Package: a4 Version: 1.22.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: c17ae72ad2115d0e5c80ca6d9c7b5cba 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4_1.22.0.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: 1.22.0 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: abb9cd7a79c892c8185314ae1112af1b 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Base_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Base_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Base_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.22.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: d61fa0ce5023e28cc77232b9d432a78e 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Classif_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Classif_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Classif_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.22.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 553294aafed8524bf5d749b5f9c2e801 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Core_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Core_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Core_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.22.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: 6be9da5f55b6e8a829d10da664d2c8cb 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Preproc_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Preproc_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Preproc_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif Package: a4Reporting Version: 1.22.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: 2403cf04ac874a22e6040548fb458fc1 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Reporting_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Reporting_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Reporting_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: ABAEnrichment Version: 1.4.0 Depends: R (>= 3.2) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), ABAData (>= 0.99.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: b5fe5f284611eb595b088c8995d5f88e 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. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr source.ver: src/contrib/ABAEnrichment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABAEnrichment_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ABAEnrichment_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABAEnrichment_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R htmlDocs: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html htmlTitles: ABAEnrichment: gene expression enrichment in human brain regions Package: ABarray Version: 1.42.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 6068090f67ebd3462fda3009f599564d 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABarray_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ABarray_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABarray_1.42.0.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: 1.20.4 Depends: R (>= 2.10), methods Imports: locfit, limma License: GPL (>= 3) MD5sum: 5833f70e751a60391524bcd76fd8deb1 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 source.ver: src/contrib/ABSSeq_1.20.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABSSeq_1.20.4.zip win64.binary.ver: bin/windows64/contrib/3.3/ABSSeq_1.20.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABSSeq_1.20.4.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: 1.4.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 80ced523a273942ff77ccf480ecdf165 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/acde_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/acde_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/acde_1.4.0.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 Package: aCGH Version: 1.52.0 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: 5921ef5304e3d8cfa998fc3dde442360 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/aCGH_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/aCGH_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/aCGH_1.52.0.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: 2.30.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 1e5d8d8b9c0791db4d8b2504937c9baa 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ACME_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ACME_2.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ACME_2.30.0.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: 2.14.0 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: 6bee49612d3ab22ab76fad81ac96164a 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ADaCGH2_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ADaCGH2_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ADaCGH2_2.14.0.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: adSplit Version: 1.44.0 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: fa75c757f6a13d43dfd8ac57df9f41ac 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/adSplit_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/adSplit_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/adSplit_1.44.0.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: 1.46.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.20.0), R.utils (>= 2.4.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: 919a3d48659a4ac1759603b5579bc738 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affxparser_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affxparser_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affxparser_1.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, rMAT, Starr importsMe: affyILM, cn.farms, crossmeta, GeneRegionScan, ITALICS, oligo, rMAT suggestsMe: TIN Package: affy Version: 1.52.0 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: 97c4fe88f8ff1178228d2560c7b33ceb 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affy_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affy_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affy_1.52.0.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, Cormotif, crossmeta, EGAD, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, LVSmiRNA, makecdfenv, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, STATegRa, TCGAbiolinks, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, ArrayExpress, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, MLSeq, oneChannelGUI, paxtoolsr, piano, PREDA, qcmetrics, siggenes Package: affycomp Version: 1.50.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 570110fd8b1681348feedc48341f234a 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affycomp_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affycomp_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affycomp_1.50.0.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: 1.34.