.0.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R Package: epigenomix Version: 1.20.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: de691f7f7daf3ef26a3068cc930db798 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein source.ver: src/contrib/epigenomix_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epigenomix_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epigenomix_1.20.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R Package: epiNEM Version: 1.4.0 Depends: R (>= 3.4) Imports: BoolNet, e1071, gtools, stats, igraph, nem, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db License: GPL-3 MD5sum: 585a6220614c6d6b823534e96c65f3f2 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl VignetteBuilder: knitr source.ver: src/contrib/epiNEM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epiNEM_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epiNEM_1.4.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.pdf vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R Package: epivizr Version: 2.10.0 Depends: R (>= 3.0), methods, Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle License: Artistic-2.0 MD5sum: 5d193406215d3ee1a8ab59c2b4dadeea NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epivizr_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epivizr_2.10.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone importsMe: metavizr Package: epivizrChart Version: 1.2.0 Depends: R (>= 3.4.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens License: Artistic-2.0 MD5sum: 63c537e978a29106cff904ec5bfe8a85 NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr source.ver: src/contrib/epivizrChart_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epivizrChart_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epivizrChart_1.2.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html vignetteTitles: Introduction to epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R Package: epivizrData Version: 1.8.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI License: MIT + file LICENSE MD5sum: 2a73c6f32a881b979b774e9356a9aee0 NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues source.ver: src/contrib/epivizrData_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epivizrData_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epivizrData_1.8.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, metavizr Package: epivizrServer Version: 1.8.1 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 108e4fccd8bf3c29e466e4d941ce7d25 NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer source.ver: src/contrib/epivizrServer_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/epivizrServer_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epivizrServer_1.8.1.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr Package: epivizrStandalone Version: 1.8.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 2d6d12dbbd5884e83add32f1d461088a NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr source.ver: src/contrib/epivizrStandalone_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/epivizrStandalone_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/epivizrStandalone_1.8.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr Package: erccdashboard Version: 1.14.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: b3f65a7b68cf4aad2434baf1b0ce1a62 NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues source.ver: src/contrib/erccdashboard_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/erccdashboard_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/erccdashboard_1.14.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R Package: erma Version: 0.12.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors, BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: 540f20d406a1129f5dd9d529688f202a NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/erma_0.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/erma_0.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/erma_0.12.0.tgz vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R importsMe: gQTLstats, ldblock suggestsMe: gQTLBase Package: esATAC Version: 1.2.3 Depends: R (>= 3.5), Rsamtools, GenomicRanges, ShortRead Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, DiagrammeR, magrittr, digest, BSgenome, AnnotationDbi, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2016, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocInstaller, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, DiagrammeRsvg, testthat, webshot License: GPL-3 | file LICENSE Archs: x64 MD5sum: 5393839bfdd2affc80239414467e657d NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang, Huan Fang, Yanda Li, Xiaowo Wang Maintainer: Zheng Wei , Wei Zhang URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_7 git_last_commit: 331f58b git_last_commit_date: 2018-10-04 Date/Publication: 2018-10-05 source.ver: src/contrib/esATAC_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/esATAC_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/esATAC_1.2.3.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: An Introduction to esATAC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R Package: esetVis Version: 1.6.3 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: 2636c24f16d10d01f31e9baca53c3373 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_7 git_last_commit: 1271764 git_last_commit_date: 2018-10-08 Date/Publication: 2018-10-08 source.ver: src/contrib/esetVis_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/esetVis_1.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/esetVis_1.6.3.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R Package: eudysbiome Version: 1.10.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 42960ed98b71af0c11d9e5256459b8fd NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou source.ver: src/contrib/eudysbiome_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/eudysbiome_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/eudysbiome_1.10.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R Package: EventPointer Version: 1.4.0 Depends: R (>= 3.4), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 681572f2637eea3ad3da33de015e3800 NeedsCompilation: no Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing Author: Juan Pablo Romero, Ander Muniategui, Fernando Carazo, Ander Aramburu, Angel Rubio Maintainer: Juan Pablo Romero VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues source.ver: src/contrib/EventPointer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/EventPointer_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/EventPointer_1.4.0.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R Package: ExiMiR Version: 2.22.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 523a20382b443f20695013446dd67dfe NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian source.ver: src/contrib/ExiMiR_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ExiMiR_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ExiMiR_2.22.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R Package: exomeCopy Version: 1.26.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: ddb7de53e2d37ec3e503a0916415e661 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love source.ver: src/contrib/exomeCopy_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/exomeCopy_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/exomeCopy_1.26.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT, Rariant Package: exomePeak Version: 2.14.0 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer, GenomicAlignments License: GPL-2 MD5sum: 318e0f60513924a5fd15aa90ad2d894c NeedsCompilation: no Title: exome-based anlaysis of MeRIP-Seq data: peak calling and differential analysis Description: The package is developed for the analysis of affinity-based epitranscriptome shortgun sequencing data from MeRIP-seq (maA-seq). It was built on the basis of the exomePeak MATLAB package (Meng, Jia, et al. "Exome-based analysis for RNA epigenome sequencing data." Bioinformatics 29.12 (2013): 1565-1567.) with new functions for differential analysis of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome. The exomePeak R-package accepts and statistically supports multiple biological replicates, internally removes PCR artifacts and multi-mapping reads, outputs exome-based binding sites (RNA methylation sites) and detects differential post-transcriptional RNA modification sites between two experimental conditions in term of percentage rather the absolute amount. The package is still under active development, and we welcome all biology and computation scientist for all kinds of collaborations and communications. Please feel free to contact Dr. Jia Meng if you have any questions. biocViews: Sequencing, HighThroughputSequencing, Methylseq, RNAseq Author: Lin Zhang , Lian Liu , Jia Meng Maintainer: Lin Zhang , Lian Liu , Jia Meng source.ver: src/contrib/exomePeak_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/exomePeak_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/exomePeak_2.14.0.tgz vignettes: vignettes/exomePeak/inst/doc/exomePeak-Overview.pdf vignetteTitles: An introduction to exomePeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak/inst/doc/exomePeak-Overview.R Package: ExperimentHub Version: 1.6.1 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.9.11) Imports: utils, S4Vectors, BiocInstaller, curl Suggests: knitr, BiocStyle Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: eddfa884c21777d1da2417acfa8159d5 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_7 git_last_commit: 5c354ff git_last_commit_date: 2018-09-05 Date/Publication: 2018-09-05 source.ver: src/contrib/ExperimentHub_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ExperimentHub_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ExperimentHub_1.6.1.tgz vignettes: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: Creating An ExperimentHub Package, ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.R, vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: MetaGxOvarian, SeqSQC importsMe: ExperimentHubData, GSEABenchmarkeR, restfulSE suggestsMe: ANF, AnnotationHub, CellMapper Package: ExperimentHubData Version: 1.6.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.9.1) Imports: methods, ExperimentHub, BiocInstaller, DBI, BiocCheck, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle License: Artistic-2.0 MD5sum: 220a4d67481cd0076a508785e5b3ac10 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/ExperimentHubData_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ExperimentHubData_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ExperimentHubData_1.6.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Creating An ExperimentHub Package, Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.R Package: explorase Version: 1.44.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 71dd775aadcbf5284759520004658f1c NeedsCompilation: no Title: GUI for exploratory data analysis of systems biology data Description: explore and analyze *omics data with R and GGobi biocViews: Visualization,Microarray,GUI Author: Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim Maintainer: Michael Lawrence URL: http://www.metnetdb.org/MetNet_exploRase.htm source.ver: src/contrib/explorase_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/explorase_1.44.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ExpressionAtlas Version: 1.8.1 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 47365178458869353dffb7961a427e55 NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays Maintainer: Maria Keays VignetteBuilder: knitr source.ver: src/contrib/ExpressionAtlas_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ExpressionAtlas_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ExpressionAtlas_1.8.1.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: Pi Package: ExpressionView Version: 1.32.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: d7c9e8492b70584978b6b4810fa0e4be NeedsCompilation: yes Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression, GO, KEGG Author: Andreas Luscher Maintainer: Gabor Csardi source.ver: src/contrib/ExpressionView_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ExpressionView_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ExpressionView_1.32.0.tgz vignettes: vignettes/ExpressionView/inst/doc/ExpressionView.format.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.pdf vignetteTitles: ExpressionView file format, How the ordering algorithm works, ExpressionView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionView/inst/doc/ExpressionView.ordering.R, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.R Package: fabia Version: 2.26.0 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: a68f254e280e9af45eb4b446528f1fc6 NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/fabia/fabia.html source.ver: src/contrib/fabia_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fabia_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fabia_2.26.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia Package: facopy Version: 1.13.0 Depends: R (>= 3.0), methods, cgdsr (>= 1.1.30), coin (>= 1.0), ggplot2, gridExtra, facopy.annot, grid Imports: annotate, data.table, DOSE, FactoMineR, GO.db, GOstats, graphite, igraph, S4Vectors, IRanges, MASS, nnet, reshape2, Rgraphviz, scales License: CC BY-NC 4.0 MD5sum: 9fcf0c1beaa64e1746767ab201a16de0 NeedsCompilation: no Title: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer Description: facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported. biocViews: Software, CopyNumberVariation, GeneSetEnrichment, GenomicVariation, Genetics, Microarray, Sequencing, Visualization Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena source.ver: src/contrib/facopy_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/facopy_1.13.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/facopy_1.13.0.tgz vignettes: vignettes/facopy/inst/doc/facopy.pdf vignetteTitles: facopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/facopy/inst/doc/facopy.R Package: factDesign Version: 1.56.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: c5007505beb226572f3ce6791f8416f1 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/factDesign_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/factDesign_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/factDesign_1.56.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R Package: FamAgg Version: 1.8.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: e2a095724b3419245143c907b4f80dbf NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues source.ver: src/contrib/FamAgg_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FamAgg_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FamAgg_1.8.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R Package: farms Version: 1.32.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: 172c0d2f83731e70dee1121919578ab0 NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html source.ver: src/contrib/farms_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/farms_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/farms_1.32.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R Package: fastLiquidAssociation Version: 1.16.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 514ff47d8a4a927394a5945a5339f4c4 NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson source.ver: src/contrib/fastLiquidAssociation_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fastLiquidAssociation_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fastLiquidAssociation_1.16.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R Package: fastseg Version: 1.26.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: e1367cb9322e1a65df82b8519eb3a8ee NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html source.ver: src/contrib/fastseg_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fastseg_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fastseg_1.26.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit Package: fCCAC Version: 1.6.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 936950c149cf39305895646cef602a0e NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). biocViews: Transcription, Genetics, Sequencing, Coverage Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/fCCAC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fCCAC_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fCCAC_1.6.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R Package: fCI Version: 1.10.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: ac673ad4d162ecc671110de36c37b6f0 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr source.ver: src/contrib/fCI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fCI_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fCI_1.10.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R Package: fdrame Version: 1.52.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 023e625d12f96b12b9b82751f4ff26d2 NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg source.ver: src/contrib/fdrame_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fdrame_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fdrame_1.52.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FELLA Version: 1.0.1 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 79ff2c233d48919bee906dbc5638caad NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_7 git_last_commit: 71e7257 git_last_commit_date: 2018-07-09 Date/Publication: 2018-07-09 source.ver: src/contrib/FELLA_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/FELLA_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FELLA_1.0.1.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/quickstart.R Package: FEM Version: 3.8.0 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db,graph,BiocGenerics Imports: graph License: GPL (>=2) MD5sum: 292e3d4cba0d3d63bd19c4a3abc0af4a NeedsCompilation: no Title: Identification of Functional Epigenetic Modules Description: The FEM package performs a systems-level integrative analysis of DNA methylation and gene expression data. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details, see Jiao et al Bioinformatics 2014. biocViews: SystemsBiology,NetworkEnrichment,DifferentialMethylation,DifferentialExpression Author: Andrew E. Teschendorff and Zhen Yang Maintainer: Zhen Yang source.ver: src/contrib/FEM_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FEM_3.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FEM_3.8.0.tgz vignettes: vignettes/FEM/inst/doc/IntroDoFEM.pdf vignetteTitles: The FEM package performs a systems-level integrative analysis of DNA methylationa and gene expression. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression,, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details,, see Jiao et al Bioinformatics 2014. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEM/inst/doc/IntroDoFEM.R dependsOnMe: ChAMP Package: ffpe Version: 1.24.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: dfa661a99d1839c37e9ebe59f93a3ce0 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron source.ver: src/contrib/ffpe_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ffpe_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ffpe_1.24.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R Package: FGNet Version: 3.14.0 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png Suggests: RGtk2, RCurl, RDAVIDWebService, gage, topGO, KEGGprofile, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi License: GPL (>= 2) MD5sum: 40d1c14426a04dc0c14ae65229a97e6b NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr source.ver: src/contrib/FGNet_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FGNet_3.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FGNet_3.14.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR Package: fgsea Version: 1.6.0 Depends: R (>= 3.3), Rcpp Imports: data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch, Matrix, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery License: MIT + file LICENCE Archs: i386, x64 MD5sum: 01f62795846ad015a9447ef23881860f NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues source.ver: src/contrib/fgsea_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fgsea_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fgsea_1.6.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html vignetteTitles: Using fgsea package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R dependsOnMe: gsean, PPInfer importsMe: CEMiTool, DOSE, mCSEA, phantasus, piano suggestsMe: mdp, Pi Package: FindMyFriends Version: 1.10.0 Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel, utils, stats LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils License: GPL (>=2) Archs: i386, x64 MD5sum: da21ac17e2e58d00d097ebe23e94d872 NeedsCompilation: yes Title: Microbial Comparative Genomics in R Description: A framework for doing microbial comparative genomics in R. The main purpose of the package is assisting in the creation of pangenome matrices where genes from related organisms are grouped by similarity, as well as the analysis of these data. FindMyFriends provides many novel approaches to doing pangenome analysis and supports a gene grouping algorithm that scales linearly, thus making the creation of huge pangenomes feasible. biocViews: ComparativeGenomics, Clustering, DataRepresentation, GenomicVariation, SequenceMatching, GraphAndNetwork Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/FindMyFriends VignetteBuilder: knitr BugReports: https://github.com/thomasp85/FindMyFriends/issues source.ver: src/contrib/FindMyFriends_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FindMyFriends_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FindMyFriends_1.10.0.tgz vignettes: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html vignetteTitles: Creating pangenomes using FindMyFriends hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R importsMe: PanVizGenerator Package: FISHalyseR Version: 1.14.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: ea58933b7fd39b25c6d8a83fd76cc9b6 NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr source.ver: src/contrib/FISHalyseR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FISHalyseR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FISHalyseR_1.14.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R Package: FitHiC Version: 1.6.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: d2d6b8ded4a295d6e1bdf7dd05a2aa5b NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr source.ver: src/contrib/FitHiC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FitHiC_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FitHiC_1.6.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R Package: flagme Version: 1.36.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: e08da24c82696261f7bf000827129287 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli source.ver: src/contrib/flagme_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flagme_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flagme_1.36.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R Package: flipflop Version: 1.18.0 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 Archs: i386, x64 MD5sum: f6a66f8aa8c5157ed505bfe2996a9a74 NeedsCompilation: yes Title: Fast lasso-based isoform prediction as a flow problem Description: Flipflop discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data. It takes an alignment file in SAM format as input. It can also discover transcripts from several samples simultaneously, increasing statistical power. biocViews: RNASeq, RNASeqData, AlternativeSplicing, Regression Author: Elsa Bernard, Laurent Jacob, Julien Mairal and Jean-Philippe Vert Maintainer: Elsa Bernard URL: http://cbio.ensmp.fr/flipflop SystemRequirements: GNU make source.ver: src/contrib/flipflop_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flipflop_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flipflop_1.18.0.tgz vignettes: vignettes/flipflop/inst/doc/flipflop.pdf vignetteTitles: FlipFlop: Fast Lasso-based Isoform Prediction as a Flow Problem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flipflop/inst/doc/flipflop.R Package: flowAI Version: 1.10.1 Depends: R (>= 3.4) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny License: GPL (>= 2) MD5sum: ecf44f71ad59fe41880ec7d700527627 NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_7 git_last_commit: 20ac798 git_last_commit_date: 2018-06-29 Date/Publication: 2018-06-30 source.ver: src/contrib/flowAI_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowAI_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowAI_1.10.1.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R Package: flowBeads Version: 1.18.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 29e49ad99ba3d806f238e559462794dd NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos source.ver: src/contrib/flowBeads_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowBeads_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowBeads_1.18.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R Package: flowBin Version: 1.16.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: a436e2702c117d2c0aff55abc6352c3b NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill source.ver: src/contrib/flowBin_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowBin_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowBin_1.16.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R Package: flowcatchR Version: 1.14.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 060f5d35d2171e7cca20b910ac224546 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues source.ver: src/contrib/flowcatchR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowcatchR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowcatchR_1.14.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R Package: flowCHIC Version: 1.14.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: ba068abc00db1935841775eec5a785ed NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 source.ver: src/contrib/flowCHIC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowCHIC_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowCHIC_1.14.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R Package: flowCL Version: 1.18.1 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: e624979a3cffd3afb1df23b4efbc4fa3 NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas source.ver: src/contrib/flowCL_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowCL_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowCL_1.18.1.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCL/inst/doc/flowCL.R Package: flowClean Version: 1.18.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 353245c857dc9f05202c6ef42d0eb0e4 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant source.ver: src/contrib/flowClean_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowClean_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowClean_1.18.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R Package: flowClust Version: 3.18.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, MCMCpack, methods, Biobase, graph, RBGL, ellipse, flowViz, flowCore, clue, mnormt Suggests: testthat, flowWorkspace, flowWorkspaceData License: Artistic-2.0 Archs: i386, x64 MD5sum: 2ef5ff7cd00f1f29672aa96db0489437 NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make source.ver: src/contrib/flowClust_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowClust_3.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowClust_3.18.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.pdf vignetteTitles: flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowTrans, flowType suggestsMe: BiocGenerics Package: flowCore Version: 1.46.2 Depends: R (>= 2.10.0) Imports: Biobase, BiocGenerics (>= 0.1.14), graph, graphics, methods, rrcov, stats, utils, stats4, corpcor, Rcpp, matrixStats LinkingTo: Rcpp, BH(>= 1.65.0.1) Suggests: Rgraphviz, flowViz, flowStats, testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: i386, x64 MD5sum: f1de08d5247c541ec2a6c7106d4f2514 NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan, J. Spidlen, M. Jiang Maintainer: M.Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_7 git_last_commit: d99c7f6 git_last_commit_date: 2018-09-11 Date/Publication: 2018-09-12 source.ver: src/contrib/flowCore_1.46.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowCore_1.46.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowCore_1.46.2.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowFP, flowMatch, flowStats, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, ncdfFlow, plateCore importsMe: CATALYST, cydar, cytofkit, CytoML, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowFit, flowMeans, flowPloidy, flowQ, flowQB, FlowSOM, flowStats, flowTrans, flowType, flowUtils, flowViz, GateFinder, MetaCyto, oneSENSE, plateCore, Sconify suggestsMe: COMPASS, FlowRepositoryR, RchyOptimyx Package: flowCyBar Version: 1.16.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 7c7103611ebd9daaf3efed678029c5a2 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 source.ver: src/contrib/flowCyBar_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowCyBar_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowCyBar_1.16.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R Package: flowDensity Version: 1.14.0 Imports: flowCore, graphics, car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.20.5), methods, stats, grDevices License: Artistic-2.0 MD5sum: f00427fbae1a897b9582fb55f9ec6289 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, StemCells, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: GEOS (>= 3.2.0);for building from source: GEOS from http://trac.osgeo.org/geos/; GEOS OSX frameworks built by William Kyngesburye at http://www.kyngchaos.com/ may be used for source installs on OSX. source.ver: src/contrib/flowDensity_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowDensity_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowDensity_1.14.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.pdf, vignettes/flowDensity/inst/doc/flowDensityVignette.pdf vignetteTitles: Automated alternative to the current manual gating practice, Automated alternative to the current manual gating practice hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R, vignettes/flowDensity/inst/doc/flowDensityVignette.R importsMe: ddPCRclust Package: flowFit Version: 1.18.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: c4651a2ae1288eb15e33e9cff493d111 NeedsCompilation: no Title: Estimate proliferation in cell-tracking dye studies Description: This package estimate the proliferation of a cell population in cell-tracking dye studies. The package uses an R implementation of the Levenberg-Marquardt algorithm (minpack.lm) to fit a set of peaks (corresponding to different generations of cells) over the proliferation-tracking dye distribution in a FACS experiment. biocViews: FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi BugReports: Davide Rambaldi source.ver: src/contrib/flowFit_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowFit_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowFit_1.18.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFit/inst/doc/HowTo-flowFit.R Package: flowFP Version: 1.38.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 9d18d01a4f52af21d6d91c3bdfdc6e6d NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst source.ver: src/contrib/flowFP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowFP_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowFP_1.38.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin Package: flowMap Version: 1.18.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 2ff9bd638a956b4b8daa154109a8b9db NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr source.ver: src/contrib/flowMap_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowMap_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowMap_1.18.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R Package: flowMatch Version: 1.16.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 Archs: i386, x64 MD5sum: 22e05f46d2c85d25b38645040831a9e2 NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad source.ver: src/contrib/flowMatch_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowMatch_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowMatch_1.16.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R Package: flowMeans Version: 1.40.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: f7273381902f99ceee4acf04166fc43b NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour source.ver: src/contrib/flowMeans_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowMeans_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowMeans_1.40.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: flowType Package: flowMerge Version: 2.28.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 96c79da4eee6f98bf5dde6e9ab582633 NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowMerge_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowMerge_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowMerge_2.28.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowMerge.pdf vignetteTitles: flowMerge package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowMerge.R importsMe: flowType Package: flowPeaks Version: 1.26.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: ece38b9b573633653940fe734d987bae NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl source.ver: src/contrib/flowPeaks_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowPeaks_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowPeaks_1.26.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust Package: flowPloidy Version: 1.6.0 Depends: R (>= 3.4) Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: 4f75e4aacc8a4914c95444b8c4c774a9 NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues source.ver: src/contrib/flowPloidy_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowPloidy_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowPloidy_1.6.0.tgz vignettes: vignettes/flowPloidy/inst/doc/ajb2017.pdf, vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.html, vignettes/flowPloidy/inst/doc/histogram-tour.html vignetteTitles: flowPloidy: BSA 2017, flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/ajb2017.R, vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R Package: flowPlots Version: 1.28.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: b095c4eefc0945acb5b7184d2bc5dfd6 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins source.ver: src/contrib/flowPlots_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowPlots_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowPlots_1.28.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R Package: flowQ Version: 1.40.0 Depends: R (>= 2.10.0), methods, BiocGenerics, outliers, lattice, flowViz, mvoutlier, bioDist, parody, RColorBrewer, latticeExtra Imports: methods, BiocGenerics, geneplotter, flowCore, flowViz, IRanges Suggests: flowStats License: Artistic-2.0 MD5sum: 8c6630b2f5fe48972a1f0c5b052a6a6d NeedsCompilation: no Title: Quality control for flow cytometry Description: Provides quality control and quality assessment tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: R. Gentleman, F. Hahne, J. Kettman, N. Le Meur, N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: ImageMagick source.ver: src/contrib/flowQ_1.40.0.tar.gz vignettes: vignettes/flowQ/inst/doc/DataQualityAssessment.pdf, vignettes/flowQ/inst/doc/Extending-flowQ.pdf vignetteTitles: Data Quality Assesment for Ungated Flow Cytometry Data, Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQ/inst/doc/DataQualityAssessment.R, vignettes/flowQ/inst/doc/Extending-flowQ.R Package: flowQB Version: 2.8.0 Imports: methods, flowCore (>= 1.32.0), stats, extremevalues Suggests: flowQBData, FlowRepositoryR, xlsx, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9e57d623e34883dbceed38f5b98ade6c NeedsCompilation: no Title: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CV instrinsic calculations Description: flowQB is a fully automated R Bioconductor package to calculate automatically the detector efficiency (Q), optical background (B) and intrinsic CV of the beads. biocViews: FlowCytometry, Regression, PeakDetection, QualityControl, MultiChannel, OneChannel Author: Josef Spidlen, Faysal El Khettabi, Wayne Moore, David Parks, Ryan Brinkman Maintainer: Josef Spidlen source.ver: src/contrib/flowQB_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowQB_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowQB_2.8.0.tgz vignettes: vignettes/flowQB/inst/doc/flowQBVignettes.pdf vignetteTitles: flowQB package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQB/inst/doc/flowQBVignettes.R Package: FlowRepositoryR Version: 1.12.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: 43a35ba878c730690d4ef7bac8b3b0e0 NeedsCompilation: no Title: FlowRepository R Interface Description: This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server. biocViews: Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen source.ver: src/contrib/FlowRepositoryR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FlowRepositoryR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FlowRepositoryR_1.12.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R suggestsMe: flowQB Package: FlowSOM Version: 1.12.0 Depends: R (>= 3.2), igraph Imports: flowCore, ConsensusClusterPlus, BiocGenerics, tsne, XML, flowUtils Suggests: BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 9d0284551176cd65fed67ee08fb422bf NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen, Britt Callebaut and Yvan Saeys Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be source.ver: src/contrib/FlowSOM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FlowSOM_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FlowSOM_1.12.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, cytofkit, diffcyt Package: flowStats Version: 3.38.0 Depends: R (>= 2.10), flowCore, fda (>= 2.2.6), cluster, flowWorkspace, ncdfFlow(>= 2.19.5) Imports: BiocGenerics, MASS, flowViz, flowCore, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, utils, KernSmooth, lattice,ks Suggests: xtable Enhances: RBGL,ncdfFlow,graph License: Artistic-2.0 MD5sum: 3b826ce30d55202543878af9b049c41d NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak and Mike Jiang source.ver: src/contrib/flowStats_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowStats_3.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowStats_3.38.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS importsMe: plateCore suggestsMe: cydar, flowCore, flowQ, ggcyto Package: flowTime Version: 1.4.0 Depends: R (>= 3.4), flowCore, plyr Imports: utils Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, moments, stats License: Artistic-2.0 MD5sum: 12d3bb6d91a69b1a2091480394572ef3 NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package was developed for analysis of both dynamic and steady state experiments examining the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using a BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as is requisite for dissemination and general ease-of-use. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr source.ver: src/contrib/flowTime_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowTime_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowTime_1.4.0.tgz vignettes: vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R Package: flowTrans Version: 1.32.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 02902bbc7f7dae7e7fc4b97f5b2f1b31 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowTrans_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowTrans_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowTrans_1.32.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R Package: flowType Version: 2.18.0 Depends: R (>= 2.10), Rcpp (>= 0.10.4), BH (>= 1.51.0-3) Imports: Biobase, graphics, grDevices, methods, flowCore, flowMeans, sfsmisc, rrcov, flowClust, flowMerge, stats LinkingTo: Rcpp, BH Suggests: xtable License: Artistic-2.0 Archs: i386, x64 MD5sum: 98649c851af57e824a58ea173cb3070f NeedsCompilation: yes Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour source.ver: src/contrib/flowType_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowType_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowType_2.18.0.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowType/inst/doc/flowType.R importsMe: RchyOptimyx Package: flowUtils Version: 1.44.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 MD5sum: dbe7e2fb5de34f0d6f57595c0fddf912 NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues source.ver: src/contrib/flowUtils_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowUtils_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowUtils_1.44.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R importsMe: CytoML, FlowSOM suggestsMe: GateFinder Package: flowViz Version: 1.44.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats,knitr License: Artistic-2.0 MD5sum: ac9ebef0ced0da291cd90f40c663d4fa NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/flowViz_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowViz_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowViz_1.44.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowQ, flowVS, plateCore importsMe: flowClust, flowFit, flowQ, flowStats, flowTrans, flowWorkspace suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto Package: flowVS Version: 1.12.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 4d26629455fbc11f13ac53b5266f26c4 NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr source.ver: src/contrib/flowVS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowVS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowVS_1.12.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R Package: flowWorkspace Version: 3.28.2 Depends: R (>= 2.16.0),flowCore(>= 1.45.14),ncdfFlow(>= 2.25.4) Imports: Biobase, BiocGenerics, graph, graphics, grDevices, lattice, methods, stats, stats4, utils, RBGL, XML, tools, gridExtra, Rgraphviz, data.table, dplyr, latticeExtra, Rcpp, RColorBrewer, stringr, scales, flowViz, matrixStats LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib, cytolib(>= 1.1.3) Suggests: testthat, flowWorkspaceData, knitr, ggcyto, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: f010995fdfe6bf2696f1391801d883b0 NeedsCompilation: yes Title: Infrastructure for representing and interacting with the gated cytometry Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_7 git_last_commit: bd5ee4c git_last_commit_date: 2018-09-12 Date/Publication: 2018-09-13 source.ver: src/contrib/flowWorkspace_3.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/flowWorkspace_3.28.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/flowWorkspace_3.28.2.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets, How to plot gated data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R dependsOnMe: flowStats, ggcyto, openCyto importsMe: CytoML, flowDensity suggestsMe: COMPASS, flowClust, flowCore Package: fmcsR Version: 1.22.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap License: Artistic-2.0 Archs: i386, x64 MD5sum: cbc608bcc743d196a3b79d3f8127e411 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr source.ver: src/contrib/fmcsR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/fmcsR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/fmcsR_1.22.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: Rcpi suggestsMe: ChemmineR Package: focalCall Version: 1.14.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 999a37ca4681f22053410eb83c3b4499 NeedsCompilation: no Title: Detection of focal aberrations in DNA copy number data Description: Detection of genomic focal aberrations in high-resolution DNA copy number data biocViews: Microarray,Preprocessing,Visualization,Sequencing Author: Oscar Krijgsman Maintainer: Oscar Krijgsman URL: https://github.com/OscarKrijgsman/focalCall source.ver: src/contrib/focalCall_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/focalCall_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/focalCall_1.14.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/focalCall/inst/doc/focalCall.R Package: FourCSeq Version: 1.14.0 Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2 (>= 1.9.11), splines, methods, LSD Imports: DESeq2, Biobase, Biostrings, GenomicRanges, SummarizedExperiment, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: 90d6434c5590fb0b863605f78fc97d76 NeedsCompilation: no Title: Package analyse 4C sequencing data Description: FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated. biocViews: Software, Preprocessing, Sequencing Author: Felix A. Klein, EMBL Heidelberg Maintainer: Felix A. Klein VignetteBuilder: knitr source.ver: src/contrib/FourCSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FourCSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FourCSeq_1.14.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FourCSeq/inst/doc/FourCSeq.R Package: FRGEpistasis Version: 1.16.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 0b8a811543617432dbd3a954b0f5c31f NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang source.ver: src/contrib/FRGEpistasis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FRGEpistasis_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FRGEpistasis_1.16.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R Package: frma Version: 1.32.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: f8b577405b336aa30d92a85c9293e40f NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org source.ver: src/contrib/frma_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/frma_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/frma_1.32.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress suggestsMe: frmaTools Package: frmaTools Version: 1.32.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 93284c7ea36cbebe79f4f2583f094e30 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org source.ver: src/contrib/frmaTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/frmaTools_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/frmaTools_1.32.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R Package: FunChIP Version: 1.6.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 Archs: i386, x64 MD5sum: e8dde95dc0a6383afca25b91625babfd NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi source.ver: src/contrib/FunChIP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FunChIP_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FunChIP_1.6.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R Package: FunciSNP Version: 1.24.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomicRanges, Rsamtools (>= 1.6.1), rtracklayer (>= 1.14.1), ChIPpeakAnno (>= 2.2.0), VariantAnnotation, plyr, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Suggests: org.Hs.eg.db Enhances: parallel License: GPL-3 MD5sum: 9b0c500a5b99d794bb8d2e876745af73 NeedsCompilation: no Title: Integrating Functional Non-coding Datasets with Genetic Association Studies to Identify Candidate Regulatory SNPs Description: FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions. biocViews: Infrastructure, DataRepresentation, DataImport, SequenceMatching, Annotation Author: Simon G. Coetzee and Houtan Noushmehr, PhD Maintainer: Simon G. Coetzee URL: http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/ source.ver: src/contrib/FunciSNP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/FunciSNP_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/FunciSNP_1.24.0.tgz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.R Package: funtooNorm Version: 1.4.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 MD5sum: bcc5d38da7414719e1565b3d222ea949 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_7 git_last_commit: 5e3e0d2 git_last_commit_date: 2018-04-30 Date/Publication: 2018-06-16 source.ver: src/contrib/funtooNorm_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/funtooNorm_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/funtooNorm_1.4.0.tgz vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k for multiple cell types with funtooNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R Package: GA4GHclient Version: 1.4.0 Depends: S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: d32864682493632e1e99d8c5dcc64d42 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues source.ver: src/contrib/GA4GHclient_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GA4GHclient_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GA4GHclient_1.4.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny Package: GA4GHshiny Version: 1.2.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 9efc323dea8eece7fa4832dc114de6e0 NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues source.ver: src/contrib/GA4GHshiny_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GA4GHshiny_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GA4GHshiny_1.2.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R Package: gaga Version: 2.26.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 526d18d480745a98dfe4537a153a8980 NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell source.ver: src/contrib/gaga_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gaga_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gaga_2.26.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper Package: gage Version: 2.30.0 Depends: R (>= 2.10) Imports: graph, KEGGREST, AnnotationDbi Suggests: pathview, gageData, GO.db, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 3ff918057d42de6de000c79d5b108c21 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: http://www.biomedcentral.com/1471-2105/10/161 source.ver: src/contrib/gage_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gage_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gage_2.30.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA importsMe: anamiR suggestsMe: FGNet, pathview Package: gaggle Version: 1.48.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 89e52c6a77fdfeb2afbc9ad7b106d63e NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ source.ver: src/contrib/gaggle_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gaggle_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gaggle_1.48.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R Package: gaia Version: 2.24.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 76a47c1cc703ee9a6e0e3ce4f71508da NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella source.ver: src/contrib/gaia_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gaia_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gaia_2.24.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R Package: GAprediction Version: 1.6.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 899f5b7552810ec2246bc26d3be8c9fc NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr source.ver: src/contrib/GAprediction_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GAprediction_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GAprediction_1.6.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R Package: garfield Version: 1.8.0 Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 356bffe134a630246e34d60cc0686212 NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr source.ver: src/contrib/garfield_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/garfield_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/garfield_1.8.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GARS Version: 1.0.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: be25533bec3448eee062b1af7eafa361 NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr source.ver: src/contrib/GARS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GARS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GARS_1.0.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: Titolo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R Package: GateFinder Version: 1.0.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP, Suggests: RUnit, flowUtils, BiocGenerics License: Artistic-2.0 MD5sum: 02fbfa1a56ef84f960a296a25f5152f5 NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour source.ver: src/contrib/GateFinder_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GateFinder_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GateFinder_1.0.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R Package: gaucho Version: 1.16.0 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: 72a1d549fcdd9ae4e8ac80268dc175eb NeedsCompilation: no Title: Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering Description: Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples biocViews: Software,Genetics,SNP,Sequencing,SomaticMutation Author: Alex Murison [aut, cre], Christopher Wardell [aut, cre] Maintainer: Alex Murison , Christopher Wardell VignetteBuilder: knitr source.ver: src/contrib/gaucho_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gaucho_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gaucho_1.16.0.tgz vignettes: vignettes/gaucho/inst/doc/gaucho_vignette.pdf vignetteTitles: An introduction to gaucho hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaucho/inst/doc/gaucho_vignette.R Package: gcapc Version: 1.4.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 MD5sum: 78990e1c4afb6d56230f0ea8157b3878 NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr source.ver: src/contrib/gcapc_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gcapc_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gcapc_1.4.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R Package: gcatest Version: 1.10.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: c710b7a13a0b5978832f6b43169571c9 NeedsCompilation: yes Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait. models. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues source.ver: src/contrib/gcatest_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gcatest_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gcatest_1.10.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R Package: gCMAP Version: 1.24.0 Depends: GSEABase, limma (>= 3.20.0) Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq, grDevices, graphics, stats, utils, bigmemory, bigmemoryExtras (>= 1.1.2) Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa License: Artistic-2.0 OS_type: unix MD5sum: 64be93d8e93a5576a408a392f8f3b4fd NeedsCompilation: no Title: Tools for Connectivity Map-like analyses Description: The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots. biocViews: Microarray, Software, Pathways, Annotation Author: Thomas Sandmann , Richard Bourgon and Sarah Kummerfeld Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAP_1.24.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gCMAP_1.24.0.tgz vignettes: vignettes/gCMAP/inst/doc/diffExprAnalysis.pdf, vignettes/gCMAP/inst/doc/gCMAP.pdf vignetteTitles: Creating reference datasets, gCMAP classes and methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAP/inst/doc/diffExprAnalysis.R, vignettes/gCMAP/inst/doc/gCMAP.R dependsOnMe: gCMAPWeb Package: gCMAPWeb Version: 1.20.0 Depends: Biobase, gCMAP (>= 1.3.0), methods, R (>= 3.4), Rook Imports: brew, BiocGenerics, annotate, AnnotationDbi, graphics, grDevices, GSEABase, hwriter, parallel, stats, utils, yaml Suggests: affy, ArrayExpress, hgfocus.db, hgu133a.db, mgug4104a.db, org.Hs.eg.db, org.Mm.eg.db, RUnit Enhances: bigmemory, bigmemoryExtras License: Artistic-2.0 OS_type: unix MD5sum: 786921ffd3885b08ac191909369eac74 NeedsCompilation: no Title: A web interface for gene-set enrichment analyses Description: The gCMAPWeb R package provides a graphical user interface for the gCMAP package. gCMAPWeb uses the Rook package and can be used either on a local machine, leveraging R's internal web server, or run on a dedicated rApache web server installation. gCMAPWeb allows users to search their own data sources and instructions to generate reference datasets from public repositories are included with the package. The package supports three common types of analyses, specifically queries with 1. one or two sets of query gene identifiers, whose members are expected to show changes in gene expression in a consistent direction. For example, an up-regulated gene set might contain genes activated by a transcription factor, a down-regulated geneset targets repressed by the same factor. 2. a single set of query gene identifiers, whose members are expected to show divergent differential expression (non-directional query). For example, members of a particular signaling pathway, some of which may be up- some down-regulated in response to a stimulus. 3. a query with the complete results of a differential expression profiling experiment. For example, gene identifiers and z-scores from a previous perturbation experiment. gCMAPWeb accepts three types of identifiers: EntreIds, gene Symbols and microarray probe ids and can be configured to work with any species supported by Bioconductor. For each query submission, significantly similar reference datasets will be identified and reported in graphical and tabular form. biocViews: GUI, GeneSetEnrichment, Visualization, GeneExpression, Transcription, Microarray, DifferentialExpression Author: Thomas Sandmann Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAPWeb_1.20.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gCMAPWeb_1.20.0.tgz vignettes: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.pdf, vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf vignetteTitles: gCMAPWeb configuration, Recreating the Broad Connectivity Map v1 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.R, vignettes/gCMAPWeb/inst/doc/referenceDatasets.R Package: gCrisprTools Version: 1.8.0 Depends: R (>= 3.3) Imports: Biobase, limma, RobustRankAggreg, ggplot2, PANTHER.db, rmarkdown, grDevices, graphics, stats, utils, parallel Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 733fd9061b3609d7a7d1d8a802b52c69 NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. biocViews: CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Peter Haverty VignetteBuilder: knitr source.ver: src/contrib/gCrisprTools_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gCrisprTools_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gCrisprTools_1.8.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R Package: gcrma Version: 2.52.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocInstaller Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL Archs: i386, x64 MD5sum: 860fa4cfc5efa39631626a8376130951 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu source.ver: src/contrib/gcrma_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gcrma_2.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gcrma_2.52.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, simpleaffy, webbioc importsMe: affycoretools, affylmGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp Package: GDCRNATools Version: 1.1.1 Depends: R (>= 3.4.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 366b68e25a47f9047612a424768b302b NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: RELEASE_3_7 git_last_commit: fbffc91 git_last_commit_date: 2018-07-20 Date/Publication: 2018-07-20 source.ver: src/contrib/GDCRNATools_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GDCRNATools_1.1.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GDCRNATools_1.1.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R Package: GDSArray Version: 1.0.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.43), IRanges, SNPRelate, SeqArray Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: bca58ff571bbf1dcad95c200d8256852 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage, Annotation, GenomeAnnotation, GenotypingArray, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues source.ver: src/contrib/GDSArray_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GDSArray_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GDSArray_1.0.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R Package: gdsfmt Version: 1.16.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, crayon, RUnit, knitr, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: 4a55d4f94fb40e190a01f4be576da94b NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Software, Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues source.ver: src/contrib/gdsfmt_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gdsfmt_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gdsfmt_1.16.0.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.R dependsOnMe: bigmelon, GDSArray, SeqArray, SNPRelate importsMe: GENESIS, GWASTools, SeqSQC, SeqVarTools suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate Package: geecc Version: 1.14.0 Depends: R (>= 3.3.0), methods Imports: MASS, hypergea (>= 1.3.0), gplots, Rcpp (>= 0.11.3), graphics, stats, utils LinkingTo: Rcpp Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) Archs: i386, x64 MD5sum: f6c3b2c2596f93b8f6da3088fc37e337 NeedsCompilation: yes Title: Gene Set Enrichment Analysis Extended to Contingency Cubes Description: Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosomal position, phylostrata, divergence-strata, .... biocViews: BiologicalQuestion, GeneSetEnrichment, WorkflowStep, GO, StatisticalMethod, GeneExpression, Transcription, RNASeq, Microarray Author: Markus Boenn Maintainer: Markus Boenn SystemRequirements: Rcpp source.ver: src/contrib/geecc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geecc_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geecc_1.14.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geecc/inst/doc/geecc.R Package: GEM Version: 1.6.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 MD5sum: 953be6f189b4d68e8c596560439aee12 NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr source.ver: src/contrib/GEM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GEM_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GEM_1.6.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R Package: genArise Version: 1.56.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: a0e4bfca771fc624f91f29444c0cdcf8 NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise source.ver: src/contrib/genArise_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genArise_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genArise_1.56.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R Package: genbankr Version: 1.8.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 04c93a2a9f8ed3232795a3064816d41e NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr source.ver: src/contrib/genbankr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genbankr_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genbankr_1.8.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R Package: GeneAnswers Version: 2.22.0 Depends: R (>= 3.0.0), igraph, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: ed7baf363cdea7ab8f7e8425d79b4b9f NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng source.ver: src/contrib/GeneAnswers_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneAnswers_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneAnswers_2.22.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/geneAnswers.R, vignettes/GeneAnswers/inst/doc/getListGIF.R suggestsMe: InterMineR Package: geneAttribution Version: 1.6.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 9b3efe59a7c5c70076f2978844e24b5a NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr source.ver: src/contrib/geneAttribution_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneAttribution_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneAttribution_1.6.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneBreak Version: 1.10.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: d7b07dba747d61a5c84d308dea96c05a NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak source.ver: src/contrib/GeneBreak_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneBreak_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneBreak_1.10.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R Package: geneClassifiers Version: 1.4.0 Depends: utils Imports: methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 9d4ff23e251835f34a713574cf5da93f NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper Maintainer: R Kuiper source.ver: src/contrib/geneClassifiers_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneClassifiers_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneClassifiers_1.4.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R Package: GeneExpressionSignature Version: 1.26.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 1d0799b206563d4923e5f3577b06dd17 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao Maintainer: Yang Cao , Fei Li ,Lu Han source.ver: src/contrib/GeneExpressionSignature_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneExpressionSignature_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneExpressionSignature_1.26.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R Package: genefilter Version: 1.62.0 Imports: S4Vectors (>= 0.9.42), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 4cec1373a506ffc1099d3247986f3050 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes biocViews: Microarray Author: R. Gentleman, V. Carey, W. Huber, F. Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/genefilter_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genefilter_1.62.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genefilter_1.62.0.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/independent_filtering.pdf vignetteTitles: Using the genefilter function to filter genes from a microarray dataset, How to find genes whose expression profile is similar to that of specified genes, Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Diagnostics for independent filtering hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R, vignettes/genefilter/inst/doc/independent_filtering.R dependsOnMe: a4Base, cellHTS2, charm, CNTools, GeneMeta, simpleaffy, sva importsMe: affyQCReport, annmap, arrayQualityMetrics, Category, cbaf, covRNA, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GISPA, GSRI, JunctionSeq, methyAnalysis, methylumi, minfi, MLInterfaces, mogsa, pcaExplorer, PECA, phenoTest, Ringo, simpleaffy, TCGAbiolinks, tilingArray, XDE, zinbwave suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, categoryCompare, clusterStab, codelink, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MCRestimate, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO Package: genefu Version: 2.12.0 Depends: survcomp, mclust, limma,biomaRt, iC10, AIMS, R (>= 2.10) Imports: amap Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival License: Artistic-2.0 MD5sum: 0d3b5d475026d40b773b646950ebb4f1 NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo, Natchar Ratanasirigulchai, Markus S. Schroder, Laia Pare, Joel S. Parker, Aleix Prat, and Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains , Markus Schroeder URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr source.ver: src/contrib/genefu_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genefu_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genefu_2.12.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R dependsOnMe: pbcmc importsMe: consensusOV Package: GeneGA Version: 1.30.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: c372e346f49ac85f11879240ff4e1050 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ source.ver: src/contrib/GeneGA_1.30.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneGA_1.30.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R Package: GeneGeneInteR Version: 1.6.0 Depends: R (>= 3.3) Imports: snpStats, mvtnorm, GGtools, Rsamtools, igraph, kernlab, FactoMineR, plspm, IRanges, GenomicRanges, data.table,rioja,grDevices, graphics,stats,utils License: GPL (>= 2) MD5sum: 0f11021ba611e598b79f2a84e56234da NeedsCompilation: no Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houee-Bigot Maintainer: Mathieu Emily , Magalie Houee-Bigot source.ver: src/contrib/GeneGeneInteR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneGeneInteR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneGeneInteR_1.6.0.tgz vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R Package: GeneMeta Version: 1.52.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 611bc36490fde9a22f4f55e8232bc323 NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GeneMeta_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneMeta_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneMeta_1.52.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu Package: GeneNetworkBuilder Version: 1.22.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb License: GPL (>= 2) Archs: i386, x64 MD5sum: 406a0d9383704b1183ffb88d0d7dab79 NeedsCompilation: yes Title: Build Regulatory Network from ChIP-chip/ChIP-seq and Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou , Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/GeneNetworkBuilder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneNetworkBuilder_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneNetworkBuilder_1.22.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R Package: GeneOverlap Version: 1.16.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: eb232293352f92b680e475af57b84a04 NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Mount Sinai Maintainer: Li Shen, Mount Sinai URL: http://shenlab-sinai.github.io/shenlab-sinai/ source.ver: src/contrib/GeneOverlap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneOverlap_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneOverlap_1.16.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R Package: geneplast Version: 1.6.2 Depends: R (>= 3.3), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: d082d40e9d595be8edcaf290d17af35c NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_7 git_last_commit: 19ddf5b git_last_commit_date: 2018-08-18 Date/Publication: 2018-08-19 source.ver: src/contrib/geneplast_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneplast_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneplast_1.6.2.tgz vignettes: vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Geneplast: evolutionary rooting and plasticity analysis." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast.R Package: geneplotter Version: 1.58.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 MD5sum: 0c7dc16ce8d028ee059db6d1bf1c3b34 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/geneplotter_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneplotter_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneplotter_1.58.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HMMcopy importsMe: biocGraph, DESeq, DESeq2, DEXSeq, EnrichmentBrowser, flowQ, GSVA, IsoGeneGUI, JunctionSeq, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.52.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 57cb3333b5413db3b46a5a8a4aef58ce NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather source.ver: src/contrib/geneRecommender_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneRecommender_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneRecommender_1.52.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R Package: GeneRegionScan Version: 1.36.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 45387854e7c19e2dd58c75e3683d34b3 NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen source.ver: src/contrib/GeneRegionScan_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneRegionScan_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneRegionScan_1.36.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R Package: geneRxCluster Version: 1.16.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 68a144b1d16d6e2269ed1567cc955453 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry source.ver: src/contrib/geneRxCluster_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneRxCluster_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneRxCluster_1.16.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R Package: GeneSelectMMD Version: 2.24.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 12b6a405729d0bb165fc417fd25ae4b8 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu source.ver: src/contrib/GeneSelectMMD_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneSelectMMD_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneSelectMMD_2.24.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck Package: GeneSelector Version: 2.30.0 Depends: R (>= 2.5.1), methods, stats, Biobase Imports: multtest, siggenes, samr, limma Suggests: multtest, siggenes, samr, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 390a49584b0b29c4640e87b9b76f4306 NeedsCompilation: yes Title: Stability and Aggregation of ranked gene lists Description: The term 'GeneSelector' refers to a filter selecting those genes which are consistently identified as differentially expressed using various statistical procedures. 'Selected' genes are those present at the top of the list in various ranking methods (currently 14). In addition, the stability of the findings can be taken into account in the final ranking by examining perturbed versions of the original data set, e.g. by leaving samples, swapping class labels, generating bootstrap replicates or adding noise. Given multiple ranked lists, one can use aggregation methods in order to find a synthesis. biocViews: StatisticalMethod, DifferentialExpression Author: Martin Slawski , Anne-Laure Boulesteix . Maintainer: Martin Slawski source.ver: src/contrib/GeneSelector_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneSelector_2.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneSelector_2.30.0.tgz vignettes: vignettes/GeneSelector/inst/doc/GeneSelector.pdf vignetteTitles: GeneSelector.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelector/inst/doc/GeneSelector.R Package: GENESIS Version: 2.10.1 Imports: Biobase, BiocGenerics, GWASTools, gdsfmt, GenomicRanges, graph, IRanges, S4Vectors, SeqArray, SeqVarTools, dplyr, graphics, grDevices, Matrix, methods, stats, utils Suggests: CompQuadForm, logistf, poibin, survey, SNPRelate, GWASdata, testthat, knitr License: GPL-3 MD5sum: 0aac18ac10443bf148adfdd7bcb5b9a9 NeedsCompilation: no Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Ken Rice, Tamar Sofer, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten , Matthew P. Conomos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_7 git_last_commit: 1cd5d4e git_last_commit_date: 2018-08-01 Date/Publication: 2018-08-02 source.ver: src/contrib/GENESIS_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GENESIS_2.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GENESIS_2.10.1.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R Package: GeneStructureTools Version: 1.0.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: c778efea715e058a7bbefc1b94a8edf2 NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr source.ver: src/contrib/GeneStructureTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneStructureTools_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneStructureTools_1.0.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R Package: geNetClassifier Version: 1.20.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: e775cefc8403d8f732c6fdd8e9ffe430 NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org source.ver: src/contrib/geNetClassifier_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geNetClassifier_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geNetClassifier_1.20.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR Package: GeneticsDesign Version: 1.48.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: 65db4e5d7a4bb00f55388de5cc82a59a NeedsCompilation: no Title: Functions for designing genetics studies Description: This package contains functions useful for designing genetics studies, including power and sample-size calculations. biocViews: Genetics Author: Gregory Warnes David Duffy , Michael Man Weiliang Qiu Ross Lazarus Maintainer: The R Genetics Project source.ver: src/contrib/GeneticsDesign_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneticsDesign_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneticsDesign_1.48.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Power Calculation for Testing If Disease is Associated a Marker in a Case-Control Study Using the \Rpackage{GeneticsDesign} Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneticsDesign/inst/doc/GPC.R Package: GeneticsPed Version: 1.42.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: 3a0063da0b64184a632b653f46dcf257 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org source.ver: src/contrib/GeneticsPed_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GeneticsPed_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GeneticsPed_1.42.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R Package: geneXtendeR Version: 1.6.0 Depends: GO.db, org.Rn.eg.db, rtracklayer, R (>= 3.3.1) Imports: AnnotationDbi, data.table, dplyr, graphics, networkD3, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, RColorBrewer, SnowballC, tm, utils, wordcloud Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 3) Archs: i386, x64 MD5sum: fc244d77efd7fe2c7dbb3786c0c2a155 NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks using fast iterative peak-coordinate/GTF alignment algorithms. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues source.ver: src/contrib/geneXtendeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/geneXtendeR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/geneXtendeR_1.6.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneXtendeR/inst/doc/geneXtendeR.R Package: GENIE3 Version: 1.2.1 Imports: stats, reshape2 Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 2f6f23f2c5696d7258740674d6d9ccda NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr source.ver: src/contrib/GENIE3_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GENIE3_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GENIE3_1.2.1.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: netbenchmark Package: genoCN Version: 1.32.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: 3e13392bc104af77c1dcd38969357bde NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun source.ver: src/contrib/genoCN_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genoCN_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genoCN_1.32.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R Package: GenoGAM Version: 1.8.0 Depends: R (>= 3.3), Rsamtools (>= 1.18.2), SummarizedExperiment (>= 1.1.19), GenomicRanges (>= 1.29.14), methods Imports: BiocParallel (>= 1.5.17), data.table (>= 1.9.4), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.11.16), mgcv (>= 1.8), reshape2 (>= 1.4.1), S4Vectors (>= 0.9.34), Biostrings (>= 2.39.14) Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter (>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr License: GPL-2 MD5sum: 8c80555f25e650efa7bffbe711c0241c NeedsCompilation: no Title: A GAM based framework for analysis of ChIP-Seq data Description: This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals. biocViews: Regression, DifferentialPeakCalling, ChIPSeq, DifferentialExpression, Genetics, Epigenetics Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut] Maintainer: Georg Stricker URL: https://github.com/gstricker/GenoGAM VignetteBuilder: knitr BugReports: https://github.com/gstricker/GenoGAM/issues source.ver: src/contrib/GenoGAM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenoGAM_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenoGAM_1.8.0.tgz vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.pdf vignetteTitles: GenoGAM: Genome-wide generalized additive models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R Package: genomation Version: 1.12.0 Depends: R (>= 3.0.0),grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), RUnit, Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 3b62c28c2c666b916d66b575f955669f NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues source.ver: src/contrib/genomation_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genomation_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genomation_1.12.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, fCCAC, RCAS suggestsMe: methylKit Package: GenomeGraphs Version: 1.40.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: 02eca6c6f78cf62f7b55a025e48bfdab NeedsCompilation: no Title: Plotting genomic information from Ensembl Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. GenomeGraphs uses the biomaRt package to perform live annotation queries to Ensembl and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Another strength of GenomeGraphs is to plot different data types such as array CGH, gene expression, sequencing and other data, together in one plot using the same genome coordinate system. biocViews: Visualization, Microarray Author: Steffen Durinck , James Bullard Maintainer: Steffen Durinck source.ver: src/contrib/GenomeGraphs_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomeGraphs_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomeGraphs_1.40.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.R dependsOnMe: Genominator, waveTiling suggestsMe: oligo, rMAT, triplex Package: GenomeInfoDb Version: 1.16.0 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Dmelanogaster.UCSC.dm3.ensGene, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 3c08742c6244a13dffcdd7bb649a326a NeedsCompilation: no Title: Utilities for manipulating chromosome and other 'seqname' identifiers Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w source.ver: src/contrib/GenomeInfoDb_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomeInfoDb_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomeInfoDb_1.16.0.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, htSeqTools, methyAnalysis, Rsamtools, VariantAnnotation importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ATACseqQC, BaalChIP, ballgown, biovizBase, BiSeq, bnbc, branchpointer, BSgenome, bsseq, CAGEfightR, CAGEr, casper, CexoR, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, cn.mops, CNEr, CNPBayes, CNVPanelizer, compEpiTools, consensusSeekeR, conumee, CopywriteR, CrispRVariants, csaw, customProDB, DeepBlueR, derfinder, derfinderPlot, DEScan2, diffHic, diffloop, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, ensembldb, ensemblVEP, epigenomix, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, GenomicScores, genoset, genotypeeval, GenVisR, ggbio, GGtools, GoogleGenomics, gQTLstats, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiTC, HTSeqGenie, IMAS, InPAS, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, metagene, metavizr, methimpute, methInheritSim, methylKit, methylPipe, methylumi, methyvim, minfi, MinimumDistance, mosaics, motifbreakR, motifmatchr, msgbsR, MutationalPatterns, myvariant, NADfinder, NarrowPeaks, normr, nucleR, ORFik, Organism.dplyr, panelcn.mops, Pi, plyranges, podkat, prebs, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RareVariantVis, Rariant, Rcade, RCAS, recount, regioneR, regionReport, Repitools, RiboProfiling, riboSeqR, RJMCMCNucleosomes, roar, RTCGAToolbox, rtracklayer, scmeth, segmentSeq, SeqArray, seqCAT, seqplots, sevenC, SGSeq, ShortRead, SNPchip, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TCGAutils, TFBSTools, TFutils, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, TxRegInfra, VanillaICE, VariantFiltering, VariantTools, wiggleplotr, YAPSA suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch, ExperimentHubData, gQTLBase, QDNAseq, recoup Package: genomeIntervals Version: 1.36.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 MD5sum: fe6c2543d5a8969e41597366513b9c1c NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur source.ver: src/contrib/genomeIntervals_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genomeIntervals_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genomeIntervals_1.36.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe importsMe: easyRNASeq Package: genomes Version: 3.10.0 Depends: readr, curl License: GPL-3 MD5sum: 5e5bd17201a598e6f6e4e2e40c408a19 NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben source.ver: src/contrib/genomes_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genomes_3.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genomes_3.10.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R Package: GenomicAlignments Version: 1.16.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.17.28), IRanges (>= 2.13.25), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.31.19), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 93ab73a88e5d1d2ed6fa867be7202df1 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, SNP, Coverage, Alignment Author: Hervé Pagès, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicAlignments_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicAlignments_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicAlignments_1.16.0.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, chimera, ChIPexoQual, exomePeak, GoogleGenomics, groHMM, Guitar, HelloRanges, hiReadsProcessor, ORFik, prebs, recoup, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: alpine, AneuFinder, ASpli, ATACseqQC, BaalChIP, biovizBase, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, contiBAIT, CopywriteR, CoverageView, CrispRVariants, customProDB, derfinder, DEScan2, DiffBind, easyRNASeq, FourCSeq, FunChIP, gcapc, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, methylPipe, mosaics, msgbsR, NADfinder, PICS, plyranges, QuasR, ramwas, Rcade, Repitools, RiboProfiling, RNAprobR, roar, Rqc, rtracklayer, seqplots, SGSeq, soGGi, SplicingGraphs, SPLINTER, srnadiff, TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect suggestsMe: amplican, BiocParallel, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, Rsamtools, similaRpeak, Streamer Package: GenomicDataCommons Version: 1.4.3 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, lazyeval, readr, GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools License: Artistic-2.0 MD5sum: 240484045f7b5412f6b05bd2407ca4fc NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_7 git_last_commit: 2436472 git_last_commit_date: 2018-09-06 Date/Publication: 2018-09-07 source.ver: src/contrib/GenomicDataCommons_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicDataCommons_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicDataCommons_1.4.3.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R importsMe: GDCRNATools, TCGAutils Package: GenomicFeatures Version: 1.32.3 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.15.4), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce2, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 79fc55139cb1875b95fda37aeda52b22 NeedsCompilation: no Title: Tools for making and manipulating transcript centric annotations Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence, V. Obenchain Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_7 git_last_commit: 80807d8 git_last_commit_date: 2018-10-04 Date/Publication: 2018-10-05 source.ver: src/contrib/GenomicFeatures_1.32.