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 17d0c75d84992710c1bcfa486dd9f416 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyCompatible_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyCompatible_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyCompatible_1.34.0.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: 1.32.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata License: Artistic-2.0 MD5sum: 86ca5bb087ecf710f3a67de4711afeea 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyContam_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyContam_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyContam_1.32.0.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: 1.46.5 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl License: Artistic-2.0 MD5sum: b3c6541610b712769b46f74ef45cf63f 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 source.ver: src/contrib/affycoretools_1.46.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/affycoretools_1.46.5.zip win64.binary.ver: bin/windows64/contrib/3.3/affycoretools_1.46.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affycoretools_1.46.5.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.pdf vignetteTitles: affycoretools,, refactored hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R Package: AffyExpress Version: 1.40.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: 3c923f92c1a1322e2b201281ccd64f53 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyExpress_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyExpress_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyExpress_1.40.0.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: 1.26.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, methods, Biobase Suggests: AffymetrixDataTestFiles License: GPL version 3 MD5sum: ba96d8400dfb1a44a7aca9f1084a15b7 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyILM_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyILM_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyILM_1.26.0.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: 1.44.0 Depends: R (>= 2.6.0) Imports: zlibbioc License: LGPL (>= 2) Archs: i386, x64 MD5sum: b859000e41ac339670f78ecd52eff33d 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/affyio source.ver: src/contrib/affyio_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyio_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyio_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyio_1.44.0.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: 1.48.0 Imports: limma, tcltk, affy, BiocInstaller, affyio, tkrplot, affyPLM, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: d16e8d0e01ac274c2b520f35540d8494 NeedsCompilation: no Title: GUI for limma package with Affymetrix microarrays Description: A Graphical User Interface 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 [aut], Ken Simpson [aut], Gordon Smyth [aut], Keith Satterley [ctb], Yifang Hu [ctb] Maintainer: Yifang Hu , Gordon Smyth , Keith Satterley URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affylmGUI_1.48.2.zip win64.binary.ver: bin/windows64/contrib/3.3/affylmGUI_1.48.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affylmGUI_1.48.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R htmlDocs: vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html htmlTitles: about.html, CustMenu.html, index.html, windowsFocus.html dependsOnMe: oneChannelGUI Package: affyPara Version: 1.34.0 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: 0c33a26476c6ff19545b65b0878a3121 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyPara_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyPara_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyPara_1.34.0.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: 1.48.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: f7cf66254588387120a3b1ddbdf41790 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affypdnn_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affypdnn_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affypdnn_1.48.0.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: 1.50.0 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: 8cd5513706ffbebc70f51466fd7433ac 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyPLM_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyPLM_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyPLM_1.50.0.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 suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, oneChannelGUI, piano Package: affyQCReport Version: 1.52.0 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: 5365471bd610f4c0e92474db87854e70 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyQCReport_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyQCReport_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyQCReport_1.52.0.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: 1.20.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: f0c7075a45f4210059eb66837be67873 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyRNADegradation_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyRNADegradation_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyRNADegradation_1.20.0.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: 1.22.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: c8bec49bf1e2135fec271934bab019fe 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AGDEX_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AGDEX_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AGDEX_1.22.0.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: 3.6.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 8d21eacaccb049c38a55b57e493adb42 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_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/agilp_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/agilp_3.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/agilp_3.6.0.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: 2.24.0 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: d2985426d555bfdfae719814fb104b40 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_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AgiMicroRna_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AgiMicroRna_2.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AgiMicroRna_2.24.0.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: 1.6.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 7c3e9f60864122267bc79487ec17f08b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AIMS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AIMS_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AIMS_1.6.0.