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicFeatures_1.32.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicFeatures_1.32.3.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: cpvSNP, ensembldb, exomePeak, GSReg, Guitar, HelloRanges, InPAS, OrganismDbi, RareVariantVis, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, ASpli, biovizBase, bumphunter, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO, crisprseekplus, csaw, customProDB, derfinder, derfinderPlot, EDASeq, ELMER, epivizrData, epivizrStandalone, esATAC, EventPointer, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gQTLstats, Gviz, gwascat, HTSeqGenie, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, methyAnalysis, msgbsR, ORFik, Organism.dplyr, PGA, proBAMr, PureCN, qpgraph, QuasR, RCAS, RiboProfiling, SGSeq, SplicingGraphs, SPLINTER, srnadiff, systemPipeR, TCGAbiolinks, TFEA.ChIP, TFutils, trackViewer, transcriptR, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: AnnotationHub, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, cummeRbund, DEXSeq, flipflop, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, IRanges, MiRaGE, recount, RIPSeeker, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, TnT, wiggleplotr Package: GenomicFiles Version: 1.16.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation, GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: f79145091aad6ba89e4b6989a0160062 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicFiles_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicFiles_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicFiles_1.16.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, gQTLBase, gQTLstats, ldblock, QuasR, Rqc, TFutils suggestsMe: TxRegInfra Package: GenomicInteractions Version: 1.14.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: 01522fba8c53777f0f1a9ac57e407816 NeedsCompilation: no Title: R package for handling genomic interaction data Description: R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots / statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr source.ver: src/contrib/GenomicInteractions_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicInteractions_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicInteractions_1.14.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R suggestsMe: Chicago, ELMER, sevenC Package: GenomicRanges Version: 1.32.7 Depends: R (>= 2.10), methods, stats4, BiocGenerics (>= 0.25.3), S4Vectors (>= 0.17.32), IRanges (>= 2.14.4), GenomeInfoDb (>= 1.15.2) Imports: utils, stats, XVector (>= 0.19.8) LinkingTo: S4Vectors, IRanges Suggests: Biobase, AnnotationDbi (>= 1.21.1), annotate, Biostrings (>= 2.25.3), Rsamtools (>= 1.13.53), SummarizedExperiment (>= 0.1.5), Matrix, GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, BiocStyle, digest, RUnit, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2, KEGG.db, hgu95av2.db, org.Hs.eg.db, org.Mm.eg.db, org.Sc.sgd.db, pasilla, pasillaBamSubset, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, RNAseqData.HNRNPC.bam.chr14, hgu95av2probe License: Artistic-2.0 Archs: i386, x64 MD5sum: 75dab3285cba01381aeecc2977235d05 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals and variables defined along a genome Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_7 git_last_commit: 4c56dc8 git_last_commit_date: 2018-09-19 Date/Publication: 2018-09-20 source.ver: src/contrib/GenomicRanges_1.32.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicRanges_1.32.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicRanges_1.32.7.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 1. An Introduction to the GenomicRanges Package, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, baySeq, biomvRCNS, BiSeq, bnbc, BPRMeth, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, CHARGE, chimera, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chroGPS, chromPlot, chromstaR, chromswitch, CINdex, cleanUpdTSeq, cn.mops, CNPBayes, cnvGSA, CNVPanelizer, compEpiTools, consensusSeekeR, CSAR, csaw, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, DOQTL, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, esATAC, exomeCopy, fastseg, fCCAC, FourCSeq, FunChIP, GeneBreak, GenoGAM, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicScores, GenomicTuples, genoset, gmapR, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HilbertCurve, HiTC, htSeqTools, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, MBASED, metagene, methimpute, methyAnalysis, methylKit, methylPipe, minfi, msgbsR, MutationalPatterns, NADfinder, ORFik, PGA, PING, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, Rariant, Rcade, recoup, regioneR, rfPred, rGREAT, riboSeqR, RIPSeeker, RJMCMCNucleosomes, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, segmentSeq, seqbias, seqCAT, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, SummarizedExperiment, TarSeqQC, TnT, trackViewer, TransView, tRNAscanImport, VanillaICE, VariantAnnotation, VariantTools, vtpnet, vulcan, wavClusteR, YAPSA importsMe: ALDEx2, alpine, amplican, AnnotationFilter, annotatr, apeglm, ArrayExpressHTS, ASpli, ATACseqQC, BadRegionFinder, ballgown, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, biovizBase, BiSeq, branchpointer, BSgenome, CAGEr, CexoR, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, ChIPSeqSpike, chromDraw, ChromHeatMap, chromVAR, CNEr, coMET, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, crisprseekplus, CrispRVariants, customProDB, DChIPRep, debrowser, DeepBlueR, DEFormats, derfinder, derfinderPlot, diffloop, DMRcate, dmrseq, DominoEffect, DRIMSeq, easyRNASeq, EDASeq, ELMER, epivizr, epivizrData, erma, EventPointer, flipflop, FourCSeq, FunciSNP, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR, GGBase, ggbio, GGtools, GOfuncR, GoogleGenomics, gQTLBase, gQTLstats, gwascat, h5vc, heatmaps, HiCcompare, hiReadsProcessor, HTSeqGenie, ideal, IMAS, INSPEcT, InterMineR, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS, karyoploteR, loci2path, LOLA, lumi, M3D, MACPET, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, methInheritSim, methyAnalysis, methylInheritance, MethylSeekR, methylumi, MinimumDistance, MIRA, MMDiff2, mosaics, motifbreakR, motifmatchr, MultiAssayExperiment, MultiDataSet, NarrowPeaks, normr, nucleR, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, panelcn.mops, Pbase, pcaExplorer, pepStat, Pi, PICS, pqsfinder, prebs, proBAMr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, recount, regioneR, regionReport, REMP, Repitools, RGMQL, RiboProfiling, RNAprobR, rnaSeqMap, roar, RTCGAToolbox, scmeth, scoreInvHap, seq2pathway, SeqArray, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sevenC, ShortRead, signeR, simulatorZ, SNPchip, soGGi, SparseSignatures, spliceR, SplicingGraphs, SPLINTER, srnadiff, STAN, SVM2CRM, systemPipeR, TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, TFutils, TitanCNA, tracktables, transcriptR, trena, triplex, tRNAscanImport, TSRchitect, TVTB, TxRegInfra, Uniquorn, VariantFiltering, waveTiling, wiggleplotr suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, Chicago, cummeRbund, epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools, HDF5Array, interactiveDisplay, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, omicsPrint, Onassis, RTCGA, S4Vectors, SeqGSEA Package: GenomicScores Version: 1.4.1 Depends: R (>= 3.4), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: utils, XML, Biobase, IRanges (>= 2.3.23), Biostrings, BSgenome, GenomeInfoDb, AnnotationHub Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer License: Artistic-2.0 MD5sum: 7510ab582012feb019bf12376fc13c64 NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues source.ver: src/contrib/GenomicScores_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicScores_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicScores_1.4.1.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R importsMe: ATACseqQC, RareVariantVis, VariantFiltering Package: GenomicTuples Version: 1.14.1 Depends: R (>= 3.3.0), GenomicRanges (>= 1.31.8), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.13.13), data.table, stats4 LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 87eff57e2402feaaecf61fffdf9c749d NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey , with contributions from Marcin Cieslik and Hervé Pagès. Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues source.ver: src/contrib/GenomicTuples_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenomicTuples_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenomicTuples_1.14.1.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R Package: Genominator Version: 1.34.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges (>= 2.5.27), GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 MD5sum: faa3748cffd7ed614f8d58b8d5eb3cdb NeedsCompilation: no Title: Analyze, manage and store genomic data Description: Tools for storing, accessing, analyzing and visualizing genomic data. biocViews: Infrastructure Author: James Bullard, Kasper Daniel Hansen Maintainer: James Bullard source.ver: src/contrib/Genominator_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Genominator_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Genominator_1.34.0.tgz vignettes: vignettes/Genominator/inst/doc/Genominator.pdf, vignettes/Genominator/inst/doc/plotting.pdf, vignettes/Genominator/inst/doc/withShortRead.pdf vignetteTitles: The Genominator User Guide, Plotting with Genominator, Working with the ShortRead Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Genominator/inst/doc/Genominator.R, vignettes/Genominator/inst/doc/plotting.R, vignettes/Genominator/inst/doc/withShortRead.R Package: genoset Version: 1.36.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.13.13), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: testthat, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 7b0accb0ac2115e69f40035f4f7c72fe NeedsCompilation: yes Title: A RangedSummarizedExperiment with methods for copy number analysis Description: GenoSet provides an extension of the RangedSummarizedExperiment class with additional API features. This class provides convenient and fast methods for working with segmented genomic data. Additionally, GenoSet provides the class RleDataFrame which stores runs of data along the genome for multiple samples and provides very fast summaries of arbitrary row sets (regions of the genome). biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset VignetteBuilder: knitr source.ver: src/contrib/genoset_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genoset_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genoset_1.36.0.tgz vignettes: vignettes/genoset/inst/doc/genoset.html vignetteTitles: genoset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R importsMe: methyAnalysis, VegaMC Package: genotypeeval Version: 1.12.0 Depends: R (>= 3.3.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, Rtsne, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 4e46de79fca6741617ca73c9326c45e0 NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown source.ver: src/contrib/genotypeeval_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genotypeeval_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genotypeeval_1.12.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: genphen Version: 1.8.0 Depends: R(>= 3.4), stats, graphics, e1071, Biostrings, rstan, ranger, parallel, foreach, doParallel License: GPL (>= 2) MD5sum: d834b4628c8cd34948212bad8b7187c1 NeedsCompilation: no Title: A tool for quantification of associations between genotypes and phenotypes with Bayesian inference and statistical learning techniques Description: Genetic association studies are an essential tool for studying the relationship between genotypes and phenotypes and thus for the discovery of disease-causing genetic variants. With genphen we propose a new method for conducting genetic association studies, using a combined approach which is based on both Bayesian inference and statistical learning techniques such as random forest and support vector machines. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski Maintainer: Simo Kitanovski source.ver: src/contrib/genphen_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/genphen_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/genphen_1.8.0.tgz vignettes: vignettes/genphen/inst/doc/genphenManual.pdf vignetteTitles: genphen overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genphen/inst/doc/genphenManual.R Package: GenRank Version: 1.8.0 Depends: R (>= 3.2.3) Imports: matrixStats, reshape2, survcomp Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 67659b82207e2085dc3b051e5058d90c NeedsCompilation: no Title: Candidate gene prioritization based on convergent evidence Description: Methods for ranking genes based on convergent evidence obtained from multiple independent evidence layers. This package adapts three methods that are popular for meta-analysis. biocViews: GeneExpression, SNP, CopyNumberVariation, Microarray, Sequencing, Software, Genetics Author: Chakravarthi Kanduri Maintainer: Chakravarthi Kanduri URL: https://github.com/chakri9/GenRank VignetteBuilder: knitr BugReports: https://github.com/chakri9/GenRank/issues source.ver: src/contrib/GenRank_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenRank_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenRank_1.8.0.tgz vignettes: vignettes/GenRank/inst/doc/GenRank_Vignette.html vignetteTitles: Introduction to GenRank Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenRank/inst/doc/GenRank_Vignette.R Package: GenVisR Version: 1.12.1 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt, BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), grid, gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, stats, utils, viridis, data.table, BSgenome, grDevices, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 3d43e0e0937a4db52a448971c0e65f05 NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues source.ver: src/contrib/GenVisR_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GenVisR_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GenVisR_1.12.1.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R Package: GEOmetadb Version: 1.42.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: d1278800b134ac531aacec400904655d NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr source.ver: src/contrib/GEOmetadb_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GEOmetadb_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GEOmetadb_1.42.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R importsMe: Onassis Package: GEOquery Version: 2.48.0 Depends: methods, Biobase Imports: httr, readr, xml2, dplyr, tidyr, magrittr, limma Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr License: GPL-2 MD5sum: 475089b6bf75785f145836a90ca2457b NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new source.ver: src/contrib/GEOquery_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GEOquery_2.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GEOquery_2.48.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: AnnotationHubData, bigmelon, ChIPXpress, coexnet, crossmeta, EGAD, GSEABenchmarkeR, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb suggestsMe: AUCell, ctsGE, diffcoexp, dyebias, ELBOW, EpiDISH, fgsea, multiClust, MultiDataSet, omicsPrint, pathprint, PGSEA, RGSEA, Rnits, runibic, skewr, TargetScore, zFPKM Package: GEOsubmission Version: 1.32.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 4c6ba6fc7be070e1dc472a74f35501ef NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/GEOsubmission_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GEOsubmission_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GEOsubmission_1.32.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R Package: gep2pep Version: 1.0.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 487543839ba3e5710ac58794fdb13106 NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr source.ver: src/contrib/gep2pep_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gep2pep_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gep2pep_1.0.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R Package: gespeR Version: 1.12.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: a98b844c5ca71a109f9a51440b35d5b9 NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr source.ver: src/contrib/gespeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gespeR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gespeR_1.12.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R Package: GEWIST Version: 1.24.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 7dee2b9f59e0f44de13a58198316b46e NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng source.ver: src/contrib/GEWIST_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GEWIST_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GEWIST_1.24.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R Package: GGBase Version: 3.42.0 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics, S4Vectors, IRanges, Matrix, AnnotationDbi, digest, GenomicRanges, SummarizedExperiment Suggests: GGtools, illuminaHumanv1.db License: Artistic-2.0 MD5sum: 12ad15f1f71afd8d34a2f7c9f52f2abc NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGBase_3.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GGBase_3.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GGBase_3.42.0.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.pdf vignetteTitles: GGBase -- infrastructure for GGtools,, genetics of gene expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGBase/inst/doc/ggbase.R dependsOnMe: GGtools Package: ggbio Version: 1.28.5 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.28.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: 27f604f76cdfb17e018e085178f5c578 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_7 git_last_commit: 594521c git_last_commit_date: 2018-08-22 Date/Publication: 2018-08-23 source.ver: src/contrib/ggbio_1.28.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/ggbio_1.28.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ggbio_1.28.5.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: derfinderPlot, FourCSeq, gwascat, msgbsR, Pi, R3CPET, Rariant, ReportingTools, RiboProfiling, SomaticSignatures suggestsMe: beadarray, ensembldb, GoogleGenomics, gQTLstats, interactiveDisplay, regionReport, RnBeads Package: ggcyto Version: 1.8.2 Depends: methods, ggplot2(>= 2.2.1.9000), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.17.24) Imports: plyr, scales, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: Artistic-2.0 MD5sum: 15dd9365e6d5d214c62a71bd8bf944d0 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_7 git_last_commit: ad88ca1 git_last_commit_date: 2018-07-13 Date/Publication: 2018-07-13 source.ver: src/contrib/ggcyto_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/ggcyto_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ggcyto_1.8.2.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R importsMe: CytoML suggestsMe: flowCore, flowWorkspace, openCyto Package: GGtools Version: 5.16.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel, Homo.sapiens Imports: methods, utils, stats, BiocGenerics (>= 0.25.1), snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP144.GRCh37, multtest, aod, rmeta Enhances: MatrixEQTL, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: ad82a6432c9bdc51222bf80fa82d0366 NeedsCompilation: no Title: software and data for analyses in genetics of gene expression Description: software and data for analyses in genetics of gene expression and/or DNA methylation biocViews: Genetics, GeneExpression, GeneticVariability, SNP Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGtools_5.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GGtools_5.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GGtools_5.16.0.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGtools/inst/doc/GGtools.R importsMe: GeneGeneInteR suggestsMe: GGBase, gQTLBase Package: ggtree Version: 1.12.7 Depends: R (>= 3.4.0) Imports: ape, dplyr, ggplot2 (>= 2.2.1.9000), grDevices, grid, magrittr, methods, purrr, rlang, rvcheck (>= 0.1.0), scales, tibble, tidyr, tidytree (>= 0.1.5), treeio (>= 1.3.14), utils Suggests: colorspace, cowplot, emojifont, ggimage, knitr, prettydoc, rmarkdown, testthat License: Artistic-2.0 MD5sum: 70e07eefab0b1a1c764b55b5c22ad1db NeedsCompilation: no Title: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees with their covariates and other associated data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Tommy Tsan-Yuk Lam [aut, ths], Justin Silverman [ctb], Watal M. Iwasaki [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ggtree VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_7 git_last_commit: 6e2d3d0 git_last_commit_date: 2018-08-07 Date/Publication: 2018-08-07 source.ver: src/contrib/ggtree_1.12.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/ggtree_1.12.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ggtree_1.12.7.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html, vignettes/ggtree/inst/doc/treeAnnotation.html, vignettes/ggtree/inst/doc/treeManipulation.html, vignettes/ggtree/inst/doc/treeVisualization.html vignetteTitles: 01 ggtree Introduction, 04 Tree Annotation, 03 Tree Manipulation, 02 Tree Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R, vignettes/ggtree/inst/doc/treeAnnotation.R, vignettes/ggtree/inst/doc/treeManipulation.R, vignettes/ggtree/inst/doc/treeVisualization.R importsMe: LINC, LymphoSeq, philr, singleCellTK suggestsMe: metagenomeFeatures, treeio Package: girafe Version: 1.32.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: 8cf777b612ba56a0fefa04cd286e26fb NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/girafe_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/girafe_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/girafe_1.32.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R Package: GISPA Version: 1.4.0 Depends: R (>= 3.3.2) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 MD5sum: 9b4579e5116438ee20f6250d2ae76556 NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr source.ver: src/contrib/GISPA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GISPA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GISPA_1.4.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R Package: GLAD Version: 2.44.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: 6d375e87f551b87bcef7a75bab3e2d77 NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. source.ver: src/contrib/GLAD_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GLAD_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GLAD_2.44.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS, MANOR, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads Package: Glimma Version: 1.8.2 Depends: R (>= 3.4.0) Imports: edgeR, grDevices, jsonlite, methods, stats, S4Vectors, utils Suggests: BiocStyle, IRanges, GenomicRanges, SummarizedExperiment, DESeq2, limma, testthat, knitr, rmarkdown, pryr License: GPL-3 | file LICENSE MD5sum: 848b3cabcb0c03853158bef1932ed766 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/Shians/Glimma VignetteBuilder: knitr BugReports: https://github.com/Shians/Glimma/issues source.ver: src/contrib/Glimma_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/Glimma_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Glimma_1.8.2.tgz vignettes: vignettes/Glimma/inst/doc/Glimma.pdf vignetteTitles: Glimma Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/Glimma.R importsMe: EGSEA Package: GlobalAncova Version: 3.48.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi Suggests: Biobase, annotate, GO.db, KEGG.db, golubEsets, hu6800.db, vsn, GSEABase, Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 47e6d5d9bb78a8e993dc4a6b92b1b48f NeedsCompilation: yes Title: Calculates a global test for differential gene expression between groups Description: We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel source.ver: src/contrib/GlobalAncova_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GlobalAncova_3.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GlobalAncova_3.48.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R Package: globalSeq Version: 1.8.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 7b16b2e70755a27bba00bab56172a394 NeedsCompilation: no Title: Testing for association between RNA-Seq and high-dimensional data Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues source.ver: src/contrib/globalSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/globalSeq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/globalSeq_1.8.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: globalSeq, pkgdown, pkgdown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R Package: globaltest Version: 5.34.1 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGG.db, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart License: GPL (>= 2) MD5sum: 3fc13a52e288c750d8632723f7668ca2 NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos and Aldo Solari Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_7 git_last_commit: b5c4b6c git_last_commit_date: 2018-08-14 Date/Publication: 2018-08-14 source.ver: src/contrib/globaltest_5.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/globaltest_5.34.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/globaltest_5.34.1.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM suggestsMe: topGO Package: gmapR Version: 1.22.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 3c477852d7da4703a551267bf6fd6e49 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence source.ver: src/contrib/gmapR_1.22.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gmapR_1.22.0.tgz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie suggestsMe: VariantTools Package: GMRP Version: 1.8.1 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics, VariantAnnotation License: GPL (>= 2) MD5sum: 533f9575427fe695c633bfc1cd1c4d9e NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan PackageStatus: Deprecated source.ver: src/contrib/GMRP_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GMRP_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GMRP_1.8.1.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R Package: GOexpress Version: 1.14.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: 52d51a82959a9ba0f9cfb13bb672e02f NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress source.ver: src/contrib/GOexpress_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOexpress_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOexpress_1.14.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R Package: GOfuncR Version: 1.0.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: d09f85fc7c8e469d4d5c509c5f17adbd NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (10-Apr-2018). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. From version 0.99.14 on it is also possible to provide custom annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr source.ver: src/contrib/GOfuncR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOfuncR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOfuncR_1.0.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R Package: GOFunction Version: 1.28.0 Depends: R (>= 2.11.0), methods, Biobase (>= 2.8.0), graph (>= 1.26.0), Rgraphviz (>= 1.26.0), GO.db (>= 2.4.1), AnnotationDbi (>= 1.10.2), SparseM (>= 0.85) Imports: methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, DBI, SparseM License: GPL (>= 2) MD5sum: 6cdcca389c52fa97d086333d79c959cf NeedsCompilation: no Title: GO-function: deriving biologcially relevant functions from statistically significant functions Description: The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing. biocViews: GO, Pathways, Microarray, GeneSetEnrichment Author: Jing Wang Maintainer: Jing Wang source.ver: src/contrib/GOFunction_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOFunction_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOFunction_1.28.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOFunction/inst/doc/GOFunction.R Package: GoogleGenomics Version: 2.2.0 Depends: R (>= 3.1.0), GenomicAlignments (>= 1.0.1), VariantAnnotation Imports: Biostrings, GenomeInfoDb, GenomicRanges, IRanges, httr, rjson, Rsamtools, S4Vectors (>= 0.9.25), Biobase, methods, utils Suggests: BiocStyle, RProtoBuf, httpuv, knitr, rmarkdown, testthat, ggbio, ggplot2, BSgenome.Hsapiens.UCSC.hg19, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Apache License (== 2.0) | file LICENSE Archs: i386, x64 MD5sum: 0f1c6bc3eac81ebbc5597d8822f625a2 NeedsCompilation: yes Title: R Client for Google Genomics API Description: Provides an R package to interact with the Google Genomics API. biocViews: DataImport, ThirdPartyClient, Genetics Author: Cassie Doll, Nicole Deflaux Maintainer: Siddhartha Bagaria URL: https://cloud.google.com/genomics/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GoogleGenomics/issues source.ver: src/contrib/GoogleGenomics_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GoogleGenomics_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GoogleGenomics_2.2.0.tgz vignettes: vignettes/GoogleGenomics/inst/doc/AnnotatingVariants.html, vignettes/GoogleGenomics/inst/doc/PlottingAlignments.html, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.html vignetteTitles: Annotating Variants, Plotting Alignments, Reproducing Variant Annotation Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GoogleGenomics/inst/doc/AnnotatingVariants.R, vignettes/GoogleGenomics/inst/doc/PlottingAlignments.R, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.R Package: GOpro Version: 1.6.0 Depends: R (>= 3.4) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 Archs: i386, x64 MD5sum: 2e02d2a8b8376110738a2c9464667576 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues source.ver: src/contrib/GOpro_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOpro_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOpro_1.6.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R Package: goProfiles Version: 1.42.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: 7c4220b16bb3bc05affdb9f2b43888e8 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez source.ver: src/contrib/goProfiles_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/goProfiles_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/goProfiles_1.42.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R Package: GOSemSim Version: 2.6.2 Depends: R (>= 3.3.2) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocInstaller, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: db1339dd1e0701c542e25dd496d97bc5 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_7 git_last_commit: 2ffe78e git_last_commit_date: 2018-08-07 Date/Publication: 2018-08-08 source.ver: src/contrib/GOSemSim_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOSemSim_2.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOSemSim_2.6.2.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: An introduction to GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, enrichplot, meshes, Rcpi suggestsMe: BioCor, epiNEM, FELLA, SemDist Package: goseq Version: 1.32.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) MD5sum: 32c7f11b94e32da71d0b746f8f836100 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Nadia Davidson , Anthony Hawkins source.ver: src/contrib/goseq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/goseq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/goseq_1.32.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, PathwaySplice, SMITE Package: GOSim Version: 1.18.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: e7511f0753b903de9dd891ce2a15550f NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich source.ver: src/contrib/GOSim_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOSim_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOSim_1.18.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R Package: goSTAG Version: 1.4.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 01f2b9e40392644e36f55eaa84c63f2d NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr source.ver: src/contrib/goSTAG_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/goSTAG_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/goSTAG_1.4.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R Package: GOstats Version: 2.46.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: edd3547a70ad51f1c4df628ef56bc729 NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GOstats_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOstats_2.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOstats_2.46.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, facopy, ideal, MAGeCKFlute, MIGSA, pcaExplorer, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, miRLAB, MLP, MmPalateMiRNA, phenoDist, qpgraph, RnBeads, safe Package: GOsummaries Version: 2.16.1 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: 4088c6cfef63e3ae8dcbb961a193676d NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries source.ver: src/contrib/GOsummaries_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOsummaries_2.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOsummaries_2.16.1.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R Package: GOTHiC Version: 1.16.0 Depends: R (>= 2.15.1), methods, utils, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2 Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 37badbe0534ebd9ff2c42eb79443b93d NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud source.ver: src/contrib/GOTHiC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GOTHiC_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GOTHiC_1.16.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R Package: goTools Version: 1.54.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 3d176ef4d0884aaf3ff3d5f37ef6ed44 NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet source.ver: src/contrib/goTools_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/goTools_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/goTools_1.54.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R Package: gpls Version: 1.52.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 9390defed32af083e08fc20d88faddc2 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/gpls_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gpls_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gpls_1.52.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MCRestimate, MLInterfaces Package: gprege Version: 1.24.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 4f846dcf8ff143df04d509ce1f7c66d8 NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, Bioinformatics, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com source.ver: src/contrib/gprege_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gprege_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gprege_1.24.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R Package: gQTLBase Version: 1.12.0 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, BiocGenerics, foreach, doParallel, bit, ff, rtracklayer, ffbase, GenomicFiles, SummarizedExperiment Suggests: geuvStore2, knitr, rmarkdown, BiocStyle, RUnit, GGtools, Homo.sapiens, IRanges, erma, GenomeInfoDb, gwascat, geuvPack License: Artistic-2.0 MD5sum: 1814ce624fd72d94c526cca3d0a3172c NeedsCompilation: no Title: gQTLBase: infrastructure for eQTL, mQTL and similar studies Description: Infrastructure for eQTL, mQTL and similar studies. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/gQTLBase_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gQTLBase_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gQTLBase_1.12.0.tgz vignettes: vignettes/gQTLBase/inst/doc/gQTLBase.html vignetteTitles: gQTLBase infrastructure for eQTL archives hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R importsMe: gQTLstats Package: gQTLstats Version: 1.12.0 Depends: R (>= 3.1.0), Homo.sapiens Imports: methods, snpStats, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicFiles, GenomicRanges, SummarizedExperiment, VariantAnnotation, Biobase, BatchJobs, gQTLBase, limma, mgcv, dplyr, AnnotationDbi, GenomicFeatures, ggplot2, reshape2, doParallel, foreach, ffbase, BBmisc, beeswarm, HardyWeinberg, graphics, stats, utils, shiny, plotly, erma, ggbeeswarm Suggests: geuvPack, geuvStore2, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, RUnit, multtest, gwascat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, ldblock License: Artistic-2.0 MD5sum: 98cc3c6cdb6ae6931f8113d7e72f7a38 NeedsCompilation: no Title: gQTLstats: computationally efficient analysis for eQTL and allied studies Description: computationally efficient analysis of eQTL, mQTL, dsQTL, etc. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/gQTLstats_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gQTLstats_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gQTLstats_1.12.0.tgz vignettes: vignettes/gQTLstats/inst/doc/gQTLstats.html vignetteTitles: gQTLstats: statistics for genetics of genomic features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R importsMe: gwascat Package: graph Version: 1.58.2 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: da18a0ded8ebbd13efb3d06b7cf26151 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_7 git_last_commit: 6455d8e git_last_commit_date: 2018-10-09 Date/Publication: 2018-10-09 source.ver: src/contrib/graph_1.58.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/graph_1.58.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/graph_1.58.2.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle, gaucho, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, NetSAM, pathRender, Pigengene, pkgDepTools, RbcBook1, RBGL, RBioinf, RCyjs, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, topGO, vtpnet importsMe: alpine, AnalysisPageServer, BgeeDB, BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, DAPAR, DEGraph, DEsubs, epiNEM, EventPointer, FEM, flowCL, flowClust, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GENESIS, GOFunction, GOSim, GraphAT, graphite, gwascat, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, MIGSA, NCIgraph, nem, netresponse, OncoSimulR, OrganismDbi, pathview, PCpheno, PhenStat, pkgDepTools, ppiStats, pwOmics, qpgraph, RchyOptimyx, RCy3, RGraph2js, rsbml, Rtreemix, signet, SplicingGraphs, Streamer, ToPASeq, trackViewer, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, DEGraph, EBcoexpress, ecolitk, GeneAnswers, KEGGlincs, MmPalateMiRNA, netbenchmark, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, VariantTools Package: GraphAlignment Version: 1.44.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: da8fc810ec4a723dc4354bfb2d97f307 NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ source.ver: src/contrib/GraphAlignment_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GraphAlignment_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GraphAlignment_1.44.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R Package: GraphAT Version: 1.52.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 1e02d651677f83f975e1734b6944a2f7 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise source.ver: src/contrib/GraphAT_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GraphAT_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GraphAT_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.26.3 Depends: R (>= 2.10), methods Imports: AnnotationDbi, checkmate, graph, httr, rappdirs, stats, utils Suggests: a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: d289e11ea489eafb1ae0f078b9d68049 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, Reactome and SPIKE databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, BioCarta, Metabolomics Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_7 git_last_commit: bbc8306 git_last_commit_date: 2018-10-12 Date/Publication: 2018-10-15 source.ver: src/contrib/graphite_1.26.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/graphite_1.26.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/graphite_1.26.3.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.html vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: ToPASeq importsMe: EnrichmentBrowser, facopy, mogsa, ReactomePA suggestsMe: clipper, signet Package: GraphPAC Version: 1.22.1 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 4fe6a5d0142de6bba326993821c82481 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: RELEASE_3_7 git_last_commit: 0697b84 git_last_commit_date: 2018-08-20 Date/Publication: 2018-08-21 source.ver: src/contrib/GraphPAC_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GraphPAC_1.22.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GraphPAC_1.22.1.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC Package: GRENITS Version: 1.32.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) Archs: i386, x64 MD5sum: d501213c120725c8c95898d66c03a821 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey source.ver: src/contrib/GRENITS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GRENITS_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GRENITS_1.32.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R Package: GreyListChIP Version: 1.12.0 Depends: R (>= 3.5), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 21218e68fe03c10b73bfb6669579aebb NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Gord Brown Maintainer: Gordon Brown source.ver: src/contrib/GreyListChIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GreyListChIP_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GreyListChIP_1.12.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R Package: GRmetrics Version: 1.6.1 Depends: R (>= 3.3), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 70f5dcba9d621740473b446af3cc7311 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_7 git_last_commit: 5a7334e git_last_commit_date: 2018-07-28 Date/Publication: 2018-07-29 source.ver: src/contrib/GRmetrics_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GRmetrics_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GRmetrics_1.6.1.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R Package: groHMM Version: 1.14.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: df519a3045c32fe45ef011f6c7e8beb7 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Anusha Nagari , Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues source.ver: src/contrib/groHMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/groHMM_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/groHMM_1.14.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R Package: GRridge Version: 1.4.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 MD5sum: 9867d30162103e574ef3a6ede870a708 NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel source.ver: src/contrib/GRridge_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GRridge_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GRridge_1.4.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R Package: GSALightning Version: 1.8.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 42a38c2e7c36b82a30eaf62cd4d1d386 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues source.ver: src/contrib/GSALightning_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSALightning_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSALightning_1.8.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R Package: GSAR Version: 1.14.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: f25e8186f0ab6263281c40ff17df352e NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko source.ver: src/contrib/GSAR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSAR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSAR_1.14.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R Package: GSCA Version: 2.10.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: d4c81277ff7384f4a7870be92ef2638a NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji source.ver: src/contrib/GSCA_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSCA_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSCA_2.10.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R Package: GSEABase Version: 1.42.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat License: Artistic-2.0 MD5sum: 91a5851135c3f3273f84864832a19689 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GSEABase_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSEABase_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSEABase_1.42.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, cpvSNP, gCMAP, npGSEA, PROMISE, splineTimeR, TissueEnrich importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, EnrichmentBrowser, gCMAPWeb, gep2pep, GISPA, GSRI, GSVA, HTSanalyzeR, MIGSA, mogsa, oppar, PCpheno, phenoTest, POST, PROMISE, RcisTarget, ReportingTools, slalom, TFutils suggestsMe: BiocCaseStudies, clusterProfiler, gage, GlobalAncova, globaltest, GOstats, GSAR, MAST, PGSEA, phenoTest, TFEA.ChIP Package: GSEABenchmarkeR Version: 1.0.0 Depends: R(>= 3.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, GEOquery, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, rappdirs, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: c7d8d3494985b27eb454512f106d5953 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues source.ver: src/contrib/GSEABenchmarkeR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSEABenchmarkeR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSEABenchmarkeR_1.0.0.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R Package: GSEAlm Version: 1.40.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: a96d2c6830bf6f782be55a0d089f2c5d NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron source.ver: src/contrib/GSEAlm_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSEAlm_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSEAlm_1.40.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R importsMe: canceR, gCMAP Package: gsean Version: 1.0.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: DESeq2, knitr, plotly, RANKS, WGCNA License: Artistic-2.0 MD5sum: 0f80522294c3fc8cd98f5a30929cf44d NeedsCompilation: no Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr source.ver: src/contrib/gsean_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gsean_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gsean_1.0.0.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R Package: GSReg Version: 1.14.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: ad40191925ae95ead929e22b13a393b5 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig source.ver: src/contrib/GSReg_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSReg_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSReg_1.14.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R Package: GSRI Version: 2.28.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: d2e4b5b71e84fc90925e9a779456a27e NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring source.ver: src/contrib/GSRI_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSRI_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSRI_2.28.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R Package: GSVA Version: 1.28.0 Depends: R (>= 3.0.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4), geneplotter, shiny, shinythemes Suggests: limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) Archs: i386, x64 MD5sum: 76173485ce80556aab9d084ddba299e1 NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: Microarray, Pathways, GeneSetEnrichment Author: Justin Guinney [aut, cre], Robert Castelo [aut], Joan Fernandez [ctb] Maintainer: Justin Guinney URL: https://github.com/rcastelo/GSVA BugReports: https://github.com/rcastelo/GSVA/issues source.ver: src/contrib/GSVA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GSVA_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GSVA_1.28.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R importsMe: consensusOV, EGSEA, oppar, singleCellTK suggestsMe: MCbiclust Package: gtrellis Version: 1.12.1 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.3.3), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.9.7), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: 32aec4833abd4cc135a7acb9df0f585f NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_7 git_last_commit: 7f3941a git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/gtrellis_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/gtrellis_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gtrellis_1.12.1.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA Package: GUIDEseq Version: 1.10.0 Depends: R (>= 3.2.0), GenomicRanges, BiocGenerics Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 1f8a583b916f350e38ab26ad7d547139 NeedsCompilation: no Title: GUIDE-seq analysis pipeline Description: The package implements GUIDE-seq analysis workflow including functions for obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites. biocViews: GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/GUIDEseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/GUIDEseq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GUIDEseq_1.10.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R importsMe: crisprseekplus Package: Guitar Version: 1.18.0 Depends: Rsamtools, GenomicFeatures, rtracklayer, GenomicAlignments, GenomicRanges, ggplot2, grid, IRanges License: GPL-2 MD5sum: 360fb9b3271af22a0fe6f8de8299366e NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription, Coverage Author: Jia Meng Maintainer: Jia Meng source.ver: src/contrib/Guitar_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Guitar_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Guitar_1.18.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Sample Guitar workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R Package: Gviz Version: 1.24.0 Depends: R (>= 2.10.0), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8) Suggests: xtable, BSgenome.Hsapiens.UCSC.hg19, BiocStyle License: Artistic-2.0 MD5sum: 131e9c29bc19fef5cdb98fd1f1593a5f NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray Author: Florian Hahne, Steffen Durinck, Robert Ivanek, Arne Mueller, Steve Lianoglou, Ge Tan , Lance Parsons , Shraddha Pai Maintainer: Robert Ivanek source.ver: src/contrib/Gviz_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Gviz_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Gviz_1.24.0.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.pdf vignetteTitles: Gviz users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, ASpli, CAGEfightR, DMRcate, ELMER, GenomicInteractions, GGtools, gwascat, InPAS, mCSEA, MEAL, methyAnalysis, methylPipe, motifbreakR, Pi, PING, SPLINTER, STAN, TFutils, trackViewer, TVTB, VariantFiltering suggestsMe: annmap, cellbaseR, CNEr, DeepBlueR, ensembldb, GenomicRanges, interactiveDisplay, InterMineR, pqsfinder, QuasR, RnBeads, SplicingGraphs, TxRegInfra Package: gwascat Version: 2.12.0 Depends: R (>= 3.5.0), Homo.sapiens Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), snpStats, Biostrings, Rsamtools, rtracklayer, gQTLstats, Gviz, VariantAnnotation, AnnotationHub, AnnotationDbi, GenomicFeatures, graph, ggbio, ggplot2, SummarizedExperiment Suggests: DO.db, DT, utils, knitr, RBGL, RUnit Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: dbb932d3c3bc79ece9c881f12436d26c NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: utils, knitr source.ver: src/contrib/gwascat_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/gwascat_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/gwascat_2.12.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat -- exploring NHGRI GWAS catalog, gwascat: exploring GWAS results using the experimental factor ontology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet suggestsMe: GenomicScores, gQTLBase, gQTLstats, hmdbQuery Package: GWASTools Version: 1.26.1 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation License: Artistic-2.0 MD5sum: 0468f16acf246d0e2097a577671f76c2 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson Maintainer: Stephanie M. Gogarten , Adrienne Stilp source.ver: src/contrib/GWASTools_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/GWASTools_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/GWASTools_1.26.1.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R importsMe: GENESIS suggestsMe: podkat Package: h5vc Version: 2.14.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.19.38), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rsamtools Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) Archs: i386, x64 MD5sum: a4efe85cd3fc24c657bb8899b595d8a9 NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl VignetteBuilder: knitr source.ver: src/contrib/h5vc_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/h5vc_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/h5vc_2.14.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R Package: hapFabia Version: 1.22.0 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: c7b6c0a8cc23dec738261d44526bcd07 NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html source.ver: src/contrib/hapFabia_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hapFabia_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hapFabia_1.22.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R Package: Harman Version: 1.8.0 Depends: R (>= 3.5) Imports: Rcpp (>= 0.11.2), graphics, stats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: i386, x64 MD5sum: 87efebaec126b3386069784ea0fce852 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr BugReports: https://github.com/JasonR055/Harman/issues source.ver: src/contrib/Harman_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Harman_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Harman_1.8.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser Package: Harshlight Version: 1.52.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 2e220ddf76f1e50ba93ec79bb2000a9a NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ source.ver: src/contrib/Harshlight_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Harshlight_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Harshlight_1.52.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R Package: HDF5Array Version: 1.8.1 Depends: R (>= 3.4), methods, DelayedArray (>= 0.5.32), rhdf5 Imports: utils, tools, BiocGenerics (>= 0.25.1), S4Vectors, IRanges Suggests: h5vcData, SummarizedExperiment (>= 1.9.6), GenomicRanges, BiocStyle License: Artistic-2.0 MD5sum: a0391441a3fc28bc407b0e55f0a84f2d NeedsCompilation: no Title: HDF5 back end for DelayedArray objects Description: An array-like container for convenient access and manipulation of HDF5 datasets. Supports delayed operations and block processing. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Hervé Pagès Maintainer: Hervé Pagès git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_7 git_last_commit: 3c9aa23 git_last_commit_date: 2018-06-20 Date/Publication: 2018-06-21 source.ver: src/contrib/HDF5Array_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/HDF5Array_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HDF5Array_1.8.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BiocSklearn importsMe: beachmat, bsseq, clusterExperiment, minfi, scmeth suggestsMe: DelayedArray, DelayedMatrixStats, DropletUtils, MultiAssayExperiment, scran, SummarizedExperiment Package: HDTD Version: 1.14.0 Depends: R (>= 3.4) Imports: stats, Rcpp (>= 0.12.13) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, markdown License: GPL-3 Archs: i386, x64 MD5sum: 6e7432828ab6a3f5302bd889101fc8c6 NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut], John C. Marioni [aut], Simon Tavar\'{e} [aut] Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues source.ver: src/contrib/HDTD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HDTD_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HDTD_1.14.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R Package: heatmaps Version: 1.4.0 Depends: R (>= 3.4) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: fd405ace8ffdd036456cb7a6267c6144 NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr source.ver: src/contrib/heatmaps_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/heatmaps_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/heatmaps_1.4.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R Package: Heatplus Version: 2.26.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 9b7ca76b5c42e5a293560f447e5e2aa0 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues source.ver: src/contrib/Heatplus_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Heatplus_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Heatplus_2.26.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: GeneAnswers, phenoTest, tRanslatome Package: HelloRanges Version: 1.6.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: 22dbe6cfe99bfa0cac3b26c5d84967c0 NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence source.ver: src/contrib/HelloRanges_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HelloRanges_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HelloRanges_1.6.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R suggestsMe: plyranges Package: HELP Version: 1.38.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: b2e59b59bca953def77c15d8984857e7 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson source.ver: src/contrib/HELP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HELP_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HELP_1.38.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R Package: HEM Version: 1.52.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 556837677f6ccd25ad5106b9648beb52 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ source.ver: src/contrib/HEM_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HEM_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HEM_1.52.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hiAnnotator Version: 1.14.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: 0210b6367bd0a660eeaeea20273d6c72 NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr source.ver: src/contrib/hiAnnotator_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hiAnnotator_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hiAnnotator_1.14.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor Package: HIBAG Version: 1.16.0 Depends: R (>= 3.2.0) Imports: methods Suggests: parallel, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6), ggplot2, reshape2 License: GPL-3 Archs: i386, x64 MD5sum: 8f585416898287afa202e3229c7e6dab NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: It is a software package for imputing HLA types using SNP data, and relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph], Bruce Weir [ctb, ths] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/HIBAG, http://zhengxwen.github.io/HIBAG, http://www.biostat.washington.edu/~bsweir/HIBAG/ VignetteBuilder: knitr source.ver: src/contrib/HIBAG_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HIBAG_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HIBAG_1.16.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.html vignetteTitles: HIBAG vignette html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.R Package: HiCcompare Version: 1.2.0 Depends: R (>= 3.4.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 8723dc4f1b6a3dae167acc64713fad56 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust permutation method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield [aut, cre], Mikhail Dozmorov [aut] Maintainer: John Stansfield VignetteBuilder: knitr source.ver: src/contrib/HiCcompare_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HiCcompare_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HiCcompare_1.2.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R Package: hicrep Version: 1.4.0 Depends: R (>= 3.4) Imports: stats Suggests: knitr, rmarkdown, testthat License: GPL (>= 2.0) MD5sum: 6393b8c3137784b3bac8c21c2646d989 NeedsCompilation: no Title: Measuring the reproducibility of Hi-C data Description: Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance-dependence. We present a novel reproducibility measure that systematically takes these features into consideration. This measure can assess pairwise differences between Hi-C matrices under a wide range of settings, and can be used to determine optimal sequencing depth. Compared to existing approaches, it consistently shows higher accuracy in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages than existing approaches. This R package `hicrep` implements our approach. biocViews: Sequencing, HiC, QualityControl Author: Tao Yang [aut, cre] Maintainer: Tao Yang VignetteBuilder: knitr source.ver: src/contrib/hicrep_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hicrep_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hicrep_1.4.0.tgz vignettes: vignettes/hicrep/inst/doc/hicrep-vigenette.html vignetteTitles: Evaluate reproducibility of Hi-C data with `hicrep` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hicrep/inst/doc/hicrep-vigenette.R Package: hierGWAS Version: 1.10.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 2799795976fee4e10e40b189c5c68436 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan source.ver: src/contrib/hierGWAS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hierGWAS_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hierGWAS_1.10.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R Package: HilbertCurve Version: 1.10.1 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3) Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.13.2), markdown, RColorBrewer, RCurl, GetoptLong License: MIT + file LICENSE MD5sum: 7b456332d46906a2ae4db889bafbc123 NeedsCompilation: no Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_7 git_last_commit: 624c7ef git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/HilbertCurve_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/HilbertCurve_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HilbertCurve_1.10.1.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R suggestsMe: ComplexHeatmap Package: HilbertVis Version: 1.38.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 378e2854aea2fe73702c62585ada4be2 NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert source.ver: src/contrib/HilbertVis_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HilbertVis_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HilbertVis_1.38.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve Package: HilbertVisGUI Version: 1.38.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) Archs: x64 MD5sum: c3e39955cb68e5c220586fd5722de37b NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make source.ver: src/contrib/HilbertVisGUI_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HilbertVisGUI_1.38.0.zip vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Package: hipathia Version: 1.0.0 Depends: R (>= 3.5), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: fae6a00a606e1cdcf0cc28d2587b7014 NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr source.ver: src/contrib/hipathia_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hipathia_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hipathia_1.0.0.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R Package: hiReadsProcessor Version: 1.16.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, rSFFreader, readxl, methods Suggests: knitr, testthat License: GPL-3 MD5sum: 3ac7aa925622a662c97ab4485610d302 NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr source.ver: src/contrib/hiReadsProcessor_1.16.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hiReadsProcessor_1.16.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R Package: HiTC Version: 1.24.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 MD5sum: c8ac44b8fbf8f22c8c7480a9f950524d NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant source.ver: src/contrib/HiTC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HiTC_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HiTC_1.24.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R Package: hmdbQuery Version: 1.0.1 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat License: Artistic-2.0 MD5sum: 681ec72db96da67b7c0bcfe92a052609 NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/hmdbQuery_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/hmdbQuery_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hmdbQuery_1.0.1.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R Package: HMMcopy Version: 1.22.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: 3ed5193c7143e0b79eebddcc519b8700 NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai , Sohrab Shah source.ver: src/contrib/HMMcopy_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HMMcopy_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HMMcopy_1.22.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea Package: hopach Version: 2.40.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: f244fe3cdfa1f92a036a53b9056439f7 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ source.ver: src/contrib/hopach_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hopach_2.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hopach_2.40.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest suggestsMe: BiocCaseStudies Package: hpar Version: 1.22.2 Depends: R (>= 2.15) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: b849c94066fca22697fc06dd7bdb3d42 NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/hpar_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/hpar_1.22.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hpar_1.22.2.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: pRoloc Package: HTqPCR Version: 1.34.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 75d6f44777e841a6486cec1f86295bc8 NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software source.ver: src/contrib/HTqPCR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HTqPCR_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HTqPCR_1.34.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR Package: HTSanalyzeR Version: 2.32.0 Depends: R (>= 2.15), igraph, methods Imports: graph, igraph, GSEABase, BioNet, cellHTS2, AnnotationDbi, biomaRt, RankProd Suggests: KEGG.db, GO.db, org.Dm.eg.db, GOstats, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, snow License: Artistic-2.0 MD5sum: b2a9779030b6c96238c05187dada493e NeedsCompilation: no Title: Gene set over-representation, enrichment and network analyses for high-throughput screens Description: This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over-representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package. biocViews: CellBasedAssays, MultipleComparison Author: Xin Wang , Camille Terfve , John C. Rose , Florian Markowetz Maintainer: Xin Wang source.ver: src/contrib/HTSanalyzeR_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HTSanalyzeR_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HTSanalyzeR_2.32.0.tgz vignettes: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.pdf vignetteTitles: Main vignette:Gene set enrichment and network analysis of high-throughput RNAi screen data using HTSanalyzeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.R importsMe: phenoTest Package: HTSeqGenie Version: 4.10.0 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: 7c0dc7fc714461db9efa21d8431724e6 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder source.ver: src/contrib/HTSeqGenie_4.10.0.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R Package: htSeqTools Version: 1.28.3 Depends: R (>= 2.12.2), methods, BiocGenerics (>= 0.1.0), Biobase, S4Vectors, IRanges, methods, MASS, BSgenome, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.11) Enhances: parallel,multicore License: GPL (>=2) MD5sum: 3bef7bdd97451eedb6194667d022be94 NeedsCompilation: no Title: Quality Control, Visualization and Processing for High-Throughput Sequencing data Description: We provide efficient, easy-to-use tools for High-Throughput Sequencing (ChIP-seq, RNAseq etc.). These include MDS plots (analogues to PCA), detecting inefficient immuno-precipitation or over-amplification artifacts, tools to identify and test for genomic regions with large accumulation of reads, and visualization of coverage profiles. biocViews: Sequencing, QualityControl Author: Evarist Planet, Camille Stephan-Otto, Oscar Reina, Oscar Flores, David Rossell Maintainer: Oscar Reina PackageStatus: Deprecated source.ver: src/contrib/htSeqTools_1.28.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/htSeqTools_1.28.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/htSeqTools_1.28.3.tgz vignettes: vignettes/htSeqTools/inst/doc/htSeqTools.pdf vignetteTitles: Manual for the htSeqTools library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/htSeqTools/inst/doc/htSeqTools.R Package: HTSFilter Version: 1.20.0 Depends: R (>= 3.4) Imports: edgeR (>= 3.9.14), DESeq2 (>= 1.10.1), DESeq (>= 1.22.1), BiocParallel (>= 1.4.3), Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq (>= 2.1.4), BiocStyle, testthat License: Artistic-2.0 MD5sum: c2894cc92c603092b84b3449b23497c6 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization Author: Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic Maintainer: Andrea Rau source.ver: src/contrib/HTSFilter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HTSFilter_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HTSFilter_1.20.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq Package: HybridMTest Version: 1.24.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 3bf57ca3e5479d9ef03239775eabd6a2 NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana source.ver: src/contrib/HybridMTest_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/HybridMTest_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/HybridMTest_1.24.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R Package: hyperdraw Version: 1.32.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 4fb1baaaa7c18cd97f5dcee2dafa9a18 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz source.ver: src/contrib/hyperdraw_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hyperdraw_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hyperdraw_1.32.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR Package: hypergraph Version: 1.52.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: f710c0bd9684bcde2f57dea5bd3910d3 NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/hypergraph_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/hypergraph_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/hypergraph_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.24.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: b354b0fc2543831cf92e7b297fac0f86 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei source.ver: src/contrib/iASeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iASeq_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iASeq_1.24.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R Package: iBBiG Version: 1.24.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 36bc3512ee778dfa37eb96d73bca9e68 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ source.ver: src/contrib/iBBiG_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iBBiG_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iBBiG_1.24.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R Package: ibh Version: 1.28.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 60a9dcb63fc4b305a86de4ea308c2d30 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz source.ver: src/contrib/ibh_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ibh_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ibh_1.28.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R Package: iBMQ Version: 1.20.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 66c6c176c037c205a8579b9a60605d37 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP source.ver: src/contrib/iBMQ_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iBMQ_1.20.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R Package: iCARE Version: 1.8.0 Depends: R (>= 3.3.0) Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 1d75e8a05e979c3743813ba6b9b1f69b NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler source.ver: src/contrib/iCARE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iCARE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iCARE_1.8.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette.R Package: Icens Version: 1.52.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: a186ff69b8a5d63610d30ae531608fde NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Icens_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Icens_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Icens_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iCheck Version: 1.10.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: 3378039cf4683cbfb3d8fef9a333ba9b NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu source.ver: src/contrib/iCheck_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iCheck_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iCheck_1.10.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R Package: iChip Version: 1.34.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: ea4ef14911d9b0599f3985fe7ef03a95 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: This package uses hidden Ising models to identify enriched genomic regions in ChIP-chip data. It can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iChip_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iChip_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iChip_1.34.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R Package: iClusterPlus Version: 1.16.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: a359ff9e05d5ea11fceb6779739724c8 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen source.ver: src/contrib/iClusterPlus_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iClusterPlus_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iClusterPlus_1.16.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet Package: iCNV Version: 1.0.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 MD5sum: 8c0f627b87e6a39dc8eb2262e03e9828 NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr source.ver: src/contrib/iCNV_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iCNV_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iCNV_1.0.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R Package: iCOBRA Version: 1.8.0 Depends: R (>= 3.4) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, testthat License: GPL (>=2) MD5sum: c7288acf78559a8e7fcbb5620a6160dd NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a shiny application for interactive exploration of results. biocViews: Classification Author: Charlotte Soneson [aut, cre] Maintainer: Charlotte Soneson VignetteBuilder: knitr source.ver: src/contrib/iCOBRA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iCOBRA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iCOBRA_1.8.0.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: SummarizedBenchmark Package: ideal Version: 1.4.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), d3heatmap, pheatmap, pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, plyr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE MD5sum: 55d28d25e488a971d034f73977c48397 NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues source.ver: src/contrib/ideal_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ideal_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ideal_1.4.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R Package: IdeoViz Version: 1.16.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 6a0961b2bbe54d2beabeb1f5840da31b NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai source.ver: src/contrib/IdeoViz_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IdeoViz_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IdeoViz_1.16.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R Package: idiogram Version: 1.56.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 0f750ee0336e1c29acc7e17514ccb46a NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/idiogram_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/idiogram_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/idiogram_1.56.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependsOnMe: reb Package: IdMappingAnalysis Version: 1.24.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: 08899b8ab338824c66eb25b670932406 NeedsCompilation: no Title: ID Mapping Analysis Description: Identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingAnalysis_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IdMappingAnalysis_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IdMappingAnalysis_1.24.0.tgz vignettes: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.pdf vignetteTitles: Critically comparing identifier maps retrieved from bioinformatics annotation resources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.R Package: IdMappingRetrieval Version: 1.28.0 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: 892662bbb62708bd9d058b3201202454 NeedsCompilation: no Title: ID Mapping Data Retrieval Description: Data retrieval for identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingRetrieval_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IdMappingRetrieval_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IdMappingRetrieval_1.28.0.tgz vignettes: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.pdf vignetteTitles: Collection and subsequent fast retrieval of identifier mapping related information from various online sources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.R Package: iGC Version: 1.10.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 9d20ad877ecd0312995971ae766ace2f NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues source.ver: src/contrib/iGC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iGC_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iGC_1.10.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R Package: igvR Version: 1.0.1 Depends: R (>= 3.5.0), GenomicRanges, VariantAnnotation, rtracklayer, BrowserViz (>= 2.0) Imports: methods, BiocGenerics, httpuv, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 875bba28fef85108c4d51758e7489f61 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_7 git_last_commit: 4c763f0 git_last_commit_date: 2018-06-30 Date/Publication: 2018-07-01 source.ver: src/contrib/igvR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/igvR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/igvR_1.0.1.tgz vignettes: vignettes/igvR/inst/doc/igvR.html vignetteTitles: "igvR: programmatic access to igv.js,, a browser-based genome track viewer" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/igvR.R Package: IHW Version: 1.8.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: 25039ee3c2193eb805bf2aeedd1c37cb NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr source.ver: src/contrib/IHW_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IHW_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IHW_1.8.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R importsMe: ideal suggestsMe: DESeq2, SummarizedBenchmark Package: illuminaio Version: 0.22.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 9e3063760dbd7171b1eb3abc0e2c71b8 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues source.ver: src/contrib/illuminaio_0.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/illuminaio_0.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/illuminaio_0.22.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon importsMe: beadarray, crlmm, methylumi, minfi suggestsMe: limma Package: imageHTS Version: 1.30.0 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: 3ce17d3c03f94d9e8a0e7704e3211903 NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry source.ver: src/contrib/imageHTS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/imageHTS_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/imageHTS_1.30.0.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependsOnMe: phenoDist Package: IMAS Version: 1.4.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 MD5sum: db898ee8e0311881dfb3fba45323a677 NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han source.ver: src/contrib/IMAS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IMAS_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IMAS_1.4.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R Package: Imetagene Version: 1.10.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 78b3707f1fe2b2056823f1e2e5d4aa23 NeedsCompilation: no Title: A graphical interface for the metagene package Description: This package provide a graphical user interface to the metagene package. This will allow people with minimal R experience to easily complete metagene analysis. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Audrey Lemacon , Charles Joly Beauparlant , Arnaud Droit Maintainer: Audrey Lemacon VignetteBuilder: knitr BugReports: https://github.com/andronekomimi/Imetagene/issues source.ver: src/contrib/Imetagene_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Imetagene_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Imetagene_1.10.0.tgz vignettes: vignettes/Imetagene/inst/doc/imetagene.html vignetteTitles: Presentation of Imetagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Imetagene/inst/doc/imetagene.R Package: IMMAN Version: 1.0.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr, BiocFileCache Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 145be196182bf725a272e062fbf08caa NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Payman Nickchi, Abdollah Safari, Minoo Ashtiani, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr source.ver: src/contrib/IMMAN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IMMAN_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IMMAN_1.0.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R Package: ImmuneSpaceR Version: 1.8.2 Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.1.136), Biobase, pheatmap, ggplot2, scales, stats, gtools, gplots, reshape2, plotly, heatmaply (>= 0.7.0), rjson, rmarkdown, preprocessCore, parallel Suggests: knitr, testthat License: GPL-2 MD5sum: 9f8a7ed76ec66cd0b16275ba0e61e66a NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Database Description: Provides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_7 git_last_commit: 0c630fd git_last_commit_date: 2018-08-29 Date/Publication: 2018-08-30 source.ver: src/contrib/ImmuneSpaceR_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/ImmuneSpaceR_1.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ImmuneSpaceR_1.8.2.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R Package: immunoClust Version: 1.12.0 Depends: R(>= 3.3), methods, stats, graphics, grid, lattice, grDevices, flowCore Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 7109433036e587cdb8d837eb0cb4a6e5 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: Model based clustering and meta-clustering of Flow Cytometry Data biocViews: Clustering, FlowCytometry, CellBasedAssays Author: Till Soerensen Maintainer: Till Soerensen source.ver: src/contrib/immunoClust_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/immunoClust_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/immunoClust_1.12.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R Package: IMPCdata Version: 1.16.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 099117abea139d4653b5cd2e661b34d3 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason source.ver: src/contrib/IMPCdata_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IMPCdata_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IMPCdata_1.16.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R Package: ImpulseDE Version: 1.6.0 Depends: graphics, grDevices, stats, utils, parallel, compiler, R (>= 3.2.3) Imports: amap, boot Suggests: longitudinal, knitr License: GPL-3 MD5sum: f6253bc7e9b069dd14a3d60b14f0fcd9 NeedsCompilation: no Title: Detection of DE genes in time series data using impulse models Description: ImpulseDE is suited to capture single impulse-like patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes whether across time points in a single experiment or between two time courses from two experiments. To optimize the running time, the code makes use of clustering steps and multi-threading. biocViews: Software, StatisticalMethod, TimeCourse Author: Jil Sander [aut, cre], Nir Yosef [aut] Maintainer: Jil Sander , Nir Yosef URL: https://github.com/YosefLab/ImpulseDE VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE/issues source.ver: src/contrib/ImpulseDE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ImpulseDE_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ImpulseDE_1.6.0.tgz vignettes: vignettes/ImpulseDE/inst/doc/ImpulseDE.pdf vignetteTitles: ImpulseDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImpulseDE/inst/doc/ImpulseDE.R Package: ImpulseDE2 Version: 1.4.0 Imports: Biobase, BiocParallel, ComplexHeatmap, circlize, compiler, cowplot, DESeq2, ggplot2, grDevices, knitr, Matrix, methods, S4Vectors, stats, SummarizedExperiment, utils License: Artistic-2.0 MD5sum: 0c766e65ade79924cdb365d9db94fd9b NeedsCompilation: no Title: Differential expression analysis of longitudinal count data sets Description: ImpulseDE2 is a differential expression algorithm for longitudinal count data sets which arise in sequencing experiments such as RNA-seq, ChIP-seq, ATAC-seq and DNaseI-seq. ImpulseDE2 is based on a negative binomial noise model with dispersion trend smoothing by DESeq2 and uses the impulse model to constrain the mean expression trajectory of each gene. The impulse model was empirically found to fit global expression changes in cells after environmental and developmental stimuli and is therefore appropriate in most cell biological scenarios. The constraint on the mean expression trajectory prevents overfitting to small expression fluctuations. Secondly, ImpulseDE2 has higher statistical testing power than generalized linear model-based differential expression algorithms which fit time as a categorial variable if more than six time points are sampled because of the fixed number of parameters. biocViews: Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays Author: David S Fischer [aut, cre], Fabian J Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE2/issues source.ver: src/contrib/ImpulseDE2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ImpulseDE2_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ImpulseDE2_1.4.0.tgz vignettes: vignettes/ImpulseDE2/inst/doc/ImpulseDE2_Tutorial.html vignetteTitles: ImpulseDE2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImpulseDE2/inst/doc/ImpulseDE2_Tutorial.R Package: impute Version: 1.54.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: afe321947d29f3876a4d23972aa60815 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan source.ver: src/contrib/impute_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/impute_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/impute_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, MetaGxOvarian, TIN importsMe: CancerSubtypes, doppelgangR, EGAD, fastLiquidAssociation, genomation, MethylMix, miRLAB, MSnbase, Pigengene, REMP, Rnits suggestsMe: BioNet, graphite, MethPed, RnBeads Package: InPAS Version: 1.12.0 Depends: R (>= 3.1), methods, Biobase, GenomicRanges, GenomicFeatures, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, Gviz, seqinr, preprocessCore, IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel Suggests: RUnit, BiocGenerics, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, org.Hs.eg.db, org.Mm.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, rtracklayer, knitr License: GPL (>= 2) MD5sum: e1fbfb070c819ac087b356693dbd85b0 NeedsCompilation: no Title: Identification of Novel alternative PolyAdenylation Sites (PAS) Description: Alternative polyadenylation (APA) is one of the important post-transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites from RNAseq data. It leverages cleanUpdTSeq to fine tune identified APA sites. biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription Author: Jianhong Ou, Sung Mi Park, Michael R. Green and Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/InPAS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/InPAS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/InPAS_1.12.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R Package: INPower Version: 1.16.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 9d9d662aa2b97075e736af23737a736d NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler source.ver: src/contrib/INPower_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/INPower_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/INPower_1.16.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R Package: INSPEcT Version: 1.10.0 Depends: R (>= 3.2), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, compiler, preprocessCore, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors Suggests: BiocStyle, knitr, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-2 MD5sum: f1138ba6588704b4c984ae5a77a42067 NeedsCompilation: no Title: Analysis of 4sU-seq and RNA-seq time-course data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates in Time-Course experiments) analyses 4sU-seq and RNA-seq time-course data in order to evaluate synthesis, processing and degradation rates and asses via modeling the rates that determines changes in mature mRNA levels. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis VignetteBuilder: knitr source.ver: src/contrib/INSPEcT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/INSPEcT_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/INSPEcT_1.10.0.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT.pdf vignetteTitles: INSPEcT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT.R Package: InTAD Version: 1.0.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 72c9af19f0207be30dfff88ad06f4e23 NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals i.e. enhancers obtained from ChIP-seq data within topologically associated domains (TADs). Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr source.ver: src/contrib/InTAD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/InTAD_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/InTAD_1.0.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R Package: intansv Version: 1.22.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: 895b62e8839b9f50610a0c6b1ad4f33c NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_7 git_last_commit: 0c4dd1e git_last_commit_date: 2018-07-19 Date/Publication: 2018-07-19 source.ver: src/contrib/intansv_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/intansv_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/intansv_1.22.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R Package: InteractionSet Version: 1.8.0 Depends: R (>= 3.5), GenomicRanges, SummarizedExperiment Imports: IRanges, S4Vectors (>= 0.17.21), GenomeInfoDb, BiocGenerics, methods, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: a0df85c8846633efec2ed538dc63a19a NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun , Malcolm Perry , Liz Ing-Simmons Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/InteractionSet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/InteractionSet_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/InteractionSet_1.8.0.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Interacting with InteractionSet classes for genomic interaction data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, GenomicInteractions, MACPET, sevenC importsMe: HiCcompare, trackViewer Package: interactiveDisplay Version: 1.18.0 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: 882cceebc507101fa3520a70013f9f76 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplay_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/interactiveDisplay_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/interactiveDisplay_1.18.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq Package: interactiveDisplayBase Version: 1.18.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: ee9de85a6c66a8e5817f0da5eaece376 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplayBase_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/interactiveDisplayBase_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/interactiveDisplayBase_1.18.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: AnnotationHub, interactiveDisplay Package: IntEREst Version: 1.4.1 Depends: R (>= 3.4), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 MD5sum: 589151abd09fc18d7753a440a7c3ff00 NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr source.ver: src/contrib/IntEREst_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/IntEREst_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IntEREst_1.4.1.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R Package: InterMineR Version: 1.2.1 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db, org.Hs.eg.db License: LGPL MD5sum: 281b842056a7f06abae59b394e1ba1b4 NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_7 git_last_commit: 978b6c2 git_last_commit_date: 2018-08-21 Date/Publication: 2018-08-21 source.ver: src/contrib/InterMineR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/InterMineR_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/InterMineR_1.2.1.tgz vignettes: vignettes/InterMineR/inst/doc/Enrichment_Analysis_and_Visualization.html, vignettes/InterMineR/inst/doc/FlyMine_Genomic_Visualizations.html, vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: Enrichment Analysis and Visualization, FlyMine Genomic Visualizations, InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/Enrichment_Analysis_and_Visualization.R, vignettes/InterMineR/inst/doc/FlyMine_Genomic_Visualizations.R, vignettes/InterMineR/inst/doc/InterMineR.R Package: IntramiRExploreR Version: 1.2.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: RDAVIDWebService, gProfileR, topGO, KEGGprofile, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: 49c9c58cd97b6b00cd115f5cb59c38c1 NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/sbhattacharya3/IntramiRExploreR/ VignetteBuilder: knitr BugReports: https://github.com/sbhattacharya3/IntramiRExploreR/issues source.ver: src/contrib/IntramiRExploreR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IntramiRExploreR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IntramiRExploreR_1.2.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R Package: inveRsion Version: 1.28.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: fca86bf343a02e1e2d63c6ad2cce9acf NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres source.ver: src/contrib/inveRsion_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/inveRsion_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/inveRsion_1.28.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R Package: IONiseR Version: 2.4.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: d696e7faf9edfc11314d030043753f78 NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/IONiseR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IONiseR_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IONiseR_2.4.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R Package: iPAC Version: 1.24.2 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 920ee1ee581835119080d0bb8244eff4 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: RELEASE_3_7 git_last_commit: cf2c4f6 git_last_commit_date: 2018-08-17 Date/Publication: 2018-08-18 source.ver: src/contrib/iPAC_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/iPAC_1.24.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iPAC_1.24.2.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC Package: IPO Version: 1.6.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: dde2b98155203178fdc9d9be7fd98361 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Riebenbauer Maintainer: Thomas Riebenbauer URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new source.ver: src/contrib/IPO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IPO_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IPO_1.6.0.tgz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R Package: IPPD Version: 1.28.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 081fad096a8d9f7897f7ad9c75da5533 NeedsCompilation: yes Title: Isotopic peak pattern deconvolution for Protein Mass Spectrometry by template matching Description: The package provides functionality to extract isotopic peak patterns from raw mass spectra. This is done by fitting a large set of template basis functions to the raw spectrum using either nonnegative least squares or least absolute deviation fittting. The package offers a flexible function which tries to estimate model parameters in a way tailored to the peak shapes in the data. The package also provides functionality to process LCMS runs. biocViews: Proteomics Author: Martin Slawski , Rene Hussong , Andreas Hildebrandt , Matthias Hein Maintainer: Martin Slawski source.ver: src/contrib/IPPD_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IPPD_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IPPD_1.28.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IPPD/inst/doc/IPPD.R Package: IRanges Version: 2.14.12 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.25.3), S4Vectors (>= 0.18.2) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 9629e7d3d2fe7d05dba45c30f184e365 NeedsCompilation: yes Title: Infrastructure for manipulating intervals on sequences Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_7 git_last_commit: 00af027 git_last_commit_date: 2018-09-19 Date/Publication: 2018-09-20 source.ver: src/contrib/IRanges_2.14.12.tar.gz win.binary.ver: bin/windows/contrib/3.5/IRanges_2.14.12.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IRanges_2.14.12.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, CNPBayes, CODEX, consensusSeekeR, CSAR, customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq, DirichletMultinomial, DMCHMM, DMRcaller, epigenomix, exomeCopy, fCCAC, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, groHMM, gtrellis, Guitar, Gviz, HelloRanges, HilbertCurve, HiTC, HMMcopy, htSeqTools, IdeoViz, InTAD, methyAnalysis, MotifDb, motifRG, NADfinder, ORFik, OTUbase, pepStat, PGA, PING, plyranges, proBAMr, PSICQUIC, RefNet, rfPred, rGADEM, rGREAT, RIPSeeker, RJMCMCNucleosomes, rMAT, Scale4C, scsR, SGSeq, SICtools, TEQC, triform, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, alpine, amplican, AneuFinder, annmap, annotatr, ArrayExpressHTS, ArrayTV, ASpli, ATACseqQC, ballgown, bamsignals, BayesPeak, BBCAnalyzer, beadarray, biovizBase, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, BSgenome, bsseq, BUMHMM, CAGEfightR, CAGEr, CHARGE, charm, ChIC, ChIPanalyser, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPSeqSpike, ChIPsim, ChromHeatMap, chromstaR, chromswitch, chromVAR, CINdex, cleaver, cn.mops, CNEr, CNVPanelizer, CNVrd2, cobindR, coMET, compEpiTools, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, dada2, debrowser, DECIPHER, DelayedMatrixStats, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffloop, DMRcate, DMRScan, dmrseq, DominoEffect, DOQTL, DRIMSeq, easyRNASeq, EDASeq, ELMER, EnrichedHeatmap, ensembldb, epivizr, epivizrData, erma, esATAC, facopy, fastseg, FindMyFriends, flipflop, flowQ, FunciSNP, GA4GHclient, gcapc, GDSArray, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicScores, GenomicTuples, genoset, genotypeeval, GenVisR, GGBase, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GOpro, GOTHiC, gQTLstats, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, HiCcompare, HTSeqGenie, ideal, IMAS, InPAS, INSPEcT, intansv, InteractionSet, IntEREst, InterMineR, IsoformSwitchAnalyzeR, isomiRs, IVAS, JunctionSeq, karyoploteR, LOLA, M3D, MACPET, MADSEQ, MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, metagene, methimpute, methInheritSim, methVisual, methyAnalysis, methylInheritance, methylKit, methylPipe, MethylSeekR, methylumi, methyvim, minfi, MinimumDistance, MIRA, mosaics, motifbreakR, motifmatchr, MotIV, msa, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, MutationalPatterns, NarrowPeaks, normr, nucleoSim, nucleR, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, panelcn.mops, Pbase, pcaExplorer, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, pqsfinder, prebs, PureCN, Pviz, QDNAseq, qpgraph, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, Rariant, Rcade, recount, REDseq, regioneR, REMP, Repitools, ReportingTools, rGADEM, RiboProfiling, riboSeqR, rMAT, RNAprobR, rnaSeqMap, RnBeads, roar, Rqc, Rsamtools, rSFFreader, RSVSim, RTCGAToolbox, RTN, rtracklayer, SCAN.UPC, segmentSeq, SeqArray, seqCAT, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sevenC, ShortRead, simulatorZ, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spliceR, SplicingGraphs, SPLINTER, srnadiff, STAN, SummarizedExperiment, SVM2CRM, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TFutils, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, triform, TSRchitect, TSSi, TVTB, TxRegInfra, Uniquorn, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, wiggleplotr, XVector, yamss suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gQTLBase, GWASTools, HilbertVis, HilbertVisGUI, martini, MiRaGE, regionReport, RTCGA, S4Vectors linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rSFFreader, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering, XVector Package: IrisSpatialFeatures Version: 1.3.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.7), SpatialTools, gplots, spatstat, tiff, RColorBrewer, methods, grDevices, graphics, stats, utils, data.table, ggplot2, dplyr, magrittr, tibble LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 5e62d27c1ce88ef38c8a7692a369187e NeedsCompilation: yes Title: A package to extract spatial features based on multiplex IF images Description: IrisSpatialFeatures reads the output of the PerkinElmer inForm software and calculates a variety of spatial statistics. In addition to simple counts, it can derive average nearest neighbors for each cell-type and interaction summary profiles for each celltype. These statistics are derived across images, both overall and regions of interest as defined by user defined masks. biocViews: FeatureExtraction, SingleCell Author: Daniel Gusenleitner, Jason L. Weirather Maintainer: Daniel Gusenleitner VignetteBuilder: knitr source.ver: src/contrib/IrisSpatialFeatures_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IrisSpatialFeatures_1.3.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IrisSpatialFeatures_1.3.0.tgz vignettes: vignettes/IrisSpatialFeatures/inst/doc/IrisSpatialFeatures.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IrisSpatialFeatures/inst/doc/IrisSpatialFeatures.R Package: iSEE Version: 1.0.1 Depends: R (>= 3.5), SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, colourpicker, igraph, vipor, mgcv, reshape2, rentrez, AnnotationDbi, graphics, grDevices, viridisLite, cowplot, scales, dplyr Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, scater, Rtsne, irlba, RColorBrewer, viridis, org.Mm.eg.db, htmltools Enhances: ExperimentHub License: MIT + file LICENSE MD5sum: 3836d46ce33aaa6eab62b293fafb5ea2 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Provides functions for creating an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. Particular attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Charlotte Soneson [aut, cre], Aaron Lun [aut], Federico Marini [aut], Kevin Rue-Albrecht [aut] Maintainer: Federico Marini URL: https://github.com/csoneson/iSEE VignetteBuilder: knitr BugReports: https://github.com/csoneson/iSEE/issues source.ver: src/contrib/iSEE_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/iSEE_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iSEE_1.0.1.tgz vignettes: vignettes/iSEE/inst/doc/iSEE_vignette.html vignetteTitles: iSEE User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/iSEE_vignette.R Package: iSeq Version: 1.32.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: cca8800be55fc7656ef64d44f5303605 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: This package uses Bayesian hidden Ising models to identify IP-enriched genomic regions from ChIP-seq data. It can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iSeq_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iSeq_1.32.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R Package: isobar Version: 1.26.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: f648d7a28b9837238041ecf6d5f10331 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues source.ver: src/contrib/isobar_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/isobar_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/isobar_1.26.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R Package: IsoformSwitchAnalyzeR Version: 1.2.0 Depends: R (>= 3.4), cummeRbund Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings, IRanges, GenomicRanges, DRIMSeq, RColorBrewer, rtracklayer, ggplot2, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport, edgeR, futile.logger Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19 License: GPL (>= 2) Archs: i386, x64 MD5sum: 31915c0aa01c502fb4a33510057e4df2 NeedsCompilation: yes Title: An R package to Identify, Annotate and Visualize Isoform Switches with Functional Consequences (from RNA-seq data) Description: IsoformSwitchAnalyzeR enables identification and analysis of isoform switches with predicted functional consequences (such as gain/loss of protein domains etc) from quantification by Kallisto, Salmon, Cufflinks/Cuffdiff, RSEM etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison Author: Kristoffer Vitting-Seerup Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues source.ver: src/contrib/IsoformSwitchAnalyzeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IsoformSwitchAnalyzeR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IsoformSwitchAnalyzeR_1.2.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R Package: IsoGeneGUI Version: 2.16.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: 85f2bdc386ce4d6d364e31d8d36f8779 NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package source.ver: src/contrib/IsoGeneGUI_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IsoGeneGUI_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IsoGeneGUI_2.16.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R Package: ISoLDE Version: 1.8.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: i386, x64 MD5sum: e4c6b0dfc255fd52cceb214867850f24 NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org source.ver: src/contrib/ISoLDE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ISoLDE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ISoLDE_1.8.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: isomiRs Version: 1.8.0 Depends: R (>= 3.4), DiscriMiner, SummarizedExperiment Imports: AnnotationDbi, assertive.sets, BiocGenerics (>= 0.7.5), Biobase, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, tidyr, S4Vectors, tidyr, tibble, targetscan.Hs.eg.db Suggests: knitr, org.Mm.eg.db, cluster, clusterProfiler, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 77bc53fe7ec392e2749009f1f41fb340 NeedsCompilation: yes Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/isomiRs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/isomiRs_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/isomiRs_1.8.0.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R Package: ITALICS Version: 2.40.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 1701d5664aae2711ba867b24fbef021b NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr source.ver: src/contrib/ITALICS_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ITALICS_2.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ITALICS_2.40.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R Package: iterativeBMA Version: 1.38.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: b536271e70307b8bd136a65d2e62cabb NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html source.ver: src/contrib/iterativeBMA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iterativeBMA_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iterativeBMA_1.38.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R Package: iterativeBMAsurv Version: 1.38.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 18a66a5b660e74474792d0e775603994 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected source.ver: src/contrib/iterativeBMAsurv_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iterativeBMAsurv_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iterativeBMAsurv_1.38.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R Package: iterClust Version: 1.2.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: 660629ec0b6ee236a6191a53a200e61a NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues source.ver: src/contrib/iterClust_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iterClust_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iterClust_1.2.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R Package: iteremoval Version: 1.0.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: 0380940c8a6075808346cf8c92c722a1 NeedsCompilation: no Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues source.ver: src/contrib/iteremoval_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/iteremoval_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/iteremoval_1.0.0.tgz vignettes: vignettes/iteremoval/inst/doc/iteremoval.html vignetteTitles: An introduction to iteremoval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R Package: IVAS Version: 2.0.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: bfefd1c7cb6bf24ff662111258701788 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han source.ver: src/contrib/IVAS_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IVAS_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IVAS_2.0.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS Package: ivygapSE Version: 1.2.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat License: Artistic-2.0 MD5sum: 9d5f7a7494faf75b1caf65ee63b7749e NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/ivygapSE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ivygapSE_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ivygapSE_1.2.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R Package: IWTomics Version: 1.4.0 Depends: GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 6529431c3a73c4aaf639f381002dca92 NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr source.ver: src/contrib/IWTomics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/IWTomics_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/IWTomics_1.4.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R Package: JASPAR2018 Version: 1.1.0 Depends: R (>= 3.4.0), methods Suggests: TFBSTools (>= 1.15.6) License: GPL-2 MD5sum: 7f6d748701d83294d6138b265657712a NeedsCompilation: no Title: Data package for JASPAR 2018 Description: Data package for JASPAR 2018. To search this databases, please use the package TFBSTools (>= 1.15.6). biocViews: FunctionalAnnotation Author: Ge Tan Maintainer: Ge Tan URL: http://jaspar.genereg.net/ source.ver: src/contrib/JASPAR2018_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/JASPAR2018_1.1.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/JASPAR2018_1.1.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: joda Version: 1.28.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: 3c2b93a5186028c1b150fe9106d9f34a NeedsCompilation: no Title: JODA algorithm for quantifying gene deregulation using knowledge Description: Package 'joda' implements three steps of an algorithm called JODA. The algorithm computes gene deregulation scores. For each gene, its deregulation score reflects how strongly an effect of a certain regulator's perturbation on this gene differs between two different cell populations. The algorithm utilizes regulator knockdown expression data as well as knowledge about signaling pathways in which the regulators are involved (formalized in a simple matrix model). biocViews: Microarray, Pathways, GraphAndNetwork, StatisticalMethod, NetworkInference Author: Ewa Szczurek Maintainer: Ewa Szczurek URL: http://www.bioconductor.org source.ver: src/contrib/joda_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/joda_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/joda_1.28.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/joda/inst/doc/JodaVignette.R Package: JunctionSeq Version: 1.10.0 Depends: R (>= 3.2.2), methods, SummarizedExperiment (>= 0.2.0), Rcpp (>= 0.11.0), RcppArmadillo (>= 0.3.4.4) Imports: DESeq2 (>= 1.10.0), statmod, Hmisc, plotrix, stringr, Biobase (>= 2.30.0), locfit, BiocGenerics (>= 0.7.5), BiocParallel, genefilter, geneplotter, S4Vectors, IRanges, GenomicRanges, LinkingTo: Rcpp, RcppArmadillo Suggests: MASS, knitr, JctSeqData, BiocStyle Enhances: Cairo, pryr License: file LICENSE Archs: i386, x64 MD5sum: 455a115a63903ef141b26433bd7a0f23 NeedsCompilation: yes Title: JunctionSeq: A Utility for Detection of Differential Exon and Splice-Junction Usage in RNA-Seq data Description: A Utility for Detection and Visualization of Differential Exon or Splice-Junction Usage in RNA-Seq data. biocViews: Sequencing, RNASeq, DifferentialExpression Author: Stephen Hartley [aut, cre] (PhD), Simon Anders [cph], Alejandro Reyes [cph] Maintainer: Stephen Hartley URL: http://hartleys.github.io/JunctionSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/hartleys/JunctionSeq/issues source.ver: src/contrib/JunctionSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/JunctionSeq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/JunctionSeq_1.10.0.tgz vignettes: vignettes/JunctionSeq/inst/doc/JunctionSeq.pdf vignetteTitles: JunctionSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: PathwaySplice Package: karyoploteR Version: 1.6.3 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures Suggests: BiocStyle, knitr, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, pasillaBamSubset License: Artistic-2.0 MD5sum: b4a550fa7354a954e58242ba8eeb9e7d NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_7 git_last_commit: 8dfb702 git_last_commit_date: 2018-09-21 Date/Publication: 2018-09-21 source.ver: src/contrib/karyoploteR_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/karyoploteR_1.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/karyoploteR_1.6.3.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R Package: KCsmart Version: 2.38.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 1bb19a3a4930b6b22898089364c46d68 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde source.ver: src/contrib/KCsmart_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/KCsmart_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/KCsmart_2.38.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R Package: kebabs Version: 1.14.0 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.5.11), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 9fd286848a877441d95eff76471e934b NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ VignetteBuilder: knitr source.ver: src/contrib/kebabs_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/kebabs_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/kebabs_1.14.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: FindMyFriends, odseq Package: KEGGgraph Version: 1.40.0 Depends: R (>= 2.10.0) Imports: methods, XML (>= 2.3-0), graph, utils Suggests: Rgraphviz, RBGL, RUnit, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 4ff3f12ccaef49e2e6b7daee6b10ab29 NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com source.ver: src/contrib/KEGGgraph_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/KEGGgraph_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/KEGGgraph_1.40.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview suggestsMe: DEGraph, GenomicRanges Package: KEGGlincs Version: 1.6.2 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocInstaller (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 88e6d369b7562ee1ffd27aeb803dbece NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data [option] Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_7 git_last_commit: 1bf004c git_last_commit_date: 2018-08-03 Date/Publication: 2018-08-03 source.ver: src/contrib/KEGGlincs_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/KEGGlincs_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/KEGGlincs_1.6.2.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R Package: keggorthology Version: 2.32.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 37d33bd883addeb12cc5f0d3e9f6750f NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/keggorthology_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/keggorthology_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/keggorthology_2.32.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces Package: KEGGprofile Version: 1.22.0 Depends: RCurl Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: 6713590edb5c0b305fe1e70e0b3b7252 NeedsCompilation: no Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao source.ver: src/contrib/KEGGprofile_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/KEGGprofile_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/KEGGprofile_1.22.0.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGprofile/inst/doc/KEGGprofile.R suggestsMe: FGNet, IntramiRExploreR Package: KEGGREST Version: 1.20.2 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 1cc6015f3cf8dccd4bf1fe7efc4d6ea5 NeedsCompilation: no Title: Client-side REST access to KEGG Description: A package that provides a client interface to the KEGG REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_7 git_last_commit: 62b4519 git_last_commit_date: 2018-09-20 Date/Publication: 2018-09-20 source.ver: src/contrib/KEGGREST_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/KEGGREST_1.20.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/KEGGREST_1.20.2.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: PAPi, ROntoTools importsMe: attract, CNEr, EnrichmentBrowser, FELLA, gage, MetaboSignal, MWASTools, pathview, PathwaySplice, StarBioTrek, YAPSA Package: kimod Version: 1.8.0 Depends: R(>= 3.3),methods Imports: cluster, graphics, Biobase License: GPL (>=2) MD5sum: cad9347662fe0a53ca8e1efc80d62b43 NeedsCompilation: no Title: A k-tables approach to integrate multiple Omics-Data Description: This package allows to work with mixed omics data (transcriptomics, proteomics, microarray-chips, rna-seq data), introducing the following improvements: distance options (for numeric and/or categorical variables) for each of the tables, bootstrap resampling techniques on the residuals matrices for all methods, that enable perform confidence ellipses for the projection of individuals, variables and biplot methodology to project variables (gene expression) on the compromise. Since the main purpose of the package is to use these techniques to omic data analysis, it includes an example data from four different microarray platforms (i.e.,Agilent, Affymetrix HGU 95, Affymetrix HGU 133 and Affymetrix HGU 133plus 2.0) on the NCI-60 cell lines.NCI60_4arrays is a list containing the NCI-60 microarray data with only few hundreds of genes randomly selected in each platform to keep the size of the package small. The data are the same that the package omicade4 used to implement the co-inertia analysis. The references in packages follow the style of the APA-6th norm. biocViews: Microarray, Visualization, GeneExpression, ExperimentData, Proteomics Author: Maria Laura Zingaretti, Johanna Altair Demey-Zambrano, Jose Luis Vicente-Villardon, Jhonny Rafael Demey Maintainer: M L Zingaretti source.ver: src/contrib/kimod_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/kimod_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/kimod_1.8.0.tgz vignettes: vignettes/kimod/inst/doc/kimod-vignette.pdf vignetteTitles: kimod A K-tables approach to integrate multiple Omics-Data in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kimod/inst/doc/kimod-vignette.R Package: kissDE Version: 1.0.0 Imports: aod, Biobase, DESeq2, DSS, ggplot2, glmnet, gplots, graphics, grDevices, matrixStats, R.utils, stats, utils Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 83259deeaa06e2c759fc6e3e69221389 NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot source.ver: src/contrib/kissDE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/kissDE_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/kissDE_1.0.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R Package: lapmix Version: 1.46.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 2ce8153bd5cc63bdfddd8c06fa4d8a86 NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch source.ver: src/contrib/lapmix_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lapmix_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lapmix_1.46.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R Package: LBE Version: 1.48.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: d371fef9ddc8ec6217c0e5caf4ad62cc NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso source.ver: src/contrib/LBE_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LBE_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LBE_1.48.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R Package: ldblock Version: 1.10.0 Depends: R (>= 3.1), methods, Homo.sapiens Imports: Matrix, snpStats, erma, VariantAnnotation, GenomeInfoDb, Rsamtools, GO.db, GenomicFiles (>= 1.13.6), BiocGenerics (>= 0.25.1) Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 85873c15eafe993310c5c45d73217bcf NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/ldblock_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ldblock_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ldblock_1.10.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.pdf vignetteTitles: LD block import and manipulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R suggestsMe: gQTLstats Package: LEA Version: 2.2.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 8474193d8af919a1f5d6931cf0ee7cb1 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to landscape genomics and ecological association tests. LEA can run analyses of population structure and genomewide tests for local adaptation. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm). LEA is mainly based on optimized C programs that can scale with the dimension of large data sets. biocViews: Software, StatisticalMethod, Clustering, Regression Author: Eric Frichot , Olivier Francois Maintainer: Eric Frichot , Olivier Francois URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr source.ver: src/contrib/LEA_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LEA_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LEA_2.2.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R Package: LedPred Version: 1.14.1 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 67eaef35609fc0c14b2ee8fa8ed9397e NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_7 git_last_commit: d752697 git_last_commit_date: 2018-08-24 Date/Publication: 2018-08-24 source.ver: src/contrib/LedPred_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/LedPred_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LedPred_1.14.1.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R Package: les Version: 1.30.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: d4532db46a83fe604a9b367bc4c8313e NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring source.ver: src/contrib/les_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/les_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/les_1.30.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI Package: lfa Version: 1.10.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 122147d5f132d306f4f70f38f01f15ec NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues source.ver: src/contrib/lfa_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lfa_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lfa_1.10.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest Package: limma Version: 3.36.5 Depends: R (>= 2.3.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: i386, x64 MD5sum: 3c5e16efd54984f7ed3c773be5c9d976 NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Genetics, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_7 git_last_commit: 3148d1c git_last_commit_date: 2018-09-20 Date/Publication: 2018-09-20 source.ver: src/contrib/limma_3.36.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/limma_3.36.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/limma_3.36.5.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, AffyExpress, birta, BLMA, CALIB, cghMCR, codelink, convert, Cormotif, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, gCMAP, HTqPCR, maigesPack, marray, metagenomeSeq, metaseqR, MmPalateMiRNA, mpra, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, snapCGH, splineTimeR, SRGnet, SSPA, tRanslatome, TurboNorm, variancePartition, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, anamiR, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, ATACseqQC, attract, ballgown, BatchQC, beadarray, biotmle, birte, bsseq, BubbleTree, bumphunter, CALIB, CancerMutationAnalysis, CancerSubtypes, casper, CATALYST, charm, ChIPpeakAnno, clusterExperiment, coexnet, compcodeR, consensusOV, CountClust, crlmm, crossmeta, csaw, ctsGE, DaMiRseq, debrowser, DEP, derfinderPlot, DEsubs, DiffBind, diffcyt, diffHic, diffloop, DMRcate, Doscheda, DRIMSeq, EBSEA, eegc, EGAD, EGSEA, EnrichmentBrowser, erccdashboard, EventPointer, explorase, flowBin, gCrisprTools, GDCRNATools, GeneSelectMMD, GeneSelector, GEOquery, GGBase, GOsummaries, gQTLstats, GUIDEseq, hipathia, HTqPCR, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, limmaGUI, Linnorm, lmdme, LVSmiRNA, mAPKL, mCSEA, MEAL, methylKit, MethylMix, methyvim, MIGSA, minfi, miRLAB, missMethyl, MLSeq, MmPalateMiRNA, monocle, MoonlightR, MSstats, MultiDataSet, NADfinder, nem, nethet, nondetects, OGSA, OLIN, omicRexposome, PAA, PADOG, PathoStat, pbcmc, pcaExplorer, PECA, pepStat, phantasus, phenoTest, polyester, psichomics, qsea, regsplice, Ringo, RNAinteract, RNAither, RTCGAToolbox, RTN, RTopper, scater, scone, scran, SEPIRA, seqsetvis, sigaR, SimBindProfiles, singleCellTK, snapCGH, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, timecourse, TPP, transcriptogramer, TVTB, tweeDEseq, vsn, yamss, yarn suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, cydar, derfinder, DEScan2, dyebias, ELBOW, fgsea, gage, GeneSelector, Glimma, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, mdgsa, methylumi, MLP, npGSEA, oligo, oppar, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, stageR, subSeq, SummarizedBenchmark, tximport, zFPKM Package: limmaGUI Version: 1.56.1 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: 04a690e1e448266968c625448da36385 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.56.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/limmaGUI_1.56.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/limmaGUI_1.56.1.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R Package: LINC Version: 1.8.0 Depends: R (>= 3.3.1), methods, stats Imports: Rcpp (>= 0.11.0), DOSE, ggtree, gridExtra, ape, grid, png, Biobase, sva, reshape2, utils, grDevices, org.Hs.eg.db, clusterProfiler, ggplot2, ReactomePA LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: 3257782c1be8c31e7f9031e0d0b97896 NeedsCompilation: yes Title: co-expression of lincRNAs and protein-coding genes Description: This package provides methods to compute co-expression networks of lincRNAs and protein-coding genes. Biological terms associated with the sets of protein-coding genes predict the biological contexts of lincRNAs according to the 'Guilty by Association' approach. biocViews: Software, BiologicalQuestion, GeneRegulation, GeneExpression Author: Manuel Goepferich, Carl Herrmann Maintainer: Manuel Goepferich VignetteBuilder: knitr source.ver: src/contrib/LINC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LINC_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LINC_1.8.0.tgz vignettes: vignettes/LINC/inst/doc/LINC.html vignetteTitles: "LINC - Co-Expression Analysis of lincRNAs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LINC/inst/doc/LINC.R Package: LineagePulse Version: 1.0.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 MD5sum: d037d915471141183248b777e750e075 NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues source.ver: src/contrib/LineagePulse_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LineagePulse_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LineagePulse_1.0.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R Package: Linnorm Version: 2.4.0 Depends: R(>= 3.4) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6d3da3e00a4bb9521799e13479d11fe1 NeedsCompilation: yes Title: Linear model and normality based transformation method (Linnorm) Description: Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented: 1. Library size/Batch effect normalization (Linnorm.Norm), 2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or Hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust), 3. Differential expression analysis or differential peak detection using limma (Linnorm.limma), 4. Highly variable gene discovery and visualization (Linnorm.HVar), 5. Gene correlation network analysis and visualization (Linnorm.Cor), 6. Stable gene selection for scRNA-seq data; for users without or do not want to rely on spike-in genes (Linnorm.SGenes). 7. Data imputation. (under development) (Linnorm.DataImput). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: http://www.jjwanglab.org/Linnorm/ VignetteBuilder: knitr source.ver: src/contrib/Linnorm_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Linnorm_2.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Linnorm_2.4.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R Package: LiquidAssociation Version: 1.34.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 6010434117c591e8f70cc6472872c1e5 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho source.ver: src/contrib/LiquidAssociation_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LiquidAssociation_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LiquidAssociation_1.34.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation Package: lmdme Version: 1.22.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: f9e8be1af355c9f46f7416347674eecb NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 source.ver: src/contrib/lmdme_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lmdme_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lmdme_1.22.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R Package: LMGene Version: 2.36.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 54eb07a4cbcf5089b38428cb2cf9b7d1 NeedsCompilation: no Title: LMGene Software for Data Transformation and Identification of Differentially Expressed Genes in Gene Expression Arrays Description: LMGene package for analysis of microarray data using a linear model and glog data transformation biocViews: Microarray, DifferentialExpression, Preprocessing Author: David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu Maintainer: Blythe Durbin-Johnson URL: http://dmrocke.ucdavis.edu/software.html source.ver: src/contrib/LMGene_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LMGene_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LMGene_2.36.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LMGene/inst/doc/LMGene.R Package: LOBSTAHS Version: 1.6.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 091fec6e73e16a65e7223d3dbfce5199 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Benjamin Van Mooy [aut] Maintainer: James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new source.ver: src/contrib/LOBSTAHS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LOBSTAHS_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LOBSTAHS_1.6.0.tgz vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R Package: loci2path Version: 1.0.0 Depends: R (>= 3.4) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: e7ada8a9e01499043ae1a82da104dd07 NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues source.ver: src/contrib/loci2path_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/loci2path_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/loci2path_1.0.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R Package: logicFS Version: 1.50.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 2a4691efc1c7a4b96a96392c0eb7fae5 NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender Maintainer: Holger Schwender source.ver: src/contrib/logicFS_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/logicFS_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/logicFS_1.50.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio Package: logitT Version: 1.38.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 17b12d01a76cccdc66716eb0e2653bfb NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org source.ver: src/contrib/logitT_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/logitT_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/logitT_1.38.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R Package: Logolas Version: 1.4.0 Depends: R (>= 3.4) Imports: grid, SQUAREM, LaplacesDemon, stats, graphics, utils, ggplot2, gridBase, Biostrings Suggests: knitr, rmarkdown, BiocStyle, Biobase, devtools, xtable, gridExtra, RColorBrewer, seqLogo, ggseqlogo License: GPL (>= 2) MD5sum: 98f5ba85f3bd1229bbc34ee5e810475c NeedsCompilation: no Title: EDLogo Plots Featuring String Logos and Adaptive Scaling of Position-Weight Matrices Description: Produces logo plots highlighting both enrichment and depletion of characters, allows for plotting of string symbols, and performs scaling of position-weights adaptively, along with several fun stylizations. biocViews: SequenceMatching, Alignment, Software, Visualization, Bayesian Author: Kushal Dey [aut, cre], Dongyue Xie [aut], Peter Carbonetto [ctb], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/Logolas VignetteBuilder: knitr BugReports: http://github.com/kkdey/Logolas/issues source.ver: src/contrib/Logolas_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Logolas_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Logolas_1.4.0.tgz vignettes: vignettes/Logolas/inst/doc/Logolas.html vignetteTitles: Guided Logolas Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Logolas/inst/doc/Logolas.R Package: lol Version: 1.28.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: 42cd520b37ba1cbd71caf388ddfdcb7a NeedsCompilation: no Title: Lots Of Lasso Description: Various optimization methods for Lasso inference with matrix warpper biocViews: StatisticalMethod Author: Yinyin Yuan Maintainer: Yinyin Yuan source.ver: src/contrib/lol_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lol_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lol_1.28.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lol/inst/doc/lol.R Package: LOLA Version: 1.10.0 Depends: R (>= 2.10) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 MD5sum: db3ae349f424d4b05cc0fdec53fe3371 NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA source.ver: src/contrib/LOLA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LOLA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LOLA_1.10.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: DeepBlueR, MIRA Package: LowMACA Version: 1.12.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: db7cfc792754d0b7ea60c0bd8bad8b59 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr source.ver: src/contrib/LowMACA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LowMACA_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LowMACA_1.12.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R Package: LPE Version: 1.54.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: edc2d303a40b95db36ea8dbe3cbd5e8f NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ source.ver: src/contrib/LPE_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LPE_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LPE_1.54.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray Package: LPEadj Version: 1.40.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 330d8dadf9cd7cae6628d814b8410f81 NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie source.ver: src/contrib/LPEadj_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LPEadj_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LPEadj_1.40.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R Package: lpNet Version: 2.12.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: 0b378c1e2145dddec0e0711458074ff3 NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali source.ver: src/contrib/lpNet_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lpNet_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lpNet_2.12.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R Package: lpsymphony Version: 1.8.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr Enhances: slam License: EPL Archs: i386, x64 MD5sum: dc04dfe2f59fa5de382e2223d781e104 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb] Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ VignetteBuilder: knitr source.ver: src/contrib/lpsymphony_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lpsymphony_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lpsymphony_1.8.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW Package: lumi Version: 2.32.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 792843922a4a8aa78f008a59751a8dab NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Pan Du , Lei Huang , Gang Feng source.ver: src/contrib/lumi_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/lumi_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/lumi_2.32.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: arrayMvout, iCheck, wateRmelon importsMe: anamiR, ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre Package: LVSmiRNA Version: 1.30.0 Depends: R (>= 3.1.0), methods, splines Imports: BiocGenerics, stats4, graphics, stats, utils, MASS, Biobase, quantreg, limma, affy, SparseM, vsn, zlibbioc Enhances: parallel,snow, Rmpi License: GPL-2 Archs: i386, x64 MD5sum: 26f7fccb6c4249529bc3f4eafbc92918 NeedsCompilation: yes Title: LVS normalization for Agilent miRNA data Description: Normalization of Agilent miRNA arrays. biocViews: Microarray,AgilentChip,OneChannel,Preprocessing Author: Stefano Calza, Suo Chen, Yudi Pawitan Maintainer: Stefano Calza source.ver: src/contrib/LVSmiRNA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LVSmiRNA_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LVSmiRNA_1.30.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R Package: LymphoSeq Version: 1.8.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: 74daf9fe14bc488d3b2e00ebf8b6a4cb NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr source.ver: src/contrib/LymphoSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/LymphoSeq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/LymphoSeq_1.8.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R Package: M3C Version: 1.2.0 Depends: R (>= 3.4.0) Imports: ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel, foreach, doParallel, matrixcalc, dendextend, sigclust Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: 8aba72a337564a77febd5a28d68a7de2 NeedsCompilation: no Title: Monte Carlo Consensus Clustering Description: Genome-wide data is used to stratify patients into classes using class discovery algorithms. However, we have observed systematic bias present in current state-of-the-art methods. This arises from not considering reference distributions while selecting the number of classes (K). As a solution, we developed a consensus clustering-based algorithm with a hypothesis testing framework called Monte Carlo consensus clustering (M3C). M3C uses a multi-core enabled Monte Carlo simulation to generate null distributions along the range of K which are used to calculate p values to select its value. P values beyond the limits of the simulation are estimated using a beta distribution. M3C can quantify structural relationships between clusters and uses spectral clustering to deal with non-gaussian and imbalanced structures. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing Author: Christopher John [aut, cre] Maintainer: Christopher John VignetteBuilder: knitr source.ver: src/contrib/M3C_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/M3C_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/M3C_1.2.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.html vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R Package: M3D Version: 1.14.0 Depends: R (>= 3.3.0) Imports: parallel, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiSeq LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: Artistic License 2.0 Archs: x64 MD5sum: f364cd8978dc56c7b4e030c91f5c3610 NeedsCompilation: yes Title: Identifies differentially methylated regions across testing groups Description: This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maximum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles. biocViews: DNAMethylation, DifferentialMethylation, Coverage, CpGIsland Author: Tom Mayo Maintainer: Tom Mayo VignetteBuilder: knitr source.ver: src/contrib/M3D_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/M3D_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/M3D_1.14.0.tgz vignettes: vignettes/M3D/inst/doc/M3D_vignette.pdf vignetteTitles: An Introduction to the M$^3$D method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3D/inst/doc/M3D_vignette.R Package: M3Drop Version: 1.6.0 Depends: R (>= 3.3), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats Suggests: ROCR, knitr, M3DExampleData License: GPL (>=2) MD5sum: 7d8daec858284901c8c22f3c5de6fdce NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues source.ver: src/contrib/M3Drop_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/M3Drop_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/M3Drop_1.6.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R Package: maanova Version: 1.50.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 41bc54f722fa3ebb10b0ab841b0bd818 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill source.ver: src/contrib/maanova_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maanova_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maanova_1.50.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: macat Version: 1.54.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 37ec08e1c1a4886ada01e12149075cad NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling source.ver: src/contrib/macat_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/macat_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/macat_1.54.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R Package: maCorrPlot Version: 1.50.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 7d494ac4ba5fec2965b7a3ec2069dafb NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 source.ver: src/contrib/maCorrPlot_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maCorrPlot_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maCorrPlot_1.50.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R Package: MACPET Version: 1.0.0 Depends: R (>= 3.5), InteractionSet (>= 1.7.6) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 1.31.3), stats (>= 3.5.0), utils (>= 3.5.0), methods (>= 3.5.0), GenomicRanges (>= 1.31.20), S4Vectors (>= 0.17.32), IRanges (>= 2.13.26), GenomeInfoDb (>= 1.15.5), gtools (>= 3.5.0), GenomicAlignments (>= 1.15.12), knitr (>= 1.19), Rcpp (>= 0.12.15), rtracklayer (>= 1.39.9), BiocParallel (>= 1.13.1), Rbowtie (>= 1.19.1), GEOquery (>= 2.47.17), Biostrings (>= 2.47.9), ShortRead (>= 1.37.1), rbamtools (>= 2.16.6), futile.logger (>= 1.4.3) LinkingTo: Rcpp Suggests: ggplot2 (>= 2.2.1), igraph (>= 1.1.2), rmarkdown (>= 1.8), reshape2 (>= 1.4.3), BiocStyle (>= 2.7.8) License: GPL-3 Archs: i386, x64 MD5sum: 1f8b3a981e0196ac59bf6efbdb3f0a12 NeedsCompilation: yes Title: Model based analysis for paired-end data Description: The MACPET package can be used for binding site analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. MACPET is mainly written in C++, and it supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/MACPET_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MACPET_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MACPET_1.0.0.tgz vignettes: vignettes/MACPET/inst/doc/MACPET.pdf vignetteTitles: MACPET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACPET/inst/doc/MACPET.R Package: made4 Version: 1.54.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 6abcdc278f0c362d651a0d2ecdee6757 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include between group analysis and coinertia analysis. It contains functions that require ADE4. biocViews: Clustering, Classification, MultipleComparison Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ source.ver: src/contrib/made4_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/made4_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/made4_1.54.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R dependsOnMe: bgafun importsMe: omicade4 Package: MADSEQ Version: 1.6.1 Depends: R(>= 3.4), rjags(>= 4-6), Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) MD5sum: 6e60f00c8528e4ba3454ef44a2c74067 NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues source.ver: src/contrib/MADSEQ_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MADSEQ_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MADSEQ_1.6.1.tgz vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R Package: maftools Version: 1.6.15 Depends: R (>= 3.3) Imports: data.table, ggplot2(>= 2.0), cowplot, cometExactTest, RColorBrewer, NMF, ggrepel, methods, ComplexHeatmap, mclust, VariantAnnotation, Biostrings, Rsamtools, rjson, grid, wordcloud, grDevices, changepoint, gridExtra, survival Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: bd0a562c28b57f28830c7efdd293b9a3 NeedsCompilation: no Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_7 git_last_commit: aa93579 git_last_commit_date: 2018-07-22 Date/Publication: 2018-07-22 source.ver: src/contrib/maftools_1.6.15.tar.gz win.binary.ver: bin/windows/contrib/3.5/maftools_1.6.15.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maftools_1.6.15.tgz vignettes: vignettes/maftools/inst/doc/maftools.html vignetteTitles: Summarize,, Analyze and Visualize MAF Files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/maftools.R importsMe: TCGAbiolinksGUI suggestsMe: TCGAbiolinks Package: MAGeCKFlute Version: 1.0.1 Depends: R (>= 3.5), ggplot2, stats, grDevices, utils, pathview, gridExtra Imports: ggExtra, ggsci, ggrepel, clusterProfiler, png, data.table, pheatmap, RColorBrewer, sva, GOstats, Category, DOSE, biomaRt, grid Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db License: GPL (>=3) MD5sum: 4bde9d4f900dabd39fd3ba3699737e08 NeedsCompilation: no Title: Integrative analysis pipeline for pooled CRISPR functional genetic screens Description: MAGeCKFlute is designed to surporting downstream analysis, utilizing the gene summary data provided through MAGeCK or MAGeCK-VISPR. Quality control, normalization, and screen hit identification for CRISPR screen data are performed in pipeline. Identified hits within the pipeline are categorized based on experimental design, and are subsequently interpreted by functional enrichment analysis. biocViews: Workflow, CRISPR, PooledScreens, QualityControl, Normalization, MultipleComparison, FunctionalGenomics, GeneSetEnrichment, Pathways, Visualization Author: Wubing Zhang, Feizhen Wu, Binbin Wang Maintainer: Wubing Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: RELEASE_3_7 git_last_commit: 87b8287 git_last_commit_date: 2018-10-05 Date/Publication: 2018-10-05 source.ver: src/contrib/MAGeCKFlute_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MAGeCKFlute_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MAGeCKFlute_1.0.1.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R Package: maigesPack Version: 1.44.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) Archs: i386, x64 MD5sum: 3d46cfffc4c6cf12c59d6e4b8e6c4349 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ source.ver: src/contrib/maigesPack_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maigesPack_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maigesPack_1.44.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R Package: MAIT Version: 1.14.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: cfea2fc5fb2fa0c8cdb7dbf0c0e6dac1 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos source.ver: src/contrib/MAIT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MAIT_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MAIT_1.14.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R Package: makecdfenv Version: 1.56.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 81fc391350bff68a3c009e4620b19fda NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald source.ver: src/contrib/makecdfenv_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/makecdfenv_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/makecdfenv_1.56.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs Package: MANOR Version: 1.52.1 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: b6be8a1717e2e8c87cdd5552b0ca651e NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation Author: Pierre Neuvial , Philippe Hupe Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html source.ver: src/contrib/MANOR_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MANOR_1.52.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MANOR_1.52.1.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R Package: manta Version: 1.26.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 376cdd57afa0b6f1ad7d4fccf8d343bc NeedsCompilation: no Title: Microbial Assemblage Normalized Transcript Analysis Description: Tools for robust comparative metatranscriptomics. biocViews: DifferentialExpression, RNASeq, Genetics, GeneExpression, Sequencing, QualityControl, DataImport, Visualization Author: Ginger Armbrust, Adrian Marchetti Maintainer: Chris Berthiaume , Adrian Marchetti URL: http://manta.ocean.washington.edu/ source.ver: src/contrib/manta_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/manta_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/manta_1.26.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/manta/inst/doc/manta.R Package: MantelCorr Version: 1.50.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 478684f53216f40d60b373e55b78b230 NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer source.ver: src/contrib/MantelCorr_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MantelCorr_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MantelCorr_1.50.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R Package: mAPKL Version: 1.10.0 Depends: R (>= 3.2.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 0726d0270bbc16a3b4b1b1b7fdc1e9e2 NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr source.ver: src/contrib/mAPKL_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mAPKL_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mAPKL_1.10.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R Package: maPredictDSC Version: 1.18.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: dd5614055ed01aa4e391cd01316f0e9a NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC source.ver: src/contrib/maPredictDSC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maPredictDSC_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maPredictDSC_1.18.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R Package: mapscape Version: 1.4.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: a92c7fa22d4424f4c2157e316b8dc92f NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr source.ver: src/contrib/mapscape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mapscape_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mapscape_1.4.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R Package: marray Version: 1.58.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 3ac77fd5b7bb596adc2676a6253be621 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ source.ver: src/contrib/marray_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/marray_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/marray_1.58.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN, OLINgui, piano, plrs, sigaR, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz Package: martini Version: 1.0.0 Depends: R (>= 3.5) Imports: igraph (>= 1.0.1), Matrix, methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), S4Vectors (>= 0.12.2), stats, utils LinkingTo: Rgin, Rcpp, RcppEigen (>= 0.3.3.3.0) Suggests: biomaRt (>= 2.34.1), httr (>= 1.2.1), IRanges (>= 2.8.2), knitr, testthat, tidyverse, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: 525ff071276fa28dcab22d8e913e0370 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr source.ver: src/contrib/martini_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/martini_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/martini_1.0.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R Package: maSigPro Version: 1.52.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 3c854571b1536caa88ecb6fb057031bf NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ source.ver: src/contrib/maSigPro_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maSigPro_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maSigPro_1.52.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: maskBAD Version: 1.24.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: ac833a3b4c2aad1467b5506eab74ce7f NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann source.ver: src/contrib/maskBAD_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/maskBAD_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/maskBAD_1.24.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R Package: MassArray Version: 1.32.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: a5ed42ad85ad605dd9edaa247dfaafde NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson source.ver: src/contrib/MassArray_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MassArray_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MassArray_1.32.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R Package: massiR Version: 1.16.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: d5057edd62d5cd33ab2dacff27894f76 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry source.ver: src/contrib/massiR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/massiR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/massiR_1.16.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R Package: MassSpecWavelet Version: 1.46.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: 262378e6da8b0dad80d2a153d0c351ab NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du source.ver: src/contrib/MassSpecWavelet_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MassSpecWavelet_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MassSpecWavelet_1.46.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms Package: MAST Version: 1.6.1 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3) Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), roxygen2(> 4.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle License: GPL(>= 2) MD5sum: 4264155acdb3932cfc8b0088fcdbbe07 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues source.ver: src/contrib/MAST_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MAST_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MAST_1.6.1.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-Intro.R importsMe: singleCellTK suggestsMe: clusterExperiment Package: matchBox Version: 1.22.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: da0e9b8652f164c64344515116045939 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta source.ver: src/contrib/matchBox_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/matchBox_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/matchBox_1.22.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R Package: MatrixRider Version: 1.12.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 Archs: i386, x64 MD5sum: 0d9a86a534e57dc4168229e1913df3a3 NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi source.ver: src/contrib/MatrixRider_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MatrixRider_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MatrixRider_1.12.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R Package: matter Version: 1.6.0 Depends: methods, stats, biglm Imports: BiocGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 67e524d9b3150676ccb029bd0ef3acc3 NeedsCompilation: yes Title: A framework for rapid prototyping with binary data on disk Description: Memory-efficient reading, writing, and manipulation of structured binary data on disk as vectors, matrices, arrays, lists, and data frames. biocViews: Software, Infrastructure Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter source.ver: src/contrib/matter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/matter_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/matter_1.6.0.tgz vignettes: vignettes/matter/inst/doc/matter-supp1.pdf, vignettes/matter/inst/doc/matter-supp2.pdf, vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Supplementary 1 - Simulations and comparative benchmarks, matter: Supplementary 2 - 3D mass spectrometry imaging case study, matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-supp1.R, vignettes/matter/inst/doc/matter-supp2.R, vignettes/matter/inst/doc/matter.R dependsOnMe: Cardinal Package: MaxContrastProjection Version: 1.4.0 Depends: R (>= 3.4) Imports: EBImage, stats, methods Suggests: knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: b9e4e2f7173ff87bd00c9f768b6d7631 NeedsCompilation: no Title: Perform a maximum contrast projection of 3D images along the z-dimension into 2D Description: A problem when recording 3D fluorescent microscopy images is how to properly present these results in 2D. Maximum intensity projections are a popular method to determine the focal plane of each pixel in the image. The problem with this approach, however, is that out-of-focus elements will still be visible, making edges and fine structures difficult to detect. This package aims to resolve this problem by using the contrast around a given pixel to determine the focal plane, allowing for a much cleaner structure detection than would be otherwise possible. For convenience, this package also contains functions to perform various other types of projections, including a maximum intensity projection. biocViews: CellBasedAssays, Preprocessing, Software, Visualization Author: Jan Sauer, Bernd Fischer Maintainer: Jan Sauer SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/MaxContrastProjection_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MaxContrastProjection_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MaxContrastProjection_1.4.0.tgz vignettes: vignettes/MaxContrastProjection/inst/doc/MaxContrastProjection.pdf vignetteTitles: MaxContrastProjection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MaxContrastProjection/inst/doc/MaxContrastProjection.R Package: MBAmethyl Version: 1.14.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 7adaa39026d6ed7d4b0bfa95db0c7c9b NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang source.ver: src/contrib/MBAmethyl_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MBAmethyl_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MBAmethyl_1.14.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R Package: MBASED Version: 1.14.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 5f9408d0cb02052ce092419cbe204c8f NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba source.ver: src/contrib/MBASED_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MBASED_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MBASED_1.14.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R Package: MBCB Version: 1.34.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 9fa98aed644e29215c4b01267a161735 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu source.ver: src/contrib/MBCB_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MBCB_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MBCB_1.34.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R Package: mBPCR Version: 1.34.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: d8dc6d5597a80fb8e346d84033e5b1d3 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: Estimates the DNA copy number profile using mBPCR to detect regions with copy number changes biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR source.ver: src/contrib/mBPCR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mBPCR_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mBPCR_1.34.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R Package: MBttest Version: 1.8.1 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: c197d15e4357cb490004b440c41f5383 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan source.ver: src/contrib/MBttest_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MBttest_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MBttest_1.8.1.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R Package: mcaGUI Version: 1.28.1 Depends: lattice, MASS, proto, foreign, gWidgets(>= 0.0-36), gWidgetsRGtk2(>= 0.0-53), OTUbase, vegan, bpca Enhances: iplots, reshape, ggplot2, cairoDevice, OTUbase License: GPL (>= 2) MD5sum: f43bb178a49cf0d5d56320501bf9dbc0 NeedsCompilation: no Title: Microbial Community Analysis GUI Description: Microbial community analysis GUI for R using gWidgets. biocViews: GUI, Visualization, Clustering, Sequencing Author: Wade K. Copeland, Vandhana Krishnan, Daniel Beck, Matt Settles, James Foster, Kyu-Chul Cho, Mitch Day, Roxana Hickey, Ursel M.E. Schutte, Xia Zhou, Chris Williams, Larry J. Forney, Zaid Abdo, Poor Man's GUI (PMG) base code by John Verzani with contributions by Yvonnick Noel Maintainer: Wade K. Copeland URL: http://www.ibest.uidaho.edu/ibest/index.php git_url: https://git.bioconductor.org/packages/mcaGUI git_branch: RELEASE_3_7 git_last_commit: 84e4491 git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/mcaGUI_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/mcaGUI_1.28.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mcaGUI_1.28.1.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCbiclust Version: 1.4.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: 39794c34e214e479ed1d3da6ed35d947 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr source.ver: src/contrib/MCbiclust_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MCbiclust_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MCbiclust_1.4.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R Package: MCRestimate Version: 2.36.0 Depends: R (>= 2.7.2), golubEsets (>= 1.4.6) Imports: e1071 (>= 1.5-12), pamr (>= 1.22), randomForest (>= 3.9-6), RColorBrewer (>= 0.1-3), Biobase (>= 2.5.5), graphics, grDevices, stats, utils Suggests: xtable (>= 1.2-1), ROC (>= 1.8.0), genefilter (>= 1.12.0), gpls (>= 1.6.0) License: GPL (>= 2) MD5sum: b388f1715178569aa9041e52c46c1cbf NeedsCompilation: no Title: Misclassification error estimation with cross-validation Description: This package includes a function for combining preprocessing and classification methods to calculate misclassification errors biocViews: Classification Author: Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth Maintainer: Marc Johannes source.ver: src/contrib/MCRestimate_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MCRestimate_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MCRestimate_2.36.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCRestimate/inst/doc/UsingMCRestimate.R Package: mCSEA Version: 1.0.1 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, knitr, leukemiasEset, rmarkdown, RUnit License: GPL-2 MD5sum: 49907d506eceb57dcd1568bbebd290e6 NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr source.ver: src/contrib/mCSEA_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/mCSEA_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mCSEA_1.0.1.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R Package: mdgsa Version: 1.12.1 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL MD5sum: f64685a32d06f2bc7ccc7501b2ad6c5b NeedsCompilation: no Title: Multi Dimensional Gene Set Analysis. Description: Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs. biocViews: GeneSetEnrichment, Annotation, Pathways, GO Author: David Montaner Maintainer: David Montaner URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com VignetteBuilder: knitr source.ver: src/contrib/mdgsa_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/mdgsa_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mdgsa_1.12.1.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdgsa/inst/doc/mdgsa_vignette.R Package: mdp Version: 1.0.0 Depends: R (>= 3.5) Imports: ggplot2, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea License: GPL-3 MD5sum: a20367f62cb82c228ef537d9037dd51c NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr source.ver: src/contrib/mdp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mdp_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mdp_1.0.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R Package: mdqc Version: 1.42.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 2725c410813a24fe195d74b3c310f989 NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue source.ver: src/contrib/mdqc_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mdqc_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mdqc_1.42.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout Package: MDTS Version: 1.0.2 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: c0146c8c6893888a68a282f47c32c27e NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_7 git_last_commit: 4c36c84 git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/MDTS_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/MDTS_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MDTS_1.0.2.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R Package: MEAL Version: 1.10.1 Depends: R (>= 3.2.0), Biobase, MultiDataSet Imports: GenomicRanges, SNPassoc, limma, DMRcate, snpStats, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: a7400e9936dac3d5ea8ae7f0b117f44d NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr source.ver: src/contrib/MEAL_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MEAL_1.10.1.zip vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R Package: MeasurementError.cor Version: 1.52.0 License: LGPL MD5sum: 9acff45c2b66202e0c5a5b03f85d1db1 NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding source.ver: src/contrib/MeasurementError.cor_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MeasurementError.cor_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MeasurementError.cor_1.52.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R Package: MEDIPS Version: 1.32.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: e62e672c46436eff46362adccd671750 NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez source.ver: src/contrib/MEDIPS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MEDIPS_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MEDIPS_1.32.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R Package: MEDME Version: 1.40.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) Archs: i386, x64 MD5sum: 955b624a3d39674b3b946d2a348c69c7 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola source.ver: src/contrib/MEDME_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MEDME_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MEDME_1.40.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R Package: MEIGOR Version: 1.14.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: c5fe2865a26b7a7713103d988f21abdd NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose Egea source.ver: src/contrib/MEIGOR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MEIGOR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MEIGOR_1.14.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R Package: MergeMaid Version: 2.52.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: d4823dcddc728e17beb5d2e69360dc32 NeedsCompilation: no Title: Merge Maid Description: The functions in this R extension are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be 'list','matrix', or 'ExpressionSet'. The main function is 'mergeExprs' which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function 'intcor' calculate the correlation coefficients. Other functions use the output from 'modelOutcome' to graphically display the results and cross-validate associations of gene expression data with survival. biocViews: Microarray, DifferentialExpression, Visualization Author: Xiaogang Zhong Leslie Cope Elizabeth Garrett Giovanni Parmigiani Maintainer: Xiaogang Zhong URL: http://astor.som.jhmi.edu/MergeMaid source.ver: src/contrib/MergeMaid_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MergeMaid_2.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MergeMaid_2.52.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: metaArray, XDE Package: Mergeomics Version: 1.8.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 0ab20cbb818e17119551164d88e4cf05 NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt source.ver: src/contrib/Mergeomics_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Mergeomics_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Mergeomics_1.8.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R Package: MeSHDbi Version: 1.16.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: edac02c8324ef3c76376d9bcd238e2e8 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki source.ver: src/contrib/MeSHDbi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MeSHDbi_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MeSHDbi_1.16.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: meshr Package: meshes Version: 1.6.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, GOSemSim (>= 1.99.3), MeSH.db, methods, rvcheck Suggests: knitr, MeSH.Cel.eg.db, MeSH.Hsa.eg.db, prettydoc License: Artistic-2.0 MD5sum: 79b0bae0517c8f98901d7199702b2528 NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/meshes VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues source.ver: src/contrib/meshes_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/meshes_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/meshes_1.6.1.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: An introduction to meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R Package: meshr Version: 1.16.0 Depends: R (>= 3.0.1), fdrtool, Category, BiocGenerics, methods, cummeRbund, org.Hs.eg.db, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db, MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, S4Vectors License: Artistic-2.0 MD5sum: c24e2f624241253ff2f688cacdd7a950 NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Itoshi Nikaido, Koki Tsuyuzaki, Gota Morota Maintainer: Koki Tsuyuzaki source.ver: src/contrib/meshr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/meshr_1.15.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/meshr_1.16.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R Package: messina Version: 1.16.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) Archs: i386, x64 MD5sum: c21ab19aa4022585216d863dab285ef8 NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems. Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr source.ver: src/contrib/messina_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/messina_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/messina_1.16.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R Package: metaArray Version: 1.58.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: 7a53b43879678e96594940185c3aa6dc NeedsCompilation: yes Title: Integration of Microarray Data for Meta-analysis Description: 1) Data transformation for meta-analysis of microarray Data: Transformation of gene expression data to signed probability scale (MCMC/EM methods) 2) Combined differential expression on raw scale: Weighted Z-score after stabilizing mean-variance relation within platform biocViews: Microarray, DifferentialExpression Author: Debashis Ghosh Hyungwon Choi Maintainer: Hyungwon Choi source.ver: src/contrib/metaArray_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metaArray_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metaArray_1.58.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaArray/inst/doc/metaArray.R Package: Metab Version: 1.14.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: c06baebe5de8cd4274a4f48a25a9f36c NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/Metab_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Metab_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Metab_1.14.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R Package: metabomxtr Version: 1.14.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 84209074426e4e54356dc0501eed893f NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski source.ver: src/contrib/metabomxtr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metabomxtr_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metabomxtr_1.14.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R Package: MetaboSignal Version: 1.10.