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: 1.6.0 Depends: methods Imports: S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel License: file LICENSE MD5sum: 86545f99da4b6619fa8f5211cf83bb86 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. For example, single-organism and meta-RNA-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected false discovery rate given the biological and sampling variation using the Wilcox rank test or Welches t-test (aldex.ttest) or the glm and Kruskal Wallis tests (aldex.glm). Reports both P and fdr values calculated by the Benjamini Hochberg correction. biocViews: DifferentialExpression, RNASeq, DNASeq, ChIPSeq, GeneExpression, Bayesian, Sequencing, Software, Microbiome, Metagenomics Author: Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Arianne Albert, Matt Links, Jia Rong Wu Maintainer: Greg Gloor source.ver: src/contrib/ALDEx2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ALDEx2_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ALDEx2_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ALDEx2_1.6.0.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 Package: AllelicImbalance Version: 1.12.0 Depends: R (>= 3.2.0), grid, GenomicRanges, SummarizedExperiment (>= 0.2.0), GenomicAlignments Imports: methods, BiocGenerics, AnnotationDbi, BSgenome, VariantAnnotation, Biostrings, S4Vectors (>= 0.9.25), IRanges, Rsamtools, GenomicFeatures, Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9952e93c00441a08ec5bdd5abce3402d 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AllelicImbalance_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AllelicImbalance_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AllelicImbalance_1.12.0.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: 1.0.0 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: f70ca32c5336aca7745ee3875bbbf28f 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/alpine_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/alpine_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/alpine_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R htmlDocs: vignettes/alpine/inst/doc/alpine.html htmlTitles: alpine Package: alsace Version: 1.10.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice License: GPL (>= 2) MD5sum: 1750971f1c5f11e4dc931a01ab567bb5 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 source.ver: src/contrib/alsace_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/alsace_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/alsace_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/alsace_1.10.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alsace/inst/doc/alsace.R Package: altcdfenvs Version: 2.36.0 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: 4db877b969bd2303155080026a4b1820 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/altcdfenvs_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/altcdfenvs_2.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/altcdfenvs_2.36.0.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: 1.0.0 Depends: R (>= 3.1.0) Suggests: BiocStyle,qgraph License: GPL (>= 2) MD5sum: a10a538f8dfc006f95737ef04832dd8a NeedsCompilation: no 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 source.ver: src/contrib/AMOUNTAIN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AMOUNTAIN_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AMOUNTAIN_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AMOUNTAIN_1.0.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.pdf vignetteTitles: Usage of AMOUNTAIN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R Package: ampliQueso Version: 1.12.0 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: 5543e5ad269aba0909def985caa93639 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ampliQueso_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ampliQueso_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ampliQueso_1.12.0.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: 1.8.1 Imports: log4r, tools, rjson, Biobase, graph Suggests: RUnit, XML, SVGAnnotation, knitr Enhances: Rook (>= 1.1), fork, FastRWeb, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: b3ed0fc654ce9715f48782d66b6335a2 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnalysisPageServer_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/AnalysisPageServer_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnalysisPageServer_1.8.1.tgz 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 htmlDocs: 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 htmlTitles: 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 Package: anamiR Version: 1.0.1 Depends: R (>= 3.3.1), SummarizedExperiment(>= 1.1.6) Imports: stats, DBI, limma, lumi, agricolae, RMySQL, DESeq2, SummarizedExperiment, gplots Suggests: knitr, rmarkdown License: GPL-2 MD5sum: f39310d0b5fb33f61d0cf2015fdfe849 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/anamiR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/anamiR_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/anamiR_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anamiR/inst/doc/IntroductionToanamiR.R htmlDocs: vignettes/anamiR/inst/doc/IntroductionToanamiR.html htmlTitles: Introduction to anamiR Package: Anaquin Version: 1.0.0 Depends: R (>= 3.3) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: deef4fc84bb7e12bd34f7d077afc600f 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Anaquin_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Anaquin_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Anaquin_1.0.0.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: 1.2.1 Depends: R (>= 3.3), GenomicRanges, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics, S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, 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: fa8f25e8d6ad19ace7518f419e0550e4 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: This package implements functions for CNV calling, plotting, export and analysis from whole-genome single cell sequencing data. biocViews: Software, 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/AneuFinder_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/AneuFinder_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AneuFinder_1.2.1.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: annaffy Version: 1.46.0 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: 887b49f983cf9106529c36b1a94901cb 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/annaffy_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/annaffy_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annaffy_1.46.0.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: 1.16.0 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: 25a66da1e4c760eed8ff6bdaae086ac8 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_1.16.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annmap_1.16.0.