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 93859d5587710d3f66961fea53559a45 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr source.ver: src/contrib/MetaboSignal_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MetaboSignal_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MetaboSignal_1.10.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R Package: metaCCA Version: 1.8.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 1cefac26067a17441c5a0a3de355dae8 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: http://biorxiv.org/content/early/2015/07/16/022665 VignetteBuilder: knitr source.ver: src/contrib/metaCCA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metaCCA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metaCCA_1.8.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R Package: MetaCyto Version: 1.2.1 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr License: GPL (>= 2) MD5sum: 5c31c7ce00ca2db74753f844943ab02b NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_7 git_last_commit: 96e2c03 git_last_commit_date: 2018-07-31 Date/Publication: 2018-07-31 source.ver: src/contrib/MetaCyto_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MetaCyto_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MetaCyto_1.2.1.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R Package: metagene Version: 2.12.2 Depends: R (>= 3.4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: f58c639b0a29866f90f90214edae8ad1 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_7 git_last_commit: 73e224c git_last_commit_date: 2018-10-10 Date/Publication: 2018-10-10 source.ver: src/contrib/metagene_2.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/metagene_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metagene_2.12.2.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependsOnMe: Imetagene Package: metagenomeFeatures Version: 2.0.0 Depends: R (>= 3.4), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), S4Vectors (>= 0.14.7), dplyr (>= 0.7.0), dbplyr(>= 1.0.0), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>= 3.3.1), lattice (>= 0.20.33), ape (>= 3.5), purrr (>= 0.2.2), DECIPHER (>= 2.4.0) Suggests: knitr (>= 1.11), testthat (>= 0.10.0), rmarkdown (>= 1.3), tidyverse (>= 1.2.1), devtools (>= 1.13.5), ggtree(>= 1.8.2) License: Artistic-2.0 MD5sum: 3a8338f9c67f0be912612b4df5931151 NeedsCompilation: no Title: Exploration of marker-gene sequence taxonomic annotations Description: metagenomeFeatures was developed for use in exploring the taxonomic annotations for a marker-gene metagenomic sequence dataset. The package can be used to explore the taxonomic composition of a marker-gene database or annotated sequences from a marker-gene metagenome experiment. biocViews: Microbiome, Metagenomics, Annotation, Infrastructure, Sequencing, Software Author: Nathan D. Olson, Joseph Nathaniel Paulson, Hector Corrada Bravo Maintainer: Nathan D. Olson URL: https://github.com/HCBravoLab/metagenomeFeatures VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/metagenomeFeatures/issues source.ver: src/contrib/metagenomeFeatures_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metagenomeFeatures_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metagenomeFeatures_2.0.0.tgz vignettes: vignettes/metagenomeFeatures/inst/doc/database-explore.html, vignettes/metagenomeFeatures/inst/doc/Exploring_a_MgDb.html, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.html vignetteTitles: Vignette Title, Vignette Title, Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeFeatures/inst/doc/database-explore.R, vignettes/metagenomeFeatures/inst/doc/Exploring_a_MgDb.R, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.R Package: metagenomeSeq Version: 1.22.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay License: Artistic-2.0 MD5sum: 9c0c28bda355351d0187a863f4b3d33e NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues source.ver: src/contrib/metagenomeSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metagenomeSeq_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metagenomeSeq_1.22.0.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: metavizr suggestsMe: interactiveDisplay, phyloseq Package: MetaGxOvarian Version: 0.99.7 Depends: Biobase, stats, lattice, impute, AnnotationHub, ExperimentHub, R (>= 3.5.0) Suggests: testthat, xtable License: Artistic-2.0 MD5sum: ddcb567a97e7133f8d2f61044592b8cb NeedsCompilation: no Title: Transcriptomic Ovarian Cancer Datasets Description: A collection of Ovarian Cancer Transcriptomic Datasets that are part of the MetaGxData package compendium. biocViews: Microarray, Software, GeneExpression, OneChannel, GeneSetEnrichment, GeneSignaling, Pathways, Preprocessing, Survival Author: Michael Zon , Deena M.A. Gendoo , Benjamin Haibe-Kains Maintainer: Michael Zon source.ver: src/contrib/MetaGxOvarian_0.99.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/MetaGxOvarian_0.99.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MetaGxOvarian_0.99.7.tgz vignettes: vignettes/MetaGxOvarian/inst/doc/MetaGxOvarian.pdf vignetteTitles: MetaGxOvarian: a package for ovarian cancer gene expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaGxOvarian/inst/doc/MetaGxOvarian.R Package: metahdep Version: 1.38.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: 9f3df717f0eaca5e9fac34e86b294a01 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens source.ver: src/contrib/metahdep_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metahdep_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metahdep_1.38.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R Package: metaMS Version: 1.16.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: ec7b1e1deeb9a8d616e6435b4a574e8d NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: MassSpectrometry, Metabolomics Author: Ron Wehrens [aut, cre] (author of GC-MS part), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline) Maintainer: Ron Wehrens URL: https://github.com/rwehrens/metaMS source.ver: src/contrib/metaMS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metaMS_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metaMS_1.16.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R Package: MetaNeighbor Version: 1.0.0 Depends: R(>= 3.5) Imports: beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1), stats (>= 3.4), SummarizedExperiment (>= 1.6.5), utils (>= 3.4) Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: a82317628812611fac1cb9edb523ddbc NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Jesse Gillis [aut] Maintainer: Manthan Shah VignetteBuilder: knitr source.ver: src/contrib/MetaNeighbor_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MetaNeighbor_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MetaNeighbor_1.0.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R Package: metaSeq Version: 1.20.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 53d3c606a8dbd28719b26facc311844b NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki source.ver: src/contrib/metaSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metaSeq_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metaSeq_1.20.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R Package: metaseqR Version: 1.20.0 Depends: R (>= 2.13.0), EDASeq, DESeq, limma, qvalue Imports: edgeR, NOISeq, baySeq, NBPSeq, biomaRt, utils, gplots, corrplot, vsn, brew, rjson, log4r Suggests: BiocGenerics, GenomicRanges, rtracklayer, Rsamtools, survcomp, VennDiagram, knitr, zoo, RUnit, BiocInstaller, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: 44c55ad53ebe19c3c192b76074c667d5 NeedsCompilation: no Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms. Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr source.ver: src/contrib/metaseqR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/metaseqR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metaseqR_1.20.0.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR/inst/doc/metaseqr-pdf.R Package: metavizr Version: 1.4.1 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, curatedMetagenomicData License: MIT + file LICENSE MD5sum: 988c53b9192835fc87e2be5f193c5cd1 NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr source.ver: src/contrib/metavizr_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/metavizr_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/metavizr_1.4.1.tgz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R Package: MetCirc Version: 1.10.0 Depends: R (>= 3.3), amap (>= 0.8), circlize (>= 0.3.9), graphics (>= 3.3), grDevices (>= 3.3), methods (>= 3.3), scales (>= 0.3.0), shiny (>= 1.0.0), stats (>= 3.3) Suggests: BiocGenerics, knitr (>= 1.11) License: GPL-2 MD5sum: c90c53afa4387f4d4b9ab5d21505ca9f NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data: create an MSP object, a format for MS/MS library data, bin m/z values of precursors, calculate similarity between precursors based on the normalised dot product and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr source.ver: src/contrib/MetCirc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MetCirc_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MetCirc_1.10.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R Package: methimpute Version: 1.2.0 Depends: R (>= 3.4.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: e63b7c2e33ecd9cc579973d07503c3da NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr source.ver: src/contrib/methimpute_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methimpute_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methimpute_1.2.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R Package: methInheritSim Version: 1.2.0 Depends: R (>= 3.4) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: c0a2742fc90a9c0ff6eefba3214b50ad NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues source.ver: src/contrib/methInheritSim_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methInheritSim_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methInheritSim_1.2.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance Package: MethPed Version: 1.8.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: fb140bac3fbbb28d3b4aa39d40d83fc7 NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr source.ver: src/contrib/MethPed_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MethPed_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MethPed_1.8.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R Package: MethTargetedNGS Version: 1.12.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 42d0df3c1dc0baebd7d9ae2d2e3ecdb7 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 source.ver: src/contrib/MethTargetedNGS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MethTargetedNGS_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MethTargetedNGS_1.12.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R Package: methVisual Version: 1.32.0 Depends: R (>= 2.11.0), Biostrings(>= 2.4.8), plotrix,gsubfn, grid,sqldf Imports: Biostrings, ca, graphics, grDevices, grid, gridBase, IRanges, stats, utils License: GPL (>= 2) MD5sum: 0e7d81f7d00e2a9d2a694f414b9175ed NeedsCompilation: no Title: Methods for visualization and statistics on DNA methylation data Description: The package 'methVisual' allows the visualization of DNA methylation data after bisulfite sequencing. biocViews: DNAMethylation, Clustering, Classification Author: A. Zackay, C. Steinhoff Maintainer: Arie Zackay source.ver: src/contrib/methVisual_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methVisual_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methVisual_1.32.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methVisual/inst/doc/methVisual.R Package: methyAnalysis Version: 1.22.0 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.34.0), org.Hs.eg.db Imports: grDevices, stats, utils, lumi, methylumi, Gviz, genoset, SummarizedExperiment, IRanges, GenomicRanges, VariantAnnotation, rtracklayer, GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 42217ad2ee2ef881eead9011e4d1ed13 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A MethyGenoSet class is defined to keep the chromosome location information together with the data. The package also includes functions of estimating the methylation levels from Methy-Seq data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Pan Du , Lei Huang , Gang Feng source.ver: src/contrib/methyAnalysis_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methyAnalysis_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methyAnalysis_1.22.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi Package: MethylAid Version: 1.14.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 0f27df511a3f459b3e84589858189b20 NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: M. van Iterson VignetteBuilder: knitr source.ver: src/contrib/MethylAid_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MethylAid_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MethylAid_1.14.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R Package: methylInheritance Version: 1.4.0 Depends: R (>= 3.4) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 MD5sum: eba4da00215a7db5d2362afca7038e5e NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements from One Generation to the Next to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes, Pascal Belleau and Arnaud Droit Maintainer: Astrid Deschenes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues source.ver: src/contrib/methylInheritance_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methylInheritance_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methylInheritance_1.4.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements from One Generation to the Next to a Treatment Effect hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim Package: methylKit Version: 1.6.3 Depends: R (>= 3.3.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.12.1), zlibbioc Suggests: testthat,knitr, rmarkdown, genomation License: Artistic-2.0 Archs: i386, x64 MD5sum: dc1ee4a973201147a0a097572b73d92a NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Perl is needed to read SAM files only. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_7 git_last_commit: fd2779b git_last_commit_date: 2018-10-11 Date/Publication: 2018-10-12 source.ver: src/contrib/methylKit_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/methylKit_1.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methylKit_1.6.3.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: methInheritSim, methylInheritance Package: MethylMix Version: 2.10.2 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: 375287a818c6a5e1f867686b11f64885 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert O. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. Pierre-Louis Cedoz, Marcos Prunello, Kevin Brennan, Olivier Gevaert; MethylMix 2.0: an R package for identifying DNA methylation genes. Bioinformatics. doi:10.1093/bioinformatics/bty156. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_7 git_last_commit: c1812cb git_last_commit_date: 2018-07-19 Date/Publication: 2018-07-19 source.ver: src/contrib/MethylMix_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/MethylMix_2.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MethylMix_2.10.2.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R Package: methylMnM Version: 1.18.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: 9f1cc95cc70703f0d41f2adf362fe488 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou source.ver: src/contrib/methylMnM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methylMnM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methylMnM_1.18.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R Package: methylPipe Version: 1.14.0 Depends: R (>= 3.2.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: i386, x64 MD5sum: edcc00ea610a5098f77d170c583b2027 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/methylPipe_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methylPipe_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methylPipe_1.14.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R importsMe: compEpiTools Package: MethylSeekR Version: 1.20.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: 1bb3d88d292dab2bcc7db3e51362d2f0 NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger source.ver: src/contrib/MethylSeekR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MethylSeekR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MethylSeekR_1.20.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe Package: methylumi Version: 2.26.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: 7e9a2ed4db462a6a41d275953a17b543 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new source.ver: src/contrib/methylumi_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methylumi_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methylumi_2.26.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl Package: methyvim Version: 1.2.0 Depends: R (>= 3.4.0) Imports: stats, cluster, methods, ggplot2, ggsci, gridExtra, superheat, dplyr, gtools, tmle, future, doFuture, BiocGenerics, BiocParallel, SummarizedExperiment, GenomeInfoDb, bumphunter, IRanges, limma, minfi Suggests: testthat, knitr, rmarkdown, BiocStyle, SuperLearner, earth, nnet, gam, arm, snow, parallel, BatchJobs, minfiData, methyvimData License: file LICENSE MD5sum: 20ca6b2d6ba619a72de2e6075550f0cc NeedsCompilation: no Title: Targeted Variable Importance for Differential Methylation Analysis Description: This package provides facilities for differential methylation analysis based on variable importance measures (VIMs), a class of statistical target parameters that arise in causal inference. The estimation and inference procedures provided are nonparametric, relying on ensemble machine learning to flexibly assess functional relationships among covariates and the outcome of interest. These tools can be applied to differential methylation at the level of CpG sites, to obtain valid statistical inference even after corrections for multiple hypothesis testing. biocViews: Clustering, DNAMethylation, DifferentialMethylation, MethylationArray, MethylSeq Author: Nima Hejazi [aut, cre, cph], Mark van der Laan [aut, ths], Alan Hubbard [ctb, ths], Rachael Phillips [ctb] Maintainer: Nima Hejazi URL: https://github.com/nhejazi/methyvim VignetteBuilder: knitr BugReports: https://github.com/nhejazi/methyvim/issues source.ver: src/contrib/methyvim_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/methyvim_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/methyvim_1.2.0.tgz vignettes: vignettes/methyvim/inst/doc/using_methyvim.html vignetteTitles: Targeted Data-Adaptive Estimation and Inference for Differential Methylation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methyvim/inst/doc/using_methyvim.R Package: mfa Version: 1.2.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 98b52de3fb420c86b60792627a3e780a NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr source.ver: src/contrib/mfa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mfa_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mfa_1.2.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter Package: Mfuzz Version: 2.40.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 47c6bb4a63a82dc8bcd098a707f26385 NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ source.ver: src/contrib/Mfuzz_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Mfuzz_2.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Mfuzz_2.40.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle suggestsMe: pwOmics Package: MGFM Version: 1.14.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 284265a52b392ab2b3b3fbc67c65d23f NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani source.ver: src/contrib/MGFM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MGFM_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MGFM_1.14.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier Package: MGFR Version: 1.6.0 Depends: R (>= 3.3) Imports: biomaRt, annotate License: GPL-3 MD5sum: 5acf749cdd5330073327ebff4d074667 NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani source.ver: src/contrib/MGFR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MGFR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MGFR_1.6.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier Package: mgsa Version: 1.28.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 376e06e62680122fe4cf964a19c89a9e NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc source.ver: src/contrib/mgsa_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mgsa_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mgsa_1.28.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R suggestsMe: gCMAP Package: MiChip Version: 1.34.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 69e78f005447e83766062f727ca624bb NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake source.ver: src/contrib/MiChip_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MiChip_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MiChip_1.34.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R Package: microbiome Version: 1.2.1 Depends: R (>= 3.4.0), phyloseq, ggplot2 Imports: dplyr, reshape2, stats, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitcitations, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: 45a27301620b5aabd50c09276588ef8f NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Clustering, Metagenomics, Microbiome, Sequencing, SystemsBiology Author: Leo Lahti [aut, cre], Sudarshan Shetty [aut] Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues source.ver: src/contrib/microbiome_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/microbiome_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/microbiome_1.2.1.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R Package: microRNA Version: 1.38.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 Archs: i386, x64 MD5sum: c4dc9c36bcd3472456bee0da4615e49f NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" source.ver: src/contrib/microRNA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/microRNA_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/microRNA_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIGSA Version: 1.4.0 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, RJSONIO, stats, utils, vegan Suggests: breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, mGSZ, pbcmc, MIGSAdata License: GPL (>= 2) MD5sum: 4cc8f373cd480f969497e7cfb793c4fb NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: http://www.bdmg.com.ar/ source.ver: src/contrib/MIGSA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MIGSA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MIGSA_1.4.0.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R Package: mimager Version: 1.4.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: 9103b2c211925b026cbfa807783481a3 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues source.ver: src/contrib/mimager_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mimager_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mimager_1.4.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R Package: MIMOSA Version: 1.18.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: ca2c7aa93f07b185d5a2c157f8b99e7b NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr source.ver: src/contrib/MIMOSA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MIMOSA_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MIMOSA_1.18.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R Package: MineICA Version: 1.20.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX Enhances: doMC License: GPL-2 MD5sum: d0eddfb37c91889fcd32af8e8f7a8dd5 NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton source.ver: src/contrib/MineICA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MineICA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MineICA_1.20.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R Package: minet Version: 3.38.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 03352d01c68ea588dd442ab9c7744b6d NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com source.ver: src/contrib/minet_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/minet_3.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/minet_3.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: coexnet, epiNEM, netbenchmark, RTN suggestsMe: CNORfeeder, predictionet, TCGAbiolinks Package: minfi Version: 1.26.2 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio, DelayedMatrixStats, mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.5.23), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 MD5sum: 98d3ac2affe325c28b4d3c087cfca866 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_7 git_last_commit: ebb07b7 git_last_commit_date: 2018-06-15 Date/Publication: 2018-06-16 source.ver: src/contrib/minfi_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/minfi_1.26.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/minfi_1.26.2.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, conumee, DMRcate, methylumi, REMP, shinyMethyl importsMe: funtooNorm, MEAL, MethylAid, methylumi, methyvim, missMethyl, quantro, skewr, TCGAbiolinksGUI suggestsMe: Harman, MultiDataSet, RnBeads Package: MinimumDistance Version: 1.24.1 Depends: R (>= 3.3), VanillaICE (>= 1.31.3) Imports: methods, BiocGenerics, Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 0.2.0), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: 41e02de110e410ddef3331376260cdb0 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert B Scharpf source.ver: src/contrib/MinimumDistance_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MinimumDistance_1.24.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MinimumDistance_1.24.1.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R Package: MiPP Version: 1.52.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 95b8e02d7983126dcf761194c44c80be NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ source.ver: src/contrib/MiPP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MiPP_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MiPP_1.52.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MIRA Version: 1.2.0 Depends: R (>= 3.4) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: b3bd99f3ff9708b7fc9a1ad399141938 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: MIRA measures the degree of "dip" in methylation level surrounding a regulatory site of interest, such as a transcription factor binding site, for instances of that type of site across the genome which can then be used to infer regulatory activity. biocViews: DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA source.ver: src/contrib/MIRA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MIRA_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MIRA_1.2.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R Package: MiRaGE Version: 1.22.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 22a599c2fbcdc20b3b715c0b36af727d NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi source.ver: src/contrib/MiRaGE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MiRaGE_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MiRaGE_1.22.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R Package: miRBaseConverter Version: 1.4.3 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils License: GPL (>= 2) MD5sum: 030a6a840329d4af64ab11c7338ffb75 NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_7 git_last_commit: 58928cb git_last_commit_date: 2018-09-09 Date/Publication: 2018-09-09 source.ver: src/contrib/miRBaseConverter_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRBaseConverter_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRBaseConverter_1.4.3.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R Package: miRcomp Version: 1.10.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: 5799c3586b06e62b1b81f09293cdc70c NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr source.ver: src/contrib/miRcomp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRcomp_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRcomp_1.10.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R Package: mirIntegrator Version: 1.10.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: f0c031d065807fecc4e3fd3f72c185bb NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ source.ver: src/contrib/mirIntegrator_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mirIntegrator_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mirIntegrator_1.10.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R Package: miRLAB Version: 1.10.0 Imports: RCurl, httr, stringr, Hmisc, energy, entropy, Roleswitch, gplots, glmnet, impute, limma, pcalg Suggests: knitr, RUnit, BiocGenerics, AnnotationDbi, org.Hs.eg.db, GOstats, Category License: GPL (>=2) MD5sum: dd4203f4470c47f11761d049fc208afc NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang Maintainer: Thuc Duy Le VignetteBuilder: knitr source.ver: src/contrib/miRLAB_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRLAB_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRLAB_1.10.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R Package: miRmine Version: 1.2.0 Depends: R (>= 3.4), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: 49f37ef85a868f63323a914f1c958fab NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr source.ver: src/contrib/miRmine_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRmine_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRmine_1.2.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R Package: miRNAmeConverter Version: 1.8.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1e9dcabe5d0d3dd53618e491ca508bec NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr source.ver: src/contrib/miRNAmeConverter_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRNAmeConverter_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRNAmeConverter_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: miRNApath Version: 1.40.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 1edb8bbc72220c8d4ef5a8781c19c4c1 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward source.ver: src/contrib/miRNApath_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRNApath_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRNApath_1.40.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R Package: miRNAtap Version: 1.14.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: a11009777d901614cdb54e52bcf1e9a2 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: Maciej Pajak source.ver: src/contrib/miRNAtap_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRNAtap_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRNAtap_1.14.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R importsMe: SpidermiR Package: miRsponge Version: 1.6.1 Depends: R (>= 3.4.1) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, varhandle, linkcomm, utils, Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, org.Hs.eg.db License: GPL-3 Archs: i386, x64 MD5sum: 6cf7c124a8a8b0fe7ecc219a84fa5a7f NeedsCompilation: yes Title: Identification and analysis of miRNA sponge interaction networks and modules Description: This package provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of modules, and conduct survival analysis of modules. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software Author: Junpeng Zhang Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRsponge git_branch: RELEASE_3_7 git_last_commit: 0dbd132 git_last_commit_date: 2018-08-09 Date/Publication: 2018-08-10 source.ver: src/contrib/miRsponge_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/miRsponge_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/miRsponge_1.6.1.tgz vignettes: vignettes/miRsponge/inst/doc/miRsponge.html vignetteTitles: miRsponge: identification and analysis of miRNA sponge interaction networks and modules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRsponge/inst/doc/miRsponge.R Package: Mirsynergy Version: 1.16.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: bca2ac0d473b47b518d8444ed241346c NeedsCompilation: no Title: Mirsynergy Description: Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion. biocViews: Clustering Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/Mirsynergy.html VignetteBuilder: knitr source.ver: src/contrib/Mirsynergy_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Mirsynergy_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Mirsynergy_1.16.0.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mirsynergy/inst/doc/Mirsynergy.R Package: missMethyl Version: 1.14.0 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod, ruv, stringr, IlluminaHumanMethylation450kanno.ilmn12.hg19, org.Hs.eg.db, AnnotationDbi, BiasedUrn, GO.db, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 Suggests: minfiData, BiocStyle, knitr, rmarkdown, edgeR, tweeDEseqCountData License: GPL-2 MD5sum: 7fee1b572a29bde3d597e42897b4a017 NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation and testing for differential variability and differential methylation for data from Illumina's Infinium HumanMethylation450 array. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr source.ver: src/contrib/missMethyl_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/missMethyl_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/missMethyl_1.14.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R importsMe: DMRcate, MEAL suggestsMe: RnBeads Package: missRows Version: 1.0.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 7c09f4171c899ae478ebbf582353fe2d NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr source.ver: src/contrib/missRows_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/missRows_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/missRows_1.0.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R Package: mitoODE Version: 1.18.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: 490902e4ede693f0ddcf50d24874bca6 NeedsCompilation: yes Title: Implementation of the differential equation model described in "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" Description: The package contains the methods to fit a cell-cycle model on cell count data and the code to reproduce the results shown in our paper "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" by Pau, G., Walter, T., Neumann, B., Heriche, J.-K., Ellenberg, J., & Huber, W., BMC Bioinformatics (2013), 14(1), 308. doi:10.1186/1471-2105-14-308 biocViews: ExperimentData, TimeCourse, CellBasedAssays, Preprocessing Author: Gregoire Pau Maintainer: Gregoire Pau SystemRequirements: source.ver: src/contrib/mitoODE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mitoODE_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mitoODE_1.18.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoODE/inst/doc/mitoODE-introduction.R Package: MLInterfaces Version: 1.60.1 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, rda, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party Enhances: parallel License: LGPL MD5sum: 88102485d406b25b00195d7a7f69eab6 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_7 git_last_commit: 019e9ed git_last_commit_date: 2018-06-22 Date/Publication: 2018-06-22 source.ver: src/contrib/MLInterfaces_1.60.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MLInterfaces_1.60.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MLInterfaces_1.60.1.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies Package: MLP Version: 1.28.0 Depends: AnnotationDbi, affy, plotrix, gplots, gmodels, gdata, gtools Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, KEGG.db, annotate, Rgraphviz, GOstats, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 1f02842dd02b9a92c66ef41ce0b4b0d5 NeedsCompilation: no Title: MLP Description: Mean Log P Analysis biocViews: Genetics, Reactome, KEGG Author: Nandini Raghavan, Tobias Verbeke, An De Bondt with contributions by Javier Cabrera, Dhammika Amaratunga, Tine Casneuf and Willem Ligtenberg Maintainer: Tobias Verbeke source.ver: src/contrib/MLP_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MLP_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MLP_1.28.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 Package: MLSeq Version: 1.20.3 Depends: caret, ggplot2 Imports: methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, testthat, BiocStyle, VennDiagram, pamr License: GPL(>=2) MD5sum: 627dd2b5a55f51936266ed67c5aa6dd3 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Ahmet Ozturk, Ahmet Ergun Karaagaoglu Maintainer: Gokmen Zararsiz VignetteBuilder: knitr source.ver: src/contrib/MLSeq_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/MLSeq_1.20.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MLSeq_1.20.3.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS Package: MMDiff2 Version: 1.8.0 Depends: R (>= 3.3), Rsamtools, Biobase, Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: aac67718d5ea752a5c631fe0cf0ce0bc NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr source.ver: src/contrib/MMDiff2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MMDiff2_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MMDiff2_1.8.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R Package: MmPalateMiRNA Version: 1.30.0 Depends: R (>= 2.13.0), methods, Biobase, xtable, limma, statmod, lattice, vsn Imports: limma, lattice, Biobase Suggests: GOstats, graph, Category, org.Mm.eg.db, microRNA, targetscan.Mm.eg.db, RSQLite, DBI, AnnotationDbi, clValid, class, cluster, multtest, RColorBrewer, latticeExtra License: GPL-3 MD5sum: 5a7050d55338866f1ab09c490c0f1e59 NeedsCompilation: no Title: Murine Palate miRNA Expression Analysis Description: R package compendium for the analysis of murine palate miRNA two-color expression data. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, Clustering, GO, Pathways, ReportWriting, SequenceMatching Author: Guy Brock , Partha Mukhopadhyay , Vasyl Pihur , Robert M. Greene , and M. Michele Pisano Maintainer: Guy Brock source.ver: src/contrib/MmPalateMiRNA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MmPalateMiRNA_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MmPalateMiRNA_1.30.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.R Package: MODA Version: 1.6.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 32d26cf512cd5b8375aa7caed6591305 NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li source.ver: src/contrib/MODA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MODA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MODA_1.6.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: mogsa Version: 1.14.0 Depends: R (>= 3.2.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster Suggests: BiocStyle, knitr License: GPL-2 MD5sum: a284c3c44e960cd053dcce3e39f10ca9 NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr source.ver: src/contrib/mogsa_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mogsa_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mogsa_1.14.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: mogsa: gene set analysis on multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R Package: monocle Version: 2.8.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-1), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, densityClust (>= 0.3), Rtsne, MASS, reshape2, limma, tibble, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 205ada9abf42f10907fff96fe41163c2 NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr source.ver: src/contrib/monocle_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/monocle_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/monocle_2.8.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R importsMe: uSORT suggestsMe: scater, scran, sincell Package: MoonlightR Version: 1.6.1 Depends: R (>= 3.3), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png License: GPL (>= 3) MD5sum: 5fdfbb9c212d22755227416c75cb73e5 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico*, Catharina Olsen*, Claudia Cava, Thilde Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena Papaleo, Gianluca Bontempi Maintainer: Antonio Colaprico , Catharina Olsen URL: https://github.com/torongs82/Moonlight VignetteBuilder: knitr BugReports: https://github.com/torongs82/Moonlight/issues source.ver: src/contrib/MoonlightR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MoonlightR_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MoonlightR_1.6.1.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R Package: MoPS Version: 1.14.0 Imports: Biobase License: GPL-3 MD5sum: 54c2a7c767a863232590725db18fac10 NeedsCompilation: no Title: MoPS - Model-based Periodicity Screening Description: Identification and characterization of periodic fluctuations in time-series data. biocViews: GeneRegulation,Classification,TimeCourse,Regression Author: Philipp Eser, Achim Tresch Maintainer: Philipp Eser source.ver: src/contrib/MoPS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MoPS_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MoPS_1.14.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoPS/inst/doc/MoPS.R Package: mosaics Version: 2.18.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: dd76bd5853583bf403902104c87c56f8 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl source.ver: src/contrib/mosaics_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mosaics_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mosaics_2.18.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R Package: motifbreakR Version: 1.10.0 Depends: R (>= 3.2), grid, MotifDb Imports: methods, compiler, grDevices, grImport, stringr, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: 3be02453201eb10269ed043fd17e9c0e NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 22). biocViews: ChIPSeq, Visualization, MotifAnnotation Author: Simon Gert Coetzee [aut, cre] Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues source.ver: src/contrib/motifbreakR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/motifbreakR_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/motifbreakR_1.10.0.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R Package: motifcounter Version: 1.4.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 Archs: i386, x64 MD5sum: c09c26ff9d3546676fe0a0f5dec6caa9 NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides functionality to compute the statistics related with motif matching and counting of motif matches in DNA sequences. As an input, 'motifcounter' requires a motif in terms of a position frequency matrix (PFM). Furthermore, a set of DNA sequences is required to estimated a higher-order background model (BGM). The package provides functions to investigate the the per-position and per strand log-likelihood scores between the PFM and the BGM across a given sequence of set of sequences. Furthermore, the package facilitates motif matching based on an automatically derived score threshold. To this end the distribution of scores is efficiently determined and the score threshold is chosen for a user-prescribed significance level. This allows to control for the false positive rate. Moreover, 'motifcounter' implements a motif match enrichment test based on two the number of motif matches that are expected in random DNA sequences. Motif enrichment is facilitated by either a compound Poisson approximation or a combinatorial approximation of the motif match counts. Both models take higher-order background models, the motif's self-similarity, and hits on both DNA strands into account. The package is in particular useful for long motifs and/or relaxed choices of score thresholds, because the implemented algorithms efficiently bypass the need for enumerating a (potentially huge) set of DNA words that can give rise to a motif match. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr source.ver: src/contrib/motifcounter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/motifcounter_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/motifcounter_1.4.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R Package: MotifDb Version: 1.22.0 Depends: R (>= 2.15.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, MotIV License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 43e733ff8c1edcb66305c3b9c9967710 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9000 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon source.ver: src/contrib/MotifDb_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MotifDb_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MotifDb_1.22.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: %%MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, trena importsMe: rTRMui suggestsMe: ATACseqQC, DiffLogo, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, vtpnet Package: motifmatchr Version: 1.2.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: b2fe3670550b925ae9773a8bce054fea NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/motifmatchr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/motifmatchr_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/motifmatchr_1.2.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: esATAC suggestsMe: chromVAR Package: motifRG Version: 1.24.0 Depends: R (>= 2.15), Biostrings (>= 2.26), IRanges, seqLogo, parallel, methods, grid, graphics, BSgenome, XVector, BSgenome.Hsapiens.UCSC.hg19 Imports: Biostrings,IRanges,seqLogo,parallel,methods,grid,graphics,XVector License: Artistic-2.0 MD5sum: b6c57962594342270552bca4e51c5ba2 NeedsCompilation: no Title: A package for discriminative motif discovery, designed for high throughput sequencing dataset Description: Tools for discriminative motif discovery using regression methods biocViews: Transcription,MotifDiscovery Author: Zizhen Yao Maintainer: Zizhen Yao source.ver: src/contrib/motifRG_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/motifRG_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/motifRG_1.24.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifRG/inst/doc/motifRG.R dependsOnMe: RCAS Package: motifStack Version: 1.24.1 Depends: R (>= 2.15.1), methods, grImport, grid, MotIV, ade4, Biostrings Imports: XML, scales, htmlwidgets,grDevices, stats, stats4, graphics, utils Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr License: GPL (>= 2) MD5sum: ba768e64102b0c29b2bdaf2032e82e31 NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_7 git_last_commit: 78c9b2c git_last_commit_date: 2018-07-20 Date/Publication: 2018-07-20 source.ver: src/contrib/motifStack_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/motifStack_1.24.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/motifStack_1.24.1.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: dagLogo importsMe: ATACseqQC, LowMACA, motifbreakR suggestsMe: ChIPpeakAnno Package: MotIV Version: 1.36.0 Depends: R (>= 2.10), BiocGenerics (>= 0.1.0) Imports: graphics, grid, methods, S4Vectors, IRanges (>= 1.13.5), Biostrings (>= 1.24.0), lattice, rGADEM, utils Suggests: rtracklayer License: GPL-2 Archs: i386, x64 MD5sum: bccf448b29338e3933659753e0914259 NeedsCompilation: yes Title: Motif Identification and Validation Description: This package makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs. biocViews: Microarray, ChIPchip, ChIPSeq, GenomicSequence, MotifAnnotation Author: Eloi Mercier, Raphael Gottardo Maintainer: Eloi Mercier , Raphael Gottardo SystemRequirements: GNU Scientific Library >= 1.6 (http://www.gnu.org/software/gsl/) source.ver: src/contrib/MotIV_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MotIV_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MotIV_1.36.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MotIV/inst/doc/MotIV.R dependsOnMe: motifStack suggestsMe: MotifDb Package: MPFE Version: 1.16.0 License: GPL (>= 3) MD5sum: 632a637010f5c667e635d6ff3043913a NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/MPFE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MPFE_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MPFE_1.16.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R Package: mpra Version: 1.2.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: a67e50dd25e9b374dbf08628e16f246b NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues source.ver: src/contrib/mpra_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mpra_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mpra_1.2.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R Package: mQTL.NMR Version: 1.14.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: 4c110f76afa6f12f3c9a6b10abc660d3 NeedsCompilation: yes Title: Metabolomic Quantitative Trait Locus Mapping for 1H NMR data Description: mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts. biocViews: Cheminformatics, Metabolomics, Genetics, SNP Author: Lyamine Hedjazi and Jean-Baptiste Cazier Maintainer: Lyamine Hedjazi URL: http://www.ican-institute.org/tools/ source.ver: src/contrib/mQTL.NMR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mQTL.NMR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mQTL.NMR_1.14.0.tgz vignettes: vignettes/mQTL.NMR/inst/doc/FAQ.pdf, vignettes/mQTL.NMR/inst/doc/mQTLUse.pdf vignetteTitles: Frequently Asked Questions, How to use the mQTL.NMR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mQTL.NMR/inst/doc/FAQ.R, vignettes/mQTL.NMR/inst/doc/mQTLUse.R Package: msa Version: 1.12.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape, phangorn License: GPL (>= 2) Archs: i386, x64 MD5sum: 3d9a32f0c75c6392c3aab329b5b7b3eb NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/msa_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/msa_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/msa_1.12.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq Package: msgbsR Version: 1.4.0 Depends: R (>= 3.4), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 MD5sum: ed1b15aff116440744ef7f85aa06c93c NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne source.ver: src/contrib/msgbsR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/msgbsR_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/msgbsR_1.4.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R Package: MSGFgui Version: 1.14.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: cbdb1af5d5bac22d0375ccef86eb1c71 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/MSGFgui_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSGFgui_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSGFgui_1.14.0.tgz vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R Package: MSGFplus Version: 1.14.0 Depends: methods Imports: mzID, ProtGenerics Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: ace234709b38597c2d6b0c435acb0439 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using the MS-GF+ algorithm. The package contains functionality for building up a parameter set both in code and through a simple GUI, as well as running the algorithm in batches, potentially asynchronously. biocViews: MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr source.ver: src/contrib/MSGFplus_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSGFplus_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSGFplus_1.14.0.tgz vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R importsMe: MSGFgui Package: msmsEDA Version: 1.18.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: d197e7aa3aed433d172ca0d5c4d3ed44 NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori source.ver: src/contrib/msmsEDA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/msmsEDA_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/msmsEDA_1.18.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman Package: msmsTests Version: 1.18.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 722466b78c3f695760e16e0a3ed910b5 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font source.ver: src/contrib/msmsTests_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/msmsTests_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/msmsTests_1.18.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R suggestsMe: MSnID Package: MSnbase Version: 2.6.4 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.13.6), BiocParallel, ProtGenerics (>= 1.9.1) Imports: IRanges (>= 2.13.28), plyr, preprocessCore, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, S4Vectors, XML, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 Archs: i386, x64 MD5sum: 062ed68647a9f181c5c3c396863f7f62 NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement. Maintainer: Laurent Gatto URL: https://github.com/lgatto/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_7 git_last_commit: 4683686 git_last_commit_date: 2018-09-21 Date/Publication: 2018-09-21 source.ver: src/contrib/MSnbase_2.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSnbase_2.6.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSnbase_2.6.4.tgz vignettes: vignettes/MSnbase/inst/doc/benchmarking.html, vignettes/MSnbase/inst/doc/MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/MSnbase-demo.html, vignettes/MSnbase/inst/doc/MSnbase-development.html, vignettes/MSnbase/inst/doc/MSnbase-io.html vignetteTitles: MSnbase2 benchmarking, MSnbase: centroiding of profile-mode MS data, Base Functions and Classes for MS-based Proteomics, A short introduction to `MSnbase` development, MSnbase IO capabilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/benchmarking.R, vignettes/MSnbase/inst/doc/MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/MSnbase-demo.R, vignettes/MSnbase/inst/doc/MSnbase-development.R, vignettes/MSnbase/inst/doc/MSnbase-io.R dependsOnMe: msmsEDA, msmsTests, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter, xcms importsMe: DAPAR, DEP, MSnID, MSstatsQC, Pbase, ProteomicsAnnotationHubData, topdownr suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, qcmetrics, readat, rpx Package: MSnID Version: 1.14.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR Suggests: BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c8e8d4bacbfe7bd5bd0a28f072605383 NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk source.ver: src/contrib/MSnID_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSnID_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSnID_1.14.0.tgz vignettes: vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/msnid_vignette.R Package: msPurity Version: 1.6.2 Depends: Rcpp Imports: plyr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA License: GPL (>= 2) MD5sum: 7b84b12b1abab796b54e5e2963590bee NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: Assess the contribution of the targeted precursor in fragmentation acquired or anticipated isolation windows using a metric called "precursor purity". Also provides simple processing steps (averaging, filtering, blank subtraction, etc) for DI-MS data. Works for both LC-MS(/MS) and DI-MS(/MS) data. Spectral matching of fragmentation spectra can also be run against a SQLite database of library spectra. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson, Ralf Weber, Martin Jones, Mark Viant, Warwick Dunn Maintainer: Thomas N. Lawson VignetteBuilder: knitr source.ver: src/contrib/msPurity_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/msPurity_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/msPurity_1.6.2.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msPurity/inst/doc/msPurity-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R Package: MSstats Version: 3.12.3 Depends: R (>= 3.4) Imports: lme4, marray, limma, gplots, ggplot2, methods, grid, ggrepel, preprocessCore, reshape2, survival, minpack.lm, utils, grDevices, graphics, stats, doSNOW, snow, foreach, data.table, MASS, dplyr, tidyr, stringr, randomForest Suggests: BiocStyle, knitr, rmarkdown, MSstatsBioData License: Artistic-2.0 MD5sum: f057dd6fd366dd5cd495fd586e272c29 NeedsCompilation: no Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Cyril Galitzine [aut], Ting Huang [aut], Tsung-Heng Tsai [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_7 git_last_commit: dd21ed2 git_last_commit_date: 2018-07-02 Date/Publication: 2018-07-02 source.ver: src/contrib/MSstats_3.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSstats_3.12.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSstats_3.12.3.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R Package: MSstatsQC Version: 1.2.0 Imports: dplyr,plotly,RecordLinkage,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 MD5sum: d460a294c66bf87dba02fad1933f8ec7 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc source.ver: src/contrib/MSstatsQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSstatsQC_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSstatsQC_1.2.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui Package: MSstatsQCgui Version: 1.0.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, RecordLinkage, grid Suggests: knitr License: Artistic License 2.0 MD5sum: e53591553dfbb4b794ba364aa12083ba NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc source.ver: src/contrib/MSstatsQCgui_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MSstatsQCgui_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MSstatsQCgui_1.0.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R Package: Mulcom Version: 1.30.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: 59ff709a34c6931ef0d29b485c711b0a NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella source.ver: src/contrib/Mulcom_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Mulcom_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Mulcom_1.30.0.tgz vignettes: vignettes/Mulcom/inst/doc/MulcomVignette.pdf vignetteTitles: Mulcom Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mulcom/inst/doc/MulcomVignette.R Package: MultiAssayExperiment Version: 1.6.0 Depends: R (>= 3.5.0) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, SummarizedExperiment (>= 1.3.81), S4Vectors (>= 0.17.5), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, R.rsp, HDF5Array, RaggedExperiment, UpSetR, survival, survminer License: Artistic-2.0 MD5sum: ac01c476ea55cdb9a3ab2aba76531db3 NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: Multi-assay 'omics experiments on a set of samples are increasingly commonplace in biomedical research. MultiAssayExperiment implements data structures and methods for representing, manipulating, and integrating multi-assay experiments via efficient construction, subsetting, and extraction operations. These methods are implemented matching Bioconductor user experience by straightforward extending concept and design of single-assay classes such as SummarizedExperiment. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/MultiAssayExperiment/wiki/MultiAssayExperiment-API VignetteBuilder: knitr, R.rsp BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues source.ver: src/contrib/MultiAssayExperiment_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MultiAssayExperiment_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MultiAssayExperiment_1.6.0.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: ClassifyR, hipathia, InTAD, missRows importsMe: CAGEr, ELMER, GOpro, omicsPrint, TCGAutils suggestsMe: BiocOncoTK, MultiDataSet, RaggedExperiment Package: multiClust Version: 1.10.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 35f678e71cb1a635863c4c709f312ee9 NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr source.ver: src/contrib/multiClust_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/multiClust_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/multiClust_1.10.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R Package: MultiDataSet Version: 1.8.0 Depends: R (>= 3.3), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: f1c378baaf2e422883004a7fbb672b02 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr source.ver: src/contrib/MultiDataSet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MultiDataSet_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MultiDataSet_1.8.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_3party_Integration.html, vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Using MultiDataSet with third party R packages, Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_3party_Integration.R, vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: omicRexposome Package: MultiMed Version: 2.2.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: ee99f0cfc605fd5101c7c8978fef1e19 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca source.ver: src/contrib/MultiMed_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MultiMed_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MultiMed_2.2.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R Package: multiMiR Version: 1.2.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: 5b28e71df76e43214201160c12ac2805 NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues source.ver: src/contrib/multiMiR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/multiMiR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/multiMiR_1.2.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R Package: multiOmicsViz Version: 1.4.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: f9fbbcf0d76f016d896393dabe9b0f96 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang source.ver: src/contrib/multiOmicsViz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/multiOmicsViz_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/multiOmicsViz_1.4.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R Package: multiscan Version: 1.40.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 9dd4691094b70f1c1c30b87c14a01e38 NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker source.ver: src/contrib/multiscan_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/multiscan_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/multiscan_1.40.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R Package: multtest Version: 2.36.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: f26f65605edd67dacb289d86d9fdca6e NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard source.ver: src/contrib/multtest_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/multtest_2.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/multtest_2.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, aCGH, BicARE, iPAC, KCsmart, LMGene, PREDA, rain, REDseq, SAGx, siggenes, webbioc importsMe: ABarray, aCGH, adSplit, ALDEx2, anota, ChIPpeakAnno, GeneSelector, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, REDseq, RTopper, singleCellTK, synapter, webbioc, xcms suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GeneSelector, GGtools, GOstats, gQTLstats, GSEAlm, maigesPack, MmPalateMiRNA, pcot2, ropls, topGO Package: muscle Version: 3.22.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: 0b447da3e7d350d632fea9004d21c6f4 NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ source.ver: src/contrib/muscle_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/muscle_3.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/muscle_3.22.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R Package: MutationalPatterns Version: 1.6.1 Depends: R (>= 3.4.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, parallel, S4Vectors, BiocGenerics (>= 0.18.0), VariantAnnotation (>= 1.18.1), reshape2 (>= 1.4.1), plyr (>= 1.8.3), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), SummarizedExperiment (>= 1.2.2), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2) Suggests: BSgenome (>= 1.40.0), BSgenome.Hsapiens.1000genomes.hs37d5 (>= 0.99.1), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), testthat License: MIT + file LICENSE MD5sum: 422f2031cbfc146fa0cc6ec1d5e1931a NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: An extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution catalogs. biocViews: Genetics, SomaticMutation Author: Francis Blokzijl, Roel Janssen, Ruben van Boxtel, Edwin Cuppen Maintainer: Francis Blokzijl , Roel Janssen URL: https://doi.org/10.1186/s13073-018-0539-0 source.ver: src/contrib/MutationalPatterns_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/MutationalPatterns_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MutationalPatterns_1.6.1.tgz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.pdf vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R Package: MVCClass Version: 1.54.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 0499f17b5a134622356c9e907b093039 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen source.ver: src/contrib/MVCClass_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MVCClass_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MVCClass_1.54.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass Package: MWASTools Version: 1.4.0 Depends: R(>= 3.4) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 48de6c7152356a7e525e585a36a53e43 NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr source.ver: src/contrib/MWASTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/MWASTools_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/MWASTools_1.4.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal Package: mygene Version: 1.16.2 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: b9a361ec079341379c57a619c4437a8d NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_7 git_last_commit: fda1a84 git_last_commit_date: 2018-06-21 Date/Publication: 2018-06-22 source.ver: src/contrib/mygene_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/mygene_1.16.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mygene_1.16.2.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal Package: myvariant Version: 1.10.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 1d24e1d2efdcf7072757ce418fc2161a NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu source.ver: src/contrib/myvariant_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/myvariant_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/myvariant_1.10.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R Package: mzID Version: 1.18.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 8d5704f656e526191ae6e19474da804d NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: DataImport, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen, Vladislav A Petyuk with contributions from Laurent Gatto and Sebastian Gibb. Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/mzID_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mzID_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mzID_1.18.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR Package: mzR Version: 2.14.0 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.9.1) LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML License: Artistic-2.0 Archs: i386, x64 MD5sum: fb94e2f14c6f1fd24dd6094316cd3e71 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make, NetCDF VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ source.ver: src/contrib/mzR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/mzR_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/mzR_2.14.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSGFgui, MSnbase importsMe: MSnID, msPurity, Pbase, ProteomicsAnnotationHubData, SIMAT, topdownr, xcms, yamss suggestsMe: AnnotationHub, qcmetrics Package: NADfinder Version: 1.4.0 Depends: R (>= 3.4), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat License: GPL (>= 2) MD5sum: 8eaf099e12222aa46f4db8b15d1fcbb7 NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including normalization, smoothing, peak calling, peak trimming and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/NADfinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NADfinder_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NADfinder_1.4.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R Package: NanoStringDiff Version: 1.10.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL Archs: i386, x64 MD5sum: a294274c5ab4b8c3ad8d6b508d18a65c NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang source.ver: src/contrib/NanoStringDiff_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NanoStringDiff_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NanoStringDiff_1.10.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R Package: NanoStringQCPro Version: 1.12.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: fa56b23bc965456f6a5432c145fce035 NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr source.ver: src/contrib/NanoStringQCPro_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NanoStringQCPro_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NanoStringQCPro_1.12.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NarrowPeaks Version: 1.24.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Archs: i386, x64 MD5sum: c61420f7faf1bfa51a02b9c538aa45df NeedsCompilation: yes Title: Shape-based Analysis of Variation in ChIP-seq using Functional PCA Description: The package applies a functional version of principal component analysis (FPCA) to: (1) Postprocess data in wiggle track format, commonly produced by generic ChIP-seq peak callers, by applying FPCA over a set of read-enriched regions (ChIP-seq peaks). This is done to study variability of the the peaks, or to shorten their genomic locations accounting for a given proportion of variation among the enrichment-score profiles. (2) Analyse differential variation between multiple ChIP-seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. An application of the package for Arabidopsis datasets is described in Mateos, Madrigal, et al. (2015) Genome Biology: 16:31. biocViews: Visualization, ChIPSeq, Transcription, Genetics, Sequencing, Sequencing Author: Pedro Madrigal , Pawel Krajewski Maintainer: Pedro Madrigal source.ver: src/contrib/NarrowPeaks_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NarrowPeaks_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NarrowPeaks_1.24.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.R Package: ncdfFlow Version: 2.26.0 Depends: R (>= 2.14.0), flowCore(>= 1.45.11), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,flowViz,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib Suggests: testthat,parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: b29860a7bc983c096daeed880a5cd212 NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang source.ver: src/contrib/ncdfFlow_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ncdfFlow_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ncdfFlow_2.26.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: flowStats, ggcyto importsMe: CytoML suggestsMe: COMPASS, cydar Package: NCIgraph Version: 1.28.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 61741e66b68692be843c2b95eb1355e2 NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob source.ver: src/contrib/NCIgraph_1.28.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph Package: ndexr Version: 1.2.0 Depends: igraph Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD MD5sum: 9271ac6d3ab58664e5624154f954de4f NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Frank Kramer , Florian Auer , Alex Ishkin , Dexter Pratt Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues source.ver: src/contrib/ndexr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ndexr_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ndexr_1.2.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R Package: nem Version: 2.54.0 Depends: R (>= 3.0) Imports: boot, e1071, graph, graphics, grDevices, methods, RBGL (>= 1.8.1), RColorBrewer, stats, utils, Rgraphviz, statmod, plotrix, limma Suggests: Biobase (>= 1.10) Enhances: doMC, snow, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 1a37190f4c5c7de08b15635ebf9545cb NeedsCompilation: yes Title: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies Description: The package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input (1.) a set of pathway components, which were perturbed, and (2.) phenotypic readout of these perturbations (e.g. gene expression, protein expression). The output is a directed graph representing the phenotypic hierarchy. biocViews: Microarray, Bioinformatics, GraphsAndNetworks, Pathways, SystemsBiology, NetworkInference Author: Holger Froehlich, Florian Markowetz, Achim Tresch, Theresa Niederberger, Christian Bender, Matthias Maneck, Claudio Lottaz, Tim Beissbarth Maintainer: Holger Froehlich URL: http://www.bioconductor.org source.ver: src/contrib/nem_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nem_2.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nem_2.54.0.tgz vignettes: vignettes/nem/inst/doc/markowetz-thesis-2006.pdf, vignettes/nem/inst/doc/nem.pdf vignetteTitles: markowetz-thesis-2006.pdf, Nested Effects Models - An example in Drosophila immune response hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nem/inst/doc/nem.R dependsOnMe: lpNet importsMe: birte, epiNEM, OncoSimulR suggestsMe: rBiopaxParser Package: netbenchmark Version: 1.12.0 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, GENIE3, c3net, PCIT, GeneNet, tools, pracma, Matrix, corpcor, fdrtool LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, graph License: CC BY-NC-SA 4.0 Archs: i386, x64 MD5sum: a6e65f2978303ea036fef7fe13ad80ad NeedsCompilation: yes Title: Benchmarking of several gene network inference methods Description: This package implements a benchmarking of several gene network inference algorithms from gene expression data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference, GeneExpression Author: Pau Bellot, Catharina Olsen, Patrick Meyer Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr source.ver: src/contrib/netbenchmark_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/netbenchmark_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netbenchmark_1.12.0.tgz vignettes: vignettes/netbenchmark/inst/doc/netbenchmark.html vignetteTitles: Netbenchmark hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netbenchmark/inst/doc/netbenchmark.R Package: netbiov Version: 1.14.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: f9d95bee3392c4d2e6a51160c72c4613 NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com source.ver: src/contrib/netbiov_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/netbiov_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netbiov_1.14.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R Package: nethet Version: 1.12.0 Imports: glasso, mvtnorm, parcor, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2 Suggests: knitr, xtable, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 265c18fcf5ff6d7cc551d953576e96bc NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr source.ver: src/contrib/nethet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nethet_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nethet_1.12.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R Package: NetPathMiner Version: 1.16.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph License: GPL (>= 2) Archs: i386, x64 MD5sum: 9de654a16ce687706234cad9f19fe787 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) source.ver: src/contrib/NetPathMiner_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NetPathMiner_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NetPathMiner_1.16.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.pdf vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R Package: netprioR Version: 1.6.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: 0f44522f15194ccfe59e123c1c7d46ca NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr source.ver: src/contrib/netprioR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/netprioR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netprioR_1.6.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R Package: netReg Version: 1.4.0 Depends: R(>= 3.4) Imports: Rcpp, stats LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, lintr, lassoshooting License: GPL-3 | BSL-1.0 + file LICENSE Archs: i386, x64 MD5sum: 4c07d24a615d73584d9c29d43e79782b NeedsCompilation: yes Title: Network-Regularized Regression Models Description: netReg fits linear regression models using network-penalization. Graph prior knowledge, in the form of biological networks, is being incorporated into the loss function of the linear model. The networks describe biological relationships such as co-regulation or dependency of the same transcription factors/metabolites/etc. yielding a part sparse and part smooth solution for coefficient profiles. biocViews: Software, StatisticalMethod, Regression, FeatureExtraction, Network, GraphAndNetwork Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/dirmeier/netReg SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/dirmeier/netReg/issues source.ver: src/contrib/netReg_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/netReg_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netReg_1.4.0.tgz vignettes: vignettes/netReg/inst/doc/netReg.html vignetteTitles: netReg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netReg/inst/doc/netReg.R Package: netresponse Version: 1.40.0 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer License: GPL (>=2) Archs: i386, x64 MD5sum: 85d39e106c69264defdccd716a47944e NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse BugReports: https://github.com/antagomir/netresponse/issues source.ver: src/contrib/netresponse_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/netresponse_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netresponse_1.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NetSAM Version: 1.20.0 Depends: R (>= 2.15.1), methods, igraph (>= 0.6-1), seriation (>= 1.0-6), graph (>= 1.34.0) Imports: methods Suggests: RUnit, BiocGenerics License: LGPL MD5sum: 02fef6777ad5403412c64411e0cb2883 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. biocViews: Visualization, Network Author: Jing Wang Maintainer: Bing Zhang source.ver: src/contrib/NetSAM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NetSAM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NetSAM_1.20.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R Package: netSmooth Version: 1.0.2 Depends: R (>= 3.5), scater (>= 1.7.11), clusterExperiment (>= 1.99.1) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel License: GPL-3 MD5sum: deb191fc3188978b94c347336353b97e NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_7 git_last_commit: 9fb462a git_last_commit_date: 2018-07-24 Date/Publication: 2018-07-24 source.ver: src/contrib/netSmooth_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/netSmooth_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/netSmooth_1.0.2.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: netSmooth example, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R Package: networkBMA Version: 2.20.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) Archs: i386, x64 MD5sum: 1f793bfac2c5ca8981dda060af40e19d NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung SystemRequirements: liblapack-dev source.ver: src/contrib/networkBMA_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/networkBMA_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/networkBMA_2.20.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R Package: NGScopy Version: 1.14.0 Depends: R (>= 3.1.0) Imports: methods, parallel, Xmisc (>= 0.2.1), rbamtools (>= 2.6.0), changepoint (>= 2.1.1) Suggests: RUnit, NGScopyData, GenomicRanges License: GPL (>=2) MD5sum: 6d3d844e71fed21066a52c8c7d8b5b61 NeedsCompilation: no Title: NGScopy: Detection of Copy Number Variations in Next Generation Sequencing sequencing Description: NGScopy provides a quantitative caller for detecting copy number variations in next generation sequencing (NGS), including whole genome sequencing (WGS), whole exome sequencing (WES) and targeted panel sequencing (TPS). The caller can be parallelized by chromosomes to use multiple processors/cores on one computer. biocViews: CopyNumberVariation, DNASeq, TargetedResequencing, ExomeSeq, WholeGenome, Sequencing Author: Xiaobei Zhao [aut, cre, cph] Maintainer: Xiaobei Zhao source.ver: src/contrib/NGScopy_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NGScopy_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NGScopy_1.14.0.tgz vignettes: vignettes/NGScopy/inst/doc/NGScopy-vignette.pdf vignetteTitles: NGScopy: Detection of copy number variations in next generation sequencing (User's Guide) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NGScopy/inst/doc/NGScopy-vignette.R Package: nnNorm Version: 2.44.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 2aaccb586b73faecbf05bfadffddc960 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ source.ver: src/contrib/nnNorm_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nnNorm_2.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nnNorm_2.44.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R Package: NOISeq Version: 2.24.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 2f3cae4c77ea4fd59eb67ea99e85b6c5 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona source.ver: src/contrib/NOISeq_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NOISeq_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NOISeq_2.24.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: CNVPanelizer, metaseqR suggestsMe: compcodeR Package: nondetects Version: 2.10.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: 25898d38e5e62a96887afe3138e30817 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr source.ver: src/contrib/nondetects_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nondetects_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nondetects_2.10.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R Package: normalize450K Version: 1.8.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 51b556f99902f80416296ce2491892bf NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss source.ver: src/contrib/normalize450K_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/normalize450K_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/normalize450K_1.8.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R Package: NormqPCR Version: 1.26.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 4490d9cb04349bfd9a5dc542491840b4 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html source.ver: src/contrib/NormqPCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NormqPCR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NormqPCR_1.26.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R Package: normr Version: 1.6.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 Archs: i386, x64 MD5sum: 151b921629a1f7a42303a4b782ca3b89 NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues source.ver: src/contrib/normr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/normr_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/normr_1.6.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R Package: npGSEA Version: 1.16.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: f79dded4af1dab758a1aeb6082151482 NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson source.ver: src/contrib/npGSEA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/npGSEA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/npGSEA_1.16.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R Package: NTW Version: 1.30.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: b1afe0eec9688f9e054bfa75fd8aaa0c NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu source.ver: src/contrib/NTW_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NTW_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NTW_1.30.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R Package: nucleoSim Version: 1.8.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: ac6017a069d54538bf8969cc88b1c86c NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues source.ver: src/contrib/nucleoSim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nucleoSim_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nucleoSim_1.8.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes Package: nucleR Version: 2.12.1 Depends: methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils Suggests: Starr, BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: e742c9fe2b1c67f0a6b2ba0658278fa2 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Ricard Illa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_7 git_last_commit: 8357444 git_last_commit_date: 2018-07-05 Date/Publication: 2018-07-05 source.ver: src/contrib/nucleR_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/nucleR_2.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nucleR_2.12.1.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: Vignette Title hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R Package: nudge Version: 1.46.0 Imports: stats License: GPL-2 MD5sum: 3d65a0b41985860b1a9941b41597d597 NeedsCompilation: no Title: Normal Uniform Differential Gene Expression detection Description: Package for normalizing microarray data in single and multiple replicate experiments and fitting a normal-uniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample) biocViews: Microarray, TwoChannel, DifferentialExpression Author: N. Dean and A. E. Raftery Maintainer: N. Dean source.ver: src/contrib/nudge_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/nudge_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/nudge_1.46.0.tgz vignettes: vignettes/nudge/inst/doc/nudge.vignette.pdf vignetteTitles: nudge Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nudge/inst/doc/nudge.vignette.R Package: NuPoP Version: 1.30.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 61321d8c4fa597b381557c4d10e25962 NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification Author: Ji-Ping Wang ; Liqun Xi Maintainer: Ji-Ping Wang source.ver: src/contrib/NuPoP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/NuPoP_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/NuPoP_1.30.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP-intro.pdf vignetteTitles: An R package for Nucleosome positioning prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP-intro.R Package: occugene Version: 1.40.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: f8a123b28709d3258314124d3d5e2931 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will source.ver: src/contrib/occugene_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/occugene_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/occugene_1.40.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R Package: OCplus Version: 1.54.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL MD5sum: b5e9dd168bcabd9c7197a65b85c6688f NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner source.ver: src/contrib/OCplus_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OCplus_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OCplus_1.54.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R Package: odseq Version: 1.8.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: d30d1213ff40cc19c0350393105d97dd NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr source.ver: src/contrib/odseq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/odseq_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/odseq_1.8.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R Package: OGSA Version: 1.10.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) MD5sum: 662fe82ccf102be87c0948ddba62c89d NeedsCompilation: no Title: Outlier Gene Set Analysis Description: OGSA provides a global estimate of pathway deregulation in cancer subtypes by integrating the estimates of significance for individual pathway members that have been identified by outlier analysis. biocViews: GeneExpression, Microarray, CopyNumberVariation Author: Michael F. Ochs Maintainer: Michael F. Ochs source.ver: src/contrib/OGSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OGSA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OGSA_1.10.0.tgz vignettes: vignettes/OGSA/inst/doc/OGSAUsersManual.pdf vignetteTitles: OGSA Users Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGSA/inst/doc/OGSAUsersManual.R Package: oligo Version: 1.44.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, GenomeGraphs, RCurl, ACME, biomaRt, AnnotationDbi, GenomeGraphs, RCurl Enhances: ff, doMC, doMPI License: LGPL (>= 2) Archs: i386, x64 MD5sum: f83fd31740291b0e567a8744d9ca9e5d NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho VignetteBuilder: knitr source.ver: src/contrib/oligo_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/oligo_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oligo_1.44.0.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, waveTiling importsMe: ArrayExpress, charm, cn.farms, crossmeta, frma, ITALICS, mimager suggestsMe: BiocGenerics, fastseg, frmaTools Package: oligoClasses Version: 1.42.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocInstaller, utils, S4Vectors (>= 0.9.25), RSQLite, DBI Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, SNPchip, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 87defae92546187178459a162e8bc551 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf source.ver: src/contrib/oligoClasses_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/oligoClasses_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oligoClasses_1.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, charm, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics Package: OLIN Version: 1.58.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 6cc011bed23d644cd772dedacea3e0c3 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu source.ver: src/contrib/OLIN_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OLIN_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OLIN_1.58.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack Package: OLINgui Version: 1.54.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 0e059b9d765a932f315cddf577c55df7 NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu source.ver: src/contrib/OLINgui_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OLINgui_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OLINgui_1.54.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R Package: OmaDB Version: 1.0.1 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: e0de6ea8a10f3429c7a0237415922e6f NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OmaDB git_branch: RELEASE_3_7 git_last_commit: e902bdf git_last_commit_date: 2018-06-29 Date/Publication: 2018-06-29 source.ver: src/contrib/OmaDB_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/OmaDB_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OmaDB_1.0.1.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R Package: omicade4 Version: 1.20.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: 471adc64a0628d27ef2375ce7d6dc278 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: Multiple co-inertia analysis of omics datasets biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng source.ver: src/contrib/omicade4_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/omicade4_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/omicade4_1.20.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: MultiDataSet Package: OmicCircos Version: 1.18.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: f7ec6cee14d85d178e73eb6b7f7617ef NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu source.ver: src/contrib/OmicCircos_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OmicCircos_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OmicCircos_1.18.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R Package: omicplotR Version: 1.0.1 Depends: R (>= 3.5) Imports: ALDEx2 (>= 1.8.0), compositions, grDevices, knitr, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: aba78005f7cb29269e0b77fab5838b9f NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_7 git_last_commit: 495d22a git_last_commit_date: 2018-07-06 Date/Publication: 2018-07-06 source.ver: src/contrib/omicplotR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/omicplotR_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/omicplotR_1.0.1.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R Package: omicRexposome Version: 1.2.0 Depends: R (>= 3.4), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: a65d10bede09c8e1bc521824cae3811d NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr source.ver: src/contrib/omicRexposome_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/omicRexposome_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/omicRexposome_1.2.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R Package: OmicsMarkeR Version: 1.12.0 Depends: R (>= 3.2.0) Imports: graphics, stats, utils, plyr (>= 1.8), data.table (>= 1.9.4), caret (>= 6.0-37), DiscriMiner (>= 0.1-29), e1071 (>= 1.6-1), randomForest (>= 4.6-10), gbm (>= 2.1), pamr (>= 1.54.1), glmnet (>= 1.9-5), caTools (>= 1.14), foreach (>= 1.4.1), permute (>= 0.7-0), assertive (>= 0.3-0), assertive.base (>= 0.0-1) Suggests: testthat, BiocStyle, knitr License: GPL-3 MD5sum: b07e6fe1fd162f76123a779ed6e90613 NeedsCompilation: no Title: Classification and Feature Selection for 'Omics' Datasets Description: Tools for classification and feature selection for 'omics' level datasets. It is a tool to provide multiple multivariate classification and feature selection techniques complete with multiple stability metrics and aggregation techniques. It is primarily designed for analysis of metabolomics datasets but potentially extendable to proteomics and transcriptomics applications. biocViews: Metabolomics, Classification, FeatureExtraction Author: Charles E. Determan Jr. Maintainer: Charles E. Determan Jr. URL: http://github.com/cdeterman/OmicsMarkeR VignetteBuilder: knitr BugReports: http://github.com/cdeterman/OmicsMarkeR/issues/new source.ver: src/contrib/OmicsMarkeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OmicsMarkeR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OmicsMarkeR_1.12.0.tgz vignettes: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.pdf vignetteTitles: A Short Introduction to the OmicMarkeR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.R Package: omicsPrint Version: 1.0.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: 3697f4ef6b6bc5737ee7a57e52eaf03d NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr source.ver: src/contrib/omicsPrint_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/omicsPrint_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/omicsPrint_1.0.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R Package: Onassis Version: 1.2.7 Depends: R (>= 3.4), rJava, OnassisJavaLibs Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils, AnnotationDbi, RCurl, stats Suggests: BiocStyle,knitr, rmarkdown, htmltools, DT, org.Hs.eg.db, gplots, GenomicRanges, kableExtra License: GPL-2 MD5sum: e835b143e1685950e1e8d1d1dd9b001d NeedsCompilation: no Title: OnASSIs Ontology Annotation and Semantic SImilarity software Description: A package that allows the annotation of text with ontology terms (mainly from OBO ontologies) and the computation of semantic similarity measures based on the structure of the ontology between different annotated samples. biocViews: Annotation, DataImport, Clustering, Network, Software, GeneTarget Author: Eugenia Galeota Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Onassis git_branch: RELEASE_3_7 git_last_commit: 6613380 git_last_commit_date: 2018-09-14 Date/Publication: 2018-09-14 source.ver: src/contrib/Onassis_1.2.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/Onassis_1.2.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Onassis_1.2.7.tgz vignettes: vignettes/Onassis/inst/doc/Onassis.html vignetteTitles: Onassis: Ontology Annotation and Semantic Similarity software hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Onassis/inst/doc/Onassis.R Package: oncomix Version: 1.2.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 921c6696034b9c7ab473ff53a97147ed NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr source.ver: src/contrib/oncomix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/oncomix_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oncomix_1.2.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R Package: OncoScore Version: 1.8.0 Depends: R (>= 3.4), Imports: biomaRt, grDevices, graphics, utils, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 49d27467ad5ef9e98fa679c73a963f5d NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [aut], Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut, cre], Roberta Spinelli [ctb] Maintainer: Daniele Ramazzotti URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore source.ver: src/contrib/OncoScore_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OncoScore_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OncoScore_1.8.0.tgz vignettes: vignettes/OncoScore/inst/doc/OncoScore.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/OncoScore.R Package: OncoSimulR Version: 2.10.0 Depends: R (>= 3.3.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, nem LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) Archs: i386, x64 MD5sum: 3581ba7d5fff7a52d582c8d6c5245ab5 NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Tress, Conjunctive Bayesian Networks, and other tumor progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, and additive models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Mark Taylor [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues source.ver: src/contrib/OncoSimulR_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OncoSimulR_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OncoSimulR_2.10.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R Package: oneSENSE Version: 1.2.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) MD5sum: 6204c9541c93d863d0b9392d13232cbb NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr source.ver: src/contrib/oneSENSE_1.2.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oneSENSE_1.2.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R Package: ontoProc Version: 1.2.1 Depends: R (>= 3.4), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, shiny Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat License: Artistic-2.0 MD5sum: 19371c5a0998f74bc041cd19528e338a NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/ontoProc_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ontoProc_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ontoProc_1.2.1.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.pdf vignetteTitles: ontoProc: RDF ontology processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R importsMe: pogos, tenXplore suggestsMe: BiocOncoTK Package: openCyto Version: 1.18.0 Depends: flowWorkspace(>= 3.17.43) Imports: methods,Biobase,gtools,flowCore(>= 1.31.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace,flowStats(>= 3.29.1),flowClust(>= 3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel, ggcyto License: Artistic-2.0 Archs: i386, x64 MD5sum: 689ec190dcec0473530da7f3bf5e391e NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/openCyto_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/openCyto_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/openCyto_1.18.0.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: flowCore, ggcyto Package: openPrimeR Version: 1.2.0 Depends: R (>= 3.4.0) Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: cabe5b9ee771bc91ef0781b6f771813f NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr source.ver: src/contrib/openPrimeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/openPrimeR_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/openPrimeR_1.2.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependsOnMe: openPrimeRui Package: openPrimeRui Version: 1.2.0 Depends: R (>= 3.4.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 MD5sum: 0d7c9b7e6638d3311a4ab9a2146b78cd NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr source.ver: src/contrib/openPrimeRui_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/openPrimeRui_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/openPrimeRui_1.2.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R Package: oposSOM Version: 1.18.0 Depends: R (>= 3.0), igraph (>= 1.0.0) Imports: som, fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, arules License: GPL (>=2) MD5sum: c6deb140c4ae9c838ba069f27c140fc1 NeedsCompilation: no Title: Comprehensive analysis of transciptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de source.ver: src/contrib/oposSOM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/oposSOM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oposSOM_1.18.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R Package: oppar Version: 1.8.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 Archs: i386, x64 MD5sum: 9fe530696d2b99f82f27b706a4c66baf NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr source.ver: src/contrib/oppar_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/oppar_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/oppar_1.8.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R Package: OPWeight Version: 1.2.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: 5d3271a01aa4a3578ea33d6eb4b2bfb7 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr source.ver: src/contrib/OPWeight_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OPWeight_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OPWeight_1.2.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R Package: OrderedList Version: 1.52.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: f9711290f05c3f701aac732497170fb3 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml source.ver: src/contrib/OrderedList_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OrderedList_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OrderedList_1.52.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R Package: ORFik Version: 1.0.0 Depends: R (>= 3.5.0), IRanges (>= 2.13.28), GenomicRanges (>= 1.31.23), GenomicAlignments (>= 1.15.13) Imports: S4Vectors (>= 0.17.39), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), rtracklayer (>= 1.39.9), Rcpp (>= 0.12.16), data.table (>= 1.10.4-3), Biostrings (>= 2.47.12), stats, tools, Rsamtools (>= 1.31.3) LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ggplot2 (>= 2.2.1) License: MIT + file LICENSE Archs: i386, x64 MD5sum: f27e79b960a23f303ec15c9022b9064b NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: Tools for manipulation of RiboSeq, RNASeq and CageSeq data. ORFik is extremely fast through use of C, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CageSeq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for the whole genomes and many more. biocViews: Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Kornel Labun [aut, cre, cph], Haakon Tjeldnes [aut, dtc], Katarzyna Chyzynska [ctb, dtc], Evind Valen [ths, fnd] Maintainer: Kornel Labun URL: https://github.com/JokingHero/ORFik VignetteBuilder: knitr BugReports: https://github.com/JokingHero/ORFik/issues source.ver: src/contrib/ORFik_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ORFik_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ORFik_1.0.0.tgz vignettes: vignettes/ORFik/inst/doc/ORFikOverview.html vignetteTitles: ORFik Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/ORFikOverview.R Package: Organism.dplyr Version: 1.8.1 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, methods, tools, utils, BiocFileCache, DBI, dbplyr Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, BiocStyle, ggplot2 License: Artistic-2.0 MD5sum: c4582b6c34ba0b3355cb24921434ae63 NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Yubo Cheng [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_7 git_last_commit: 1079816 git_last_commit_date: 2018-08-09 Date/Publication: 2018-08-09 source.ver: src/contrib/Organism.dplyr_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/Organism.dplyr_1.8.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Organism.dplyr_1.8.1.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R Package: OrganismDbi Version: 1.22.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.23.31) Imports: Biobase, BiocInstaller, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 MD5sum: 45397c14bf01b6e64588e09f354cb151 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Biocore Data Team source.ver: src/contrib/OrganismDbi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OrganismDbi_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OrganismDbi_1.22.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R importsMe: AnnotationHubData, epivizrData, ggbio suggestsMe: ChIPpeakAnno, epivizrStandalone Package: OSAT Version: 1.28.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 2e06f0ba6cf29c6e407cffde7bdd0df9 NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 source.ver: src/contrib/OSAT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OSAT_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OSAT_1.28.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R Package: Oscope Version: 1.10.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 4a650adf93cc2afa5d7259c8a2a2a3dd NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng source.ver: src/contrib/Oscope_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Oscope_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Oscope_1.10.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R Package: OTUbase Version: 1.30.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: f32d248efea53cef8943c86347a918ec NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck source.ver: src/contrib/OTUbase_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OTUbase_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OTUbase_1.30.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependsOnMe: mcaGUI Package: OutlierD Version: 1.44.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: 95239b994f5713928844a8efe8277208 NeedsCompilation: no Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/OutlierD_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/OutlierD_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/OutlierD_1.44.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutlierD/inst/doc/OutlierD.R Package: PAA Version: 1.14.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: f8eb8d20b95ade6716963f686a0baec3 NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle source.ver: src/contrib/PAA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PAA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PAA_1.14.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R Package: PADOG Version: 1.22.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGG.db, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: b4df9a97316b6e899b6930d2268d1487 NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi Laurentiu Tarca source.ver: src/contrib/PADOG_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PADOG_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PADOG_1.22.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA Package: paircompviz Version: 1.18.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 3c85fdd45635f61665b63d4ecc2ea654 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda source.ver: src/contrib/paircompviz_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/paircompviz_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/paircompviz_1.18.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R Package: pandaR Version: 1.12.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr License: GPL-2 MD5sum: 1810fed369c26c17016d4eb806e474ac NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr source.ver: src/contrib/pandaR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pandaR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pandaR_1.12.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R Package: panelcn.mops Version: 1.2.0 Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: c671bd8b511ff4055939119d380bd815 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid, Gundula Povysil Maintainer: Gundula Povysil VignetteBuilder: knitr source.ver: src/contrib/panelcn.mops_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/panelcn.mops_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/panelcn.mops_1.2.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R Package: PAnnBuilder Version: 1.43.0 Depends: R (>= 2.7.0), methods, utils, RSQLite, Biobase (>= 1.17.0), AnnotationDbi (>= 1.3.12) Imports: methods, utils, Biobase, DBI, RSQLite, AnnotationDbi Suggests: org.Hs.ipi.db License: LGPL (>= 2.0) MD5sum: a4615f79443702bf90601870582dfac4 NeedsCompilation: no Title: Protein annotation data package builder Description: Processing annotation data from public data repositories and building protein-centric annotation data packages. biocViews: Annotation, Proteomics Author: Li Hong lihong@sibs.ac.cn Maintainer: Li Hong URL: http://www.biosino.org/PAnnBuilder source.ver: src/contrib/PAnnBuilder_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PAnnBuilder_1.43.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PAnnBuilder_1.43.0.tgz vignettes: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.pdf vignetteTitles: Using the PAnnBuilder Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.R Package: panp Version: 1.50.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 224b90a490beb2dcf46382ad6d4c928e NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren source.ver: src/contrib/panp_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/panp_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/panp_1.50.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R Package: PANR Version: 1.26.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: c4fa36c72d81502bc2baddd6f56976ff NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang source.ver: src/contrib/PANR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PANR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PANR_1.26.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R Package: PanVizGenerator Version: 1.8.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: e2ef7352e1320c2fada7f04419317674 NeedsCompilation: no Title: Generate PanViz visualisations from your pangenome Description: PanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed. biocViews: ComparativeGenomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/PanVizGenerator VignetteBuilder: knitr BugReports: https://github.com/thomasp85/PanVizGenerator/issues source.ver: src/contrib/PanVizGenerator_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PanVizGenerator_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PanVizGenerator_1.8.0.tgz vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html vignetteTitles: Creating PanViz visualizations with PanVizGenerator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R Package: PAPi Version: 1.20.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: eecf474fc1d364151fc054968bec0c1f NeedsCompilation: no Title: Predict metabolic pathway activity based on metabolomics data Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics. biocViews: MassSpectrometry, Metabolomics Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/PAPi_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PAPi_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PAPi_1.20.0.tgz vignettes: vignettes/PAPi/inst/doc/PAPi.pdf, vignettes/PAPi/inst/doc/PAPiPackage.pdf vignetteTitles: PAPi.pdf, Applying PAPi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAPi/inst/doc/PAPiPackage.R Package: parglms Version: 1.12.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS License: Artistic-2.0 MD5sum: 55ad9ef1602cdb33b35ddee86d4dea54 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: support for parallelized estimation of GLMs/GEEs, catering for dispersed data Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parglms_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/parglms_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/parglms_1.12.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: parody Version: 1.38.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: c487a62319206357b5a311c8352f9190 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics biocViews: MultipleComparison Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parody_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/parody_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/parody_1.38.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout, flowQ Package: Path2PPI Version: 1.10.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: ed964d7be85436909b053ad760383c2b NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr source.ver: src/contrib/Path2PPI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Path2PPI_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Path2PPI_1.10.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R Package: pathifier Version: 1.20.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: e39f7ee9a2137f4974fbcf33b9ea528d NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_7 git_last_commit: cf5066b git_last_commit_date: 2018-07-17 Date/Publication: 2018-07-17 source.ver: src/contrib/pathifier_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pathifier_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pathifier_1.20.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R Package: PathNet Version: 1.20.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 8e4236ec979d8f0ab6551674775d68b5 NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Jason B. Smith source.ver: src/contrib/PathNet_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PathNet_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PathNet_1.20.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R Package: PathoStat Version: 1.6.1 Depends: R (>= 3.4) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: b168fe548cbc3a49bb2b08eec5d922a1 NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues source.ver: src/contrib/PathoStat_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/PathoStat_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PathoStat_1.6.1.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R Package: pathprint Version: 1.10.4 Depends: R (>= 3.4) Imports: stats, utils Suggests: ALL, GEOquery, pathprintGEOData, SummarizedExperiment License: GPL MD5sum: 460856d8f873cd65ca1c80d58b9c9073 NeedsCompilation: no Title: Pathway fingerprinting for analysis of gene expression arrays Description: Algorithms to convert a gene expression array provided as an expression table or a GEO reference to a 'pathway fingerprint', a vector of discrete ternary scores representing high (1), low(-1) or insignificant (0) expression in a suite of pathways. biocViews: Transcription, GeneExpression, KEGG, Reactome Author: Gabriel Altschuler, Sokratis Kariotis Maintainer: Sokratis Kariotis source.ver: src/contrib/pathprint_1.10.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/pathprint_1.10.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pathprint_1.10.4.tgz vignettes: vignettes/pathprint/inst/doc/exampleFingerprint.pdf vignetteTitles: pathprint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathprint/inst/doc/exampleFingerprint.R Package: pathRender Version: 1.48.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: bc48da6f87ad2368355c6c1a79483b68 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org source.ver: src/contrib/pathRender_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pathRender_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pathRender_1.48.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R Package: pathVar Version: 1.10.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 05919baa51eed9e04635c05317b399e9 NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman source.ver: src/contrib/pathVar_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pathVar_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pathVar_1.10.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R Package: pathview Version: 1.20.0 Depends: R (>= 2.10), org.Hs.eg.db Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 8253bd9aa3925d18de431fd30d53b4df NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://pathview.uncc.edu/ source.ver: src/contrib/pathview_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pathview_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pathview_1.20.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: BioNetStat, EGSEA, MAGeCKFlute importsMe: CompGO, debrowser, EnrichmentBrowser, GDCRNATools, TCGAbiolinksGUI suggestsMe: clusterProfiler, gage, Pi, TCGAbiolinks Package: PathwaySplice Version: 1.4.0 Depends: R (>= 3.5.0) Imports: goseq, Biobase, DOSE, reshape2, igraph, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, AnnotationDbi, JunctionSeq, BiasedUrn, GO.db,gdata, geneLenDataBase, grDevices, graphics, stats, utils, VennDiagram, RColorBrewer, ensembldb, AnnotationHub, S4Vectors, dplyr, plotly, webshot, htmlwidgets , mgcv ,gridExtra, grid ,gplots, tibble , EnrichmentBrowser, annotate , KEGGREST Suggests: testthat, knitr, rmarkdown License: LGPL(>=2) MD5sum: fe2963fad429723cd93e5ce4a6a20dce NeedsCompilation: no Title: An R Package for Unbiased Splicing Pathway Analysis Description: Pathway analysis of alternative splicing would be biased without accounting for the different number of exons associated with each gene, because genes with higher number of exons are more likely to be included in the 'significant' gene list in alternative splicing. PathwaySplice is an R package that: (1) performs pathway analysis that explicitly adjusts for the number of exons associated with each gene (2) visualizes selection bias due to different number of exons for each gene (3) formally tests for presence of bias using logistic regression (4) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets (5) identifies the significant genes driving pathway significance (6) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph biocViews: AlternativeSplicing, DifferentialSplicing, GeneSetEnrichment, GO, RNASeq, Sequencing, Software, Visualization, NetworkEnrichment, Network, Pathways, GraphAndNetwork, Regression Author: Aimin Yan, Xi Chen, Lily Wang Maintainer: Aimin Yan VignetteBuilder: knitr source.ver: src/contrib/PathwaySplice_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PathwaySplice_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PathwaySplice_1.4.0.tgz vignettes: vignettes/PathwaySplice/inst/doc/tutorial.html vignetteTitles: PathwaySplice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathwaySplice/inst/doc/tutorial.R Package: paxtoolsr Version: 1.14.0 Depends: R (>= 3.2), rJava (>= 0.9-8), XML Imports: httr, igraph, plyr, rjson, R.utils, data.table, jsonlite Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma, foreach, doSNOW, parallel, org.Hs.eg.db License: LGPL-3 MD5sum: c575f5d3316e002fb133c4cc45b36cda NeedsCompilation: no Title: PaxtoolsR: Access Pathways from Multiple Databases through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr source.ver: src/contrib/paxtoolsr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/paxtoolsr_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/paxtoolsr_1.14.0.tgz vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R Package: Pbase Version: 0.20.0 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings (>= 2.47.5), IRanges (>= 2.13.11), S4Vectors (>= 0.17.24), mzID, mzR (>= 1.99.1), MSnbase (>= 1.15.5), Pviz, biomaRt, GenomicRanges (>= 1.31.7), rtracklayer (>= 1.39.6), ensembldb (>= 1.99.13), BiocParallel, AnnotationFilter Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationHub, knitr, rmarkdown, BiocStyle, EnsDb.Hsapiens.v86 (>= 2.0.0) License: GPL-3 MD5sum: b1afd3e66d39a616bd522ce0ce8188be NeedsCompilation: no Title: Manipulating and exploring protein and proteomics data Description: A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment. biocViews: Infrastructure, Proteomics, MassSpectrometry, Visualization, DataImport, DataRepresentation Author: Laurent Gatto [aut], Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb , Laurent Gatto URL: https://github.com/ComputationalProteomicsUnit/Pbase VignetteBuilder: knitr BugReports: https://github.com/ComputationalProteomicsUnit/Pbase/issues source.ver: src/contrib/Pbase_0.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Pbase_0.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Pbase_0.20.0.tgz vignettes: vignettes/Pbase/inst/doc/Pbase-data.html vignetteTitles: Pbase data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/Pbase-data.R Package: pbcmc Version: 1.8.0 Depends: R (>= 3.4), genefu Imports: Biobase, BiocGenerics, BiocParallel (>= 1.3.13), parallel, reshape2, grid, utils, cowplot, methods, limma, ggplot2, gridExtra, grDevices, stats Suggests: breastCancerUPP, breastCancerNKI, breastCancerVDX, breastCancerTRANSBIG, breastCancerMAINZ, breastCancerUNT License: GPL (>=2) MD5sum: 501bc4148641f07279482374ebd08eee NeedsCompilation: no Title: Permutation-Based Confidence for Molecular Classification Description: The pbcmc package characterizes uncertainty assessment on gene expression classifiers, a. k. a. molecular signatures, based on a permutation test. In order to achieve this goal, synthetic simulated subjects are obtained by permutations of gene labels. Then, each synthetic subject is tested against the corresponding subtype classifier to build the null distribution. Thus, classification confidence measurement can be provided for each subject, to assist physician therapy choice. At present, it is only available for PAM50 implementation in genefu package but it can easily be extend to other molecular signatures. biocViews: Classification, GeneExpression, Microarray, MultipleComparison, QualityControl, Normalization, Clustering, mRNAMicroarray, OneChannel, TwoChannel, RNASeq, KEGG, DifferentialExpression Author: Cristobal Fresno, German A. Gonzalez, Andrea S. Llera and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/ source.ver: src/contrib/pbcmc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pbcmc_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pbcmc_1.8.0.tgz vignettes: vignettes/pbcmc/inst/doc/pbcmc-vignette.pdf vignetteTitles: PermutationBased Confidence for Molecular Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pbcmc/inst/doc/pbcmc-vignette.R suggestsMe: MIGSA Package: pcaExplorer Version: 2.6.0 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), d3heatmap, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, tidyr, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 3d0df14394e5f0343144548c4735600b NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues source.ver: src/contrib/pcaExplorer_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pcaExplorer_2.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pcaExplorer_2.6.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html vignetteTitles: pcaExplorer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R importsMe: ideal Package: pcaGoPromoter Version: 1.24.0 Depends: R (>= 2.14.0), ellipse, Biostrings Imports: AnnotationDbi Suggests: Rgraphviz, GO.db, hgu133plus2.db, mouse4302.db, rat2302.db, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, serumStimulation, parallel License: GPL (>= 2) MD5sum: bcf9722fbd71807cd3b01ba130912e3d NeedsCompilation: no Title: pcaGoPromoter is used to analyze DNA micro array data Description: This package contains functions to ease the analyses of DNA micro arrays. It utilizes principal component analysis as the initial multivariate analysis, followed by functional interpretation of the principal component dimensions with overrepresentation analysis for GO terms and regulatory interpretations using overrepresentation analysis of predicted transcription factor binding sites with the primo algorithm. biocViews: GeneExpression, Microarray, GO , Visualization Author: Morten Hansen, Jorgen Olsen Maintainer: Morten Hansen source.ver: src/contrib/pcaGoPromoter_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pcaGoPromoter_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pcaGoPromoter_1.24.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.R Package: pcaMethods Version: 1.72.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) Archs: i386, x64 MD5sum: 9e4dd8e98c630176c9b3047192352033 NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues source.ver: src/contrib/pcaMethods_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pcaMethods_1.72.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pcaMethods_1.72.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq importsMe: CompGO, MSnbase, PanVizGenerator, scde, SomaticSignatures Package: PCAN Version: 1.8.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: a9f8a40668b5f97fe91f7fff075352d5 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr source.ver: src/contrib/PCAN_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PCAN_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PCAN_1.8.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R Package: pcot2 Version: 1.48.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: 418638e33404a9e47cdf210c05f5710c NeedsCompilation: no Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression, Network Author: Sarah Song, Mik Black Maintainer: Sarah Song source.ver: src/contrib/pcot2_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pcot2_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pcot2_1.48.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcot2/inst/doc/pcot2.R Package: PCpheno Version: 1.42.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 4d251784654b022dc88142940aebc226 NeedsCompilation: no Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/PCpheno_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PCpheno_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PCpheno_1.42.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCpheno/inst/doc/PCpheno.R Package: pcxn Version: 2.2.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE MD5sum: c7001fa5bce6540da373d911dee195d0 NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis source.ver: src/contrib/pcxn_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pcxn_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pcxn_2.2.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R Package: pdInfoBuilder Version: 1.44.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 Archs: i386, x64 MD5sum: 6d57eb90d8a50f2c77b9683e2e6563f0 NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho source.ver: src/contrib/pdInfoBuilder_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pdInfoBuilder_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pdInfoBuilder_1.44.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R Package: PECA Version: 1.16.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 5df775f5216b1078610460fcd2f6dd11 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi source.ver: src/contrib/PECA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PECA_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PECA_1.16.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R suggestsMe: SWATH2stats Package: pepStat Version: 1.14.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 8ff46f74244580e59177a23cfd9bb26d NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr source.ver: src/contrib/pepStat_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pepStat_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pepStat_1.14.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R Package: pepXMLTab Version: 1.14.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 43c3a771ff60490099c73bf2f5dee641 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/pepXMLTab_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pepXMLTab_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pepXMLTab_1.14.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R Package: perturbatr Version: 1.0.0 Depends: R (>= 3.5), methods, stats Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph, foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid, utils, graphics, scales, magrittr, formula.tools, rlang Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: e4f10507f265001a3b824432f38af1f1 NeedsCompilation: no Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens Description: perturbatr does stage-wise analysis of large-scale genetic perturbation screens for integrated data sets consisting of multiple screens. For multiple integrated perturbation screens a hierarchical model that considers the variance between different biological conditions is fitted. The resulting list of gene effects is then further extended using a network propagation algorithm to correct for false negatives. biocViews: Regression, CellBasedAssays, Network Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/cbg-ethz/perturbatr VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/perturbatr/issues source.ver: src/contrib/perturbatr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/perturbatr_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/perturbatr_1.0.0.tgz vignettes: vignettes/perturbatr/inst/doc/perturbatr.html vignetteTitles: perturbatr cookbook hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/perturbatr/inst/doc/perturbatr.R Package: PGA Version: 1.10.1 Depends: R (>= 3.0.1), IRanges, GenomicRanges, Biostrings (>= 2.26.3), data.table, rTANDEM Imports: S4Vectors (>= 0.9.25), Rsamtools (>= 1.10.2), GenomicFeatures (>= 1.19.8), biomaRt (>= 2.17.1), stringr, RCurl, Nozzle.R1, VariantAnnotation (>= 1.7.28), rtracklayer, RSQLite, ggplot2, AnnotationDbi, customProDB (>= 1.7.0), pheatmap Suggests: BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocGenerics, BiocStyle, knitr, R.utils License: GPL-2 MD5sum: 1e1ae78576cdf4edd269f15d2dcea2dc NeedsCompilation: no Title: An package for identification of novel peptides by customized database derived from RNA-Seq Description: This package provides functions for construction of customized protein databases based on RNA-Seq data with/without genome guided, database searching, post-processing and report generation. This kind of customized protein database includes both the reference database (such as Refseq or ENSEMBL) and the novel peptide sequences form RNA-Seq data. biocViews: Proteomics, MassSpectrometry, Software, ReportWriting, RNASeq, Sequencing Author: Shaohang Xu, Bo Wen Maintainer: Bo Wen , Shaohang Xu VignetteBuilder: knitr source.ver: src/contrib/PGA_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/PGA_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PGA_1.10.1.tgz vignettes: vignettes/PGA/inst/doc/PGA.pdf vignetteTitles: PGA tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGA/inst/doc/PGA.R Package: pgca Version: 1.4.0 Imports: utils, stats Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 2ff9cdd9091085c25aeac9e0110a883a NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr source.ver: src/contrib/pgca_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pgca_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pgca_1.4.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R Package: PGSEA Version: 1.54.0 Depends: R (>= 2.10), GO.db, KEGG.db, AnnotationDbi, annaffy, methods, Biobase (>= 2.5.5) Suggests: GSEABase, GEOquery, org.Hs.eg.db, hgu95av2.db, limma License: GPL-2 MD5sum: 5a1c41fd71e8e2a9ee4587bdbb529cf5 NeedsCompilation: no Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema source.ver: src/contrib/PGSEA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PGSEA_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PGSEA_1.54.0.tgz vignettes: vignettes/PGSEA/inst/doc/PGSEA.pdf, vignettes/PGSEA/inst/doc/PGSEA2.pdf vignetteTitles: HOWTO: PGSEA, HOWTO: PGSEA Example Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGSEA/inst/doc/PGSEA.R, vignettes/PGSEA/inst/doc/PGSEA2.R dependsOnMe: GeneExpressionSignature Package: phantasus Version: 1.0.2 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea, svglite Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table, svglite License: MIT + file LICENSE MD5sum: 6375a931e1251e1f8c8dc2e699772005 NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_7 git_last_commit: 0e0aa32 git_last_commit_date: 2018-07-22 Date/Publication: 2018-07-22 source.ver: src/contrib/phantasus_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/phantasus_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phantasus_1.0.2.tgz vignettes: vignettes/phantasus/inst/doc/developer-tutorial.html, vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Developing new tools, Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/developer-tutorial.R, vignettes/phantasus/inst/doc/phantasus-tutorial.R Package: PharmacoGx Version: 1.10.3 Depends: R (>= 3.4) Imports: Biobase, piano, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, lsa, reshape2 Suggests: xtable, testthat License: Artistic-2.0 MD5sum: 83f53979ff4870b9b5488a80fa889992 NeedsCompilation: no Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmacogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov, Zhaleh Safikhani, Mark Freeman, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_7 git_last_commit: 6a270be git_last_commit_date: 2018-07-25 Date/Publication: 2018-07-25 source.ver: src/contrib/PharmacoGx_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/PharmacoGx_1.10.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PharmacoGx_1.10.3.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf, vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf vignetteTitles: Creating a PharmacoSet object, PharmacoGx: an R package for analysis of large pharmacogenomic datasets hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R Package: phenoDist Version: 1.27.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: 1d96128d30dc68ce6d7bc7da4980248a NeedsCompilation: no Title: Phenotypic distance measures Description: PhenoDist is designed for measuring phenotypic distance in image-based high-throughput screening, in order to identify strong phenotypes and to group treatments into functional clusters. biocViews: CellBasedAssays Author: Xian Zhang, Gregoire Pau, Wolfgang Huber, Michael Boutros Maintainer: Xian Zhang URL: http://www.dkfz.de/signaling, http://www.embl.de/research/units/genome_biology/huber/ source.ver: src/contrib/phenoDist_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/phenoDist_1.27.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phenoDist_1.27.0.tgz vignettes: vignettes/phenoDist/inst/doc/phenoDist.pdf vignetteTitles: Introduction to phenoDist hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoDist/inst/doc/phenoDist.R Package: phenopath Version: 1.4.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) Archs: i386, x64 MD5sum: 2faae548a07d2073e3426f1eade83fd7 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr source.ver: src/contrib/phenopath_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/phenopath_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phenopath_1.4.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter Package: phenoTest Version: 1.28.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2 Imports: survival, limma, Hmisc, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, SNPchip, hgu133a.db, HTSanalyzeR, ellipse Suggests: GSEABase, KEGG.db, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: c9d6dadc716abb7aa04102ab7e59d490 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet source.ver: src/contrib/phenoTest_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/phenoTest_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phenoTest_1.28.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR Package: PhenStat Version: 2.17.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: cbbeeda703c6ee5fb35c8eb523cc9a51 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: RELEASE_3_7 git_last_commit: dcdc9c6 git_last_commit_date: 2018-09-07 Date/Publication: 2018-09-07 source.ver: src/contrib/PhenStat_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PhenStat_2.17.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PhenStat_2.17.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf, vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf vignetteTitles: PhenStat Vignette, PhenStatUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R Package: philr Version: 1.6.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet, dplyr License: GPL-3 MD5sum: f0de692b0040a09cae343bb7a606106c NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues source.ver: src/contrib/philr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/philr_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/philr_1.6.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R Package: phosphonormalizer Version: 1.4.0 Depends: R (>= 3.4.0) Imports: plyr, stats, graphics, matrixStats Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: c0c9833562516667cd2ebc93136d3166 NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in phosphoproteomics Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei, Tomi Suomi, Otto Kauko, Laura L. Elo Maintainer: Sohrab Saraei VignetteBuilder: knitr source.ver: src/contrib/phosphonormalizer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/phosphonormalizer_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phosphonormalizer_1.4.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R Package: phyloseq Version: 1.24.2 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: 1d42281f8a02b756c9925cc6f78d0801 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_7 git_last_commit: 829992f git_last_commit_date: 2018-07-15 Date/Publication: 2018-07-15 source.ver: src/contrib/phyloseq_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/phyloseq_1.24.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/phyloseq_1.24.2.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT importsMe: metavizr, PathoStat, RPA suggestsMe: decontam, philr Package: Pi Version: 1.8.0 Depends: XGR, igraph, dnet, ggplot2, graphics Imports: Matrix, MASS, ggbio, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, Gviz, lattice, caret, plot3D, stats Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ExpressionAtlas, ggforce, fgsea, pathview, tidyr License: GPL-3 MD5sum: 03808678f11c2c7a53faebbc8e8aeb87 NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system, with the focus on leveraging human genetic data to prioritise potential drug targets at the gene, pathway and network level. The long term goal is to use such information to enhance early-stage target validation. Based on evidence of disease association from genome-wide association studies (GWAS), this prioritisation system is able to generate evidence to support identification of the specific modulated genes (seed genes) that are responsible for the genetic association signal by utilising knowledge of linkage disequilibrium (co-inherited genetic variants), distance of associated variants from the gene, evidence of independent genetic association with gene expression in disease-relevant tissues, cell types and states, and evidence of physical interactions between disease-associated genetic variants and gene promoters based on genome-wide capture HiC-generated promoter interactomes in primary blood cell types. Seed genes are scored in an integrative way, quantifying the genetic influence. Scored seed genes are subsequently used as baits to rank seed genes plus additional (non-seed) genes; this is achieved by iteratively exploring the global connectivity of a gene interaction network. Genes with the highest priority are further used to identify/prioritise pathways that are significantly enriched with highly prioritised genes. Prioritised genes are also used to identify a gene network interconnecting highly prioritised genes and a minimal number of less prioritised genes (which act as linkers bringing together highly prioritised genes). biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues source.ver: src/contrib/Pi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Pi_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Pi_1.8.0.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R Package: piano Version: 1.20.1 Depends: R (>= 2.14.0) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi License: GPL (>=2) MD5sum: aabe227f8086c1632bed953b79203044 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo and Intawat Nookaew Maintainer: Leif Varemo URL: http://www.sysbio.se/piano BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_7 git_last_commit: 97a8e33 git_last_commit_date: 2018-09-04 Date/Publication: 2018-09-04 source.ver: src/contrib/piano_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/piano_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/piano_1.20.1.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf vignetteTitles: Piano - Platform for Integrative Analysis of Omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R importsMe: PharmacoGx Package: pickgene Version: 1.52.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 0a5622c084eb5eff0bc7c89ebede12b2 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen source.ver: src/contrib/pickgene_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pickgene_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pickgene_1.52.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PICS Version: 2.24.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.1.3) Imports: methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments, S4Vectors Suggests: ShortRead, rtracklayer, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: e057fba29d2faf1ba80dc3706dd422bf NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPSeq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud SystemRequirements: GSL (GNU Scientific Library) source.ver: src/contrib/PICS_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PICS_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PICS_2.24.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING Package: Pigengene Version: 1.6.0 Depends: R (>= 3.3.0), graph Imports: bnlearn, C50, MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8) Suggests: org.Hs.eg.db, org.Mm.eg.db, biomaRt (>= 2.30.0), knitr, BiocStyle, AnnotationDbi, energy License: GPL (>=2) MD5sum: b662f7e2d4c1f105fb2f435d09572d3c NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization Author: Habil Zare, Amir Foroushani, and Rupesh Agrahari Maintainer: Habil Zare VignetteBuilder: knitr source.ver: src/contrib/Pigengene_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Pigengene_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Pigengene_1.6.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R Package: PING Version: 2.24.0 Depends: R(>= 2.15.0), chipseq, IRanges, GenomicRanges Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: 62c2e28df4a51459b8cb3b80ea020ef3 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud source.ver: src/contrib/PING_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PING_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PING_2.24.0.tgz vignettes: vignettes/PING/inst/doc/PING-PE.pdf, vignettes/PING/inst/doc/PING.pdf vignetteTitles: Using PING with paired-end sequencing data, The PING users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PING/inst/doc/PING-PE.R, vignettes/PING/inst/doc/PING.R Package: pint Version: 1.30.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: b17840149685132c591188689775f14c NeedsCompilation: no Title: Pairwise INTegration of functional genomics data Description: Pairwise data integration for functional genomics, including tools for DNA/RNA/miRNA dependency screens. biocViews: aCGH, GeneExpression, Genetics, DifferentialExpression, Microarray Author: Olli-Pekka Huovilainen and Leo Lahti Maintainer: Olli-Pekka Huovilainen URL: https://github.com/antagomir/pint BugReports: https://github.com/antagomir/pint/issues source.ver: src/contrib/pint_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pint_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pint_1.30.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pint/inst/doc/depsearch.R Package: pkgDepTools Version: 1.46.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: cb25d6d8eb0328338be66581e40e237a NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Core Team [cre] Maintainer: Bioconductor Core Team source.ver: src/contrib/pkgDepTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pkgDepTools_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pkgDepTools_1.46.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R Package: plateCore Version: 1.38.0 Depends: R (>= 2.10), flowCore, flowViz, lattice, latticeExtra Imports: Biobase, flowCore, graphics, grDevices, lattice, MASS, methods, robustbase, stats, utils, flowStats Suggests: gplots License: Artistic-2.0 MD5sum: 9784a5531a21355450a36d2def9a252f NeedsCompilation: no Title: Statistical tools and data structures for plate-based flow cytometry Description: Provides basic S4 data structures and routines for analyzing plate based flow cytometry data. biocViews: FlowCytometry, Infrastructure, CellBasedAssays Author: Errol Strain, Florian Hahne, and Perry Haaland Maintainer: Errol Strain URL: http://www.bioconductor.org source.ver: src/contrib/plateCore_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plateCore_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plateCore_1.38.0.tgz vignettes: vignettes/plateCore/inst/doc/plateCoreVig.pdf vignetteTitles: An R Package for Analysis of High Throughput Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plateCore/inst/doc/plateCoreVig.R Package: plethy Version: 1.18.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: a7088a8ebd9388f4ffe69d8453894cbb NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly source.ver: src/contrib/plethy_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plethy_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plethy_1.18.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R Package: plgem Version: 1.52.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: 421ada8fd3b9df1261c527f804b97964 NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it source.ver: src/contrib/plgem_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plgem_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plgem_1.52.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R Package: plier Version: 1.50.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 20e6a82e158da83cf3ea6290d98169f0 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller source.ver: src/contrib/plier_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plier_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plier_1.50.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.40.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 762ce859c994218d9a5f5b8cc7102b83 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/PLPE_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PLPE_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PLPE_1.40.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R Package: plrs Version: 1.20.0 Depends: R (>= 2.10), Biobase Imports: BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils Suggests: mvtnorm, methods License: GPL (>=2.0) MD5sum: 220aa038225fcc71fc6c4a5666b9531e NeedsCompilation: no Title: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression Description: The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS). biocViews: Regression Author: Gwenael G.R. Leday Maintainer: Gwenael G.R. Leday to source.ver: src/contrib/plrs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plrs_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plrs_1.20.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plrs/inst/doc/plrs_vignette.R Package: plw Version: 1.40.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: e21648842202d2a7233f3fbb1e5a29f6 NeedsCompilation: yes Title: Probe level Locally moderated Weighted t-tests. Description: Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). biocViews: Microarray, OneChannel, TwoChannel, DifferentialExpression Author: Magnus Astrand Maintainer: Magnus Astrand source.ver: src/contrib/plw_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/plw_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plw_1.40.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plw/inst/doc/HowToPLW.R Package: plyranges Version: 1.0.3 Depends: R (>= 3.5), methods, BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: dplyr, rlang (>= 0.2.0), magrittr, tidyr, tidyselect, rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.17.41), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, ggplot2, HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr License: Artistic-2.0 MD5sum: 90e4f59a35572aa3b7bb04d6b1ae1175 NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut, cre], Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb] Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ source.ver: src/contrib/plyranges_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/plyranges_1.0.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/plyranges_1.0.3.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R Package: pmm Version: 1.12.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: c8c5f3ae3146ca018e367212347d6486 NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek source.ver: src/contrib/pmm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pmm_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pmm_1.12.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R Package: podkat Version: 1.12.0 Depends: R (>= 3.2.0), methods, Rsamtools, GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rsamtools Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: a97675da27cece8da5f7226680144a05 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ VignetteBuilder: knitr source.ver: src/contrib/podkat_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/podkat_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/podkat_1.12.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R Package: pogos Version: 1.0.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat License: Artistic-2.0 MD5sum: 03c563582f54df74f64dbe6a9b9fbe99 NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/pogos_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pogos_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pogos_1.0.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK Package: polyester Version: 1.16.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 5bbb96b9e31f624e8259ee0bee40b010 NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr source.ver: src/contrib/polyester_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/polyester_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/polyester_1.16.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R Package: Polyfit Version: 1.14.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: 35c39bde827ddbcd93d63a56c87f8def NeedsCompilation: no Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/Polyfit_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Polyfit_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Polyfit_1.14.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polyfit/inst/doc/polyfit.R Package: POST Version: 1.4.0 Depends: R (>= 3.4.0) Imports: stats, CompQuadForm, Matrix, survival, Biobase, GSEABase License: GPL (>= 2) MD5sum: b4fa3e3c58093ba5e6e127c503ce1903 NeedsCompilation: no Title: Projection onto Orthogonal Space Testing for High Dimensional Data Description: Perform orthogonal projection of high dimensional data of a set, and statistical modeling of phenotye with projected vectors as predictor. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao source.ver: src/contrib/POST_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/POST_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/POST_1.4.0.tgz vignettes: vignettes/POST/inst/doc/POST.pdf vignetteTitles: An introduction to POST hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POST/inst/doc/POST.R Package: PowerExplorer Version: 1.0.1 Depends: R (>= 3.5.0), SummarizedExperiment Imports: DESeq2, ROTS, vsn, stats, utils, methods, gridExtra, MASS, data.table, ggplot2, Biobase, S4Vectors, BiocParallel Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 7295d6e803f6275e815361a82da35a5b NeedsCompilation: no Title: Power Estimation Tool for RNA-Seq and proteomics data Description: Estimate and predict power among groups and multiple sample sizes with simulated data, the simulations are operated based on distribution parameters estimated from the provided input dataset. biocViews: RNASeq, Proteomics, DifferentialExpression, MultipleComparison, Sequencing, Coverage, ChIPSeq Author: Xu Qiao [aut, cre], Laura Elo [cph] Maintainer: Xu Qiao URL: https://gitlab.utu.fi/CompBioMedNGSTools/PowerExplorer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PowerExplorer git_branch: RELEASE_3_7 git_last_commit: 156566c git_last_commit_date: 2018-07-04 Date/Publication: 2018-07-04 source.ver: src/contrib/PowerExplorer_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/PowerExplorer_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PowerExplorer_1.0.1.tgz vignettes: vignettes/PowerExplorer/inst/doc/PowerExplore_vignette.pdf vignetteTitles: PowerExplorer Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PowerExplorer/inst/doc/PowerExplore_vignette.R Package: powerTCR Version: 1.0.0 Imports: cubature, evmix, magrittr, methods, purrr, stats, tcR, truncdist, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 27100f23cddfac979bc3c0ba544587bb NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr source.ver: src/contrib/powerTCR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/powerTCR_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/powerTCR_1.0.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R Package: PPInfer Version: 1.6.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Suggests: testthat License: Artistic-2.0 MD5sum: 762974ad6faabefdb14bfb0965771a63 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, Statistical Method, Network, Graph And Network, GeneSetEnrichment Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung source.ver: src/contrib/PPInfer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PPInfer_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PPInfer_1.6.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean Package: ppiStats Version: 1.46.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: d04799218fc11640a304147c17c57c84 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ppiStats_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ppiStats_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ppiStats_1.46.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML Package: pqsfinder Version: 1.8.0 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.62.0) Suggests: BiocStyle, knitr, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 583c964423e6e4eb06e6171aac1359d2 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: The main functionality of this package is to detect DNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, this package is able to detect sequences responsible for G4s folded from imperfect G-runs containing bulges or mismatches and as such is more sensitive than competing algorithms. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon SystemRequirements: GNU make, C++11 VignetteBuilder: knitr source.ver: src/contrib/pqsfinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pqsfinder_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pqsfinder_1.8.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R Package: prada Version: 1.56.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS2, tcltk License: LGPL Archs: i386, x64 MD5sum: 5d582f2b22488d38d217269001882a82 NeedsCompilation: yes Title: Data analysis for cell-based functional assays Description: Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy). biocViews: CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne source.ver: src/contrib/prada_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/prada_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/prada_1.56.0.tgz vignettes: vignettes/prada/inst/doc/norm2.pdf, vignettes/prada/inst/doc/prada2cellHTS.pdf vignetteTitles: Removal of contaminants from FACS data, Combining prada output and cellHTS2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prada/inst/doc/norm2.R, vignettes/prada/inst/doc/prada2cellHTS.R dependsOnMe: RNAither importsMe: cellHTS2 Package: prebs Version: 1.20.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: 62ed41c8929327754df80d873ab7ea92 NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela source.ver: src/contrib/prebs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/prebs_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/prebs_1.20.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R Package: PREDA Version: 1.26.1 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 1e499f9cf84ad9161da8b6deb05c61fd NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_7 git_last_commit: 4e91c57 git_last_commit_date: 2018-09-14 Date/Publication: 2018-09-18 source.ver: src/contrib/PREDA_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/PREDA_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PREDA_1.26.1.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R Package: predictionet Version: 1.26.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: a84b49396ff4305909611dbb0b699805 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg source.ver: src/contrib/predictionet_1.26.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/predictionet_1.26.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R Package: preprocessCore Version: 1.42.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 4c2bf925c511f83f7de3eab9c16ad2b3 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore source.ver: src/contrib/preprocessCore_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/preprocessCore_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/preprocessCore_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus importsMe: affy, bnbc, charm, cn.farms, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, INSPEcT, lumi, MADSEQ, MBCB, MEDIPS, mimager, minfi, MSnbase, MSstats, oligo, PECA, Pigengene, soGGi, waveTiling, yarn suggestsMe: multiClust linksToMe: affy, affyPLM, crlmm, oligo Package: Prize Version: 1.10.0 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: e7e5ccf6014e6fa7549fd29e12b304e9 NeedsCompilation: no Title: Prize: an R package for prioritization estimation based on analytic hierarchy process Description: The high throughput studies often produce large amounts of numerous genes and proteins of interest. While it is difficult to study and validate all of them. Analytic Hierarchy Process (AHP) offers a novel approach to narrowing down long lists of candidates by prioritizing them based on how well they meet the research goal. AHP is a mathematical technique for organizing and analyzing complex decisions where multiple criteria are involved. The technique structures problems into a hierarchy of elements, and helps to specify numerical weights representing the relative importance of each element. Numerical weight or priority derived from each element allows users to find alternatives that best suit their goal and their understanding of the problem. biocViews: Software, MultipleComparison, GeneExpression, CellBiology, RNASeq Author: Daryanaz Dargahi Maintainer: Daryanaz Dargahi source.ver: src/contrib/Prize_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Prize_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Prize_1.10.0.tgz vignettes: vignettes/Prize/inst/doc/Prize.pdf vignetteTitles: Prize: an R package for prioritization estimation based on analytic hierarchy process hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prize/inst/doc/Prize.R Package: proBAMr Version: 1.14.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 4e9809295be261ddb1371b119d1ef7e2 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/proBAMr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/proBAMr_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/proBAMr_1.14.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R Package: PROcess Version: 1.56.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 810daf53daea7148722fa1ffbd2790f2 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li source.ver: src/contrib/PROcess_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PROcess_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PROcess_1.56.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R Package: procoil Version: 2.8.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: d79bcaff2c5ded8ab960b655fe71f909 NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ VignetteBuilder: knitr source.ver: src/contrib/procoil_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/procoil_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/procoil_2.8.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R Package: ProCoNA Version: 1.18.0 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: f8a667e7441df87a450841491105534b NeedsCompilation: no Title: Protein co-expression network analysis (ProCoNA). Description: Protein co-expression network construction using peptide level data, with statisical analysis. (Journal of Clinical Bioinformatics 2013, 3:11 doi:10.1186/2043-9113-3-11) biocViews: GraphAndNetwork, Software, Proteomics Author: David L Gibbs Maintainer: David L Gibbs source.ver: src/contrib/ProCoNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ProCoNA_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ProCoNA_1.18.0.tgz vignettes: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.pdf vignetteTitles: De Novo Peptide Network Example hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.R Package: proFIA Version: 1.6.0 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL Archs: i386, x64 MD5sum: 9020d44d08d471df272800dae3f51963 NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr source.ver: src/contrib/proFIA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/proFIA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/proFIA_1.6.0.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R Package: profileScoreDist Version: 1.8.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: i386, x64 MD5sum: 20a8670d7df9fc6a1849377b74b67464 NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr source.ver: src/contrib/profileScoreDist_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/profileScoreDist_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/profileScoreDist_1.8.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R Package: progeny Version: 1.2.0 Depends: R (>= 3.4.0) Imports: Biobase Suggests: airway, biomaRt, BiocFileCache, broom, DESeq2, dplyr, knitr, readr, readxl License: Apache License (== 2.0) | file LICENSE MD5sum: 3ecf2dd252cbf51edd6a6b431f086500 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: This package provides a function to infer pathway activity from gene expression using PROGENy. It contains the linear model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression". biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert Maintainer: Michael Schubert URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues source.ver: src/contrib/progeny_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/progeny_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/progeny_1.2.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: narray Usage Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R Package: pRoloc Version: 1.20.2 Depends: R (>= 2.15), MSnbase (>= 1.19.20), MLInterfaces (>= 1.37.1), methods, Rcpp (>= 0.10.3), BiocParallel Imports: Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, synapter, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals License: GPL-2 Archs: i386, x64 MD5sum: f94f4c3fef1ae3fba076861386216596 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_7 git_last_commit: dfbe646 git_last_commit_date: 2018-09-24 Date/Publication: 2018-09-24 source.ver: src/contrib/pRoloc_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/pRoloc_1.20.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pRoloc_1.20.2.tgz vignettes: vignettes/pRoloc/inst/doc/pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/pRoloc-ml.html, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.html, vignettes/pRoloc/inst/doc/pRoloc-tutorial.html vignetteTitles: Annotating spatial proteomics data, Machine learning techniques available in pRoloc, A transfer learning algorithm for spatial proteomics, Using pRoloc for spatial proteomics data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/pRoloc-ml.R, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.R, vignettes/pRoloc/inst/doc/pRoloc-tutorial.R dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.14.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.11.1), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2 Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 MD5sum: 85e956799eae8d36a44e250fbf58761e NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa M Breckels, Thomas Naake and Laurent Gatto Maintainer: Laurent Gatto , Lisa M Breckels URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues source.ver: src/contrib/pRolocGUI_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pRolocGUI_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pRolocGUI_1.14.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R Package: PROMISE Version: 1.32.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 3bbb7fb18b4a8aacf5b479abaed4b4de NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao source.ver: src/contrib/PROMISE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PROMISE_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PROMISE_1.32.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE Package: PROPER Version: 1.12.0 Depends: R (>= 2.10) Imports: edgeR Suggests: BiocStyle,DESeq,DSS,knitr License: GPL MD5sum: ce7ab013fe1cdf92dcc6b9a01d8737eb NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr source.ver: src/contrib/PROPER_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PROPER_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PROPER_1.12.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R Package: PROPS Version: 1.2.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: dafe59a7d1f2b9b1619cc8ecddc17d78 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr source.ver: src/contrib/PROPS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PROPS_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PROPS_1.2.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R Package: Prostar Version: 1.12.18 Depends: R (>= 3.5) Imports: DAPAR (>= 1.12.10), DAPARdata (>= 1.10.2), rhandsontable, data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, R.utils, shinythemes, XML,later Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 02612737c39dab027fc2f1afba45554a NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: MassSpectrometry, Normalization, Preprocessing, R.utils Proteomics,GO, GUI Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_7 git_last_commit: 7724921 git_last_commit_date: 2018-08-26 Date/Publication: 2018-08-26 source.ver: src/contrib/Prostar_1.12.18.tar.gz win.binary.ver: bin/windows/contrib/3.5/Prostar_1.12.18.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Prostar_1.12.18.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R suggestsMe: DAPAR Package: prot2D Version: 1.18.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: b187054ef2077cf953eddbe12c7bbace NeedsCompilation: no Title: Statistical Tools for volume data from 2D Gel Electrophoresis Description: The purpose of this package is to analyze (i.e. Normalize and select significant spots) data issued from 2D GEl experiments biocViews: DifferentialExpression, MultipleComparison, Proteomics Author: Sebastien Artigaud Maintainer: Sebastien Artigaud source.ver: src/contrib/prot2D_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/prot2D_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/prot2D_1.18.0.tgz vignettes: vignettes/prot2D/inst/doc/prot2D.pdf vignetteTitles: prot2D hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prot2D/inst/doc/prot2D.R Package: proteinProfiles Version: 1.20.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 043d96da6b4034a4bb320f1c98d372f7 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring source.ver: src/contrib/proteinProfiles_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/proteinProfiles_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/proteinProfiles_1.20.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R Package: ProteomicsAnnotationHubData Version: 1.10.0 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocInstaller, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 4496dddd7af3da78ad580a0fae4c8bf3 NeedsCompilation: no Title: Transform public proteomics data resources into Bioconductor Data Structures Description: These recipes convert a variety and a growing number of public proteomics data sets into easily-used standard Bioconductor data structures. biocViews: DataImport, Proteomics Author: Gatto Laurent [aut, cre], Sonali Arora [aut] Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProteomicsAnnotationHubData VignetteBuilder: knitr BugReports: https://github.com/lgatto/ProteomicsAnnotationHubData/issues source.ver: src/contrib/ProteomicsAnnotationHubData_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ProteomicsAnnotationHubData_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ProteomicsAnnotationHubData_1.10.0.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html vignetteTitles: Proteomics Data in Annotation Hub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R Package: proteoQC Version: 1.16.0 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, rpx, tidyr, dplyr, plotly, rmarkdown, Suggests: RforProteomics, knitr, BiocStyle, R.utils, RUnit,BiocGenerics License: LGPL-2 MD5sum: 575d3838d00bfaa36951e870ee42db72 NeedsCompilation: no Title: An R package for proteomics data quality control Description: This package creates an HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. biocViews: Proteomics, MassSpectrometry, QualityControl, Visualization, ReportWriting Author: Bo Wen , Laurent Gatto Maintainer: Bo Wen URL: https://github.com/wenbostar/proteoQC VignetteBuilder: knitr source.ver: src/contrib/proteoQC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/proteoQC_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/proteoQC_1.16.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.html vignetteTitles: 00 proteoQC introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R Package: ProtGenerics Version: 1.12.0 Depends: methods License: Artistic-2.0 MD5sum: 43517bebdaaa914f08cf551c803b9706 NeedsCompilation: no Title: S4 generic functions for Bioconductor proteomics infrastructure Description: S4 generic functions needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProtGenerics source.ver: src/contrib/ProtGenerics_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ProtGenerics_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ProtGenerics_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, tofsims, topdownr importsMe: ensembldb, MSGFplus, MSnID, mzID, mzR, xcms Package: PSEA Version: 1.14.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: ee3826f2c5de9fab6794584df3396357 NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/PSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PSEA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PSEA_1.14.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R Package: psichomics Version: 1.6.2 Depends: R (>= 3.5), shiny (>= 1.0.3), shinyBS Imports: AnnotationHub, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, miscTools, pairsD3, plyr, Rcpp (>= 0.12.14), recount, R.utils, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi License: MIT + file LICENSE Archs: i386, x64 MD5sum: 5bb9a3b2599b9f01c953804c53a89953 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre], Nuno Luís Barbosa-Morais [aut, led, ths], André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://github.com/nuno-agostinho/psichomics VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_7 git_last_commit: 670cb8a git_last_commit_date: 2018-10-02 Date/Publication: 2018-10-02 source.ver: src/contrib/psichomics_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/psichomics_1.6.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/psichomics_1.6.2.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom-data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing alternative splicing annotations, Case study: command-line interface (CLI), SRA and user-provided RNA-seq data analysis, Case study: visual interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom-data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R Package: PSICQUIC Version: 1.18.1 Depends: R (>= 3.2.2), methods, IRanges, biomaRt (>= 2.34.1), BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 9caf80cc8bd457906baf9bf6ba4cf37f NeedsCompilation: no Title: Proteomics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_7 git_last_commit: 0456d6a git_last_commit_date: 2018-07-09 Date/Publication: 2018-07-09 source.ver: src/contrib/PSICQUIC_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/PSICQUIC_1.18.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PSICQUIC_1.18.1.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependsOnMe: RefNet Package: psygenet2r Version: 1.12.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, BiocInstaller, Biobase, labeling, GO.db Suggests: testthat, knitr License: MIT + file LICENSE MD5sum: 29e0c80ae875c6e7d8eadb93ef849fbb NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr source.ver: src/contrib/psygenet2r_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/psygenet2r_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/psygenet2r_1.12.0.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R importsMe: rexposome Package: puma Version: 3.22.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL Archs: i386, x64 MD5sum: 1ec00412f245afa5082d374add71a115 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma source.ver: src/contrib/puma_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/puma_3.