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: 1.52.1 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: b2512b78686d031af9b9024f91b7db7b 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_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/annotate_1.52.1.zip win64.binary.ver: bin/windows64/contrib/3.3/annotate_1.52.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annotate_1.52.1.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, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DOQTL, DrugVsDisease, facopy, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, MGFR, mvGST, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, GenomicRanges, GlobalAncova, GOstats, GSAR, GSEAlm, maigesPack, metagenomeSeq, MLP, oneChannelGUI, RnBeads, siggenes, SummarizedExperiment Package: AnnotationDbi Version: 1.36.2 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), IRanges Imports: methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase, S4Vectors (>= 0.9.25), IRanges Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), 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: ef0bfa62487e2ec5f301a182897006f7 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_1.36.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationDbi_1.36.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationDbi_1.36.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationDbi_1.36.2.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: How to use bimaps from the ".db" annotation packages, AnnotationDbi: 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, ASpli, attract, Category, chimera, ChromHeatMap, customProDB, DEXSeq, EGSEA, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, miRNAtap, MLP, OrganismDbi, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, beadarray, biomaRt, BioNet, biovizBase, bumphunter, CancerMutationAnalysis, categoryCompare, ccmap, cellity, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, crisprseekplus, CrispRVariants, crossmeta, csaw, customProDB, debrowser, derfinder, domainsignatures, DOSE, dSimer, EDASeq, eegc, EnrichmentBrowser, ensembldb, erma, ExpressionView, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, GenVisR, GGBase, ggbio, GGtools, GlobalAncova, globaltest, GOFunction, GOpro, GOSemSim, goseq, GOSim, GOstats, goTools, gQTLstats, graphite, GSEABase, Gviz, gwascat, HTSanalyzeR, InPAS, interactiveDisplay, IVAS, limmaGUI, lumi, mAPKL, mdgsa, MeSHDbi, meshes, MetaboSignal, methyAnalysis, methylumi, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, mvGST, NanoStringQCPro, PADOG, PAnnBuilder, pathview, pcaExplorer, pcaGoPromoter, PCpheno, PGA, phenoTest, pwOmics, qpgraph, RCAS, ReactomePA, REDseq, rgsepd, rTRM, ScISI, SGSeq, SLGI, SMITE, SpidermiR, StarBioTrek, SVM2CRM, tigre, ToPASeq, trackViewer, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, DEGreport, esetVis, FGNet, fgsea, gCrisprTools, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, limma, miRLAB, MmPalateMiRNA, oligo, oneChannelGUI, piano, Pigengene, pRoloc, qcmetrics, R3CPET, recount, sigPathway, SummarizedExperiment Package: AnnotationForge Version: 1.16.1 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, 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: 8cbc423604c2274e3f3e06372e3b368d 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, Herve Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationForge_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationForge_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationForge_1.16.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationForge_1.16.1.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf 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. 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 htmlDocs: vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html htmlTitles: Making New Organism Packages importsMe: AnnotationHubData, GOstats suggestsMe: AnnotationDbi, AnnotationHub Package: AnnotationFuncs Version: 1.24.0 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: f709876dd4917cfd337a3ab01b4e76f3 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationFuncs_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationFuncs_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationFuncs_1.24.0.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: 2.6.5 Depends: BiocGenerics (>= 0.15.10) Imports: utils, methods, grDevices, RSQLite, BiocInstaller, AnnotationDbi, S4Vectors, interactiveDisplayBase, httr, yaml Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: c19ef29f4826ff2357685a44babd1cbc 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 source.ver: src/contrib/AnnotationHub_2.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationHub_2.6.5.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationHub_2.6.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationHub_2.6.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R htmlDocs: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html htmlTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service dependsOnMe: AnnotationHubData, ExperimentHub, ProteomicsAnnotationHubData, RefNet importsMe: annotatr, ensembldb, gwascat, psichomics, pwOmics suggestsMe: Chicago, CINdex, clusterProfiler, DNAshapeR, dupRadar, GenomicRanges, GOSemSim, MSnbase, OrganismDbi, Pbase, VariantAnnotation Package: AnnotationHubData Version: 1.4.1 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocInstaller, httr, AnnotationDbi, Biobase, Biostrings, DBI, GEOquery, GenomeInfoDb, OrganismDbi, RSQLite, rBiopaxParser, AnnotationForge, futile.logger (>= 1.3.0), XML, xml2, curl Suggests: RUnit, knitr,RMySQL, BiocStyle, grasp2db License: Artistic-2.0 MD5sum: 28f4bbecf9aa35a67568e7f5ec8c0da4 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 source.ver: src/contrib/AnnotationHubData_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationHubData_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationHubData_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationHubData_1.4.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.R htmlDocs: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html htmlTitles: Introduction to AnnotationHubData dependsOnMe: ExperimentHubData Package: annotationTools Version: 1.48.0 Imports: Biobase, stats License: GPL MD5sum: d8395a287e1330c2002691468e544e08 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/annotationTools_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/annotationTools_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annotationTools_1.48.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: DOQTL Package: annotatr Version: 1.0.3 Depends: R (>= 3.3.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, org.Dm.