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/puma_3.22.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre Package: PureCN Version: 1.10.0 Depends: R (>= 3.3), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, rhdf5, Matrix Suggests: PSCBS, BiocStyle, BiocParallel, knitr, optparse, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: b6ff96b121769d1801518adf819c5a44 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage Author: Markus Riester [aut, cre], Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr source.ver: src/contrib/PureCN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PureCN_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PureCN_1.10.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.pdf vignetteTitles: PureCN overview, Quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R Package: pvac Version: 1.28.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: f2402111e9fc04bf2c68addfb291dfd6 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel source.ver: src/contrib/pvac_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pvac_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pvac_1.28.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R Package: pvca Version: 1.20.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 901a8da33cbedadb39b763f6d67e40c3 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI source.ver: src/contrib/pvca_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pvca_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pvca_1.20.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R Package: Pviz Version: 1.14.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: e3df6bb236480d8e9c1d292c94f7a16b NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr source.ver: src/contrib/Pviz_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Pviz_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Pviz_1.14.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R importsMe: Pbase suggestsMe: pepStat Package: PWMEnrich Version: 4.16.0 Depends: methods, grid, BiocGenerics, Biostrings, Imports: seqLogo, gdata, evd Suggests: MotifDb, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 20902f643f8023eaa744c84ed2d2afc6 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Robert Stojnic VignetteBuilder: knitr source.ver: src/contrib/PWMEnrich_4.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/PWMEnrich_4.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/PWMEnrich_4.16.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R suggestsMe: rTRM Package: pwOmics Version: 1.12.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: 08c5407d83f9d1a42d4bda6ea005770d NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte source.ver: src/contrib/pwOmics_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/pwOmics_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/pwOmics_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: qcmetrics Version: 1.18.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, Nozzle.R1, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, yaqcaffy, MAQCsubsetAFX, RforProteomics, AnnotationDbi, mzR, hgu133plus2cdf, BiocStyle License: GPL-2 MD5sum: 230663ca4e0b8876cc64344a97baec48 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues source.ver: src/contrib/qcmetrics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qcmetrics_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qcmetrics_1.18.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf, vignettes/qcmetrics/inst/doc/vig-index.html vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting, Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R, vignettes/qcmetrics/inst/doc/vig-index.R importsMe: MSstatsQC Package: QDNAseq Version: 1.16.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.50.2), R.utils (>= 2.3.0), Rsamtools (>= 1.20), BiocParallel (>= 1.6.6), Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.8), GenomeInfoDb (>= 1.6.0), future (>= 0.14.0), R.cache (>= 0.12.0) License: GPL MD5sum: eab5bee00b5e49ec1e5858609453e39d NeedsCompilation: no Title: Quantitative DNA sequencing for chromosomal aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues source.ver: src/contrib/QDNAseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/QDNAseq_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QDNAseq_1.16.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak importsMe: HiCcompare Package: qpcrNorm Version: 1.38.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 27d51051f8da4dc172217d135128d719 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar source.ver: src/contrib/qpcrNorm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qpcrNorm_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qpcrNorm_1.38.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R suggestsMe: EasyqpcR Package: qpgraph Version: 2.14.0 Depends: R (>= 3.4) Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: i386, x64 MD5sum: f094d0140b69b2d09bc63a3ab7f7e1b0 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues source.ver: src/contrib/qpgraph_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qpgraph_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qpgraph_2.14.0.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper Package: qrqc Version: 1.34.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, Rsamtools (>= 1.19.38), testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rsamtools License: GPL (>=2) Archs: i386, x64 MD5sum: 08450b418a90a27be56bca70bf7ce3fa NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc source.ver: src/contrib/qrqc_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qrqc_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qrqc_1.34.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R Package: qsea Version: 1.6.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) Archs: i386, x64 MD5sum: dbb23b9c7536782cc8b8fe4193bf6d4f NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr source.ver: src/contrib/qsea_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qsea_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qsea_1.6.0.tgz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R Package: QUALIFIER Version: 1.24.1 Depends: R (>= 2.14.0),flowCore,flowViz,ncdfFlow,flowWorkspace, data.table,reshape Imports: MASS,hwriter,lattice,stats4,flowCore,flowViz,methods,flowWorkspace,latticeExtra,grDevices,tools, Biobase,XML,grid Suggests: RSVGTipsDevice, knitr License: Artistic-2.0 MD5sum: 4ab90044029c06392ac7e368cad2abdf NeedsCompilation: no Title: Quality Control of Gated Flow Cytometry Experiments Description: Provides quality control and quality assessment tools for gated flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Mike Jiang,Greg Finak,Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/QUALIFIER_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/QUALIFIER_1.24.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QUALIFIER_1.24.1.tgz vignettes: vignettes/QUALIFIER/inst/doc/QUALIFIER.html vignetteTitles: Quick plot for cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QUALIFIER/inst/doc/QUALIFIER.R Package: quantro Version: 1.14.0 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: 96ee54f3c42991078840097435fa669d NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks and Rafael Irizarry Maintainer: Stephanie Hicks VignetteBuilder: knitr source.ver: src/contrib/quantro_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/quantro_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/quantro_1.14.0.tgz vignettes: vignettes/quantro/inst/doc/quantro-vignette.pdf vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro-vignette.R importsMe: yarn Package: quantsmooth Version: 1.46.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 8796c0214ade8cbe07b75b8364561be6 NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting source.ver: src/contrib/quantsmooth_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/quantsmooth_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/quantsmooth_1.46.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA Package: QuartPAC Version: 1.12.1 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 0bf52f723907d6a7c7fabfc29e1871e4 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: RELEASE_3_7 git_last_commit: 1396f49 git_last_commit_date: 2018-08-20 Date/Publication: 2018-08-21 source.ver: src/contrib/QuartPAC_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/QuartPAC_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QuartPAC_1.12.1.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R Package: QuasR Version: 1.20.0 Depends: parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, grDevices, graphics, utils, zlibbioc, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, BiocInstaller, Biobase, Biostrings, BSgenome, Rsamtools (>= 1.19.38), GenomicFeatures (>= 1.17.13), ShortRead (>= 1.19.1), GenomicAlignments, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles LinkingTo: Rsamtools Suggests: Gviz, RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 790411ca7940ad3f5d25dbaf7b815448 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl Author: Anita Lerch, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler VignetteBuilder: knitr source.ver: src/contrib/QuasR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/QuasR_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QuasR_1.20.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R Package: QuaternaryProd Version: 1.14.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) Archs: i386, x64 MD5sum: 9312cdf21382533884190ae19d52d3b1 NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr source.ver: src/contrib/QuaternaryProd_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/QuaternaryProd_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QuaternaryProd_1.14.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R Package: QUBIC Version: 1.8.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE Archs: i386, x64 MD5sum: 7c700b7cc32ecf235b3bec63aa67c9d8 NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues source.ver: src/contrib/QUBIC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/QUBIC_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/QUBIC_1.8.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R suggestsMe: runibic Package: qusage Version: 2.14.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, lsmeans License: GPL (>= 2) MD5sum: 238df5352a01c6be41cb02f373e77fed NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage source.ver: src/contrib/qusage_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qusage_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qusage_2.14.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R suggestsMe: SigCheck Package: qvalue Version: 2.12.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: 84d0710268ed6b024bb68b068dd9f4b2 NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey with contributions from Andrew J. Bass, Alan Dabney and David Robinson Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr source.ver: src/contrib/qvalue_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/qvalue_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/qvalue_2.12.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, CancerMutationAnalysis, DEGseq, DrugVsDisease, metaseqR, r3Cseq, SSPA, webbioc importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge, erccdashboard, methylKit, msmsTests, MWASTools, netresponse, normr, OPWeight, Rnits, SDAMS, sights, sRAP, subSeq, synapter, trigger, webbioc suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark Package: R3CPET Version: 1.12.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, clues, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) Archs: i386, x64 MD5sum: 97ad1df65f138c30be6d8a1f1ecd0778 NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues source.ver: src/contrib/R3CPET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/R3CPET_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/R3CPET_1.12.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R Package: r3Cseq Version: 1.26.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 MD5sum: 5c46aae01411f7ebec1f6eb9c04c95e5 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is an implementation of data analysis for the long-range interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea URL: http://r3cseq.genereg.net source.ver: src/contrib/r3Cseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/r3Cseq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/r3Cseq_1.26.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R Package: R453Plus1Toolbox Version: 1.30.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: d569d944ed95b458354396e3daaeddd9 NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein source.ver: src/contrib/R453Plus1Toolbox_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/R453Plus1Toolbox_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/R453Plus1Toolbox_1.30.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R Package: R4RNA Version: 1.8.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: 11a6227131e9cb1da3d5d7b2e3f703dc NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ source.ver: src/contrib/R4RNA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/R4RNA_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/R4RNA_1.8.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R Package: RaggedExperiment Version: 1.4.0 Depends: R (>= 3.4.0), GenomicRanges Imports: BiocGenerics, IRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: knitr, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: a2b041e5a6b463fc8ab12b5b167253b2 NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut, cre], Marcel Ramos [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues source.ver: src/contrib/RaggedExperiment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RaggedExperiment_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RaggedExperiment_1.4.0.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: TxRegInfra importsMe: omicsPrint, RTCGAToolbox suggestsMe: MultiAssayExperiment, MultiDataSet Package: rain Version: 1.14.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 9002494dd1b05ec95d9554948f557e12 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben source.ver: src/contrib/rain_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rain_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rain_1.14.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R Package: rama Version: 1.54.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 52a6849b4ccea85952e0a6a658888863 NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/rama_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rama_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rama_1.54.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge Package: RamiGO Version: 1.28.0 Depends: gsubfn,methods Imports: igraph,RCurl,png,graph License: Artistic-2.0 MD5sum: 50ef76afb41b80fc6d5616c3cbc5dafd NeedsCompilation: no Title: AmiGO visualize R interface Description: R interface sending requests to AmiGO visualize, retrieving DAG GO trees, parsing GraphViz DOT format files and exporting GML files for Cytoscape. Deprecated:Also uses RCytoscape to interactively display AmiGO trees in Cytoscape. biocViews: GO, Visualization, GraphAndNetwork, Classification, ThirdPartyClient Author: Markus Schroeder, Daniel Gusenleitner, John Quackenbush, Aedin Culhane, Benjamin Haibe-Kains Maintainer: Markus Schroeder source.ver: src/contrib/RamiGO_1.28.0.tar.gz vignettes: vignettes/RamiGO/inst/doc/RamiGO.pdf vignetteTitles: RamiGO: An Introduction (HowTo) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RamiGO/inst/doc/RamiGO.R Package: ramwas Version: 1.4.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 Archs: i386, x64 MD5sum: 7b7e1761ce2311067c8fa179113bf8c7 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues source.ver: src/contrib/ramwas_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ramwas_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ramwas_1.4.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R Package: RandomWalkRestartMH Version: 1.0.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: b68d18766a07a07d3ab6e97fb78d2527 NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks". https://www.biorxiv.org/content/early/2017/08/30/134734 . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas Urbelz Maintainer: Alberto Valdeolivas Urbelz URL: https://www.biorxiv.org/content/early/2017/08/30/134734 source.ver: src/contrib/RandomWalkRestartMH_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RandomWalkRestartMH_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RandomWalkRestartMH_1.0.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.pdf vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.R Package: randPack Version: 1.26.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 0caaae6002e8617c60d995d69fca2f15 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman source.ver: src/contrib/randPack_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/randPack_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/randPack_1.26.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R Package: RankProd Version: 3.6.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: c49884102aaa04a79b75b7797433f565 NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore source.ver: src/contrib/RankProd_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RankProd_3.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RankProd_3.6.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: RNAither, tRanslatome importsMe: HTSanalyzeR, synlet Package: RareVariantVis Version: 2.8.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: a66475c9150a7b86f3a96ced197463fa NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr source.ver: src/contrib/RareVariantVis_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RareVariantVis_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RareVariantVis_2.8.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R Package: Rariant Version: 1.16.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, VGAM, dplyr, reshape2 Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 0e2572e6ad3e2dc9a676d12196f4ddf2 NeedsCompilation: no Title: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies Description: The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples. biocViews: Sequencing, StatisticalMethod, GenomicVariation, SomaticMutation, VariantDetection, Visualization Author: Julian Gehring, Simon Anders, Bernd Klaus Maintainer: Julian Gehring URL: https://github.com/juliangehring/Rariant VignetteBuilder: knitr BugReports: https://support.bioconductor.org source.ver: src/contrib/Rariant_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rariant_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rariant_1.16.0.tgz vignettes: vignettes/Rariant/inst/doc/Rariant-vignette.html vignetteTitles: Rariant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R Package: RbcBook1 Version: 1.48.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 1ed4e3e8ebebe5e1736f3e38112027dc NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey source.ver: src/contrib/RbcBook1_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RbcBook1_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RbcBook1_1.48.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R Package: RBGL Version: 1.56.0 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 45276ff3f44dbd2d13187cbea8c769d2 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org source.ver: src/contrib/RBGL_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RBGL_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RBGL_1.56.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, joda, pkgDepTools, RpsiXML importsMe: alpine, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowClust, flowWorkspace, GeneAnswers, GOSim, GOstats, MIGSA, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, signet, Streamer, VariantFiltering suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools Package: RBioinf Version: 1.40.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 02a173e622915a06baf016b35c67f256 NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman source.ver: src/contrib/RBioinf_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RBioinf_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RBioinf_1.40.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R Package: rBiopaxParser Version: 2.20.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL, igraph License: GPL (>= 2) MD5sum: 5e879f6367717f4d59125fd041caf8fd NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser source.ver: src/contrib/rBiopaxParser_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rBiopaxParser_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rBiopaxParser_2.20.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R importsMe: AnnotationHubData, pwOmics suggestsMe: AnnotationHub, NetPathMiner Package: RBM Version: 1.12.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: a5690e01d87821cf5041f4be467ae395 NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li source.ver: src/contrib/RBM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RBM_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RBM_1.12.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R Package: Rbowtie Version: 1.20.0 Suggests: parallel, BiocStyle, knitr, rmarkdown License: Artistic-1.0 | file LICENSE Archs: x64 MD5sum: 7e5ba2337516de483e9b3fd2db672129 NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/Rbowtie_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rbowtie_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rbowtie_1.20.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: MACPET Package: Rbowtie2 Version: 1.2.0 Depends: R (>= 3.5) Suggests: knitr License: GPL (>= 3) Archs: x64 MD5sum: 2dee257666fb04b5af7fcea6d60a8959 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/Rbowtie2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rbowtie2_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rbowtie2_1.2.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: esATAC Package: rbsurv Version: 2.38.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 35a0589509bb280b131599b5d91df2cc NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/rbsurv_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rbsurv_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rbsurv_2.38.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R Package: Rcade Version: 1.22.0 Depends: R (>= 2.14.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors, IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 4bc865a23cdb0f55e23d5fdc7e439869 NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns source.ver: src/contrib/Rcade_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rcade_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rcade_1.22.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R Package: RCAS Version: 1.6.0 Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table, topGO, motifRG Imports: biomaRt, AnnotationDbi, GenomicRanges, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, org.Hs.eg.db, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, stats, plotrix, pbapply, RSQLite, proxy, DBI, pheatmap, ggplot2, cowplot, ggseqlogo, methods, utils Suggests: BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Celegans.UCSC.ce10, BSgenome.Dmelanogaster.UCSC.dm3, org.Mm.eg.db, org.Ce.eg.db, org.Dm.eg.db, testthat, covr License: Artistic-2.0 MD5sum: a306e660b378a55037ab8eeb1f478969 NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an automated system that provides dynamic genome annotations for custom input files that contain transcriptomic regions. Such transcriptomic regions could be, for instance, peak regions detected by CLIP-Seq analysis that detect protein-RNA interactions, RNA modifications (alias the epitranscriptome), CAGE-tag locations, or any other collection of target regions at the level of the transcriptome. RCAS is designed as a reporting tool for the functional analysis of RNA-binding sites detected by high-throughput experiments. It takes as input a BED format file containing the genomic coordinates of the RNA binding sites and a GTF file that contains the genomic annotation features usually provided by publicly available databases such as Ensembl and UCSC. RCAS performs overlap operations between the genomic coordinates of the RNA binding sites and the genomic annotation features and produces in-depth annotation summaries such as the distribution of binding sites with respect to gene features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions, and whole transcripts). Moreover, by detecting the collection of targeted transcripts, RCAS can carry out functional annotation tables for enriched gene sets (annotated by the Molecular Signatures Database) and GO terms. As one of the most important questions that arise during protein-RNA interaction analysis; RCAS has a module for detecting sequence motifs enriched in the targeted regions of the transcriptome. A full interactive report in HTML format can be generated that contains interactive figures and tables that are ready for publication purposes. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr source.ver: src/contrib/RCAS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RCAS_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RCAS_1.6.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R Package: RCASPAR Version: 1.26.0 License: GPL (>=3) MD5sum: b00278cd722d68c413f2010395d073ec NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali source.ver: src/contrib/RCASPAR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RCASPAR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RCASPAR_1.26.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R Package: rcellminer Version: 2.2.0 Depends: R (>= 3.2), Biobase, rcdk, fingerprint, rcellminerData Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, d3heatmap, glmnet, foreach, doSNOW, parallel License: LGPL-3 + file LICENSE MD5sum: c246485c7e010d1ec825a5191af92bda NeedsCompilation: no Title: rcellminer: Molecular Profiles and Drug Response for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr source.ver: src/contrib/rcellminer_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rcellminer_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rcellminer_2.2.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R Package: rCGH Version: 1.10.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 2b03c9aee2c8e1da4eefd60187d96332 NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr source.ver: src/contrib/rCGH_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rCGH_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rCGH_1.10.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R Package: Rchemcpp Version: 2.18.0 Depends: R (>= 2.15.0) Imports: Rcpp (>= 0.11.1), methods, ChemmineR LinkingTo: Rcpp Suggests: apcluster, kernlab License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 0670b7a1be78ca2b784c0c84ef17f2a5 NeedsCompilation: yes Title: Similarity measures for chemical compounds Description: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. biocViews: Bioinformatics, CellBasedAssays, Clustering, DataImport, Infrastructure, MicrotitrePlateAssay, Proteomics, Software, Visualization Author: Michael Mahr, Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/Rchemcpp SystemRequirements: GNU make source.ver: src/contrib/Rchemcpp_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rchemcpp_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rchemcpp_2.18.0.tgz vignettes: vignettes/Rchemcpp/inst/doc/Rchemcpp.pdf vignetteTitles: Rchemcpp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rchemcpp/inst/doc/Rchemcpp.R Package: RchyOptimyx Version: 2.20.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: b37df936d40c87bcb33487f9af281bc0 NeedsCompilation: yes Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour source.ver: src/contrib/RchyOptimyx_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RchyOptimyx_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RchyOptimyx_2.20.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.R Package: RcisTarget Version: 1.0.2 Depends: R (>= 3.4) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather, graphics, GSEABase, methods, R.utils, stats, SummarizedExperiment, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: dd8d209c078babdfc33927033c6af923 NeedsCompilation: no Title: RcisTarget: Identify transcription factor binding motifs enriched on a gene list Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues source.ver: src/contrib/RcisTarget_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/RcisTarget_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RcisTarget_1.0.2.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget.R Package: Rcpi Version: 1.16.2 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 | file LICENSE MD5sum: 23a698cc953e8e7967bb6d90bcbebf36 NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: Rcpi offers a molecular informatics toolkit with a comprehensive integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/road2stat/Rcpi VignetteBuilder: knitr BugReports: https://github.com/road2stat/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_7 git_last_commit: 8419b95 git_last_commit_date: 2018-07-15 Date/Publication: 2018-07-16 source.ver: src/contrib/Rcpi_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rcpi_1.16.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rcpi_1.16.2.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R Package: RCy3 Version: 2.0.88 Depends: R (>= 3.4) Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, igraph, stats, graph Suggests: RUnit, RColorBrewer, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f3dd4871e69be0ef07230b35fd4ec25f NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alexander Pico, Tanja Muetze, Georgi Kolishovski, Paul Shannon Maintainer: Alexander Pico , Tanja Muetze , Paul Shannon URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.6.0), CyREST (>= 3.6.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_7 git_last_commit: fa8a68d git_last_commit_date: 2018-08-10 Date/Publication: 2018-08-11 source.ver: src/contrib/RCy3_2.0.88.tar.gz win.binary.ver: bin/windows/contrib/3.5/RCy3_2.0.88.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RCy3_2.0.88.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, NCIgraph suggestsMe: graphite Package: RCyjs Version: 2.2.2 Depends: R (>= 3.4.2), BrowserViz (>= 2.0.0), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.4.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, RefNet License: MIT + file LICENSE MD5sum: 4c950a1078efd124944af5e4a288c2e4 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_7 git_last_commit: 694250d git_last_commit_date: 2018-07-03 Date/Publication: 2018-07-03 source.ver: src/contrib/RCyjs_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/RCyjs_2.2.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RCyjs_2.2.2.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.pdf vignetteTitles: RCyjs hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R Package: RDAVIDWebService Version: 1.18.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: c498f32d78e5d4d5d870d82eb661deec NeedsCompilation: no Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ source.ver: src/contrib/RDAVIDWebService_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RDAVIDWebService_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RDAVIDWebService_1.18.0.tgz vignettes: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.pdf vignetteTitles: RDAVIDWebService: a versatile R interface to DAVID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.R dependsOnMe: CompGO suggestsMe: FGNet, IntramiRExploreR Package: rDGIdb Version: 1.6.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 4d1839f46b01b9d4d10fdac81a5034ed NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (http://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Thomas Thurnherr VignetteBuilder: knitr source.ver: src/contrib/rDGIdb_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rDGIdb_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rDGIdb_1.6.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R Package: Rdisop Version: 1.40.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: c8569eead39a1a12f4eb80b6a76aa1b5 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new source.ver: src/contrib/Rdisop_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rdisop_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rdisop_1.40.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE suggestsMe: MSnbase Package: RDRToolbox Version: 1.30.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 30aff8161a756dc2418b76c82593cd78 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen source.ver: src/contrib/RDRToolbox_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RDRToolbox_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RDRToolbox_1.30.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R Package: ReactomePA Version: 1.24.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: b8a96464cf9a1ac5a73bcd61799d8cf3 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ReactomePA VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues source.ver: src/contrib/ReactomePA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ReactomePA_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ReactomePA_1.24.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R importsMe: bioCancer, LINC, miRsponge suggestsMe: ChIPseeker, CINdex, clusterProfiler Package: readat Version: 1.6.0 Depends: R (>= 3.4.0) Imports: assertive.base (>= 0.0-7), assertive.files (>= 0.0-2), assertive.numbers (>= 0.0-2), assertive.properties (>= 0.0-4), assertive.sets (>= 0.0-3), assertive.types (>= 0.0-3), Biobase (>= 2.34.0), data.table (>= 1.10.4), dplyr (>= 0.5.0), magrittr (>= 1.5), openxlsx (>= 4.0.17), pathological (>= 0.1-2), reshape2 (>= 1.4.2), stats, stringi (>= 1.1.5), SummarizedExperiment (>= 1.4.0), testthat (>= 1.0.2), tidyr (>= 0.6.2), utils Suggests: knitr, MSnbase, rmarkdown, withr License: GPL-3 MD5sum: f0f490e01a6ff5c45b16bd57cfbd224d NeedsCompilation: no Title: Functionality to Read and Manipulate SomaLogic ADAT files Description: This package contains functionality to import, transform and annotate data from ADAT files generated by the SomaLogic SOMAscan platform. biocViews: GeneExpression, DataImport, Proteomics, OneChannel, ProprietaryPlatforms Author: Richard Cotton [cre, aut], Aditya Bhagwat [aut] Maintainer: Richard Cotton URL: https://bitbucket.org/graumannlabtools/readat VignetteBuilder: knitr BugReports: https://bitbucket.org/graumannlabtools/readat/issues source.ver: src/contrib/readat_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/readat_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/readat_1.6.0.tgz vignettes: vignettes/readat/inst/doc/introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/readat/inst/doc/introduction.R Package: ReadqPCR Version: 1.26.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: 8ae5a9cc5229d5d5ce2bdef75ebf65eb NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html source.ver: src/contrib/ReadqPCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ReadqPCR_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ReadqPCR_1.26.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR Package: reb Version: 1.58.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: cf8864e3295bb395e1d1f5b2f90acf65 NeedsCompilation: yes Title: Regional Expression Biases Description: A set of functions to dentify regional expression biases biocViews: Microarray, CopyNumberVariation, Visualization Author: Kyle A. Furge and Karl Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/reb_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/reb_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/reb_1.58.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reb/inst/doc/reb.R Package: recount Version: 1.6.3 Depends: R (>= 3.3.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocStyle (>= 2.5.19), DESeq2, devtools (>= 1.6), EnsDb.Hsapiens.v79, GenomicFeatures, knitcitations, knitr (>= 1.6), org.Hs.eg.db, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat License: Artistic-2.0 MD5sum: 0004b6e66579ca900a8a81e9142e5da5 NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre], Abhinav Nellore [ctb], Andrew E. Jaffe [ctb], Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb], Kasper Daniel Hansen [ctb], Ben Langmead [ctb], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_7 git_last_commit: 9b98081 git_last_commit_date: 2018-07-28 Date/Publication: 2018-07-29 source.ver: src/contrib/recount_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/recount_1.6.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/recount_1.6.3.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics Package: recoup Version: 1.8.0 Depends: R (>= 2.13.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, circlize, graphics, grDevices, methods, rtracklayer, plyr, stats, utils Suggests: grid, GenomeInfoDb, Rsamtools, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocInstaller, BSgenome, RSQLite, RMySQL Enhances: parallel License: GPL (>= 3) MD5sum: 0d82f10e5244e24936a0bf1046861ec3 NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr source.ver: src/contrib/recoup_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/recoup_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/recoup_1.8.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R Package: RedeR Version: 1.28.0 Depends: R (>= 3.3.3), methods Imports: igraph Suggests: pvclust, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 8ee8619ca95313adf0ec61ecc7a95d98 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr source.ver: src/contrib/RedeR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RedeR_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RedeR_1.28.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical network representation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R importsMe: PANR, RTN, transcriptogramer Package: REDseq Version: 1.26.0 Depends: R (>= 2.15.0), BiocGenerics (>= 0.1.0), BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: BiocGenerics, AnnotationDbi, Biostrings, ChIPpeakAnno, graphics, IRanges (>= 1.13.5), multtest, stats, utils License: GPL (>=2) MD5sum: 3c6ef9611bf60ac314b199accdd79644 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu and Thomas Fazzio Maintainer: Lihua Julie Zhu source.ver: src/contrib/REDseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/REDseq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/REDseq_1.26.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R Package: RefNet Version: 1.16.0 Depends: R (>= 2.15.0), methods, IRanges, PSICQUIC, AnnotationHub, RCurl, shiny Imports: BiocGenerics Suggests: RUnit, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: f2c91ce6b71024360137245862c1d63c NeedsCompilation: no Title: A queryable collection of molecular interactions, from many sources Description: Molecular interactions with metadata, some archived, some dynamically obtained biocViews: GraphAndNetwork Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/RefNet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RefNet_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RefNet_1.16.0.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefNet/inst/doc/RefNet.R suggestsMe: RCyjs Package: RefPlus Version: 1.50.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 1ac154acd7a05ed21dab3a119070e3c2 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang source.ver: src/contrib/RefPlus_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RefPlus_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RefPlus_1.50.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R Package: regioneR Version: 1.12.0 Depends: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Imports: memoise, GenomicRanges, BSgenome, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, IRanges, S4Vectors Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 57dd0bad225fcac79afa10343c5ed390 NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr source.ver: src/contrib/regioneR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/regioneR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/regioneR_1.12.0.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.pdf vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR importsMe: annotatr, ChIPpeakAnno, karyoploteR Package: regionReport Version: 1.14.3 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.1.0), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment Suggests: biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.3.2), devtools (>= 1.6), DT, DESeq, edgeR, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: e9284e0d7776f459a75aa2c5d198cb72 NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_7 git_last_commit: ae8244e git_last_commit_date: 2018-07-24 Date/Publication: 2018-07-24 source.ver: src/contrib/regionReport_1.14.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/regionReport_1.14.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/regionReport_1.14.3.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/bumphunterExampleOutput.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Basic genomic regions exploration, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/bumphunterExampleOutput.R, vignettes/regionReport/inst/doc/regionReport.R suggestsMe: recount Package: regsplice Version: 1.6.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: c29f333b2ae8ffbbd2fa162537787106 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues source.ver: src/contrib/regsplice_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/regsplice_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/regsplice_1.6.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.pdf vignetteTitles: Example workflow for regsplice package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R Package: REMP Version: 1.4.1 Depends: R (>= 3.4), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 Imports: graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, org.Hs.eg.db, impute, iterators Suggests: knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: f3abc2965e7e9888b3bffaeccba6a5c2 NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, GenePrediction, Sequencing, Alignment, Epigenetics, Normalization, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_7 git_last_commit: 4dd31e1 git_last_commit_date: 2018-08-12 Date/Publication: 2018-08-12 source.ver: src/contrib/REMP_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/REMP_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/REMP_1.4.1.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R Package: Repitools Version: 1.26.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: bf057e512af04c2ba5777e7aa136d36e NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson source.ver: src/contrib/Repitools_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Repitools_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Repitools_1.26.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R Package: ReportingTools Version: 2.20.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 1fbaf0d8e50303503f40713d5ea86137 NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr source.ver: src/contrib/ReportingTools_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ReportingTools_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ReportingTools_2.20.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R importsMe: affycoretools, EnrichmentBrowser suggestsMe: cpvSNP, GSEABase, npGSEA Package: ReQON Version: 1.26.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: ab091f89c2ca74bfcfe38fc37380caf2 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 source.ver: src/contrib/ReQON_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ReQON_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ReQON_1.26.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R Package: restfulSE Version: 1.2.3 Depends: R (>= 3.5), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle License: Artistic-2.0 MD5sum: da737d9dcd504a2db0dd6b350c55d505 NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_7 git_last_commit: 55e4a84 git_last_commit_date: 2018-09-27 Date/Publication: 2018-09-27 source.ver: src/contrib/restfulSE_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/restfulSE_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/restfulSE_1.2.3.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn Package: rexposome Version: 1.2.0 Depends: R (>= 3.4), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, psygenet2r, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 414c218130249c3dce3635b42212d8b0 NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonz?lez [aut] Maintainer: Carles Hernandez-Ferrer VignetteBuilder: knitr source.ver: src/contrib/rexposome_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rexposome_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rexposome_1.2.0.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome Package: rfPred Version: 1.18.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: 6dd1098b4a524026dade73465d3f97c6 NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to be connected on the Internet or to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet URL: http://www.sbim.fr/rfPred source.ver: src/contrib/rfPred_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rfPred_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rfPred_1.18.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R Package: rGADEM Version: 2.28.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, IRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: i386, x64 MD5sum: 5c5a8db28760490d66c9a48e84d3cbe4 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit source.ver: src/contrib/rGADEM_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rGADEM_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rGADEM_2.28.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: MotIV Package: RGalaxy Version: 1.24.0 Depends: XML, methods, tools, optparse Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: 0cb219306abcfe7622e28e45e838ff5a NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/RGalaxy_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RGalaxy_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RGalaxy_1.24.0.tgz vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html vignetteTitles: Introduction to RGalaxy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R Package: Rgin Version: 1.0.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.3.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: a6d099e3c17bb2d6565a5e70054d9f62 NeedsCompilation: yes Title: gin in R Description: C++ implementation of SConES. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm [aut], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente VignetteBuilder: knitr source.ver: src/contrib/Rgin_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rgin_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rgin_1.0.0.tgz vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html vignetteTitles: Using Rgin C++ libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE linksToMe: martini Package: RGMQL Version: 1.0.2 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 3b0ec60d0cc3fc439b7037335255e484 NeedsCompilation: no Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, SingleCell Author: Simone Pallotta, Marco Masseroli Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: RELEASE_3_7 git_last_commit: 025f71e git_last_commit_date: 2018-08-25 Date/Publication: 2018-08-25 source.ver: src/contrib/RGMQL_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/RGMQL_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RGMQL_1.0.2.tgz vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.pdf vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R Package: RGraph2js Version: 1.8.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: a6c725676c3ad993f824fee8f424459d NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). source.ver: src/contrib/RGraph2js_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RGraph2js_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RGraph2js_1.8.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R Package: Rgraphviz Version: 2.24.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: 2abe5006c490e7825a51b50e359efe61 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) source.ver: src/contrib/Rgraphviz_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rgraphviz_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rgraphviz_2.24.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, gaucho, GOFunction, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE importsMe: apComplex, biocGraph, chimeraviz, CompGO, CytoML, DEGraph, EnrichmentBrowser, facopy, flowWorkspace, GeneNetworkBuilder, GOFunction, GOstats, hyperdraw, MIGSA, mirIntegrator, nem, OncoSimulR, paircompviz, pathview, Pigengene, qpgraph, RchyOptimyx, SplicingGraphs, trackViewer, TRONCO suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, KEGGgraph, MLP, NCIgraph, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, vtpnet Package: rGREAT Version: 1.12.1 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats Suggests: testthat (>= 0.3), knitr, circlize License: MIT + LICENSE MD5sum: 0e70c75582c2fe47fb15cb2850d8185c NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: RELEASE_3_7 git_last_commit: d2c2d1a git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/rGREAT_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/rGREAT_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rGREAT_1.12.1.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: Analyze with GREAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R Package: RGSEA Version: 1.14.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 649c71c83fdf8154ff745b3036114ae9 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr source.ver: src/contrib/RGSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RGSEA_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RGSEA_1.14.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R Package: rgsepd Version: 1.12.0 Depends: R (>= 3.5.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: c76be0a79fee91a04a102bbe69bdec75 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at RefSeq IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr source.ver: src/contrib/rgsepd_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rgsepd_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rgsepd_1.12.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R Package: rhdf5 Version: 2.24.0 Depends: methods Imports: Rhdf5lib LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 3bad1a2a67232ef739b46f56c557eb49 NeedsCompilation: yes Title: HDF5 interface to R Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Gregoire Pau [aut], Mike Smith [aut, cre], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/rhdf5_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rhdf5_2.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rhdf5_2.24.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, HDF5Array importsMe: beachmat, biomformat, diffHic, DOQTL, DropletUtils, h5vc, IONiseR, phantasus, PureCN, scater, scone suggestsMe: slalom, SummarizedExperiment, tximport Package: rhdf5client Version: 1.2.3 Depends: R (>= 3.5), methods, DelayedArray Imports: S4Vectors, httr, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, reticulate License: Artistic-2.0 MD5sum: e9cc1e6b75a7592f8fb5b8e377804714 NeedsCompilation: no Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [cre, aut], Shweta Gopaulakrishnan [aut], Vincent Carey [aut] Maintainer: Samuela Pollack VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_7 git_last_commit: 9163236 git_last_commit_date: 2018-08-18 Date/Publication: 2018-08-18 source.ver: src/contrib/rhdf5client_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/rhdf5client_1.2.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rhdf5client_1.2.3.tgz vignettes: vignettes/rhdf5client/inst/doc/h5client.html vignetteTitles: h5client -- notes on Bioconductor and remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/h5client.R importsMe: restfulSE Package: Rhdf5lib Version: 1.2.1 Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: af352e4c40101c3f1630e1303aec7615 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith Maintainer: Mike Smith SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib source.ver: src/contrib/Rhdf5lib_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rhdf5lib_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rhdf5lib_1.2.1.tgz vignettes: vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: beachmat, rhdf5 linksToMe: beachmat, DropletUtils, mzR, ncdfFlow, rhdf5, scater, scran Package: Rhtslib Version: 1.12.1 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: f564b7d3eab406942414ff8fd3339546 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.7 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/nhayden/Rhtslib, http://www.htslib.org/ VignetteBuilder: knitr BugReports: https://github.com/nhayden/Rhtslib source.ver: src/contrib/Rhtslib_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rhtslib_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rhtslib_1.12.1.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R dependsOnMe: deepSNV importsMe: csaw, deepSNV, diffHic, scPipe linksToMe: bamsignals, csaw, deepSNV, diffHic, methylKit, scPipe Package: rHVDM Version: 1.46.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: 7d65fca4d86189999a943c305dbcc9ce NeedsCompilation: no Title: Hidden Variable Dynamic Modeling Description: A R implementation of HVDM (Genome Biol 2006, V7(3) R25) biocViews: Microarray, GraphAndNetwork, Transcription, Classification, NetworkInference Author: Martino Barenco Maintainer: Martino Barenco source.ver: src/contrib/rHVDM_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rHVDM_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rHVDM_1.46.0.tgz vignettes: vignettes/rHVDM/inst/doc/rHVDM.pdf vignetteTitles: rHVDM primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rHVDM/inst/doc/rHVDM.R Package: RiboProfiling Version: 1.10.0 Depends: R (>= 3.2.2), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 MD5sum: 0d6a64f25ce6ff51379da2d5553cc322 NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr source.ver: src/contrib/RiboProfiling_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RiboProfiling_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RiboProfiling_1.10.0.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R Package: riboSeqR Version: 1.14.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: ea7a872e73f490e34e52a8417191fb60 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/riboSeqR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/riboSeqR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/riboSeqR_1.14.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R Package: RImmPort Version: 1.8.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 1cba70d0b91ec7ac57ecfdbe80aa030d NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr source.ver: src/contrib/RImmPort_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RImmPort_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RImmPort_1.8.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R Package: Ringo Version: 1.44.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 80ed44c880a6412121e84f301107effa NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/Ringo_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Ringo_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Ringo_1.44.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, Starr importsMe: Repitools Package: RIPSeeker Version: 1.20.0 Depends: R (>= 2.15), methods, S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: e86b9c232d210057f32665751060bbef NeedsCompilation: no Title: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments Description: Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation. biocViews: Sequencing, RIPSeq Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/RIPSeeker_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RIPSeeker_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RIPSeeker_1.20.0.tgz vignettes: vignettes/RIPSeeker/inst/doc/RIPSeeker.pdf vignetteTitles: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPSeeker/inst/doc/RIPSeeker.R Package: Risa Version: 1.22.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: 0bc365c85c5ce5cba7c51e062d440955 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues source.ver: src/contrib/Risa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Risa_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Risa_1.22.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R Package: RITAN Version: 1.4.2 Depends: R (>= 3.4), Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, sqldf, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata Suggests: rmarkdown License: file LICENSE MD5sum: 5a69536e139ab54043b1ace93de45e60 NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics Author: Michael Zimmermann Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_7 git_last_commit: 430ab20 git_last_commit_date: 2018-08-13 Date/Publication: 2018-08-22 source.ver: src/contrib/RITAN_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/RITAN_1.4.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RITAN_1.4.2.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R Package: RIVER Version: 1.4.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: 70999b65d557270f3e09194775942f9e NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues source.ver: src/contrib/RIVER_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RIVER_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RIVER_1.4.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R Package: RJMCMCNucleosomes Version: 1.4.0 Depends: R (>= 3.4), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors, BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 306977fdd2de13db189f345a4f3b754e NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues source.ver: src/contrib/RJMCMCNucleosomes_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RJMCMCNucleosomes_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RJMCMCNucleosomes_1.4.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R Package: RLMM Version: 1.42.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 630b207f1c48eab5d10c25fc535c663a NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) source.ver: src/contrib/RLMM_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RLMM_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RLMM_1.42.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R Package: Rmagpie Version: 1.36.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 14f729d52f9072ebae6755cada3c5c9c NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ source.ver: src/contrib/Rmagpie_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rmagpie_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rmagpie_1.36.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R Package: RMassBank Version: 2.8.0 Depends: Rcpp Imports: XML,RCurl,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, ontoCAT, RUnit, enviPat License: Artistic-2.0 MD5sum: 9d864c6238575171783f0fa7d5a9f96c NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel source.ver: src/contrib/RMassBank_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RMassBank_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RMassBank_2.8.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.pdf, vignettes/RMassBank/inst/doc/RMassBankNonstandard.pdf, vignettes/RMassBank/inst/doc/RMassBankXCMS.pdf vignetteTitles: RMassBank walkthrough, RMassBank non-standard usage, RMassBank using XCMS walkthrough hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R Package: rMAT Version: 3.30.0 Depends: R(>= 2.9.0), BiocGenerics (>= 0.1.3), IRanges (>= 1.13.10), Biobase (>= 2.15.1), affxparser Imports: stats, methods, BiocGenerics, IRanges, Biobase, affxparser, stats4 Suggests: GenomeGraphs, rtracklayer License: Artistic-2.0 MD5sum: c10600e7cb6d18e4abb780edb482c432 NeedsCompilation: yes Title: R implementation from MAT program to normalize and analyze tiling arrays and ChIP-chip data. Description: This package is an R version of the package MAT and contains functions to parse and merge Affymetrix BPMAP and CEL tiling array files (using C++ based Fusion SDK and Bioconductor package affxparser), normalize tiling arrays using sequence specific models, detect enriched regions from ChIP-chip experiments. Note: users should have GSL and GenomeGraphs installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Snow Leopard users can take advantage of increase speed with Grand Central Dispatch! biocViews: Microarray, Preprocessing Author: Charles Cheung and Arnaud Droit and Raphael Gottardo Maintainer: Arnaud Droit and Raphael Gottardo URL: http://www.rglab.org SystemRequirements: GSL (GNU Scientific Library) source.ver: src/contrib/rMAT_3.30.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rMAT_3.30.0.tgz vignettes: vignettes/rMAT/inst/doc/rMAT.pdf vignetteTitles: The rMAT users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rMAT/inst/doc/rMAT.R Package: RmiR Version: 1.36.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: db1a145446227acaf37f2215bccea6a9 NeedsCompilation: no Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero source.ver: src/contrib/RmiR_1.36.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RmiR_1.36.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R Package: RNAdecay Version: 1.0.2 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 0ce5d66e02f14494d7413486dad7e824 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: a function is provided to easily visualize the data and the selected model using ggplot2 package functions. biocViews: Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: RELEASE_3_7 git_last_commit: d48ca15 git_last_commit_date: 2018-07-02 Date/Publication: 2018-07-03 source.ver: src/contrib/RNAdecay_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/RNAdecay_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RNAdecay_1.0.2.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R Package: RNAinteract Version: 1.28.0 Depends: R (>= 2.12.0), abind, locfit, Biobase Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12) License: Artistic-2.0 MD5sum: 9bf15ca3750c0aefc6c6997dce297e61 NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer source.ver: src/contrib/RNAinteract_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RNAinteract_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RNAinteract_1.28.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R Package: RNAither Version: 2.28.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 7776300b58dd909dc217c93185ec96a7 NeedsCompilation: no Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali source.ver: src/contrib/RNAither_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RNAither_2.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RNAither_2.28.0.tgz vignettes: vignettes/RNAither/inst/doc/vignetteRNAither.pdf vignetteTitles: RNAither,, an automated pipeline for the statistical analysis of high-throughput RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAither/inst/doc/vignetteRNAither.R Package: RNAprobR Version: 1.12.0 Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1), BiocGenerics(>= 0.10.0) Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), IRanges(>= 2.10.5), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12), S4Vectors(>= 0.14.7), graphics, stats, utils Suggests: BiocStyle License: GPL (>=2) MD5sum: feeb8c722058640254c1dcb20c77c2ac NeedsCompilation: no Title: An R package for analysis of massive parallel sequencing based RNA structure probing data Description: This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file. biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation Author: Lukasz Jan Kielpinski [aut], Nikos Sidiropoulos [cre, aut], Jeppe Vinther [aut] Maintainer: Nikos Sidiropoulos source.ver: src/contrib/RNAprobR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RNAprobR_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RNAprobR_1.12.0.tgz vignettes: vignettes/RNAprobR/inst/doc/RNAprobR.pdf vignetteTitles: RNAprobR: An R package for analysis of the massive parallel sequencing based methods of RNA structure probing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAprobR/inst/doc/RNAprobR.R Package: rnaseqcomp Version: 1.10.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 4696ad10819bffa98fbaf86b17fa0ca7 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr source.ver: src/contrib/rnaseqcomp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rnaseqcomp_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rnaseqcomp_1.10.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.pdf vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R suggestsMe: SummarizedBenchmark Package: rnaSeqMap Version: 2.38.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: 2de65e0f50ca556f9878465aa3b1eed7 NeedsCompilation: yes Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/rnaSeqMap_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rnaSeqMap_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rnaSeqMap_2.38.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.R dependsOnMe: ampliQueso Package: RNASeqPower Version: 1.20.0 License: LGPL (>=2) MD5sum: 3a8fc592b3bbf44d0f2cc5e5d5408ad5 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau source.ver: src/contrib/RNASeqPower_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RNASeqPower_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RNASeqPower_1.20.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R Package: RnaSeqSampleSize Version: 1.12.0 Depends: R (>= 2.10), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: fc3f397874c77d1cc76edc472e06e8c9 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data biocViews: ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao, Chung-I Li, Yan Guo, Quanhu Sheng, Yu Shyr Maintainer: Shilin Zhao VignetteBuilder: knitr source.ver: src/contrib/RnaSeqSampleSize_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RnaSeqSampleSize_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RnaSeqSampleSize_1.12.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R Package: RnBeads Version: 1.12.1 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, argparse, glmnet, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit License: GPL-3 MD5sum: c1ff6c1de518811c144d3cffc26008e9 NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, TwoChannel, DataImport Author: Yassen Assenov [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller source.ver: src/contrib/RnBeads_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/RnBeads_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RnBeads_1.12.1.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R Package: Rnits Version: 1.14.0 Depends: R (>= 3.1.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: dd6fe61272b4715aa0c471dbef08b05a NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr source.ver: src/contrib/Rnits_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rnits_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rnits_1.14.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R Package: roar Version: 1.16.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: 59209c852a278dc117ac9be9424a17c2 NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ source.ver: src/contrib/roar_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/roar_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/roar_1.16.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R importsMe: XBSeq Package: ROC Version: 1.56.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: fc48dfa6b674a5ada53bfb6b1d589500 NeedsCompilation: yes Title: utilities for ROC, with uarray focus Description: utilities for ROC, with uarray focus biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org source.ver: src/contrib/ROC_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ROC_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ROC_1.56.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.pdf vignetteTitles: ROC notes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROC/inst/doc/ROCnotes.R dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate Package: Roleswitch Version: 1.18.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: a82bb129da231059310ee5097c8e41ea NeedsCompilation: no Title: Infer miRNA-mRNA interactions using paired expression data from a single sample Description: Infer Probabilities of MiRNA-mRNA Interaction Signature (ProMISe) using paired expression data from a single sample. Roleswitch operates in two phases by inferring the probability of mRNA (miRNA) being the targets ("targets") of miRNA (mRNA), taking into account the expression of all of the mRNAs (miRNAs) due to their potential competition for the same miRNA (mRNA). Due to dynamic miRNA repression in the cell, Roleswitch assumes that the total transcribed mRNA levels are higher than the observed (equilibrium) mRNA levels and iteratively updates the total transcription of each mRNA targets based on the above inference. NB: in the paper, we used ProMISe as both the model name and inferred score name. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/roleswitch.html source.ver: src/contrib/Roleswitch_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Roleswitch_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Roleswitch_1.18.0.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Roleswitch/inst/doc/Roleswitch.R importsMe: miRLAB Package: rols Version: 2.8.2 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: e323ae49b5e3bf37328268e4f3901303 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto , with contributions from Tiago Chedraoui Silva. Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_7 git_last_commit: c437df8 git_last_commit_date: 2018-10-07 Date/Publication: 2018-10-07 source.ver: src/contrib/rols_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/rols_2.8.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rols_2.8.2.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R suggestsMe: MSnbase Package: ROntoTools Version: 2.8.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: cdc43026ad4d8f1b9335e1abbe38b77d NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Calin Voichita source.ver: src/contrib/ROntoTools_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ROntoTools_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ROntoTools_2.8.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA Package: ropls Version: 1.12.0 Imports: Biobase, methods Suggests: BiocGenerics, BiocStyle, CAMERA, faahKO, knitr, multtest, rmarkdown, RUnit, xcms License: CeCILL MD5sum: 9c7f9d67e210a4a7fd71acbf74c364be NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis(PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Etienne A. Thevenot Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr source.ver: src/contrib/ropls_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ropls_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ropls_1.12.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R importsMe: ASICS, biosigner, proFIA Package: ROTS Version: 1.8.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: ec337e743df50714601a95801812907a NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Fatemeh Seyednasrollah source.ver: src/contrib/ROTS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ROTS_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ROTS_1.8.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, PowerExplorer Package: RPA Version: 1.36.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: cbd80d4706319d98eda603a49a363c71 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA source.ver: src/contrib/RPA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RPA_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RPA_1.36.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs Package: RProtoBufLib Version: 1.2.0 License: BSD_3_clause Archs: i386, x64 MD5sum: 153603ab959d48d6cfabe1d4ca6bedbf NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers 2.6.0 for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make source.ver: src/contrib/RProtoBufLib_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RProtoBufLib_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RProtoBufLib_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE linksToMe: cytolib, flowWorkspace Package: RpsiXML Version: 2.22.0 Depends: methods, annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), XML (>= 2.4.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db,hom.Hs.inp.db, hom.Mm.inp.db, hom.Dm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db,Rgraphviz, ppiStats, ScISI License: LGPL-3 MD5sum: d95da5142808ac6a8086fb2e8027b08f NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang, Stefan Wiemann, Marc Carlson, with contributions from Tony Chiang Maintainer: Jitao David Zhang URL: http://www.bioconductor.org source.ver: src/contrib/RpsiXML_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RpsiXML_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RpsiXML_2.22.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependsOnMe: ScISI importsMe: ScISI Package: rpx Version: 1.16.0 Depends: methods Imports: xml2, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, testthat, knitr License: GPL-2 MD5sum: 1334821496dbf8dbef01187eb9a9f63f NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues source.ver: src/contrib/rpx_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rpx_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rpx_1.16.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R importsMe: proteoQC suggestsMe: MSnbase Package: Rqc Version: 1.14.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, digest, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: c6c34d574213b41d7303d155f9e035f9 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues source.ver: src/contrib/Rqc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rqc_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rqc_1.14.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R Package: rqt Version: 1.6.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: a7f6dc1ac1a6b6043026ed0d338a3fed NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues source.ver: src/contrib/rqt_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rqt_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rqt_1.6.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R Package: rqubic Version: 1.26.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: bffe211f65d80d497ab47b1bd9aa804e NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Microarray, Clustering Author: Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang source.ver: src/contrib/rqubic_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rqubic_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rqubic_1.26.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R Package: rRDP Version: 1.14.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: d3ee02933d6db6cfc92c8a75deb7475d NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java source.ver: src/contrib/rRDP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rRDP_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rRDP_1.14.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R Package: RRHO Version: 1.20.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: a3eca86edf8e7b73583ab042a292e592 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt source.ver: src/contrib/RRHO_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RRHO_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RRHO_1.20.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R Package: Rsamtools Version: 1.32.3 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops, BiocParallel LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, KEGG.db, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 4871359ed80f29266b9fce817ec9fe9f NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities (see 'LICENCE') for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Herv\'e Pag\`es, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_7 git_last_commit: 0aa3f13 git_last_commit_date: 2018-08-22 Date/Publication: 2018-08-22 source.ver: src/contrib/Rsamtools_1.32.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rsamtools_1.32.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rsamtools_1.32.3.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.pdf vignetteTitles: An introduction to Rsamtools, Using samtools C libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, exomePeak, GenoGAM, GenomicAlignments, GenomicFiles, girafe, gmapR, Guitar, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, qrqc, r3Cseq, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, ArrayExpressHTS, ASpli, ATACseqQC, BadRegionFinder, BBCAnalyzer, biovizBase, BSgenome, CAGEr, casper, cellbaseR, CexoR, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChIPSeqSpike, chromstaR, chromVAR, cn.mops, CNVPanelizer, CNVrd2, compEpiTools, CopywriteR, CrispRVariants, csaw, customProDB, derfinder, DEXSeq, DiffBind, diffHic, DOQTL, easyRNASeq, EDASeq, ensembldb, epigenomix, eudysbiome, FourCSeq, FunChIP, FunciSNP, gcapc, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, GGtools, GoogleGenomics, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HTSeqGenie, IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, maftools, MDTS, metagene, methylKit, mosaics, motifmatchr, msgbsR, NADfinder, nucleR, ORFik, panelcn.mops, PGA, PICS, plyranges, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rariant, Repitools, RiboProfiling, riboSeqR, RNAprobR, Rqc, rtracklayer, segmentSeq, seqplots, seqsetvis, soGGi, SplicingGraphs, srnadiff, TCseq, TFutils, TitanCNA, tracktables, trackViewer, transcriptR, TransView, TSRchitect, TVTB, VariantFiltering, VariantTools suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gQTLstats, IRanges, metaseqR, omicsPrint, recoup, SeqArray, seqbias, SigFuge, similaRpeak, Streamer linksToMe: ArrayExpressHTS, BitSeq, DiffBind, h5vc, podkat, qrqc, QuasR, seqbias, TransView, VariantAnnotation Package: rsbml Version: 2.38.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: f15b04a9e7e923fa5ea3c15d68d14c47 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) source.ver: src/contrib/rsbml_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rsbml_2.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rsbml_2.38.0.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR Package: RSeqAn Version: 1.0.0 Suggests: knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 48015da0a859fea04dcffd8b8fffb681 NeedsCompilation: no Title: R SeqAn Description: Headers from the SeqAn C++ library for easy of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues source.ver: src/contrib/RSeqAn_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RSeqAn_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RSeqAn_1.0.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R Package: rSFFreader Version: 0.28.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors (>= 0.13.8), IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: 4de3ac7b0caf34325728096cb54610ce NeedsCompilation: yes Title: rSFFreader reads in sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers Description: rSFFreader reads sequence, qualities and clip point values from sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers into similar classes as are present for fastq files. biocViews: DataImport, Sequencing Author: Matt Settles , Sam Hunter, Brice Sarver, Ilia Zhbannikov, Kyu-Chul Cho Maintainer: Matt Settles source.ver: src/contrib/rSFFreader_0.28.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rSFFreader_0.28.0.tgz vignettes: vignettes/rSFFreader/inst/doc/rSFFreader.pdf vignetteTitles: An introduction to rSFFreader hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSFFreader/inst/doc/rSFFreader.R importsMe: hiReadsProcessor Package: Rsubread Version: 1.30.9 License: GPL-3 MD5sum: ab4459595b112203a68bca59a482daff NeedsCompilation: yes Title: Subread sequence alignment and counting for R Description: Rsubread is a toolbox developed for the analyses of second and third generation sequencing data. It can be used for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. It can be applied to all major sequencing techologies and it is suitable for the analysis of both short and long reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, GeneExpression, GeneRegulation, Genetics, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, Software Author: Wei Shi and Yang Liao with contributions from Gordon K Smyth, Jenny Dai and Timothy Triche, Jr. Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/release/bioc/html/Rsubread.html git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_7 git_last_commit: 75993b5 git_last_commit_date: 2018-10-03 Date/Publication: 2018-10-04 source.ver: src/contrib/Rsubread_1.30.9.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rsubread_1.30.9.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R importsMe: dupRadar suggestsMe: singleCellTK Package: RSVSim Version: 1.20.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: 6860396305a3d423d123cf763d6fd15e NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen source.ver: src/contrib/RSVSim_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RSVSim_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RSVSim_1.20.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R Package: rTANDEM Version: 1.20.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: 60b579054140a883057b361dd754f9d9 NeedsCompilation: yes Title: Interfaces the tandem protein identification algorithm in R Description: This package interfaces the tandem protein identification algorithm in R. Identification can be launched in the X!Tandem style, by using as sole parameter the path to a parameter file. But rTANDEM aslo provides extended syntax and functions to streamline launching analyses, as well as function to convert results, parameters and taxonomy to/from R. A related package, shinyTANDEM, provides visualization interface for result objects. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Charles Joly Beauparlant , Rene Paradis , Arnaud Droit Maintainer: Frederic Fournier SystemRequirements: rTANDEM uses expat and pthread libraries. See the README file for details. source.ver: src/contrib/rTANDEM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rTANDEM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rTANDEM_1.20.0.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rTANDEM/inst/doc/rTANDEM.R dependsOnMe: PGA, shinyTANDEM importsMe: proteoQC Package: RTCA Version: 1.32.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: a67f7b5ba104227b23a122f9f0bf69fc NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming source.ver: src/contrib/RTCA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTCA_1.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTCA_1.32.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R Package: RTCGA Version: 1.10.0 Depends: R (>= 3.3.0) Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr License: GPL-2 MD5sum: a6e2636b4d9d2bac6be2c3d3a5b638c4 NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: Software, DataImport, DataRepresentation, Preprocessing, RNASeq Author: Marcin Kosinski , Przemyslaw Biecek Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues source.ver: src/contrib/RTCGA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTCGA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTCGA_1.10.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RTCGAToolbox Version: 2.10.0 Depends: R (>= 3.4.0) Imports: Biobase, BiocGenerics, data.table (>= 1.9.4), GenomicRanges, GenomeInfoDb, httr, IRanges, limma (>= 3.18), methods, plyr, RaggedExperiment, RCircos, RCurl, RJSONIO, S4Vectors, stringr, SummarizedExperiment, survival, XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: file LICENSE MD5sum: 089160e798374bb0089ad09f2dc3617f NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre], Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues source.ver: src/contrib/RTCGAToolbox_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTCGAToolbox_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTCGAToolbox_2.10.0.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R suggestsMe: CVE, TCGAutils Package: RTN Version: 2.4.6 Depends: R (>= 3.3.3), methods Imports: RedeR, minet, viper, mixtools, snow, limma, data.table, IRanges, igraph, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: f632198a12dd1a188a553d4798c3ad7b NeedsCompilation: no Title: Reconstruction of transcriptional networks and analysis of master regulators Description: This package provides classes and methods for transcriptional network inference and analysis. Modulators of transcription factor activity are assessed by conditional mutual information, and master regulators are mapped to phenotypes using different strategies, e.g., gene set enrichment, shadow and synergy analyses. Additionally, master regulators can be linked to genetic markers using eQTL/VSE analysis, taking advantage of the haplotype block structure mapped to the human genome in order to explore risk-associated SNPs identified in GWAS studies. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment,GeneticVariability,SNP Author: Mauro Castro, Xin Wang, Michael Fletcher, Florian Markowetz and Kerstin Meyer Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_7 git_last_commit: b3fb7ce git_last_commit_date: 2018-09-03 Date/Publication: 2018-09-03 source.ver: src/contrib/RTN_2.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTN_2.4.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTN_2.4.6.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional networks and analysis of master regulators."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival suggestsMe: geneplast Package: RTNduals Version: 1.4.4 Depends: R(>= 3.5), RTN(>= 2.4.5), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 4279b4fcbcb086efbadc2a0a49b4edc5 NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_7 git_last_commit: 7049dec git_last_commit_date: 2018-09-03 Date/Publication: 2018-09-03 source.ver: src/contrib/RTNduals_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTNduals_1.4.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTNduals_1.4.4.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival Package: RTNsurvival Version: 1.4.5 Depends: R(>= 3.5), RTN(>= 2.4.5), RTNduals(>= 1.4.4), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales Suggests: Fletcher2013b, pheatmap, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 238f442913942d39ed2cefc03a93a1ad NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_7 git_last_commit: 41e23d2 git_last_commit_date: 2018-09-04 Date/Publication: 2018-09-05 source.ver: src/contrib/RTNsurvival_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTNsurvival_1.4.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTNsurvival_1.4.5.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R Package: RTopper Version: 1.26.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 7e4688181938624473057f404a1a55e6 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni source.ver: src/contrib/RTopper_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RTopper_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RTopper_1.26.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R Package: rtracklayer Version: 1.40.6 Depends: R (>= 3.3), methods, GenomicRanges (>= 1.31.8) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: e83d4ec4be2335103cd758b6147a370c NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_7 git_last_commit: ba9a6e7 git_last_commit_date: 2018-08-30 Date/Publication: 2018-08-31 source.ver: src/contrib/rtracklayer_1.40.6.tar.gz win.binary.ver: bin/windows/contrib/3.5/rtracklayer_1.40.6.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rtracklayer_1.40.6.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CAGEfightR, ChIPSeqSpike, CoverageView, cummeRbund, exomePeak, geneXtendeR, GenomicFiles, groHMM, Guitar, HelloRanges, igvR, MethylSeekR, r3Cseq, regioneR, RIPSeeker, spliceR importsMe: AnnotationHubData, annotatr, ATACseqQC, ballgown, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPseeker, ChromHeatMap, chromswitch, CNEr, coMET, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, DeepBlueR, derfinder, DEScan2, diffloop, DMCHMM, dmrseq, ensembldb, erma, esATAC, FourCSeq, FunciSNP, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, GGtools, gmapR, GOTHiC, gQTLBase, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, IsoformSwitchAnalyzeR, karyoploteR, MACPET, MADSEQ, MEDIPS, metagene, methyAnalysis, methylKit, motifbreakR, MotifDb, NADfinder, normr, ORFik, Pbase, PGA, plyranges, proBAMr, PureCN, qsea, QuasR, RCAS, recount, recoup, regioneR, Repitools, RGMQL, RiboProfiling, RNAprobR, roar, seqplots, seqsetvis, sevenC, SGSeq, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, tRNAscanImport, TSRchitect, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr suggestsMe: alpine, AnnotationHub, BiocFileCache, biovizBase, ChIPpeakAnno, CINdex, compEpiTools, CrispRVariants, ELMER, epivizrData, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, miRBaseConverter, MotIV, MutationalPatterns, NarrowPeaks, OrganismDbi, PICS, PING, pqsfinder, R453Plus1Toolbox, Ringo, rMAT, RnBeads, RSVSim, signeR, similaRpeak, triplex, TSSi, TVTB Package: Rtreemix Version: 1.42.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: 087d55ecd84ad16579961e432b02a025 NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska source.ver: src/contrib/Rtreemix_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Rtreemix_1.42.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Rtreemix_1.42.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R Package: rTRM Version: 1.18.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: b6f755ba994d7bfe3e346a075fa95839 NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues source.ver: src/contrib/rTRM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rTRM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rTRM_1.18.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui Package: rTRMui Version: 1.18.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 00a20a4455949739bc2b31fdcc5a6bb0 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues source.ver: src/contrib/rTRMui_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/rTRMui_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rTRMui_1.18.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R Package: runibic Version: 1.2.5 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE Archs: i386, x64 MD5sum: 9281f5d8d77c8b278cd9e1fd58fcf21c NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_7 git_last_commit: 20c968d git_last_commit_date: 2018-09-27 Date/Publication: 2018-09-27 source.ver: src/contrib/runibic_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/runibic_1.2.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/runibic_1.2.5.tgz vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: RUVcorr Version: 1.12.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 MD5sum: 7e093abb473a713288a8c3002db965f1 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr source.ver: src/contrib/RUVcorr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RUVcorr_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RUVcorr_1.12.0.tgz vignettes: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.pdf vignetteTitles: RUVcorr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.R Package: RUVnormalize Version: 1.14.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: d1396d0764d330337e0f8107cbe2f744 NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob source.ver: src/contrib/RUVnormalize_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RUVnormalize_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RUVnormalize_1.14.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R Package: RUVSeq Version: 1.14.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 3b53bd1c4f0dea988b04a50025ddd5f3 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues source.ver: src/contrib/RUVSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/RUVSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RUVSeq_1.14.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R importsMe: scone suggestsMe: DEScan2 Package: RVS Version: 1.2.1 Depends: R (>= 3.4.0) Imports: gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-2 MD5sum: 5b784864c438e7dc9a8110b5a291da29 NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_7 git_last_commit: bb654c4 git_last_commit_date: 2018-08-14 Date/Publication: 2018-08-14 source.ver: src/contrib/RVS_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/RVS_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/RVS_1.2.1.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R Package: rWikiPathways Version: 1.0.5 Depends: R (>= 3.5) Imports: caTools, httr, RJSONIO, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 9d50f36c679d99d1e2101de6f2d2334e NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (), Alex Pico [aut] () Maintainer: Egon Willighagen , Alexander Pico URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_7 git_last_commit: f86fd89 git_last_commit_date: 2018-10-06 Date/Publication: 2018-10-07 source.ver: src/contrib/rWikiPathways_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/rWikiPathways_1.0.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/rWikiPathways_1.0.5.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R Package: S4Vectors Version: 0.18.3 Depends: R (>= 3.3.0), methods, utils, stats, stats4, BiocGenerics (>= 0.23.3) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: e57a3a01a9807d65ebdc688b4bdc60b6 NeedsCompilation: yes Title: S4 implementation of vector-like and list-like objects Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pagès, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/S4Vectors_0.18.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/S4Vectors_0.18.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/S4Vectors_0.18.3.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf, vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, Biostrings, BiSeq, BSgenome, bumphunter, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, ClassifyR, CODEX, coseq, CSAR, DelayedArray, DESeq2, DEXSeq, DirichletMultinomial, DMCHMM, DMRcaller, epigenomix, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicScores, GenomicTuples, girafe, groHMM, Gviz, HelloRanges, htSeqTools, InPAS, InTAD, IntEREst, IRanges, meshr, MotifDb, NADfinder, OTUbase, plethy, RIPSeeker, RnBeads, segmentSeq, SummarizedBenchmark, topdownr, triplex, VariantTools, vulcan, XVector importsMe: affycoretools, ALDEx2, AllelicImbalance, alpine, amplican, anamiR, AneuFinder, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, ArrayTV, ASpli, BadRegionFinder, ballgown, BASiCS, BiocOncoTK, biovizBase, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, BSgenome, bsseq, CAGEfightR, CAGEr, casper, CATALYST, ccfindR, ChIC, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, ChIPSeqSpike, chromstaR, chromswitch, chromVAR, cleaver, clusterExperiment, cn.mops, CNEr, CNPBayes, CNVPanelizer, coMET, compEpiTools, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, CTDquerier, cummeRbund, customProDB, cydar, DChIPRep, debrowser, DECIPHER, DEFormats, DEGreport, DelayedMatrixStats, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffcyt, diffHic, diffloop, DMRcate, dmrseq, DOSE, DRIMSeq, DropletUtils, easyRNASeq, eegc, ELMER, EnrichmentBrowser, ensembldb, ensemblVEP, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, ExperimentHub, facopy, fastseg, FindMyFriends, FunciSNP, GA4GHshiny, gcapc, GDSArray, genbankr, genefilter, GeneRegionScan, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, genoset, GGBase, ggbio, GGtools, Glimma, gmapR, GoogleGenomics, GOpro, GOTHiC, gQTLBase, gQTLstats, GRmetrics, GSEABenchmarkeR, GUIDEseq, gwascat, h5vc, HDF5Array, HiCcompare, hipathia, hmdbQuery, HTSeqGenie, ideal, IMAS, ImpulseDE2, INSPEcT, InteractionSet, InterMineR, iSEE, isomiRs, IVAS, ivygapSE, JunctionSeq, karyoploteR, kebabs, loci2path, LOLA, M3D, MACPET, MADSEQ, martini, MAST, mCSEA, MEAL, metagenomeFeatures, methInheritSim, methylInheritance, methylKit, methylPipe, methylumi, mimager, minfi, MinimumDistance, MIRA, MiRaGE, missRows, MMDiff2, mosaics, motifbreakR, motifmatchr, MotIV, mpra, msa, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, MutationalPatterns, mygene, myvariant, NarrowPeaks, nucleoSim, nucleR, oligoClasses, ontoProc, openPrimeR, ORFik, Organism.dplyr, OrganismDbi, panelcn.mops, PathwaySplice, Pbase, pcaExplorer, pdInfoBuilder, PGA, PICS, PING, plyranges, pogos, polyester, PowerExplorer, pqsfinder, prebs, procoil, PureCN, qcmetrics, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, regioneR, regionReport, regsplice, REMP, Repitools, restfulSE, rexposome, RGMQL, rhdf5client, RiboProfiling, RJMCMCNucleosomes, RNAprobR, roar, Rqc, Rsamtools, RTCGAToolbox, RTN, rtracklayer, SC3, scater, scDD, scmap, SCnorm, scPipe, scran, SeqArray, seqCAT, seqplots, seqsetvis, SeqSQC, SeqVarTools, sevenbridges, sevenC, SGSeq, ShortRead, simulatorZ, SingleCellExperiment, singleCellTK, skewr, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ, TFutils, TnT, trackViewer, transcriptR, TransView, Trendy, tRNAscanImport, TSRchitect, TSSi, TVTB, twoddpcr, TxRegInfra, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, wiggleplotr, xcms, XVector, yamss suggestsMe: BiocGenerics, epivizrChart, GWASTools, RTCGA, splatter, TFEA.ChIP linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, IRanges, kebabs, MatrixRider, Rsamtools, rSFFreader, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering, XVector Package: safe Version: 3.20.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: 703d7726993370ba31dd252aa9f4bbb7 NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: William T. Barry source.ver: src/contrib/safe_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/safe_3.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/safe_3.20.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R importsMe: EGSEA, EnrichmentBrowser Package: sagenhaft Version: 1.50.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: fa585fd674c04113aa51828d5bac9444 NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth and Lavinia Hyde . Maintainer: Tim Beissbarth URL: http://tagcalling.mbgproject.org source.ver: src/contrib/sagenhaft_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sagenhaft_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sagenhaft_1.50.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R Package: SAGx Version: 1.54.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 5499d99c81f06104269f12e07756d84e NeedsCompilation: yes Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison, GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html source.ver: src/contrib/SAGx_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SAGx_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SAGx_1.54.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAGx/inst/doc/samroc-ex.R Package: samExploreR Version: 1.4.0 Depends: ggplot2,Rsubread,RNAseqData.HNRNPC.bam.chr14,edgeR,R (>= 3.4.0) Imports: grDevices, stats, graphics Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL-3 MD5sum: a74f452e696f72ce8d8527f1387f2a93 NeedsCompilation: no Title: samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation Description: This R package is designed for subsampling procedure to simulate sequencing experiments with reduced sequencing depth. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA, HiSeq, MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. Was developed with usage of Rsubread. biocViews: Sequencing, SequenceMatching, RNASeq, ChIPSeq, DNASeq, WholeGenome, GeneTarget, Alignment, GeneExpression, GeneticVariability, GeneRegulation, Preprocessing, GenomeAnnotation, Software Author: Alexey Stupnikov, Shailesh Tripathi and Frank Emmert-Streib Maintainer: shailesh tripathi source.ver: src/contrib/samExploreR_1.4.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/samExploreR_1.4.0.tgz vignettes: vignettes/samExploreR/inst/doc/Manual.pdf vignetteTitles: samExploreR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/samExploreR/inst/doc/Manual.R Package: sampleClassifier Version: 1.4.0 Depends: R (>= 3.4), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: 4b7498bfc9c768a93107293f33f294b9 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify gene expression profiles. biocViews: Classification, Microarray, RNASeq, GeneExpression Author: Khadija El-Amrani Maintainer: Khadija El Amrani source.ver: src/contrib/sampleClassifier_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sampleClassifier_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sampleClassifier_1.4.0.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R Package: SamSPECTRAL Version: 1.34.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: fb015d240d3fa5339e593db1dc795c83 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data. Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data sample. For instructions on manual installation, refer to the PDF file provided in the following documentation. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV Author: Habil Zare and Parisa Shooshtari Maintainer: Habil Zare source.ver: src/contrib/SamSPECTRAL_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SamSPECTRAL_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SamSPECTRAL_1.34.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust Package: sangerseqR Version: 1.16.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 845e4a030edab5ac6ce100b1af1f9196 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr source.ver: src/contrib/sangerseqR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sangerseqR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sangerseqR_1.16.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R suggestsMe: CrispRVariants Package: SANTA Version: 2.18.0 Depends: R (>= 2.14), igraph Imports: Matrix, snow Suggests: RUnit, BiocGenerics, knitr, knitcitations, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) Archs: i386, x64 MD5sum: 51b72f7f960152b7287fdf788b1c9ced NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex J. Cornish and Florian Markowetz Maintainer: Alex J. Cornish VignetteBuilder: knitr source.ver: src/contrib/SANTA_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SANTA_2.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SANTA_2.18.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R Package: sapFinder Version: 1.18.0 Depends: R (>= 3.0.0),rTANDEM (>= 1.3.5) Imports: pheatmap,Rcpp (>= 0.10.6),graphics,grDevices,stats, utils LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 4bfa39421be8f1dede1849af9e6ac876 NeedsCompilation: yes Title: A package for variant peptides detection and visualization in shotgun proteomics. Description: sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics. biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen source.ver: src/contrib/sapFinder_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sapFinder_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sapFinder_1.18.0.tgz vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sapFinder/inst/doc/sapFinder.R Package: savR Version: 1.18.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: d2b8f270a1a8a745d31e73a65f00c644 NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues source.ver: src/contrib/savR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/savR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/savR_1.18.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R Package: SBMLR Version: 1.76.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: a5247f7e8a34e1d3b1db891f503c8982 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html source.ver: src/contrib/SBMLR_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SBMLR_1.76.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SBMLR_1.76.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R Package: SC3 Version: 1.8.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater License: GPL-3 Archs: i386, x64 MD5sum: a134c4530ff0385297d5c210e619f832 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ source.ver: src/contrib/SC3_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SC3_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SC3_1.8.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SC3/inst/doc/SC3.R Package: Scale4C Version: 1.2.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: b9ef294de53e0640bb471b25414aca33 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Scale4C_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Scale4C_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Scale4C_1.2.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R Package: SCAN.UPC Version: 2.22.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 8227a8a81042be4e926ce94f3aae38c2 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc source.ver: src/contrib/SCAN.UPC_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SCAN.UPC_2.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SCAN.UPC_2.22.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R Package: scater Version: 1.8.4 Depends: R (>= 3.5), Biobase, ggplot2, SingleCellExperiment, SummarizedExperiment Imports: BiocGenerics, data.table, dplyr, edgeR, ggbeeswarm, grid, limma, Matrix, DelayedMatrixStats, methods, parallel, plyr, reshape2, rhdf5, rjson, S4Vectors, shiny, shinydashboard, stats, tximport, utils, viridis, Rcpp (>= 0.12.14), DelayedArray LinkingTo: Rhdf5lib, Rcpp, beachmat Suggests: BiocStyle, biomaRt, beachmat, cowplot, cluster, destiny, knitr, monocle, mvoutlier, rmarkdown, Rtsne, testthat, magrittr, pheatmap, DropletUtils, irlba License: GPL (>= 2) Archs: i386, x64 MD5sum: acd46e7a4717b947117cf892e8699aa2 NeedsCompilation: yes Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut, cre], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb] Maintainer: Davis McCarthy URL: http://bioconductor.org/packages/scater/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_7 git_last_commit: d560a9a git_last_commit_date: 2018-08-13 Date/Publication: 2018-08-13 source.ver: src/contrib/scater_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/scater_1.8.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scater_1.8.4.tgz vignettes: vignettes/scater/inst/doc/vignette-dataviz.html, vignettes/scater/inst/doc/vignette-intro.html, vignettes/scater/inst/doc/vignette-qc.html vignetteTitles: Data visualisation methods in scater, An introduction to the scater package, Quality control with scater hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/vignette-dataviz.R, vignettes/scater/inst/doc/vignette-intro.R, vignettes/scater/inst/doc/vignette-qc.R dependsOnMe: netSmooth importsMe: scran, splatter suggestsMe: iSEE, monocle, SC3, slalom, SummarizedBenchmark Package: scDD Version: 1.4.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: bceea8c7c2563c4e5d1902ea07f451a6 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues source.ver: src/contrib/scDD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scDD_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scDD_1.4.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter Package: scde Version: 2.8.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: c2327039bb5907c859e2189253cda315 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut] Maintainer: Jean Fan URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues source.ver: src/contrib/scde_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scde_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scde_2.8.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: scFeatureFilter Version: 1.0.0 Depends: R (>= 3.5) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 5d1a218d5c9e27207bb59d7dce77d26b NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr source.ver: src/contrib/scFeatureFilter_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scFeatureFilter_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scFeatureFilter_1.0.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R Package: scfind Version: 1.2.0 Depends: R(>= 3.4) Imports: SingleCellExperiment, SummarizedExperiment, methods, stats, bit, dplyr, hash, reshape2, Rcpp(>= 0.12.12) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: da727606c91067b3fcfbfda310c1ef67 NeedsCompilation: yes Title: A search tool for single cell RNA-seq data by gene lists Description: Recently a very large collection of single-cell RNA-seq (scRNA-seq) datasets has been generated and publicly released. For the collection to be useful, the information must be organized in a way that supports queries that are relevant to researchers. `scfind` builds an index from scRNA-seq datasets which organizes the information in a suitable and compact manner so that the datasets can be very efficiently searched for either cells or cell types in which a given list of genes is expressed. biocViews: SingleCell, Software, RNASeq, Transcriptomics, DataRepresentation, Transcription, Sequencing, GeneExpression Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scfind VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scfind/ source.ver: src/contrib/scfind_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scfind_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scfind_1.2.0.tgz vignettes: vignettes/scfind/inst/doc/scfind.html vignetteTitles: `scfind` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scfind/inst/doc/scfind.R Package: ScISI Version: 1.52.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL MD5sum: 2d30438c0b3fdf12840a5afd2961420d NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang source.ver: src/contrib/ScISI_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ScISI_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ScISI_1.52.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML Package: scmap Version: 1.2.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 8952309c2a139f264f35ebcdfe3e3825 NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ source.ver: src/contrib/scmap_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scmap_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scmap_1.2.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmap/inst/doc/scmap.R Package: scmeth Version: 1.0.1 Depends: R (>= 3.5.0) Imports: ggplot2, knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, ggthemes, scales, viridis, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics License: GPL-2 MD5sum: f3885edac8104e45aa16047e8fc7d00e NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr source.ver: src/contrib/scmeth_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/scmeth_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scmeth_1.0.1.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R Package: SCnorm Version: 1.2.1 Depends: R (>= 3.4.0), Imports: stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, SingleCellExperiment, SummarizedExperiment, S4Vectors, ggplot2, forcats Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 3571c635f8ee20266f3a31624db2efa0 NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_7 git_last_commit: 77e07d4 git_last_commit_date: 2018-07-26 Date/Publication: 2018-07-26 source.ver: src/contrib/SCnorm_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/SCnorm_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SCnorm_1.2.1.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R Package: scone Version: 1.4.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork License: Artistic-2.0 MD5sum: ef84196e30ca723b3f6e1ae3661742e7 NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cre, cph], Davide Risso [aut, cph] Maintainer: Michael Cole VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues source.ver: src/contrib/scone_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scone_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scone_1.4.0.tgz vignettes: vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: scone Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/sconeTutorial.R Package: Sconify Version: 1.0.4 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: ffc14e0bfde3a75534224ca3e45ea1b1 NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_7 git_last_commit: 47d26b1 git_last_commit_date: 2018-08-31 Date/Publication: 2018-08-31 source.ver: src/contrib/Sconify_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/Sconify_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Sconify_1.0.4.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R Package: scoreInvHap Version: 1.2.1 Depends: R (>= 3.4.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 10d36d8e6a5aa8aa28cf270fe5e9d41f NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for two well known inversions (8p23 and 17q21.31) and for two additional regions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut, cre], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_7 git_last_commit: 6b99cbe git_last_commit_date: 2018-06-15 Date/Publication: 2018-06-15 source.ver: src/contrib/scoreInvHap_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/scoreInvHap_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scoreInvHap_1.2.1.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Call haplotype inversions with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R Package: scPipe Version: 1.2.1 Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3), reshape, BiocGenerics, robustbase, scales, utils, stats, S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db LinkingTo: Rcpp, Rhtslib (>= 1.12.1), zlibbioc Suggests: knitr, rmarkdown, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 3727275db5292f29e2395e99a032255c NeedsCompilation: yes Title: pipeline for single cell RNA-seq data analysis Description: A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation Author: Luyi Tian Maintainer: Luyi Tian URL: https://github.com/LuyiTian/scPipe VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe source.ver: src/contrib/scPipe_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/scPipe_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scPipe_1.2.1.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R Package: scran Version: 1.8.4 Depends: R (>= 3.5), BiocParallel, SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp (>= 0.12.14), stats, methods, utils, graphics, grDevices, Matrix, scater, edgeR, limma, dynamicTreeCut, FNN, igraph, shiny, statmod, ggplot2, DT, viridis, DelayedArray, DelayedMatrixStats LinkingTo: beachmat, Rcpp, Rhdf5lib Suggests: testthat, BiocStyle, knitr, beachmat, HDF5Array, limSolve, org.Mm.eg.db, DESeq2, monocle, pracma, Biobase, irlba, aroma.light License: GPL-3 Archs: i386, x64 MD5sum: 5dcee3729a55934693bb89bda025efc5 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements functions for low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, detection of highly variable and significantly correlated genes, correction of batch effects, identification of marker genes, and other common tasks in single-cell analysis workflows. biocViews: Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Visualization, BatchEffect, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_7 git_last_commit: bbf1d09 git_last_commit_date: 2018-08-07 Date/Publication: 2018-08-07 source.ver: src/contrib/scran_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/scran_1.8.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scran_1.8.4.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to perform basic analyses of single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R importsMe: BASiCS, scDD suggestsMe: scone, splatter Package: scsR Version: 1.16.0 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 MD5sum: 32ebc178ebf90988613ffc674bc82ae8 NeedsCompilation: no Title: SiRNA correction for seed mediated off-target effect Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package. biocViews: Preprocessing Author: Andrea Franceschini Maintainer: Andrea Franceschini , Roger Meier , Christian von Mering source.ver: src/contrib/scsR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/scsR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/scsR_1.16.0.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scsR/inst/doc/scsR.R Package: SDAMS Version: 1.0.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 62278f928749b61ee3962b969a08b9aa NeedsCompilation: no Title: Differential Abundant Analysis for Metabolomics and Proteomics Data Description: This Package utilizes a Semi-parametric Differential Abundance analysis (SDA) method for metabolomics and proteomics data from mass spectrometry. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li source.ver: src/contrib/SDAMS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SDAMS_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SDAMS_1.0.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R Package: segmentSeq Version: 2.14.0 Depends: R (>= 3.0.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 2a928544f9f1fd8a65e49cc0a8259c52 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/segmentSeq_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/segmentSeq_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/segmentSeq_2.14.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R Package: SELEX Version: 1.12.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: 9d86c56721b6ad41aca7bacafa5b611b NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, and Harmen Bussemaker Maintainer: Harmen Bussemaker URL: http://bussemakerlab.org/software/SELEX/ SystemRequirements: Java (>= 1.5) source.ver: src/contrib/SELEX_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SELEX_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SELEX_1.12.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R Package: SemDist Version: 1.14.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 312c3a1fdd67d73f93af256852c16849 NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist source.ver: src/contrib/SemDist_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SemDist_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SemDist_1.14.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R Package: semisup Version: 1.4.0 Depends: R (>= 3.0.0) Imports: SummarizedExperiment, VGAM Suggests: knitr, testthat License: GPL-3 MD5sum: 354aa3380cb0e7c7028f23a426fabba7 NeedsCompilation: no Title: Detecting SNPs with interactive effects on a quantitative trait Description: This R packages moves away from testing interaction terms, and move towards testing whether an individual SNP is involved in any interaction. This reduces the multiple testing burden to one test per SNP, and allows for interactions with unobserved factors. Analysing one SNP at a time, it splits the individuals into two groups, based on the number of minor alleles. If the quantitative trait differs in mean between the two groups, the SNP has a main effect. If the quantitative trait differs in distribution between some individuals in one group and all other individuals, it possibly has an interactive effect. Implicitly, the membership probabilities may suggest potential interacting variables. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues source.ver: src/contrib/semisup_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/semisup_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/semisup_1.4.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: semisup, pkgdown, pkgdown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R Package: SEPA Version: 1.10.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, topGO, segmented, reshape2, org.Hs.eg.db, org.Mm.eg.db Suggests: knitr License: GPL(>=2) MD5sum: 459b9b1b7796706e92fce5d4dbd76c86 NeedsCompilation: no Title: SEPA Description: Given single-cell RNA-seq data and true experiment time of cells or pseudo-time cell ordering, SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns. biocViews: GeneExpression, Visualization, GUI, GO Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/SEPA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SEPA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SEPA_1.10.0.tgz vignettes: vignettes/SEPA/inst/doc/SEPA.pdf vignetteTitles: SEPA: Single-Cell Gene Expression Pattern Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPA/inst/doc/SEPA.R Package: SEPIRA Version: 1.0.2 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 MD5sum: e7dc2a1bff55b9881f810a78f043ade2 NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_7 git_last_commit: fd794e3 git_last_commit_date: 2018-10-09 Date/Publication: 2018-10-10 source.ver: src/contrib/SEPIRA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/SEPIRA_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SEPIRA_1.0.2.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R Package: seq2pathway Version: 1.12.0 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: f9c96209ac0241cf341c82c043c310b9 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Xinan Yang with contribution from Lorenzo Pesce and Ana Marija Sokovic source.ver: src/contrib/seq2pathway_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seq2pathway_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seq2pathway_1.12.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R Package: SeqArray Version: 1.20.2 Depends: R (>= 3.5.0), gdsfmt (>= 1.10.1) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: digest, crayon, RUnit, knitr, Rcpp, SNPRelate, Biobase, BiocParallel, BiocGenerics, Rsamtools, VariantAnnotation License: GPL-3 Archs: i386, x64 MD5sum: ff12fda2a426c4ff47376b5f0f69b2cd NeedsCompilation: yes Title: Big Data Management of Whole-genome Sequence Variant Calls Description: Big data management of whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_7 git_last_commit: 7277e82 git_last_commit_date: 2018-09-10 Date/Publication: 2018-09-10 source.ver: src/contrib/SeqArray_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/SeqArray_1.20.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SeqArray_1.20.2.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/R_Integration.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/R_Integration.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SeqVarTools importsMe: GDSArray, GENESIS Package: seqbias Version: 1.28.0 Depends: R (>= 2.13.0), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods Imports: zlibbioc LinkingTo: Rsamtools (>= 1.19.38) Suggests: Rsamtools, ggplot2 License: LGPL-3 Archs: i386, x64 MD5sum: 0e835d8b791510c1fbaade3f7f185634 NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones source.ver: src/contrib/seqbias_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqbias_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqbias_1.28.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON Package: seqCAT Version: 1.2.1 Depends: R (>= 3.5), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), lazyeval (>= 0.2.0), scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat License: MIT + file LICENCE MD5sum: f0316a9031df3a51a19a10ecf894620f NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr source.ver: src/contrib/seqCAT_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqCAT_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqCAT_1.2.1.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R Package: seqCNA Version: 1.26.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 Archs: i386, x64 MD5sum: 7968410e0a9d422366473bb15888d4bf NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools source.ver: src/contrib/seqCNA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqCNA_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqCNA_1.26.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R Package: seqcombo Version: 1.2.0 Depends: R (>= 3.4.0) Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr, methods, rvcheck, utils Suggests: emojifont, knitr, prettydoc, tibble License: Artistic-2.0 MD5sum: 41def7640525fe159b4a5349a2f6bc1d NeedsCompilation: no Title: Visualization Tool for Sequence Recombination and Reassortment Description: Provides useful functions for visualizing sequence recombination and virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues source.ver: src/contrib/seqcombo_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqcombo_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqcombo_1.2.0.tgz vignettes: vignettes/seqcombo/inst/doc/reassortment.html, vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment, seqcombo introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/reassortment.R, vignettes/seqcombo/inst/doc/seqcombo.R Package: SeqGSEA Version: 1.20.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: 907e50919eea0894d334c622a2842e39 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression Author: Xi Wang Maintainer: Xi Wang source.ver: src/contrib/SeqGSEA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SeqGSEA_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SeqGSEA_1.20.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R Package: seqLogo Version: 1.46.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: 3a72bddab0fea350d7055c85489a0e21 NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom Maintainer: Oliver Bembom source.ver: src/contrib/seqLogo_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqLogo_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqLogo_1.46.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: motifRG, rGADEM importsMe: IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, Logolas, motifcounter, MotifDb Package: seqPattern Version: 1.12.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: c373393e0935e8db75b7e61c3a8f2736 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle source.ver: src/contrib/seqPattern_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqPattern_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqPattern_1.12.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation Package: seqplots Version: 1.18.0 Depends: R (>= 3.2.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.13.0), DBI, RSQLite, plotrix, fields, grid, kohonen, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT (>= 0.1.0), RColorBrewer, Rsamtools, GenomicAlignments Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: 8f66ca668f4acd7289cf26041738f347 NeedsCompilation: no Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor [aut, cph, cre] Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues source.ver: src/contrib/seqplots_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqplots_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqplots_1.18.0.tgz vignettes: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html vignetteTitles: Vignette Title, Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R importsMe: ChIPSeqSpike Package: seqsetvis Version: 1.0.2 Depends: R (>= 3.5), ggplot2 Imports: data.table, eulerr, GenomicRanges, grDevices, grid, IRanges, limma, methods, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats Suggests: BiocFileCache, BiocStyle, ChIPpeakAnno, covr, cowplot, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 72331389af632055d6c139bace9bee1c NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of multiple genomic datasets. Although seqsetvis was designed for the comparison of mulitple ChIP-seq datasets, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files or bam pileups). biocViews: Software, ChIPSeq, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr source.ver: src/contrib/seqsetvis_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqsetvis_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqsetvis_1.0.2.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R Package: SeqSQC Version: 1.2.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: de873204b35ca0523b3f49bddf1e0983 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu VignetteBuilder: knitr source.ver: src/contrib/SeqSQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SeqSQC_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SeqSQC_1.2.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R Package: seqTools Version: 1.14.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 88017a4675ad5a59eafffafb1e7401fc NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/seqTools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/seqTools_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/seqTools_1.14.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R Package: SeqVarTools Version: 1.18.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, dplyr, tidyr Suggests: BiocGenerics, BiocStyle, RUnit, stringr License: GPL-3 MD5sum: dbdba7520b54afcd864583b9bccae177 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten source.ver: src/contrib/SeqVarTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SeqVarTools_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SeqVarTools_1.18.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS Package: sevenbridges Version: 1.10.5 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, dplyr, shiny (>= 0.13), miniUI (>= 0.1.1), rstudioapi (>= 0.5) Suggests: knitr, rmarkdown, testthat, readr, clipr License: Apache License 2.0 | file LICENSE MD5sum: 33932a27cebcfd3364a5ffd4758d8fea NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Nan Xiao [aut, cre], Tengfei Yin [aut], Emile Young [ctb], Dusan Randjelovic [aut], Seven Bridges Genomics [cph, fnd] Maintainer: Nan Xiao URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_7 git_last_commit: ba1fba3 git_last_commit_date: 2018-10-10 Date/Publication: 2018-10-11 source.ver: src/contrib/sevenbridges_1.10.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/sevenbridges_1.10.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sevenbridges_1.10.5.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-sparql.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Exploerer,, SPARQL,, and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-sparql.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R Package: sevenC Version: 1.0.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: 38976073cca933cb6f37cdc1999648a9 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues source.ver: src/contrib/sevenC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sevenC_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sevenC_1.0.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R Package: SGSeq Version: 1.14.0 Depends: IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics, Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.17.28), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: 147a43d4fcac49175ef4d7e957973bba NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr source.ver: src/contrib/SGSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SGSeq_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SGSeq_1.14.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer Package: shinyMethyl Version: 1.16.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: cbca0b5dd7ae6207006e180d8581cd19 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr source.ver: src/contrib/shinyMethyl_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/shinyMethyl_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/shinyMethyl_1.16.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R Package: shinyTANDEM Version: 1.18.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: a69e5a5869a0bd45dc9d0668cb7ff7a8 NeedsCompilation: no Title: Provides a GUI for rTANDEM Description: This package provides a GUI interface for rTANDEM. The GUI is primarily designed to visualize rTANDEM result object or result xml files. But it will also provides an interface for creating parameter objects, launching searches or performing conversions between R objects and xml files. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier source.ver: src/contrib/shinyTANDEM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/shinyTANDEM_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/shinyTANDEM_1.18.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ShortRead Version: 1.38.0 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: 0e2f638c64a9700c38d822473494d8cc NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ShortRead_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ShortRead_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ShortRead_1.38.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, GOTHiC, IONiseR, MACPET, nucleR, QuasR, R453Plus1Toolbox, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools, S4Vectors Package: SIAMCAT Version: 1.0.0 Depends: R (>= 3.5.0), mlr, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC, PRROC, RColorBrewer, stats, utils Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 686562eb3500dccc6d91744ed694f687 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison, Regression Author: Georg Zeller [aut] (), Konrad Zych [aut, cre] (), Jakob Wirbel [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Konrad Zych VignetteBuilder: knitr source.ver: src/contrib/SIAMCAT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SIAMCAT_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SIAMCAT_1.0.0.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R Package: SICtools Version: 1.10.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: 3a3aff75c6c12e1803400f50a856462a NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr source.ver: src/contrib/SICtools_1.10.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SICtools_1.10.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R Package: sigaR Version: 1.28.0 Depends: Biobase, CGHbase, methods, mvtnorm, Imports: corpcor (>= 1.6.2), graphics, igraph, limma, marray, MASS, penalized, quadprog, snowfall, stats Suggests: CGHcall License: GPL (>= 2) MD5sum: beddafd3b2158085d2de2316b220387a NeedsCompilation: no Title: Statistics for Integrative Genomics Analyses in R Description: Facilitates the joint analysis of high-throughput data from multiple molecular levels. Contains functions for manipulation of objects, various analysis types, and some visualization. biocViews: Microarray, DifferentialExpression, aCGH, GeneExpression, Pathways Author: Wessel N. van Wieringen Maintainer: Wessel N. van Wieringen URL: http://www.few.vu.nl/~wvanwie source.ver: src/contrib/sigaR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sigaR_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sigaR_1.28.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigaR/inst/doc/sigaR.R Package: SigCheck Version: 2.12.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: feac70f95f3e3be53af9feac42be6845 NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark source.ver: src/contrib/SigCheck_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SigCheck_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SigCheck_2.12.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R Package: SigFuge Version: 1.18.0 Depends: R (>= 3.1.1), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: 69a6b43a83b3ed57a7edb678d2527170 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes source.ver: src/contrib/SigFuge_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SigFuge_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SigFuge_1.18.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R Package: siggenes Version: 1.54.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: 29b1b27308fb274c1f8cef874415f82c NeedsCompilation: no Title: Multiple testing using SAM and Efron's empirical Bayes approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender source.ver: src/contrib/siggenes_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/siggenes_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/siggenes_1.54.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf vignetteTitles: siggenes Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: charm, coexnet, DAPAR, GeneSelector, minfi, XDE suggestsMe: GeneSelector, logicFS, trio Package: sights Version: 1.6.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 535636cb56b291690cec0942e586176b NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues source.ver: src/contrib/sights_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sights_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sights_1.6.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R Package: signeR Version: 1.6.1 Depends: VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, graphics, grDevices, GenomicRanges, nloptr, methods, stats, utils, PMCMR LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 Archs: i386, x64 MD5sum: 0932dead936d53ca4717b0d81df89e85 NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/signeR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/signeR_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/signeR_1.6.1.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html vignetteTitles: signeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R Package: signet Version: 1.0.2 Depends: R (>= 3.4.0) Imports: graph, igraph, RBGL, graphics, utils, stats, methods Suggests: graphite, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 97dbaca0b8178c6b72156af50b4a3a2c NeedsCompilation: no Title: signet: Selection Inference in Gene NETworks Description: An R package to detect selection in biological pathways. Using gene selection scores and biological pathways data, one can search for high-scoring subnetworks of genes within pathways and test their significance. biocViews: Software, Pathways, DifferentialExpression, GeneExpression, NetworkEnrichment, GraphAndNetwork, KEGG Author: Alexandre Gouy [aut, cre] Maintainer: Alexandre Gouy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signet git_branch: RELEASE_3_7 git_last_commit: f3d0673 git_last_commit_date: 2018-06-18 Date/Publication: 2018-06-19 source.ver: src/contrib/signet_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/signet_1.0.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/signet_1.0.2.tgz vignettes: vignettes/signet/inst/doc/signet.html vignetteTitles: signet tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signet/inst/doc/signet.R Package: sigPathway Version: 1.48.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 Archs: i386, x64 MD5sum: 048312d9982f8306471b97191adbe93a NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html source.ver: src/contrib/sigPathway_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sigPathway_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sigPathway_1.48.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome Package: sigsquared Version: 1.12.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 960d4353f85ecbb9914dc711a795bd50 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee source.ver: src/contrib/sigsquared_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sigsquared_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sigsquared_1.12.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R Package: SIM Version: 1.50.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: bd56e523cdc59354168a7e016e3d0574 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes source.ver: src/contrib/SIM_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SIM_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SIM_1.50.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R Package: SIMAT Version: 1.12.1 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 94f984bf709a0925f08b3fd676e6cc1f NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_7 git_last_commit: 5ce47d8 git_last_commit_date: 2018-09-10 Date/Publication: 2018-09-10 source.ver: src/contrib/SIMAT_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/SIMAT_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SIMAT_1.12.1.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R Package: SimBindProfiles Version: 1.18.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: b2330af81cacdf24ad53d388f5bf9938 NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer source.ver: src/contrib/SimBindProfiles_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SimBindProfiles_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SimBindProfiles_1.18.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R Package: similaRpeak Version: 1.12.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 9b7e94be18a014e03d80c660fda1bb1c NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues source.ver: src/contrib/similaRpeak_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/similaRpeak_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/similaRpeak_1.12.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R suggestsMe: metagene Package: SIMLR Version: 1.6.0 Depends: R (>= 3.4), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE Archs: i386, x64 MD5sum: 237e9b2e9225b92520d5334b5acba51b NeedsCompilation: yes Title: Title: SIMLR and CIMLR Multi-kernel LeaRning methods Description: In this package we provide implementations of both SIMLR and CIMLR. These methods were originally applied to single-cell and cancer genomic data, but they are in principle capable of effectively and efficiently learning similarities in all the contexts where diverse and heterogeneous statistical characteristics of the data make the problem harder for standard approaches. biocViews: Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut, cre], Bo Wang [aut], Luca De Sano [aut], Serafim Batzoglou [ctb] Maintainer: Daniele Ramazzotti URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR source.ver: src/contrib/SIMLR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SIMLR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SIMLR_1.6.0.tgz vignettes: vignettes/SIMLR/inst/doc/SIMLR.pdf vignetteTitles: SIMLR and CIMLR Multi-kernel LeaRning methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/SIMLR.R Package: simpleaffy Version: 2.56.0 Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma License: GPL (>= 2) Archs: i386, x64 MD5sum: 32f9a400f4d9046eac6638c4295fd528 NeedsCompilation: yes Title: Very simple high level analysis of Affymetrix data Description: Provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. Makes heavy use of the affy library. Also has some basic scatter plot functions and mechanisms for generating high resolution journal figures... biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Transcription, DataImport, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Crispin J Miller Maintainer: Crispin Miller URL: http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/ source.ver: src/contrib/simpleaffy_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/simpleaffy_2.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/simpleaffy_2.56.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleaffy/inst/doc/simpleAffy.R dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW Package: simulatorZ Version: 1.14.0 Depends: R (>= 3.1), methods, BiocGenerics, Biobase, SummarizedExperiment, survival, CoxBoost Imports: graphics, stats, gbm, Hmisc, S4Vectors, IRanges, GenomicRanges Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE, superpc License: Artistic-2.0 Archs: i386, x64 MD5sum: 0f48ee1bf5da7ccf22cbad52c5fc1142 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSet and RangedSummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ BugReports: https://github.com/zhangyuqing/simulatorZ source.ver: src/contrib/simulatorZ_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/simulatorZ_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/simulatorZ_1.14.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.R suggestsMe: doppelgangR Package: sincell Version: 1.12.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Archs: i386, x64 MD5sum: c75ce15ced881cf74d826d9ac8123b6e NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr source.ver: src/contrib/sincell_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sincell_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sincell_1.12.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R Package: SingleCellExperiment Version: 1.2.0 Depends: R (>= 3.5), SummarizedExperiment Imports: S4Vectors, methods, BiocGenerics, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, magrittr, Rtsne License: GPL-3 MD5sum: c5c0f9836e6c2c199dea59314a552d94 NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb] Maintainer: Davide Risso VignetteBuilder: knitr source.ver: src/contrib/SingleCellExperiment_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SingleCellExperiment_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SingleCellExperiment_1.2.0.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: BASiCS, clusterExperiment, DropletUtils, iSEE, MAST, scater, scPipe, scran, singleCellTK, splatter, switchde, zinbwave importsMe: BEARscc, ccfindR, LineagePulse, netSmooth, SC3, scDD, scfind, scmap, SCnorm, slalom suggestsMe: DEsingle, destiny, phenopath, scFeatureFilter Package: singleCellTK Version: 1.0.3 Depends: R (>= 3.5), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, colourpicker, cluster, ComplexHeatmap, data.table, DESeq2, DT, ggplot2, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, limma, MAST, matrixStats, methods, multtest, plotly, RColorBrewer, Rtsne, S4Vectors, shiny, shinyjs, sva, reshape2, AnnotationDbi, shinyalert, circlize Suggests: testthat, Rsubread, BiocStyle, knitr, bladderbatch, rmarkdown, org.Mm.eg.db, org.Hs.eg.db, scRNAseq, xtable License: MIT + file LICENSE MD5sum: 554e2f7dc0b1cb8b225a16fbdac364a3 NeedsCompilation: no Title: Interactive Analysis of Single Cell RNA-Seq Data Description: Run common single cell analysis directly through your browser including differential expression, downsampling analysis, and clustering. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering Author: David Jenkins Maintainer: David Jenkins URL: https://compbiomed.github.io/sctk_docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_7 git_last_commit: 35dab22 git_last_commit_date: 2018-06-20 Date/Publication: 2018-06-21 source.ver: src/contrib/singleCellTK_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/singleCellTK_1.0.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/singleCellTK_1.0.3.tgz vignettes: vignettes/singleCellTK/inst/doc/v01-Introduction_to_singleCellTK.html, vignettes/singleCellTK/inst/doc/v02-Processing_and_Visualizing_Data_in_the_SingleCellTK.html vignetteTitles: 1. Introduction to singleCellTK, 2. Processing and Visualizing Data in the Single Cell Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/v01-Introduction_to_singleCellTK.R, vignettes/singleCellTK/inst/doc/v02-Processing_and_Visualizing_Data_in_the_SingleCellTK.R Package: singscore Version: 1.0.0 Depends: R (>= 3.5),GSEABase Imports: methods, stats, graphics, ggplot2, ggsci, grDevices, ggrepel, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 35d8b14a45eea16d1ecfe6c2b88148ac NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Momeneh Foroutan [aut, ctb], Dharmesh Bhuva [aut, ctb], Ruqian Lyu [aut, cre] Maintainer: Ruqian Lyu URL: https://github.com/DavisLaboratory/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues source.ver: src/contrib/singscore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/singscore_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/singscore_1.0.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: singscore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R Package: SISPA Version: 1.10.0 Depends: R (>= 3.2),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: 5ed5d10919b508b1f921492684b88b9e NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr source.ver: src/contrib/SISPA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SISPA_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SISPA_1.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sizepower Version: 1.50.0 Depends: stats License: LGPL MD5sum: 0e911ffd13834880452a607b788495e5 NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu source.ver: src/contrib/sizepower_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sizepower_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sizepower_1.50.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R Package: skewr Version: 1.12.1 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.18.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: 35f4b3a9458e1e5bc261ecde3c089c64 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr source.ver: src/contrib/skewr_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/skewr_1.12.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/skewr_1.12.1.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R Package: slalom Version: 1.2.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rhdf5, scater, testthat License: GPL-2 Archs: i386, x64 MD5sum: d0625babc1785899ef8b875fb73496be NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. biocViews: SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr source.ver: src/contrib/slalom_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/slalom_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/slalom_1.2.0.tgz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R Package: SLGI Version: 1.40.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: fa72edd76b33aad4ee4bc3fcbbebd761 NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/SLGI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SLGI_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SLGI_1.40.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependsOnMe: PCpheno Package: SLqPCR Version: 1.46.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: f70aac4fe7b79514fcd64b633b69d33d NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl source.ver: src/contrib/SLqPCR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SLqPCR_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SLqPCR_1.46.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R suggestsMe: EasyqpcR Package: SMAP Version: 1.44.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 069dd65707cb066a1e6aeb4c6230f4a5 NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson source.ver: src/contrib/SMAP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SMAP_1.44.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SMAP_1.44.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R Package: SMITE Version: 1.8.0 Depends: R (>= 3.3), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGG.db, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr License: GPL (>=2) MD5sum: f66d16ebfdeac8bfc82afb7957fa348a NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues source.ver: src/contrib/SMITE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SMITE_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SMITE_1.8.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R Package: SNAGEE Version: 1.20.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 654664883d5f5f8835571241d5f43798 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ source.ver: src/contrib/SNAGEE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SNAGEE_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SNAGEE_1.20.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R Package: snapCGH Version: 1.50.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 34dab67fedfba9414f5e8d166ff8b5b2 NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data. Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni source.ver: src/contrib/snapCGH_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/snapCGH_1.50.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/snapCGH_1.50.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP Package: snm Version: 1.28.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: fb9d8ae8c53b7c71071651632a39f1ec NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/snm_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/snm_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/snm_1.28.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: edge Package: SNPchip Version: 2.26.0 Depends: R (>= 2.14.0) Imports: methods, graphics, lattice, grid, foreach, utils, Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, oligoClasses (>= 1.31.1) Suggests: crlmm (>= 1.17.14), RUnit Enhances: doSNOW, VanillaICE (>= 1.21.24), RColorBrewer License: LGPL (>= 2) MD5sum: 490bcc657da575f1b4cb7af290a19967 NeedsCompilation: no Title: Visualizations for copy number alterations Description: Functions for plotting SNP array data; maintained for historical reasons biocViews: CopyNumberVariation, SNP, GeneticVariability, Visualization Author: Robert Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf URL: http://www.biostat.jhsph.edu/~iruczins/software/snpchip.html source.ver: src/contrib/SNPchip_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SNPchip_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SNPchip_2.26.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPchip/inst/doc/PlottingIdiograms.R dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: Category, MinimumDistance, oligoClasses, VanillaICE Package: SNPediaR Version: 1.6.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8b529c7041e3d368740409258ce2ceef NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues source.ver: src/contrib/SNPediaR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SNPediaR_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SNPediaR_1.6.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R Package: SNPhood Version: 1.10.0 Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: b828334a8cf0112f8765667f874ef6df NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: christian.arnold@embl.de source.ver: src/contrib/SNPhood_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SNPhood_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SNPhood_1.10.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R Package: SNPRelate Version: 1.14.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) LinkingTo: gdsfmt Suggests: parallel, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 Archs: i386, x64 MD5sum: 976516d37a12d21a700f78b39eb9cc4d NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph], Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/tutorials/SNPRelate/ VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues source.ver: src/contrib/SNPRelate_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SNPRelate_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SNPRelate_1.14.0.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.R dependsOnMe: SeqSQC importsMe: GDSArray suggestsMe: GENESIS, GWASTools, HIBAG, SeqArray Package: snpStats Version: 1.30.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 97935a03423d6130d746ebb70d86641f NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton source.ver: src/contrib/snpStats_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/snpStats_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/snpStats_1.30.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: GGBase importsMe: FunciSNP, GeneGeneInteR, GGtools, gQTLstats, gwascat, ldblock, martini, MEAL, RVS, scoreInvHap suggestsMe: crlmm, GWASTools, omicRexposome, omicsPrint, VariantAnnotation Package: soGGi Version: 1.12.0 Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: c548485c837d6ddccb5d7c7f54884e3b NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/soGGi_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/soGGi_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/soGGi_1.12.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: DChIPRep Package: SomaticSignatures Version: 2.16.0 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE MD5sum: 3bea1e83e4d7ead1eaba864b2989b4b5 NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org source.ver: src/contrib/SomaticSignatures_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SomaticSignatures_2.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SomaticSignatures_2.16.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: Rariant, YAPSA Package: SpacePAC Version: 1.18.3 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 856b9f94ed18b77609d74f2ea24d0957 NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: RELEASE_3_7 git_last_commit: 3b691da git_last_commit_date: 2018-08-24 Date/Publication: 2018-08-24 source.ver: src/contrib/SpacePAC_1.18.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/SpacePAC_1.18.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SpacePAC_1.18.3.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC Package: sparseDOSSA Version: 1.4.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: 54222c2ae95e81112384c4c0580b5550 NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr source.ver: src/contrib/sparseDOSSA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sparseDOSSA_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sparseDOSSA_1.4.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R Package: SparseSignatures Version: 1.0.3 Depends: R (>= 3.5), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, GenomeInfoDb, ggplot2, gridExtra Suggests: BiocGenerics, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 4a3ef6c18f6b54ee16da55ee0c6c0c5d NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient’s tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [cre, aut], Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [ctb], Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_7 git_last_commit: eb6a9a8 git_last_commit_date: 2018-09-27 Date/Publication: 2018-09-27 source.ver: src/contrib/SparseSignatures_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/SparseSignatures_1.0.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SparseSignatures_1.0.3.tgz vignettes: vignettes/SparseSignatures/inst/doc/SparseSignatures.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/SparseSignatures.R Package: specL Version: 1.14.0 Depends: R (>= 3.3.2), DBI (>= 0.5.1), methods (>= 3.3.2), protViz (>= 0.2.45), RSQLite (>= 1.1.2), seqinr (>= 3.3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2.1), knitr (>= 1.15.1), msqc1 (>= 1.0.0), plotrix (>= 3.6.4), prozor (>= 0.2.2), RUnit (>= 0.4.31) License: GPL-3 MD5sum: 030098b6f40d290453fa9fb34d843d07 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a function for generating spectra libraries which can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. biocViews: MassSpectrometry, Proteomics Author: Christian Trachsel [aut], Christian Panse [aut, cre] (0000-0003-1975-3064), Jonas Grossmann [aut] (0000-0002-6899-9020), Witold E. Wolski [ctb] Maintainer: Christian Panse , Witold E. Wolski URL: https://github.com/fgcz/specL VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues source.ver: src/contrib/specL_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/specL_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/specL_1.14.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf, vignettes/specL/inst/doc/cdsw.html, vignettes/specL/inst/doc/report.html, vignettes/specL/inst/doc/ssrc.html vignetteTitles: Introduction to specL, Computing Dynamic SWATH Windows, Automatic Workflow, Retention Time Prediction using the ssrc Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/cdsw.R, vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R, vignettes/specL/inst/doc/ssrc.R Package: SpeCond Version: 1.34.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: e19f83877f09963994477d1d6433ab93 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli source.ver: src/contrib/SpeCond_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SpeCond_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SpeCond_1.34.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R Package: SPEM Version: 1.20.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 6a5a24118ef9942a8b31ca7a69b0d402 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG source.ver: src/contrib/SPEM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SPEM_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SPEM_1.20.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust Package: SPIA Version: 2.32.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 93392be56d3a532b5a78d89d419c121b NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 source.ver: src/contrib/SPIA_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SPIA_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SPIA_2.32.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph Package: SpidermiR Version: 1.10.0 Depends: R (>= 3.0.0) Imports: networkD3, httr, igraph, utils, stats, TCGAbiolinks, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, gdata Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 53785a651156c09731d7c7d05f30d038 NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miR2Disease,miRTar, miRTarBase, miRandola,Pharmaco-miR,DIANA, Miranda, PicTar and TargetScan) and the use of standard analysis (igraph) and visualization methods (networkD3). biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues source.ver: src/contrib/SpidermiR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SpidermiR_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SpidermiR_1.10.0.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiRcasestudy.pdf, vignettes/SpidermiR/inst/doc/SpidermiR.html vignetteTitles: SpidermiR examples, Working with SpidermiR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R, vignettes/SpidermiR/inst/doc/SpidermiRcasestudy.R importsMe: StarBioTrek Package: spikeLI Version: 2.40.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 8d1580011f5b923d5bcf8ebd012499b2 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon source.ver: src/contrib/spikeLI_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/spikeLI_2.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/spikeLI_2.40.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spkTools Version: 1.36.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: af3931a51269c589fc2201ceba95154c NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org source.ver: src/contrib/spkTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/spkTools_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/spkTools_1.36.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R Package: splatter Version: 1.4.3 Depends: R (>= 3.4), SingleCellExperiment Imports: akima, BiocGenerics, BiocParallel, checkmate, edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.7.4), stats, SummarizedExperiment, utils, crayon Suggests: BiocStyle, covr, cowplot, knitr, limSolve, lme4, progress, pscl, testthat, rmarkdown, S4Vectors, scDD, scran, mfa, phenopath, BASiCS, zinbwave, SparseDC License: GPL-3 + file LICENSE MD5sum: bf400fff20ea4eb3f508ce4d76986082 NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software Author: Luke Zappia Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_7 git_last_commit: 9cca297 git_last_commit_date: 2018-08-19 Date/Publication: 2018-08-20 source.ver: src/contrib/splatter_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/splatter_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/splatter_1.4.3.tgz vignettes: vignettes/splatter/inst/doc/splatter.html vignetteTitles: An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splatter.R suggestsMe: SummarizedBenchmark Package: splicegear Version: 1.52.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: 606d1f403ed79c73342ae6d5c7e0b3e0 NeedsCompilation: no Title: splicegear Description: A set of tools to work with alternative splicing biocViews: Infrastructure, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/splicegear_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/splicegear_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/splicegear_1.52.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splicegear/inst/doc/splicegear.R Package: spliceR Version: 1.22.0 Depends: R (>= 2.15.0), methods, cummeRbund, rtracklayer, VennDiagram, RColorBrewer, plyr Imports: GenomicRanges, IRanges Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome License: GPL (>=2) Archs: i386, x64 MD5sum: 02a70ea710c237c91fa66c7fa32a6ab7 NeedsCompilation: yes Title: Classification of alternative splicing and prediction of coding potential from RNA-seq data. Description: An R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, Visualization Author: Johannes Waage , Kristoffer Vitting-Seerup Maintainer: Johannes Waage , Kristoffer Vitting-Seerup PackageStatus: Deprecated source.ver: src/contrib/spliceR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/spliceR_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/spliceR_1.22.0.tgz vignettes: vignettes/spliceR/inst/doc/spliceR.pdf vignetteTitles: spliceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceR/inst/doc/spliceR.R Package: spliceSites Version: 1.28.0 Depends: methods,rbamtools (>= 2.14.3),refGenome (>= 1.6.0),Biobase,Biostrings (>= 2.28.0) Imports: BiocGenerics,doBy,seqLogo,IRanges License: GPL-2 Archs: i386, x64 MD5sum: ae0aad505efcc0b78965609b4e1ca353 NeedsCompilation: yes Title: A bioconductor package for exploration of alignment gap positions from RNA-seq data Description: Performs splice centered analysis on RNA-seq data. biocViews: RNAseq,GeneExpression,DifferentialExpression,Proteomics Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/spliceSites_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/spliceSites_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/spliceSites_1.28.0.tgz vignettes: vignettes/spliceSites/inst/doc/spliceSites.pdf vignetteTitles: spliceSites hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceSites/inst/doc/spliceSites.R Package: SplicingGraphs Version: 1.20.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), IRanges (>= 2.3.21), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 60192b4bf067b84f268d7d78dee6c7c3 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès source.ver: src/contrib/SplicingGraphs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SplicingGraphs_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SplicingGraphs_1.20.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R Package: splineTimeR Version: 1.8.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: b4e7b2545515a7e2e543b78052383d84 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann VignetteBuilder: knitr source.ver: src/contrib/splineTimeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/splineTimeR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/splineTimeR_1.8.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R Package: SPLINTER Version: 1.6.0 Depends: R (>= 3.4.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr, BSgenome.Mmusculus.UCSC.mm9 Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: b1dc00bc8908bfc79f70e8696a11c3b0 NeedsCompilation: no Title: Splice Interpreter Of Transcripts Description: SPLINTER provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/SPLINTER_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SPLINTER_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SPLINTER_1.6.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R Package: splots Version: 1.46.0 Imports: grid, RColorBrewer License: LGPL MD5sum: befc717f4da12bca5e50c1e4f69eb928 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: The splots package provides the plotScreen function for visualising data in microtitre plate or slide format. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber source.ver: src/contrib/splots_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/splots_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/splots_1.46.0.tgz vignettes: vignettes/splots/inst/doc/splotsHOWTO.pdf vignetteTitles: Visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splotsHOWTO.R dependsOnMe: cellHTS2 importsMe: RNAinteract, RNAither Package: SPONGE Version: 1.2.0 Depends: R (>= 3.4) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: ba56499558927e9d5729218b4711f6c5 NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression Author: Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz Maintainer: Markus List VignetteBuilder: knitr source.ver: src/contrib/SPONGE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SPONGE_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SPONGE_1.2.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R Package: spotSegmentation Version: 1.54.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 9297375b7a1178ce2c39608cb947d42d NeedsCompilation: no Title: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots Description: Spot segmentation via model-based clustering and gridding for blocks within microarray slides, as described in Li et al, Robust Model-Based Segmentation of Microarray Images, Technical Report no. 473, Department of Statistics, University of Washington. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Maintainer: Chris Fraley URL: http://www.stat.washington.edu/fraley source.ver: src/contrib/spotSegmentation_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/spotSegmentation_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/spotSegmentation_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.30.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: b00658a17e196132c9b8e0916e4c2632 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html source.ver: src/contrib/SQUADD_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SQUADD_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SQUADD_1.30.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD package, SQUADD package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R Package: SRAdb Version: 1.42.2 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 8087290fbbc07ff92b7c489165ae218b NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new source.ver: src/contrib/SRAdb_1.42.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/SRAdb_1.42.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SRAdb_1.42.2.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R Package: sRAP Version: 1.20.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 3256b4e131fde5f17559054fddb95703 NeedsCompilation: no Title: Simplified RNA-Seq Analysis Pipeline Description: This package provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data. biocViews: GeneExpression, RNAseq, Microarray, Preprocessing, QualityControl, Statistics, DifferentialExpression, Visualization, GeneSetEnrichment, GO Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/sRAP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sRAP_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sRAP_1.20.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sRAP/inst/doc/sRAP.R Package: SRGnet Version: 1.6.0 Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc Suggests: knitr, rmarkdown License: GPL-2 MD5sum: f1802c377e431871218493d8015078bf NeedsCompilation: no Title: SRGnet: An R package for studying synergistic response to gene mutations from transcriptomics data from transcriptomics data Description: We developed SRGnet to analyze synergistic regulatory mechanisms in transcriptome profiles that act to enhance the overall cell response to combination of mutations, drugs or environmental exposure. This package can be used to identify regulatory modules downstream of synergistic response genes, prioritize synergistic regulatory genes that may be potential intervention targets, and contextualize gene perturbation experiments. biocViews: Software, StatisticalMethod, Regression Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre] Maintainer: Isar Nassiri VignetteBuilder: knitr source.ver: src/contrib/SRGnet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SRGnet_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SRGnet_1.6.0.tgz vignettes: vignettes/SRGnet/inst/doc/vignette.html vignetteTitles: SRGnet An R package for studying synergistic response to gene mutations from transcriptomics data \ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: srnadiff Version: 1.0.0 Depends: R (>= 3.5) Imports: Rcpp (>= 0.12.8), methods, utils, devtools, BiocStyle, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, Rsamtools, GenomicFeatures, GenomicAlignments, ggplot2, BiocParallel LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 0fdfe723d07134fbfd502652511a0792 NeedsCompilation: yes Title: Differential Expression of Small RNA-Seq Description: Differential expression of small RNA-seq when reference annotation is not given. biocViews: GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre] Maintainer: Matthias Zytnicki SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/srnadiff_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/srnadiff_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/srnadiff_1.0.0.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R Package: sscore Version: 1.52.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 8a93adab7dd7b38613500ec6fb9ba1d2 NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html source.ver: src/contrib/sscore_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sscore_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sscore_1.52.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R Package: sscu Version: 2.10.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 2375e57ac086ad7b59cea53da33b3865 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr source.ver: src/contrib/sscu_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sscu_2.