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, readr, regioneR, reshape2, rtracklayer, S4Vectors, stats, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, 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, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: ccc5adb46cc83cc372e1044bb65346a4 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_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/annotatr_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/annotatr_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annotatr_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R htmlDocs: vignettes/annotatr/inst/doc/annotatr-vignette.html htmlTitles: Vignette Title Package: anota Version: 1.22.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: b917630b2767f8f5cf80f10e4cdf0145 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/anota_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/anota_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/anota_1.22.0.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: antiProfiles Version: 1.14.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 9e3d405c92d5accbb16aabc057db6afd 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/antiProfiles_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/antiProfiles_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/antiProfiles_1.14.0.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: 2.40.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 98d55585f38ba25d8920aba50a65f935 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_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/apComplex_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/apComplex_2.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/apComplex_2.40.0.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: aroma.light Version: 3.4.0 Depends: R (>= 2.15.2) Imports: R.methodsS3 (>= 1.7.1), R.oo (>= 1.20.0), R.utils (>= 2.4.0), matrixStats (>= 0.50.2) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: 8e28dc69064f12b22dd5f08f0aadb974 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] 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_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/aroma.light_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/aroma.light_3.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/aroma.light_3.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq suggestsMe: TIN Package: ArrayExpress Version: 1.34.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 596bc81db538f194072222fe13fc3ec3 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayExpress_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayExpress_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayExpress_1.34.0.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: 1.24.0 Depends: sampling, Rsamtools (>= 1.19.36), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges, R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rsamtools License: Artistic License 2.0 MD5sum: a3aa8a654b7110e3d21f8b74f211bb5b 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_1.24.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayExpressHTS_1.24.0.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: 1.32.0 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: b03a66949b8e9553e0e9612d0a35838f 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayMvout_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayMvout_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayMvout_1.32.0.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: 1.52.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 7e0a162bb2757e04fa588d8810f9d6fe 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayQuality_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayQuality_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayQuality_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.30.0 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, SVGAnnotation (>= 0.9-0), utils, vsn (>= 3.23.3), XML Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 5b12789f63770a5e170e8b6cd3702c51 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: Audrey Kauffmann VignetteBuilder: knitr source.ver: src/contrib/arrayQualityMetrics_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayQualityMetrics_3.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayQualityMetrics_3.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayQualityMetrics_3.30.0.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: 1.34.0 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: d05dc675980ef3f9c5f5894e84ade223 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayTools_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayTools_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayTools_1.34.0.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: 1.12.0 Depends: R (>= 2.14) Imports: methods, foreach, S4Vectors (>= 0.9.25), 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: 96307adc39396050f82a1482495bad3a 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayTV_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayTV_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayTV_1.12.0.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: 1.14.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 6e691ea88f981a38da1d4eba6d5c5be8 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ARRmNormalization_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ARRmNormalization_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ARRmNormalization_1.14.0.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: 1.0.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 95c7030231ed950b25c420c98bfd6255 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASAFE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASAFE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASAFE_1.0.0.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: 1.18.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 63b9a05ce0069401dcb55bfbbfc49619 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASEB_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASEB_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASEB_1.18.0.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: 1.8.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: b4a51222c608ed671e99cda8db34c798 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASGSCA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASGSCA_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASGSCA_1.8.0.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: ASpli Version: 1.0.0 Depends: methods,GenomicRanges,GenomicFeatures,edgeR,BiocGenerics, IRanges, GenomicAlignments, DESeq2, DEXSeq, Gviz, grDevices, stats, utils, S4Vectors, AnnotationDbi, parallel Suggests: RNAseqData.HNRNPC.bam.chr14, BiocStyle License: GPL MD5sum: 093115b1a53d00cab058e5edc944586e NeedsCompilation: no Title: Analysis of alternative splicing using RNA-Seq Description: Integrative pipeline for the analyisis of alternative splicing using RNAseq. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini source.ver: src/contrib/ASpli_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASpli_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASpli_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASpli_1.0.0.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: 1.12.0 Depends: MASS, msm, rmeta, mvtnorm, tmvnsim Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 508e1e033360a70b69f812c2ee143813 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, Bioinformatics Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/ASSET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASSET_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASSET_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASSET_1.12.0.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: 1.10.0 Depends: Rlab, msm, gplots Imports: graphics, grDevices, stats, utils License: MIT MD5sum: 2b826747e52791b492b3cffb2a568a3c 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 literatures 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, and W. Evan Johnson Maintainer: Ying Shen source.ver: src/contrib/ASSIGN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASSIGN_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASSIGN_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASSIGN_1.10.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R Package: AtlasRDF Version: 1.10.0 Depends: R (>= 2.10), hash, SPARQL, methods License: Apache License 2.0 MD5sum: 5e4183598648d2ee26ebd8b0ebd8bcaf NeedsCompilation: no Title: Gene Expression Atlas query and gene set enrichment package. Description: Query the Gene Expression Atlas RDF data at the European Bioinformatics Institute using genes, experimental factors (such as disease, cell type, compound treatments), pathways and proteins. Also contains a function to perform an enrichment of your gene list across Experimental Factor Ontology (EFO) using the Atlas background set. biocViews: Microarray, DataImport, GeneSetEnrichment, GeneExpression, DifferentialExpression, DataRepresentation Author: James Malone, Simon Jupp, Maryam Soleimani Maintainer: Simon Jupp source.ver: src/contrib/AtlasRDF_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AtlasRDF_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AtlasRDF_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AtlasRDF_1.10.0.tgz vignettes: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.pdf vignetteTitles: An introduction to the AtlasRDF-R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.R Package: attract Version: 1.26.0 Depends: R (>= 3.3.1), methods, AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: 493ba2ec49ca18ec206e452e52178e66 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/attract_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/attract_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/attract_1.26.0.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: BaalChIP Version: 1.0.0 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: 6804b4b818003cce8fcfb618de9b31c1 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, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr source.ver: src/contrib/BaalChIP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BaalChIP_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BaalChIP_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BaalChIP_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R htmlDocs: vignettes/BaalChIP/inst/doc/BaalChIP.html htmlTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package Package: BAC Version: 1.34.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: dcc911644ca89c81e66205f8b66f0606 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BAC_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BAC_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BAC_1.34.0.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: 1.2.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: c636081ffdeb0289e2f546c971cfac3e 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 URL: https://github.com/mvaniterson/bacon VignetteBuilder: knitr BugReports: https://github.com/mvaniterson/bacon/issues source.ver: src/contrib/bacon_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bacon_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bacon_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bacon_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R htmlDocs: vignettes/bacon/inst/doc/bacon.html htmlTitles: Controlling bias and inflation in association studies using the empirical null distribution Package: BADER Version: 1.12.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: c1379646a5568064e56fa0cfa14ca6f1 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BADER_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BADER_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BADER_1.12.0.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: 1.2.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: cc23937db14423f27cb10fde218224e1 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BadRegionFinder_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BadRegionFinder_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BadRegionFinder_1.2.0.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: 2.14.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 83909ade219a1b0081f8d060cf1c2030 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BAGS_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BAGS_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BAGS_2.14.0.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: 2.6.0 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: 40935656d2f7378be303eeff7255b4c4 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ballgown_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ballgown_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ballgown_2.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R htmlDocs: vignettes/ballgown/inst/doc/ballgown.html htmlTitles: Flexible isoform-level differential expression analysis with Ballgown suggestsMe: polyester, variancePartition Package: bamsignals Version: 1.6.0 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 4fa449e37d4944a5ad1f723dc8ad1257 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bamsignals_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bamsignals_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bamsignals_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R htmlDocs: vignettes/bamsignals/inst/doc/bamsignals.html htmlTitles: Introduction to the bamsignals package importsMe: AneuFinder, chromstaR, normr Package: BaseSpaceR Version: 1.18.