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sscu_2.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sSeq Version: 1.18.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 1209ebc8e3d16d21c6c18521fd11c06c NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu source.ver: src/contrib/sSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sSeq_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sSeq_1.18.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq Package: ssize Version: 1.54.0 Depends: gdata, xtable License: LGPL MD5sum: 2cebe464b7898b11f5cc55a534121b41 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes source.ver: src/contrib/ssize_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ssize_1.54.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ssize_1.54.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R Package: SSPA Version: 2.20.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: 3ea4a5ca61b38aaa6595b00e2ace7b8e NeedsCompilation: yes Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/SSPA_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SSPA_2.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SSPA_2.20.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R Package: ssviz Version: 1.14.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats Suggests: knitr License: GPL-2 MD5sum: 16d18ffa1fa4a85c2695ec2b3f0a6c63 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/ssviz_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ssviz_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ssviz_1.14.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R Package: stageR Version: 1.2.22 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: 2f90bb44799ddcf06c3068d27641aac2 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_7 git_last_commit: 02bd126 git_last_commit_date: 2018-06-13 Date/Publication: 2018-06-14 source.ver: src/contrib/stageR_1.2.22.tar.gz win.binary.ver: bin/windows/contrib/3.5/stageR_1.2.22.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/stageR_1.2.22.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R Package: STAN Version: 2.8.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 4cbadd613aa85a93c7c92b2e97d0f976 NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription Author: Benedikt Zacher, Julia Ertl, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr source.ver: src/contrib/STAN_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/STAN_2.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/STAN_2.8.0.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R Package: staRank Version: 1.22.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: 06e016954db94618e12a40cf690cda5e NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg source.ver: src/contrib/staRank_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/staRank_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/staRank_1.22.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R Package: StarBioTrek Version: 1.6.0 Depends: R (>= 3.3) Imports: SpidermiR, KEGGREST, org.Hs.eg.db, AnnotationDbi, e1071, ROCR, grDevices, igraph Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: afcd5dd7b15385a55c696fcdbf3f7a2e NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues source.ver: src/contrib/StarBioTrek_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/StarBioTrek_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/StarBioTrek_1.6.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek_Application_Examples.pdf, vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: StarBioTrek:Application Examples, Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek_Application_Examples.R, vignettes/StarBioTrek/inst/doc/StarBioTrek.R Package: Starr Version: 1.36.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: 54253a78734a684ca005c487dc614018 NeedsCompilation: yes Title: Simple tiling array analysis of Affymetrix ChIP-chip data Description: Starr facilitates the analysis of ChIP-chip data, in particular that of Affymetrix tiling arrays. The package provides functions for data import, quality assessment, data visualization and exploration. Furthermore, it includes high-level analysis features like association of ChIP signals with annotated features, correlation analysis of ChIP signals and other genomic data (e.g. gene expression), peak-finding with the CMARRT algorithm and comparative display of multiple clusters of ChIP-profiles. It uses the basic Bioconductor classes ExpressionSet and probeAnno for maximum compatibility with other software on Bioconductor. All functions from Starr can be used to investigate preprocessed data from the Ringo package, and vice versa. An important novel tool is the the automated generation of correct, up-to-date microarray probe annotation (bpmap) files, which relies on an efficient mapping of short sequences (e.g. the probe sequences on a microarray) to an arbitrary genome. biocViews: Microarray,OneChannel,DataImport,QualityControl,Preprocessing,ChIPchip Author: Benedikt Zacher, Johannes Soeding, Pei Fen Kuan, Matthias Siebert, Achim Tresch Maintainer: Benedikt Zacher source.ver: src/contrib/Starr_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Starr_1.36.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Starr_1.36.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Starr/inst/doc/Starr.R suggestsMe: nucleR Package: STATegRa Version: 1.16.1 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: a346d5383ca3946d8143ecc43f8bef59 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr source.ver: src/contrib/STATegRa_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/STATegRa_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/STATegRa_1.16.1.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R Package: statTarget Version: 1.10.0 Depends: R (>= 3.3.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute,gWidgets2,gWidgets2RGtk2 Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: efec1d245d726fb7692dcac820d5202d NeedsCompilation: no Title: Statistical Analysis of Metabolite Profile Description: An easy to use tool provides a graphical user interface for quality control based shift signal correction, integration of metabolomic data from multi-batch experiments, and the comprehensive statistic analysis in non-targeted or targeted metabolomics. biocViews: Metabolomics, MassSpectrometry, QualityControl, Regression, GUI Author: Hemi Luan Maintainer: Hemi Luan URL: https://github.com/13479776/statTarget VignetteBuilder: knitr source.ver: src/contrib/statTarget_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/statTarget_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/statTarget_1.10.0.tgz vignettes: vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: statTargetIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/statTarget.R Package: stepNorm Version: 1.52.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: c2665161cb25247bfec3ff01e33477ac NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ source.ver: src/contrib/stepNorm_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/stepNorm_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/stepNorm_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Streamer Version: 1.26.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: bf2fa470dc8a2851edd765e32423721a NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan source.ver: src/contrib/Streamer_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Streamer_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Streamer_1.26.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy Package: STRINGdb Version: 1.20.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 4a2c16f2747b9b1da26b813c6e3ecb7a NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (http://www.string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk source.ver: src/contrib/STRINGdb_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/STRINGdb_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/STRINGdb_1.20.0.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer, scsR importsMe: coexnet, IMMAN, pwOmics, RITAN suggestsMe: epiNEM, GeneNetworkBuilder, netSmooth, PCAN Package: STROMA4 Version: 1.4.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: 63b5fe447c845797863f3a178f129381 NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh source.ver: src/contrib/STROMA4_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/STROMA4_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/STROMA4_1.4.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R Package: subSeq Version: 1.10.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: 237709a8d4a72644fc37d290c92d5007 NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr source.ver: src/contrib/subSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/subSeq_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/subSeq_1.10.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R Package: SummarizedBenchmark Version: 1.0.4 Depends: R (>= 3.5), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr Suggests: iCOBRA, BiocStyle, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) MD5sum: 50f468265bccf095bbda0ddd49a234b5 NeedsCompilation: no Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes , Patrick Kimes Maintainer: Alejandro Reyes , Patrick Kimes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_7 git_last_commit: 705ef69 git_last_commit_date: 2018-07-23 Date/Publication: 2018-07-23 source.ver: src/contrib/SummarizedBenchmark_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/SummarizedBenchmark_1.0.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SummarizedBenchmark_1.0.4.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/Appendix.html, vignettes/SummarizedBenchmark/inst/doc/QuantificationBenchmark.html, vignettes/SummarizedBenchmark/inst/doc/SingleCellBenchmark.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark.html vignetteTitles: Data objects and non-standard use., Case Study: Benchmarking Methods Not Written in R, Case Study: Single-Cell RNA-Seq Simulation, Benchmarking with SummarizedBenchmark hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/Appendix.R, vignettes/SummarizedBenchmark/inst/doc/QuantificationBenchmark.R, vignettes/SummarizedBenchmark/inst/doc/SingleCellBenchmark.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark.R Package: SummarizedExperiment Version: 1.10.1 Depends: R (>= 3.2), methods, GenomicRanges (>= 1.31.17), Biobase, DelayedArray (>= 0.3.20) Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.17.25), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.13.1) Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown, digest, jsonlite, rhdf5, HDF5Array (>= 1.7.5), airway, RUnit License: Artistic-2.0 MD5sum: 7a6005bf6f09560d13ceeebc2c18ef98 NeedsCompilation: no Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/SummarizedExperiment_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/SummarizedExperiment_1.10.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SummarizedExperiment_1.10.1.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: SummarizedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AllelicImbalance, anamiR, BiocSklearn, BiSeq, bnbc, bsseq, CAGEfightR, clusterExperiment, coseq, csaw, cydar, DaMiRseq, deepSNV, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, DMCHMM, ENmix, EnrichmentBrowser, epigenomix, EventPointer, ExpressionAtlas, GenoGAM, GenomicAlignments, GenomicFiles, genoset, GRmetrics, GSEABenchmarkeR, HelloRanges, hipathia, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, JunctionSeq, MBASED, methylPipe, minfi, miRmine, mpra, NADfinder, PowerExplorer, recount, REMP, RIPSeeker, rqt, runibic, Scale4C, scater, scone, SDAMS, SGSeq, simulatorZ, SingleCellExperiment, singleCellTK, soGGi, stageR, SummarizedBenchmark, TissueEnrich, VanillaICE, VariantAnnotation, yamss, zinbwave importsMe: adaptest, ALDEx2, alpine, anamiR, anota2seq, apeglm, ASICS, AUCell, BASiCS, BBCAnalyzer, biotmle, biovizBase, BiSeq, BUMHMM, CAGEr, CATALYST, ccfindR, CHARGE, ChIPpeakAnno, chromVAR, CNPBayes, coexnet, DChIPRep, debrowser, DEComplexDisease, DEFormats, DEGreport, DEP, DEScan2, destiny, diffcyt, DominoEffect, easyRNASeq, ELMER, ensemblVEP, epivizrData, erma, FourCSeq, GARS, GenomicDataCommons, GGBase, ggbio, gQTLBase, gQTLstats, GreyListChIP, gwascat, HTSeqGenie, ideal, ImpulseDE2, InterMineR, iteremoval, LineagePulse, M3D, MADSEQ, MAST, mCSEA, MEAL, MetaNeighbor, methyAnalysis, MethylAid, methylumi, methyvim, MinimumDistance, MLSeq, MoonlightR, motifmatchr, msgbsR, MultiAssayExperiment, MultiDataSet, multiOmicsViz, MutationalPatterns, MWASTools, netSmooth, oligoClasses, omicRexposome, omicsPrint, oncomix, pcaExplorer, phenopath, psichomics, PureCN, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, RcisTarget, readat, regionReport, regsplice, rgsepd, roar, RTCGAToolbox, SC3, scDD, scfind, scmap, scmeth, SCnorm, scoreInvHap, scPipe, scran, semisup, seqCAT, singscore, slalom, SNPchip, SNPhood, splatter, srnadiff, SVAPLSseq, switchde, systemPipeR, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, Trendy, TSRchitect, TTMap, TVTB, TxRegInfra, VariantFiltering, vidger, zFPKM suggestsMe: AnnotationHub, biobroom, DelayedArray, epivizr, epivizrChart, esetVis, GENIE3, GenomicRanges, Glimma, globalSeq, HDF5Array, interactiveDisplay, MSnbase, pathprint, podkat, RiboProfiling, S4Vectors, scFeatureFilter, TFutils Package: supraHex Version: 1.18.0 Depends: R (>= 3.3), hexbin Imports: ape, MASS, grDevices, graphics, stats, utils License: GPL-2 MD5sum: 8f28404bea907c66daf96c4a5857168f NeedsCompilation: no Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org source.ver: src/contrib/supraHex_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/supraHex_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/supraHex_1.18.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R importsMe: Pi suggestsMe: TCGAbiolinks Package: survcomp Version: 1.30.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, CPE, clinfun, xtable, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 02ced4af5c9c7af61aaacba6200ec3b4 NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush, Samuel Branders, Zhaleh Safikhani Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/survcomp_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/survcomp_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/survcomp_1.30.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: GenRank suggestsMe: metaseqR Package: Sushi Version: 1.18.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: 22b9d5d04f7f07a6f7ce1753ab149fff NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel source.ver: src/contrib/Sushi_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Sushi_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Sushi_1.18.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R importsMe: diffloop Package: sva Version: 3.28.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a5ac4961915ad56e9f307ab0794a0a1c NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , John D. Storey , Yuqing Zhang , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson source.ver: src/contrib/sva_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/sva_3.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/sva_3.28.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: SCAN.UPC importsMe: ASSIGN, ballgown, BatchQC, bnbc, charm, crossmeta, DaMiRseq, debrowser, doppelgangR, edge, LINC, MAGeCKFlute, omicRexposome, PAA, PROPS, singleCellTK, TCGAbiolinks, trigger suggestsMe: Harman, RnBeads, SomaticSignatures Package: SVAPLSseq Version: 1.6.0 Depends: R (>= 3.4) Imports: methods, stats, SummarizedExperiment, edgeR, ggplot2, limma, lmtest, parallel, pls Suggests: BiocStyle License: GPL-3 MD5sum: c2af4d5da15fa7318e5e172285ef8f69 NeedsCompilation: no Title: SVAPLSseq-An R package to estimate the hidden factors of unwanted variability and adjust for them to enable a more powerful and accurate differential expression analysis based on RNAseq data Description: The package contains functions that are intended for extracting the signatures of latent variation in RNAseq data and using them to perform an improved differential expression analysis for a set of features (genes, transcripts) between two specified biological groups. biocViews: GeneExpression, RNASeq, Normalization, BatchEffect Author: Sutirtha Chakraborty Maintainer: Sutirtha Chakraborty source.ver: src/contrib/SVAPLSseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SVAPLSseq_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SVAPLSseq_1.6.0.tgz vignettes: vignettes/SVAPLSseq/inst/doc/SVAPLSseq.pdf vignetteTitles: SVAPLSseq tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVAPLSseq/inst/doc/SVAPLSseq.R Package: SVM2CRM Version: 1.12.0 Depends: R (>= 3.2.0), LiblineaR, SVM2CRMdata Imports: AnnotationDbi, mclust, GenomicRanges, IRanges, zoo, squash, pls,rtracklayer,ROCR,verification License: GPL-3 MD5sum: 3bc38ecb5fded35031220dc2bf73e835 NeedsCompilation: no Title: SVM2CRM: support vector machine for cis-regulatory elements detections Description: Detection of cis-regulatory elements using svm implemented in LiblineaR. biocViews: ChIPSeq, SupportVectorMachine, Software, Preprocessing, ChipOnChip Author: Guidantonio Malagoli Tagliazucchi Maintainer: Guidantonio Malagoli Tagliazucchi source.ver: src/contrib/SVM2CRM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SVM2CRM_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SVM2CRM_1.12.0.tgz vignettes: vignettes/SVM2CRM/inst/doc/SVM2CRM.pdf vignetteTitles: The \Rpackage{SVM2CRM} Package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVM2CRM/inst/doc/SVM2CRM.R Package: SWATH2stats Version: 1.10.2 Depends: R(>= 2.10.0) Imports: data.table, reshape2, grid, ggplot2, stats, grDevices, graphics, utils Suggests: testthat, aLFQ, knitr, PECA Enhances: imsbInfer, MSstats License: GPL-3 MD5sum: 5dcf5dbb69d80346d0329785fa308b44 NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry Author: Peter Blattmann, Moritz Heusel and Ruedi Aebersold Maintainer: Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_7 git_last_commit: 0a5ebec git_last_commit_date: 2018-06-15 Date/Publication: 2018-06-15 source.ver: src/contrib/SWATH2stats_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.5/SWATH2stats_1.10.2.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SWATH2stats_1.10.2.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R Package: SwathXtend Version: 2.2.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 34f7eee51baacab90f0eaf4bf7d52582 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu source.ver: src/contrib/SwathXtend_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SwathXtend_2.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SwathXtend_2.2.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R Package: swfdr Version: 1.6.0 Depends: R (>= 3.4) Imports: stats4, ggplot2, reshape2, stats, dplyr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: bde9dd4388bfbb422769ffb4ba71ecc2 NeedsCompilation: no Title: Science-wise false discovery rate and proportion of true null hypotheses estimation Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the proportion of true null hypotheses in the presence of covariates, using a regression framework, as per Boca and Leek, "A regression framework for the proportion of true null hypotheses," 2015, bioRxiv preprint. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca Maintainer: Simina M. Boca , Jeffrey T. Leek VignetteBuilder: knitr source.ver: src/contrib/swfdr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/swfdr_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/swfdr_1.6.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrTutorial.R Package: SwimR Version: 1.18.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 MD5sum: 1a0a222f5e485c9b3beaa040e6becdfd NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely source.ver: src/contrib/SwimR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/SwimR_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/SwimR_1.18.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R Package: switchBox Version: 1.16.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 Archs: i386, x64 MD5sum: 3a869f4a03dfd81d4d28fb1a2e794961 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara source.ver: src/contrib/switchBox_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/switchBox_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/switchBox_1.16.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R Package: switchde Version: 1.6.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: 9c5464cbaa83f776c3c268fde4063e63 NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde source.ver: src/contrib/switchde_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/switchde_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/switchde_1.6.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R Package: synapter Version: 2.4.1 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle License: GPL-2 MD5sum: 68699e54a12e13ae0a98cbccc9cecef2 NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr source.ver: src/contrib/synapter_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/synapter_2.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/synapter_2.4.1.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R suggestsMe: pRoloc Package: synergyfinder Version: 1.6.1 Imports: drc (>= 2.5-12), reshape2 (>= 1.4.1), SpatialExtremes (>= 2.0-2), ggplot2 (>= 2.1.0), gridBase (>= 0.4-7), grid (>= 3.2.4), lattice (>= 0.20-33), gplots (>= 3.0.0), nleqslv(>= 3.0), stats (>= 3.3.0), graphics (>= 3.3.0), grDevices (>= 3.3.0) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 + file LICENSE MD5sum: a165f809fbdc17e1fbadc5e669811d84 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for all the popular synergy scoring models for drug combinations, including HSA, Loewe, Bliss and ZIP and visualization of the synergy scores as either a two-dimensional or a three-dimensional interaction surface over the dose matrix. biocViews: Software, Statistical Method Author: Liye He , Jing Tang Maintainer: Liye He VignetteBuilder: knitr source.ver: src/contrib/synergyfinder_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/synergyfinder_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/synergyfinder_1.6.1.tgz vignettes: vignettes/synergyfinder/inst/doc/synergyfinder.pdf vignetteTitles: synergyfinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synergyfinder/inst/doc/synergyfinder.R Package: synlet Version: 1.10.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: ef86cb2d4b085fd94ba29cf689ab5757 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr source.ver: src/contrib/synlet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/synlet_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/synlet_1.10.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R Package: systemPipeR Version: 1.14.0 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: BiocGenerics, GenomicRanges, GenomicFeatures (>= 1.31.3), SummarizedExperiment, VariantAnnotation (>= 1.25.11), rjson, ggplot2, grid, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel License: Artistic-2.0 MD5sum: 07f2603205756648936902b9e34359e5 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl Author: Thomas Girke Maintainer: Thomas Girke URL: http://tgirke.github.io/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr source.ver: src/contrib/systemPipeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/systemPipeR_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/systemPipeR_1.14.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeRIBOseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeVARseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: ChIP-Seq Workflow Template, Ribo-Seq Workflow Template, RNA-Seq Workflow Template, VAR-Seq Workflow Template, Overview Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.R, vignettes/systemPipeR/inst/doc/systemPipeR.R, vignettes/systemPipeR/inst/doc/systemPipeRIBOseq.R, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.R, vignettes/systemPipeR/inst/doc/systemPipeVARseq.R importsMe: DiffBind Package: TargetScore Version: 1.18.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 2ef52feaa5930aa59c18889071b27d35 NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/TargetScore_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TargetScore_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TargetScore_1.18.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R Package: TargetSearch Version: 1.36.1 Depends: ncdf4 Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 8ab1aafc555bc77501bc2ddd80ee5bbb NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree Author: Alvaro Cuadros-Inostroza , Jan Lisec , Henning Redestig , Matt Hannah Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch BugReports: https://github.com/acinostroza/TargetSearch/issues source.ver: src/contrib/TargetSearch_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/TargetSearch_1.36.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TargetSearch_1.36.1.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R Package: TarSeqQC Version: 1.10.0 Depends: R (>= 3.4.1), methods, GenomicRanges, Rsamtools (>= 1.20.4), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: RUnit License: GPL (>=2) MD5sum: 4864081578c90ef2510220a71e4034ad NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar source.ver: src/contrib/TarSeqQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TarSeqQC_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TarSeqQC_1.10.0.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R Package: TCC Version: 1.20.1 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 28a52850b6ea513ec0ecf765eb391143 NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_7 git_last_commit: 2fae396 git_last_commit_date: 2018-07-31 Date/Publication: 2018-07-31 source.ver: src/contrib/TCC_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/TCC_1.20.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TCC_1.20.1.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR Package: TCGAbiolinks Version: 2.8.4 Depends: R (>= 3.2) Imports: downloader (>= 0.4), survminer, grDevices, dplyr, gridExtra, graphics, tibble, grid, GenomicRanges, XML (>= 3.98.0), data.table, EDASeq (>= 2.0.0), edgeR (>= 3.0.0), jsonlite (>= 1.0.0), plyr, knitr, methods, biomaRt, ggplot2, ggthemes, survival, stringr (>= 1.0.0), IRanges, scales, rvest (>= 0.3.0), stats, utils, selectr, S4Vectors, ComplexHeatmap (>= 1.10.2), R.utils, SummarizedExperiment (>= 1.4.0), genefilter, ConsensusClusterPlus, readr, RColorBrewer, doParallel, GenomeInfoDb, GenomicFeatures, parallel, tools, sva, limma, xml2, httr (>= 1.2.1), matlab, circlize, ggrepel (>= 0.6.3) Suggests: png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, pathview, clusterProfiler, igraph, supraHex License: GPL (>= 3) MD5sum: 8b85aaab626006bf4b8ee2f12a306957 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Antonio Colaprico , Tiago Chedraoui Silva URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: RELEASE_3_7 git_last_commit: 458b021 git_last_commit_date: 2018-09-10 Date/Publication: 2018-09-10 source.ver: src/contrib/TCGAbiolinks_2.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/TCGAbiolinks_2.8.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TCGAbiolinks_2.8.4.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: ELMER, MoonlightR, SpidermiR, TCGAbiolinksGUI Package: TCGAbiolinksGUI Version: 1.6.1 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, minfi, caret, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, pander License: GPL (>= 3) MD5sum: 2135eb034d47735e7fce558ad3821f29 NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: RELEASE_3_7 git_last_commit: 3519878 git_last_commit_date: 2018-08-29 Date/Publication: 2018-08-30 source.ver: src/contrib/TCGAbiolinksGUI_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/TCGAbiolinksGUI_1.6.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TCGAbiolinksGUI_1.6.1.tgz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R Package: TCGAutils Version: 1.0.1 Depends: R (>= 3.5.0) Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: BiocStyle, curatedTCGAData, devtools, knitr, magrittr, readr, RTCGAToolbox (>= 2.7.5), testthat License: Artistic-2.0 MD5sum: 1c75b88cb53b302d173914d133423808 NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. biocViews: Software, WorkflowStep, Preprocessing Author: Marcel Ramos [aut, cre], Lucas Schiffer [ctb], WaldronLab [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: RELEASE_3_7 git_last_commit: 24cf27c git_last_commit_date: 2018-06-18 Date/Publication: 2018-06-21 source.ver: src/contrib/TCGAutils_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/TCGAutils_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TCGAutils_1.0.1.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R Package: TCseq Version: 1.4.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: 4b7b8e38325fc753be4f9d6d2f9487a0 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu source.ver: src/contrib/TCseq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TCseq_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TCseq_1.4.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R Package: TDARACNE Version: 1.30.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 0571a7e3304b39d58642a61423942780 NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro source.ver: src/contrib/TDARACNE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TDARACNE_1.30.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TDARACNE_1.30.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R Package: tenXplore Version: 1.2.0 Depends: R (>= 3.4), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr License: Artistic-2.0 MD5sum: 6f22ec9a7295dcd314b274e67c9567fb NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr source.ver: src/contrib/tenXplore_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tenXplore_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tenXplore_1.2.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.pdf vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R Package: TEQC Version: 4.2.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: c29dcb49bb865878e0a7d7c71d603a27 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Manuela Hummel source.ver: src/contrib/TEQC_4.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TEQC_4.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TEQC_4.2.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R Package: ternarynet Version: 1.24.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: a56419d085cc8682ec307d0cd3e4c33d NeedsCompilation: yes Title: Ternary Network Estimation Description: A computational Bayesian approach to ternary gene regulatory network estimation from gene perturbation experiments. biocViews: Software, CellBiology, GraphAndNetwork Author: Matthew N. McCall , Anthony Almudevar Maintainer: Matthew N. McCall source.ver: src/contrib/ternarynet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/ternarynet_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ternarynet_1.24.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R Package: TFARM Version: 1.2.0 Depends: R (>= 3.4) Imports: arules, fields, GenomicRanges, graphics, grDevices, stringr, methods, stats Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 3537027070d27a8f1d6e1fa084f3de32 NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr source.ver: src/contrib/TFARM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TFARM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TFARM_1.2.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R Package: TFBSTools Version: 1.18.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0) License: GPL-2 Archs: i386, x64 MD5sum: 7a96730da0bf9426d147cbbeb0703bf2 NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues source.ver: src/contrib/TFBSTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TFBSTools_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TFBSTools_1.18.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: chromVAR, esATAC, MatrixRider, motifmatchr suggestsMe: JASPAR2018 Package: TFEA.ChIP Version: 1.0.0 Depends: R (>= 3.5), dplyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, stats, utils, R.utils Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, GSEABase, DESeq2, BiocGenerics License: Artistic-2.0 MD5sum: fdb1673c843129fe14dbee16998e4162 NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr source.ver: src/contrib/TFEA.ChIP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TFEA.ChIP_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TFEA.ChIP_1.0.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R Package: TFHAZ Version: 1.2.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: f5b83eb8b617e274a4190a2df3217e58 NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Marco Masseroli Maintainer: Alberto Marchesi VignetteBuilder: knitr source.ver: src/contrib/TFHAZ_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TFHAZ_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TFHAZ_1.2.0.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R Package: TFutils Version: 1.0.0 Depends: R (>= 3.5.0) Imports: methods, GenomicRanges, IRanges, S4Vectors, GSEABase, shiny, miniUI, data.table, dplyr, magrittr, AnnotationDbi, Rsamtools, GenomeInfoDb, GenomicFiles, AnnotationFilter, GenomicFeatures, Gviz, utils, stats, Biobase Suggests: knitr, DT, Homo.sapiens, GO.db, org.Hs.eg.db, ensembldb, EnsDb.Hsapiens.v75, testthat, BiocParallel, BiocStyle, SummarizedExperiment, UpSetR, ggplot2 License: Artistic-2.0 MD5sum: 49be3bab4b3ec35c51a5d2ab9e0f969b NeedsCompilation: no Title: TFutils Description: Package to work with TF data. biocViews: Transcriptomics Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr source.ver: src/contrib/TFutils_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TFutils_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TFutils_1.0.0.tgz vignettes: vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/TFutils.R Package: tigre Version: 1.34.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle License: AGPL-3 Archs: i386, x64 MD5sum: 11c613acf2084e5d2bb3f11b7d8f82c0 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues source.ver: src/contrib/tigre_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tigre_1.34.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tigre_1.34.0.tgz vignettes: vignettes/tigre/inst/doc/tigre_quick.pdf, vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre Quick Guide, tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre_quick.R, vignettes/tigre/inst/doc/tigre.R Package: tilingArray Version: 1.58.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 179eda84888e1b562fa674f34cc8202e NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu source.ver: src/contrib/tilingArray_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tilingArray_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tilingArray_1.58.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R importsMe: ADaCGH2, snapCGH Package: timecourse Version: 1.52.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 6c229f53d5a654eb0b5bd5f051e82a43 NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org source.ver: src/contrib/timecourse_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/timecourse_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/timecourse_1.52.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R Package: timescape Version: 1.4.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 62f53bab930da188bb8b251c8d9979f7 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr source.ver: src/contrib/timescape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/timescape_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/timescape_1.4.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R Package: TIN Version: 1.12.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 5f081a6919747df8ee28b1737caf26c4 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr source.ver: src/contrib/TIN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TIN_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TIN_1.12.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R Package: TissueEnrich Version: 1.0.7 Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 5e8c17298c02b842dd502c3af68a23b9 NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_7 git_last_commit: 9e57e75 git_last_commit_date: 2018-10-10 Date/Publication: 2018-10-10 source.ver: src/contrib/TissueEnrich_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.5/TissueEnrich_1.0.7.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TissueEnrich_1.0.7.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R Package: TitanCNA Version: 1.18.0 Depends: R (>= 3.3.2) Imports: IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), Rsamtools (>= 1.24.0), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 Archs: i386, x64 MD5sum: d0d549ccda5bfed29564b8051c9b9cba NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalenece of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation Author: Gavin Ha, Sohrab P Shah Maintainer: Gavin Ha , Sohrab P Shah URL: https://github.com/gavinha/TitanCNA source.ver: src/contrib/TitanCNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TitanCNA_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TitanCNA_1.18.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R Package: tkWidgets Version: 1.58.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 288500e64d31d566a8b68252c5b13342 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang source.ver: src/contrib/tkWidgets_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tkWidgets_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tkWidgets_1.58.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: TMixClust Version: 1.2.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: 28fcfab05adbe92298aa2057dceb3244 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr source.ver: src/contrib/TMixClust_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TMixClust_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TMixClust_1.2.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R Package: TnT Version: 1.2.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, rmarkdown, testthat License: AGPL-3 MD5sum: 0f521bcbcd839c25bcf4171737977f47 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues source.ver: src/contrib/TnT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TnT_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TnT_1.2.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R Package: tofsims Version: 1.8.0 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, ChemometricsWithR, signal, KernSmooth, graphics, grDevices, stats LinkingTo: Rcpp, RcppArmadillo Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData, BiocParallel, RColorBrewer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: 210623af7305c7048717dfc0059ae1bf NeedsCompilation: yes Title: Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data Description: This packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality. biocViews: Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber URL: https://github.com/lorenzgerber/tofsims VignetteBuilder: knitr BugReports: https://github.com/lorenzgerber/tofsims/issues source.ver: src/contrib/tofsims_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tofsims_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tofsims_1.8.0.tgz vignettes: vignettes/tofsims/inst/doc/workflow.html vignetteTitles: Workflow with the `tofsims` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R Package: ToPASeq Version: 1.14.1 Depends: R(>= 3.5.0), graphite Imports: Rcpp, graph, methods LinkingTo: Rcpp Suggests: BiocStyle, EnrichmentBrowser, airway, knitr, rmarkdown License: AGPL-3 Archs: i386, x64 MD5sum: 02e46e88abaaff5f284f43b80c48d71a NeedsCompilation: yes Title: Topology-based pathway analysis of RNA-seq data Description: Implementation of methods for topology-based pathway analysis of RNA-seq data. This includes Topological Analysis of Pathway Phenotype Association (TAPPA; Gao and Wang, 2007), PathWay Enrichment Analysis (PWEA; Hung et al., 2010), and the Pathway Regulation Score (PRS; Ibrahim et al., 2012). biocViews: GeneExpression, RNASeq, DifferentialExpression, GraphAndNetwork, Pathways, NetworkEnrichment, Visualization Author: Ivana Ihnatova, Eva Budinska, Ludwig Geistlinger Maintainer: Ludwig Geistlinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToPASeq git_branch: RELEASE_3_7 git_last_commit: bd4f779 git_last_commit_date: 2018-06-19 Date/Publication: 2018-06-19 source.ver: src/contrib/ToPASeq_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/ToPASeq_1.14.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/ToPASeq_1.14.1.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq.html vignetteTitles: Topology-based pathway analysis of RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq.R Package: topdownr Version: 1.2.0 Depends: R (>= 3.4), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.2.10), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.11.4) Suggests: topdownrdata (>= 0.2), knitr, ranger, testthat, BiocStyle License: GPL (>= 3) MD5sum: 626e31e052c76cefbae7c57e9be7981e NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (), Pavel Shliaha [aut], Ole Nørregaard Jensen [aut] Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ source.ver: src/contrib/topdownr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/topdownr_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/topdownr_1.2.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R Package: topGO Version: 2.32.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: 2a94d6b451e9f7383fd01d680ab56e8a NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa source.ver: src/contrib/topGO_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/topGO_2.32.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/topGO_2.32.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, ideal, RCAS, RNAither, tRanslatome importsMe: cellity, EnrichmentBrowser, GOSim, OmaDB, pcaExplorer, psygenet2r, SEPA, transcriptogramer suggestsMe: FGNet, IntramiRExploreR, miRNAtap, Ringo Package: TPP Version: 3.8.5 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, sme, splines, stats, stringr, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 12e67e46397d996278d84940662812ef NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_7 git_last_commit: 24f1ed3 git_last_commit_date: 2018-10-06 Date/Publication: 2018-10-07 source.ver: src/contrib/TPP_3.8.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/TPP_3.8.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TPP_3.8.5.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R Package: tracktables Version: 1.14.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: c4b6d9adb1efb221647cc8c79170ff09 NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/tracktables_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tracktables_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tracktables_1.14.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R Package: trackViewer Version: 1.16.1 Depends: R (>= 3.1.0), grDevices, methods, GenomicRanges, grid Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation License: GPL (>= 2) MD5sum: 35172fa646419700a7bd80af06a5e633 NeedsCompilation: no Title: A R/Bioconductor package for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou, Yong-Xu Wang, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_7 git_last_commit: 5ff9aff git_last_commit_date: 2018-09-20 Date/Publication: 2018-09-20 source.ver: src/contrib/trackViewer_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/trackViewer_1.16.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/trackViewer_1.16.1.tgz vignettes: vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R importsMe: NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno Package: transcriptogramer Version: 1.2.1 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 07c4da432d85c990410f6b8afeec10b3 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expression are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr source.ver: src/contrib/transcriptogramer_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/transcriptogramer_1.2.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/transcriptogramer_1.2.1.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R Package: transcriptR Version: 1.8.0 Depends: methods, R (>= 3.3) Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: c9c3650bd9287e57dce3f6aa69135ee4 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr source.ver: src/contrib/transcriptR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/transcriptR_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/transcriptR_1.8.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.pdf vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R Package: tRanslatome Version: 1.18.5 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 68ce81e7bebd3df06ee96793e2b35552 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_7 git_last_commit: 5624488 git_last_commit_date: 2018-08-04 Date/Publication: 2018-08-04 source.ver: src/contrib/tRanslatome_1.18.5.tar.gz win.binary.ver: bin/windows/contrib/3.5/tRanslatome_1.18.5.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tRanslatome_1.18.5.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R Package: TransView Version: 1.24.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, Rsamtools (>= 1.19.38), zlibbioc, gplots LinkingTo: Rsamtools Suggests: RUnit, pasillaBamSubset License: GPL-3 Archs: i386, x64 MD5sum: 62d957014ef2f3e4b5d9db8e9b1370ed NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html source.ver: src/contrib/TransView_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TransView_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TransView_1.24.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R Package: traseR Version: 1.10.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: 6b2dc68877e0619f3786af148842ad10 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen source.ver: src/contrib/traseR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/traseR_1.10.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/traseR_1.10.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R Package: treeio Version: 1.4.3 Depends: R (>= 3.4.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, rvcheck, tibble, tidytree (>= 0.1.7) Suggests: ggplot2, ggtree, knitr, prettydoc, testthat, tidyr License: Artistic-2.0 MD5sum: 7827aa7b35529f4abce55c69f162e6bb NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: Base classes and functions for parsing and exporting phylogenetic trees. 'treeio' supports parsing analysis findings from commonly used software packages, allows linking external data to phylogeny and merging tree data obtained from different sources. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment Author: Guangchuang Yu [aut, cre] (), Tommy Tsan-Yuk Lam [ctb, ths], Casey Dunn [ctb], Bradley Jones [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/treeio VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_7 git_last_commit: 6eff9cd git_last_commit_date: 2018-08-13 Date/Publication: 2018-08-13 source.ver: src/contrib/treeio_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.5/treeio_1.4.3.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/treeio_1.4.3.tgz vignettes: vignettes/treeio/inst/doc/Exporter.html, vignettes/treeio/inst/doc/Importer.html vignetteTitles: 02 Exporting trees with data, 01 Importing trees with data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/Exporter.R, vignettes/treeio/inst/doc/Importer.R importsMe: ggtree Package: trena Version: 1.2.0 Depends: R (>= 3.4.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, lassopv, randomForest, flare, vbsr, BiocParallel, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, RMySQL License: GPL-3 MD5sum: 6ea4d238187b4945a497ea1163cf8e90 NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon VignetteBuilder: knitr source.ver: src/contrib/trena_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/trena_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/trena_1.2.0.tgz vignettes: vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/TReNA_Vignette.R Package: Trendy Version: 1.2.11 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: b455c70ebad3393651b041d557358c7a NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_7 git_last_commit: 220e557 git_last_commit_date: 2018-10-10 Date/Publication: 2018-10-10 source.ver: src/contrib/Trendy_1.2.11.tar.gz win.binary.ver: bin/windows/contrib/3.5/Trendy_1.2.11.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Trendy_1.2.11.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R Package: triform Version: 1.22.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: BiocGenerics, IRanges (>= 2.5.27), yaml Suggests: RUnit License: GPL-2 MD5sum: 8f23d69222dc7cc7be85a4fd7262323b NeedsCompilation: no Title: Triform finds enriched regions (peaks) in transcription factor ChIP-sequencing data Description: The Triform algorithm uses model-free statistics to identify peak-like distributions of TF ChIP sequencing reads, taking advantage of an improved peak definition in combination with known profile characteristics. biocViews: Sequencing, ChIPSeq Author: Karl Kornacker Developer [aut], Tony Handstad Developer [aut, cre] Maintainer: Thomas Carroll source.ver: src/contrib/triform_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/triform_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/triform_1.22.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/triform/inst/doc/triform.R Package: trigger Version: 1.26.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: e0bbb0f2d7e1d777be4f92864e2453b3 NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/trigger_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/trigger_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/trigger_1.26.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R Package: trio Version: 3.18.0 Depends: R (>= 3.0.1) Suggests: survival, haplo.stats, mcbiopi, siggenes, splines, LogicReg (>= 1.5.3), logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: 58bd02f5ddf2db488671d0de3c38e7fb NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender source.ver: src/contrib/trio_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/trio_3.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/trio_3.18.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R Package: triplex Version: 1.20.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer, GenomeGraphs License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: f610c131786aa0ff3eb119bb3c18441f NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ source.ver: src/contrib/triplex_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/triplex_1.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/triplex_1.20.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R Package: tRNAscanImport Version: 1.0.1 Depends: R (>= 3.5), GenomicRanges Imports: methods, assertive, stringr, reshape2, BiocGenerics, Biostrings, S4Vectors, GenomicRanges, GenomeInfoDb, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, graphics, ggplot2, scales License: GPL-3 + file LICENSE MD5sum: d344f0e4ba8c4bae9587295a6a1b8cc6 NeedsCompilation: no Title: Imports a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix GM Ernst [aut, cre] Maintainer: Felix GM Ernst VignetteBuilder: knitr source.ver: src/contrib/tRNAscanImport_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/tRNAscanImport_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tRNAscanImport_1.0.1.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R Package: TRONCO Version: 2.12.0 Depends: R (>= 3.4), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: f565cfef1974e9e6fcaf29acee35a507 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De Sano [aut], Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] Maintainer: BIMIB Group URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO source.ver: src/contrib/TRONCO_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TRONCO_2.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TRONCO_2.12.0.tgz vignettes: vignettes/TRONCO/inst/doc/TRONCO.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRONCO/inst/doc/TRONCO.R Package: TSCAN Version: 1.18.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: d62cf0af2b55b9b2fab7572376484f00 NeedsCompilation: no Title: TSCAN: Tools for Single-Cell ANalysis Description: TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/TSCAN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TSCAN_1.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TSCAN_1.18.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R Package: tspair Version: 1.38.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: e4deb5cf01aa014a1ddf8ef0d627fc21 NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek source.ver: src/contrib/tspair_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tspair_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tspair_1.38.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R Package: TSRchitect Version: 1.8.9 Depends: R (>= 3.5) Imports: AnnotationHub, BiocGenerics, BiocParallel, GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools, IRanges, methods, Rsamtools (>= 1.14.3), rtracklayer, S4Vectors, SummarizedExperiment, utils Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown License: GPL-3 MD5sum: 0129d30a7c448a96bdf09dd5c21507da NeedsCompilation: no Title: Promoter identification from large-scale TSS profiling data Description: In recent years, large-scale transcriptional sequence data has yielded considerable insights into the nature of gene expression and regulation in eukaryotes. Techniques that identify the 5' end of mRNAs, most notably CAGE, have mapped the promoter landscape across a number of model organisms. Due to the variability of TSS distributions and the transcriptional noise present in datasets, precisely identifying the active promoter(s) for genes from these datasets is not straightforward. TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq). In addition, (new with version 1.3.0) TSRchitect provides the ability to import aligned EST and cDNA data. Along with the coordiantes of identified TSRs, TSRchitect also calculates the width, abundance and two forms of the Shape Index, and handles biological replicates for expression profiling. Finally, TSRchitect imports annotation files, allowing the user to associate identified promoters with genes and other genomic features. Three detailed examples of TSRchitect's utility are provided in the User's Guide, included with this package. biocViews: Clustering, FunctionalGenomics, GeneExpression, GeneRegulation, GenomeAnnotation, Sequencing, Transcription Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph] Krishnakumar Sridharan [ctb] Maintainer: R. Taylor Raborn URL: https://github.com/brendelgroup/tsrchitect VignetteBuilder: knitr BugReports: https://github.com/brendelgroup/tsrchitect/issues git_url: https://git.bioconductor.org/packages/TSRchitect git_branch: RELEASE_3_7 git_last_commit: f4b1c7e git_last_commit_date: 2018-10-08 Date/Publication: 2018-10-09 source.ver: src/contrib/TSRchitect_1.8.9.tar.gz win.binary.ver: bin/windows/contrib/3.5/TSRchitect_1.8.9.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TSRchitect_1.8.9.tgz vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf, vignettes/TSRchitect/inst/doc/TSRchitect.html, vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html vignetteTitles: TSRchitect User's Guide, TSRchitect vignette, TSRchitect User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R Package: TSSi Version: 1.26.0 Depends: R (>= 2.13.2) Imports: methods, BiocGenerics (>= 0.3.2), S4Vectors, Hmisc, minqa, stats, Biobase (>= 0.3.2), plyr, IRanges Suggests: rtracklayer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: b3ae39f270b6e72e988406d16b3814ec NeedsCompilation: yes Title: Transcription Start Site Identification Description: Identify and normalize transcription start sites in high-throughput sequencing data. biocViews: Sequencing, RNASeq, Genetics, Preprocessing Author: Julian Gehring, Clemens Kreutz Maintainer: Julian Gehring source.ver: src/contrib/TSSi_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TSSi_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TSSi_1.26.0.tgz vignettes: vignettes/TSSi/inst/doc/TSSi.pdf vignetteTitles: Introduction to the TSSi package: Identification of Transcription Start Sites hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSSi/inst/doc/TSSi.R Package: TTMap Version: 1.2.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: 1c3bbc3face560c5ac79f2d4c0b66105 NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner source.ver: src/contrib/TTMap_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TTMap_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TTMap_1.2.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R Package: TurboNorm Version: 1.28.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL Archs: i386, x64 MD5sum: 16beaf9dd6e0d7010a3512a554124d50 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/TurboNorm_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TurboNorm_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TurboNorm_1.28.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R Package: TVTB Version: 1.6.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.7.1), reshape2, Rsamtools, S4Vectors (>= 0.11.11), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 0f464da7bcfa8ae9c0a5285938c61d3b NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues source.ver: src/contrib/TVTB_1.6.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TVTB_1.6.0.tgz vignettes: vignettes/TVTB/inst/doc/tSVE.pdf, vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: The Shiny Variant Explorer, Introduction to TVTB, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R Package: tweeDEseq Version: 1.26.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 69b456860054eae71497406924929901 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm source.ver: src/contrib/tweeDEseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tweeDEseq_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tweeDEseq_1.26.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R Package: twilight Version: 1.56.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: i386, x64 MD5sum: 4f31d4bb9bd2c06563b924efbc022373 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml source.ver: src/contrib/twilight_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/twilight_1.56.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/twilight_1.56.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList importsMe: OrderedList Package: twoddpcr Version: 1.4.1 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: bdf0554f2cebca9eda3ae25745239390 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ source.ver: src/contrib/twoddpcr_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/twoddpcr_1.4.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/twoddpcr_1.4.1.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R Package: tximport Version: 1.8.0 Imports: utils Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite License: GPL (>=2) MD5sum: 6b3faf6771c87ea6b7312c4ae25ee82d NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: RNASeq, Transcription, GeneExpression, DataImport Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb] Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/tximport_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/tximport_1.8.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/tximport_1.8.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: IsoformSwitchAnalyzeR, scater suggestsMe: DESeq2, SummarizedBenchmark, variancePartition Package: TxRegInfra Version: 1.0.1 Depends: RaggedExperiment (>= 1.3.11), mongolite Imports: methods, rjson, GenomicRanges, IRanges, BiocParallel, GenomeInfoDb, S4Vectors, SummarizedExperiment, utils Suggests: knitr, GenomicFiles, EnsDb.Hsapiens.v75, testthat, biovizBase (>= 1.27.2), Gviz, AnnotationFilter, ensembldb License: Artistic-2.0 MD5sum: ae56261c1f115427096f6a5ae4ab064b NeedsCompilation: no Title: Metadata management for multiomic specification of transcriptional regulatory networks Description: This package provides interfaces to genomic metadata employed in regulatory network creation, with a focus on noSQL solutions. Currently quantitative representations of eQTLs, DnaseI hypersensitivity sites and digital genomic footprints are assembled using an out-of-memory extension of the RaggedExperiment API. biocViews: Network Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TxRegInfra git_branch: RELEASE_3_7 git_last_commit: 974261a git_last_commit_date: 2018-10-12 Date/Publication: 2018-10-12 source.ver: src/contrib/TxRegInfra_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/TxRegInfra_1.0.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TxRegInfra_1.0.1.tgz vignettes: vignettes/TxRegInfra/inst/doc/TxRegInfra.html vignetteTitles: TxRegInfra -- classes and methods for TxRegQuery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TxRegInfra/inst/doc/TxRegInfra.R Package: TypeInfo Version: 1.46.0 Depends: methods Suggests: Biobase License: BSD MD5sum: f28aaeaf6d6f79def779ed5654d2ec01 NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang source.ver: src/contrib/TypeInfo_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/TypeInfo_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/TypeInfo_1.46.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R Package: UNDO Version: 1.22.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: ef95f0e2804e20cecbd7ec5396ccb9cb NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang source.ver: src/contrib/UNDO_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/UNDO_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/UNDO_1.22.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R Package: unifiedWMWqPCR Version: 1.16.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: d5acefd47ea54ebfdaf5054893510e0f NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys source.ver: src/contrib/unifiedWMWqPCR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/unifiedWMWqPCR_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/unifiedWMWqPCR_1.16.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R Package: UniProt.ws Version: 2.20.4 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, rappdirs Suggests: RUnit License: Artistic License 2.0 MD5sum: 29ba8e24054f3ce375994520f977c00d NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the UniProt web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut, cre], Csaba Ortutay [ctb] Maintainer: Marc Carlson git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_7 git_last_commit: 1a14832 git_last_commit_date: 2018-10-15 Date/Publication: 2018-10-15 source.ver: src/contrib/UniProt.ws_2.20.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/UniProt.ws_2.20.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/UniProt.ws_2.20.4.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R suggestsMe: cleaver, dagLogo Package: Uniquorn Version: 2.0.0 Depends: R (>= 3.4) Imports: stringr, R.utils, WriteXLS, stats, data.table, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: ce42515dd373b58f047e589bc1611329 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: This packages enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology. RNA-seq data is very likely to work as well but hasn't been rigiously tested yet. Panel-seq will require manual adjustment of thresholds biocViews: Software, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr source.ver: src/contrib/Uniquorn_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Uniquorn_2.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Uniquorn_2.0.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R Package: uSORT Version: 1.6.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: 134d741883bd46b5edb9d6e777ca18c9 NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr source.ver: src/contrib/uSORT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/uSORT_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/uSORT_1.6.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R Package: VanillaICE Version: 1.42.4 Depends: R (>= 3.0.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: Biobase, S4Vectors (>= 0.9.25), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, SNPchip, human610quadv1bCrlmm, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: 5dd19cffabe5e1e722d14ceb8b85e92b NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf , Kevin Scharpf, and Ingo Ruczinski Maintainer: Robert Scharpf source.ver: src/contrib/VanillaICE_1.42.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/VanillaICE_1.42.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/VanillaICE_1.42.4.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses Package: variancePartition Version: 1.10.4 Depends: R (>= 3.0.0), ggplot2, limma, foreach, scales, Biobase, methods Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, iterators, splines, colorRamps, gplots, reshape2, lme4 (>= 1.1-10), doParallel, grDevices, graphics, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL (>= 2) MD5sum: 18c9de61e032d1493040d787d7c53961 NeedsCompilation: no Title: Quantify and interpret divers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, Regression, Transcription, Normalization, Preprocessing, Software Author: Gabriel E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_7 git_last_commit: 4455edd git_last_commit_date: 2018-10-11 Date/Publication: 2018-10-12 source.ver: src/contrib/variancePartition_1.10.4.tar.gz win.binary.ver: bin/windows/contrib/3.5/variancePartition_1.10.4.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/variancePartition_1.10.4.tgz vignettes: vignettes/variancePartition/inst/doc/additional_visualization.pdf, vignettes/variancePartition/inst/doc/theory_practice_random_effects.pdf, vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/dream.html vignetteTitles: 2) Additional visualizations, 3) Theory and practice of random effects, 1) Tutorial on using variancePartition, 4) Differential expression testing with repeated measures designs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R Package: VariantAnnotation Version: 1.26.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 1.9.9), Rsamtools (>= 1.31.2) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.17.24), IRanges (>= 2.13.13), XVector (>= 0.19.7), Biostrings (>= 2.47.6), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rsamtools Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: e60fa4f6031485c784f526706607d60d NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Valerie Obenchain [aut, cre], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Valerie Obenchain Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_7 git_last_commit: 60ae675 git_last_commit_date: 2018-07-04 Date/Publication: 2018-07-04 source.ver: src/contrib/VariantAnnotation_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/VariantAnnotation_1.26.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/VariantAnnotation_1.26.1.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, DOQTL, ensemblVEP, genotypeeval, GoogleGenomics, HelloRanges, HTSeqGenie, igvR, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, Rariant, seqCAT, signeR, SomaticSignatures, VariantFiltering, VariantTools importsMe: AllelicImbalance, BadRegionFinder, BBCAnalyzer, biovizBase, customProDB, DominoEffect, FunciSNP, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, GGtools, gmapR, gQTLstats, gwascat, ldblock, MADSEQ, maftools, methyAnalysis, motifbreakR, MutationalPatterns, PGA, scoreInvHap, SNPhood, systemPipeR, TitanCNA, TVTB, Uniquorn, YAPSA suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, GenomicRanges, GenomicScores, GMRP, GWASTools, omicsPrint, podkat, SeqArray, trackViewer, trio, vtpnet Package: VariantFiltering Version: 1.16.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 7725a9d715a09c33d3ca7053dec3ee14 NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues source.ver: src/contrib/VariantFiltering_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/VariantFiltering_1.16.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/VariantFiltering_1.16.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R Package: VariantTools Version: 1.22.0 Depends: S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3) License: Artistic-2.0 MD5sum: 8553187bdd9bff8c0e672cd437170b93 NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/VariantTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/VariantTools_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/VariantTools_1.22.0.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie Package: vbmp Version: 1.48.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 51c1fbaa856372f31c684c54e5d2cac1 NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 source.ver: src/contrib/vbmp_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/vbmp_1.48.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/vbmp_1.48.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R Package: Vega Version: 1.28.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 4e5cc083ab70f1a67b1d5684acf389d8 NeedsCompilation: yes Title: An R package for copy number data segmentation Description: Vega (Variational Estimator for Genomic Aberrations) is an algorithm that adapts a very popular variational model (Mumford and Shah) used in image segmentation so that chromosomal aberrant regions can be efficiently detected. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella Maintainer: Sandro Morganella source.ver: src/contrib/Vega_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/Vega_1.28.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/Vega_1.28.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Vega/inst/doc/Vega.R Package: VegaMC Version: 3.18.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods, genoset License: GPL-2 Archs: i386, x64 MD5sum: d5361720b764c82b18ee30712e50ba6a NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella source.ver: src/contrib/VegaMC_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/VegaMC_3.18.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/VegaMC_3.18.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R Package: vidger Version: 1.0.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, knitr, rmarkdown, GGally, ggplot2, scales, stats, SummarizedExperiment, tidyr, utils Suggests: testthat, BiocStyle License: GPL-2 | file LICENSE MD5sum: d769b77dd2ff5cb8f60cc22b53400378 NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: Visualization, RNASeq, DifferentialExpression, GeneExpression Author: Brandon Monier, Adam McDermaid, Qin Ma Maintainer: Brandon Monier VignetteBuilder: knitr source.ver: src/contrib/vidger_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/vidger_1.0.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/vidger_1.0.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R Package: viper Version: 1.14.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 7803933d0a76d991681dc20d330e6006 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez source.ver: src/contrib/viper_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/viper_1.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/viper_1.14.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan importsMe: diggit, RTN Package: vsn Version: 3.48.1 Depends: R (>= 3.4.0), Biobase Imports: methods, affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 2b18cf596459dbe0e43861759c8cc154 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr source.ver: src/contrib/vsn_3.48.1.tar.gz win.binary.ver: bin/windows/contrib/3.5/vsn_3.48.1.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/vsn_3.48.1.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, coexnet, DEP, Doscheda, imageHTS, LVSmiRNA, metaseqR, MSnbase, PowerExplorer, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight Package: vtpnet Version: 0.20.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 382df78fe44727d8964c49139cbcb172 NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/vtpnet_0.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/vtpnet_0.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/vtpnet_0.20.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R Package: vulcan Version: 1.2.0 Depends: R (>= 3.4), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: fb7e14f12f6a9f641a214c425f09a4e3 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi source.ver: src/contrib/vulcan_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/vulcan_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/vulcan_1.2.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R Package: wateRmelon Version: 1.24.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Enhances: minfi License: GPL-3 MD5sum: d128464977cab718b75d4dda3f63e695 NeedsCompilation: no Title: Illumina 450 methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic, Tyler Gorrie-Stone, Louis El Khoury Maintainer: Leo source.ver: src/contrib/wateRmelon_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/wateRmelon_1.24.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/wateRmelon_1.24.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: The \Rpackage{wateRmelon} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP suggestsMe: RnBeads Package: wavClusteR Version: 2.14.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr, wmtsa Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: fd01b5fbe3fa23f7064c4bb830b5ab60 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr source.ver: src/contrib/wavClusteR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/wavClusteR_2.14.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/wavClusteR_2.14.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R Package: waveTiling Version: 1.22.0 Depends: oligo, oligoClasses, Biobase, Biostrings, GenomeGraphs Imports: methods, affy, preprocessCore, GenomicRanges, waveslim, IRanges Suggests: BSgenome, BSgenome.Athaliana.TAIR.TAIR9, waveTilingData, pd.atdschip.tiling, TxDb.Athaliana.BioMart.plantsmart22 License: GPL (>=2) Archs: i386, x64 MD5sum: 304d3b90ac7ef0dcd2e0babcc65d719b NeedsCompilation: yes Title: Wavelet-Based Models for Tiling Array Transcriptome Analysis Description: This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models. biocViews: Microarray, DifferentialExpression, TimeCourse, GeneExpression Author: Kristof De Beuf , Peter Pipelers and Lieven Clement Maintainer: Kristof De Beuf URL: https://r-forge.r-project.org/projects/wavetiling/ source.ver: src/contrib/waveTiling_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/waveTiling_1.22.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/waveTiling_1.22.0.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/waveTiling/inst/doc/waveTiling-vignette.R Package: weaver Version: 1.46.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: ac3d2c379e2f34d3eca9adb82c441e58 NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon source.ver: src/contrib/weaver_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/weaver_1.46.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/weaver_1.46.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R suggestsMe: BiocCaseStudies Package: webbioc Version: 1.52.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: 3f3ef17bfc2bf00f65412bee065fb8c2 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm source.ver: src/contrib/webbioc_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/webbioc_1.52.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/webbioc_1.52.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: widgetTools Version: 1.58.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 503158c58f4add539bdb77754763fef8 NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang source.ver: src/contrib/widgetTools_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/widgetTools_1.58.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/widgetTools_1.58.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui suggestsMe: affy Package: wiggleplotr Version: 1.4.0 Depends: R (>= 3.4) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 MD5sum: f6edc1128c55c46f2e2b24f8b76208da NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr source.ver: src/contrib/wiggleplotr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/wiggleplotr_1.4.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/wiggleplotr_1.4.0.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R Package: XBSeq Version: 1.12.0 Depends: DESeq2, R (>= 3.3) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, magrittr, roar Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) MD5sum: b6010993fce8f44170921d313aeca1ea NeedsCompilation: no Title: Test for differential expression for RNA-seq data Description: We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes. biocViews: RNASeq, DifferentialExpression, Sequencing, Software, ExperimentalDesign Author: Yuanhang Liu Maintainer: Yuanhang Liu URL: https://github.com/Liuy12/XBSeq VignetteBuilder: knitr source.ver: src/contrib/XBSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/XBSeq_1.12.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/XBSeq_1.12.0.tgz vignettes: vignettes/XBSeq/inst/doc/XBSeq.html vignetteTitles: Differential expression and apa usage analysis of count data using XBSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XBSeq/inst/doc/XBSeq.R Package: xcms Version: 3.2.0 Depends: R (>= 2.14.0), methods, Biobase, BiocParallel (>= 1.8.0), MSnbase (>= 2.5.10) Imports: mzR (>= 2.13.1), BiocGenerics, ProtGenerics, lattice, RColorBrewer, plyr, RANN, multtest, MassSpecWavelet (>= 1.5.2), S4Vectors Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata, ncdf4, rgl, microbenchmark, testthat, pander, magrittr Enhances: Rgraphviz, Rmpi, XML, robustbase License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 719f96370304b4d0b8f8e3ca55252dac NeedsCompilation: yes Title: LC/MS and GC/MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley , Johannes Rainer Maintainer: Steffen Neumann URL: http://metlin.scripps.edu/download/ and https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new source.ver: src/contrib/xcms_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/xcms_3.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/xcms_3.2.0.tgz vignettes: vignettes/xcms/inst/doc/new_functionality.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms.html, vignettes/xcms/inst/doc/xcmsMSn.html vignetteTitles: New and modified functionality in xcms, Grouping FTICR-MS data with xcms, LCMS data preprocessing and analysis with xcms, Processing Tandem-MS and MSn data with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/new_functionality.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms.R, vignettes/xcms/inst/doc/xcmsMSn.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, proFIA importsMe: CAMERA, cosmiq, Risa suggestsMe: MassSpecWavelet, msPurity, RMassBank, ropls Package: XDE Version: 2.26.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: 1a57520544a7cd1414ac281c0b327073 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf source.ver: src/contrib/XDE_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/XDE_2.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/XDE_2.26.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R Package: xmapbridge Version: 1.38.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 825cd922ff043c6c6e5e6c1e7fae8b54 NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org source.ver: src/contrib/xmapbridge_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/xmapbridge_1.38.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/xmapbridge_1.38.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R Package: xps Version: 1.40.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) MD5sum: e938917896f61a795b1ce05c51bf61a4 NeedsCompilation: yes Title: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays Description: The package handles pre-processing, normalization, filtering and analysis of Affymetrix GeneChip expression arrays, including exon arrays (Exon 1.0 ST: core, extended, full probesets), gene arrays (Gene 1.0 ST) and plate arrays on computers with 1 GB RAM only. It imports Affymetrix .CDF, .CLF, .PGF and .CEL as well as annotation files, and computes e.g. RMA, MAS5, FARMS, DFW, FIRMA, tRMA, MAS5-calls, DABG-calls, I/NI-calls. It is an R wrapper to XPS (eXpression Profiling System), which is based on ROOT, an object-oriented framework developed at CERN. Thus, the prior installation of ROOT is a prerequisite for the usage of this package, however, no knowledge of ROOT is required. ROOT is licensed under LGPL and can be downloaded from http://root.cern.ch. biocViews: ExonArray, GeneExpression, Microarray, OneChannel, DataImport, Preprocessing, Transcription, DifferentialExpression Author: Christian Stratowa, Vienna, Austria Maintainer: Christian Stratowa SystemRequirements: GNU make, root_v5.34.36 - See README file for installation instructions. source.ver: src/contrib/xps_1.40.0.tar.gz mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/xps_1.40.0.tgz vignettes: vignettes/xps/inst/doc/APTvsXPS.pdf, vignettes/xps/inst/doc/xps.pdf, vignettes/xps/inst/doc/xpsClasses.pdf, vignettes/xps/inst/doc/xpsPreprocess.pdf vignetteTitles: 3. XPS Vignette: Comparison APT vs XPS, 1. XPS Vignette: Overview, 2. XPS Vignette: Classes, 4. XPS Vignette: Function express() hasREADME: TRUE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/xps/inst/doc/APTvsXPS.R, vignettes/xps/inst/doc/xps.R, vignettes/xps/inst/doc/xpsClasses.R, vignettes/xps/inst/doc/xpsPreprocess.R Package: XVector Version: 0.20.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.19.2), S4Vectors (>= 0.17.24), IRanges (>= 2.13.16) Imports: methods, utils, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 091118a7e7736427ca2157ad4aa5adbe NeedsCompilation: yes Title: Representation and manipulation of external sequences Description: Memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès source.ver: src/contrib/XVector_0.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/XVector_0.20.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/XVector_0.20.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, IONiseR, kebabs, MatrixRider, R453Plus1Toolbox, Rsamtools, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rSFFreader, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering Package: yamss Version: 1.6.0 Depends: R (>= 3.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: e9841bf96bff448bed32ae2f6fe220d8 NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. biocViews: MassSpectrometry, Metabolomics, Software Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues source.ver: src/contrib/yamss_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/yamss_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/yamss_1.6.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R Package: YAPSA Version: 1.6.0 Depends: R (>= 3.3.0), GenomicRanges, ggplot2, grid Imports: lsei, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, PMCMR, ComplexHeatmap, KEGGREST, grDevices Suggests: BSgenome.Hsapiens.UCSC.hg19, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9fb3e41d93a54ead3c5cf924b11e5d9b NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines useful in the analysis of somatic signatures (cf. L. Alexandrov et al., Nature 2013). In particular, functions to perform a signature analysis with known signatures (LCD = linear combination decomposition) and a signature analysis on stratified mutational catalogue (SMC = stratify mutational catalogue) are provided. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann, Zuguang Gu, Matthias Schlesner Maintainer: Daniel Huebschmann VignetteBuilder: knitr source.ver: src/contrib/YAPSA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/YAPSA_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/YAPSA_1.6.0.tgz vignettes: vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/YAPSA.R Package: yaqcaffy Version: 1.40.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: b760a199aa1199734f400efaf657319d NeedsCompilation: no Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto source.ver: src/contrib/yaqcaffy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/yaqcaffy_1.40.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/yaqcaffy_1.40.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yaqcaffy/inst/doc/yaqcaffy.R suggestsMe: qcmetrics Package: yarn Version: 1.6.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: 334aa756d4e8d6e59ede3427dac27a43 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr source.ver: src/contrib/yarn_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/yarn_1.6.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/yarn_1.6.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R Package: zFPKM Version: 1.2.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr License: GPL-3 | file LICENSE MD5sum: 8d3dd2a3ac74ad60f0e31183cc990c47 NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues source.ver: src/contrib/zFPKM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/zFPKM_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/zFPKM_1.2.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R Package: zinbwave Version: 1.2.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: copula, glmnet, BiocParallel, softImpute, stats, genefilter, edgeR Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, Seurat License: Artistic-2.0 MD5sum: 10220b2ad39cdbe48db32422ca95312b NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues source.ver: src/contrib/zinbwave_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/zinbwave_1.2.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/zinbwave_1.2.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R suggestsMe: splatter Package: zlibbioc Version: 1.26.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: e77c3f5eeac79d5b579234822bc86065 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Zlibbioc.html source.ver: src/contrib/zlibbioc_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.5/zlibbioc_1.26.0.zip mac.binary.el-capitan.ver: bin/macosx/el-capitan/contrib/3.5/zlibbioc_1.26.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BitSeq importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, DiffBind, LVSmiRNA, MADSEQ, makecdfenv, oligo, polyester, QuasR, Rhtslib, Rsamtools, rtracklayer, seqbias, ShortRead, snpStats, Starr, TransView, VariantAnnotation, XVector linksToMe: bamsignals, BitSeq, csaw, diffHic, methylKit, mzR, Rhtslib, scPipe, seqTools