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 2c70c8d061b707341b606a6e53e9ef0a 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BaseSpaceR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BaseSpaceR_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BaseSpaceR_1.18.0.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: 1.10.0 Depends: R (>= 3.0.0), Biostrings, GenomicAlignments, caTools, GenomicRanges Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 4b8d6cc0ee8ec509829b05f242c4ec66 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 Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Basic4Cseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Basic4Cseq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Basic4Cseq_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Basic4Cseq_1.10.0.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: BasicSTARRseq Version: 1.2.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 8763a47d423fd6b385313b1a1a2a968d 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BasicSTARRseq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BasicSTARRseq_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BasicSTARRseq_1.2.0.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: 1.2.1 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 Suggests: testthat License: GPL (>= 2) MD5sum: 009884d4a0fe91a41bd32b85cb4467fe 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 source.ver: src/contrib/BatchQC_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/BatchQC_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/BatchQC_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BatchQC_1.2.1.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf vignetteTitles: BatchQC_usage_advanced hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R htmlDocs: vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html htmlTitles: BatchQC_examples, BatchQCIntro importsMe: PathoStat Package: BayesKnockdown Version: 1.0.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 181a7217110805f605959f0bf4c58fd3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BayesKnockdown_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BayesKnockdown_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BayesKnockdown_1.0.0.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: 1.26.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 2a53cf0c53bde590b2221f5868f95bec 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BayesPeak_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BayesPeak_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BayesPeak_1.26.0.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: 2.8.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: cd4fbec5ad9d468eac59b8e7122bce6d 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/baySeq_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/baySeq_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/baySeq_2.8.0.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: Rcade, segmentSeq, TCC importsMe: debrowser, EDDA, metaseqR, riboSeqR suggestsMe: compcodeR, oneChannelGUI Package: BBCAnalyzer Version: 1.4.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: d770f93ca9fc1ddf382911f5bac05970 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BBCAnalyzer_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BBCAnalyzer_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BBCAnalyzer_1.4.0.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: 1.36.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: d46a00897669624518bdfa87b0ddf209 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BCRANK_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BCRANK_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BCRANK_1.36.0.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: beadarray Version: 2.24.0 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: c1065a9d4d4be174f43285648b57d817 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_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/beadarray_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/beadarray_2.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/beadarray_2.24.0.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: FALSE 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: 1.40.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 68de514c889de49347c7b03d99040ca6 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/beadarraySNP_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/beadarraySNP_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/beadarraySNP_1.40.0.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: 1.26.0 Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 9811a4688527a507bc1cb0cd3d4d9024 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BeadDataPackR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BeadDataPackR_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BeadDataPackR_1.26.0.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: BEAT Version: 1.12.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 2511c71a9fd020e10f89d42a99fa4e08 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BEAT_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BEAT_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BEAT_1.12.0.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: 1.6.0 Depends: snowfall, Matrix License: GPL-2 MD5sum: a693f475f19761bc5b75820336d625e9 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: Markus Merl source.ver: src/contrib/BEclear_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BEclear_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BEclear_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BEclear_1.6.0.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: betr Version: 1.32.0 Depends: R(>= 2.6.0) Imports: Biobase (>= 2.5.5), limma, mvtnorm, methods, stats Suggests: Biobase License: LGPL MD5sum: 9ff64c84a1b471d37daf381b5011405a NeedsCompilation: no Title: Identify differentially expressed genes in microarray time-course data Description: The betr package implements the BETR (Bayesian Estimation of Temporal Regulation) algorithm to identify differentially expressed genes in microarray time-course data. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Martin Aryee Maintainer: Martin Aryee PackageStatus: Deprecated source.ver: src/contrib/betr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/betr_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/betr_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/betr_1.32.0.tgz vignettes: vignettes/betr/inst/doc/betr.pdf vignetteTitles: BETR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betr/inst/doc/betr.R Package: bgafun Version: 1.36.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: 33cf7d7645a01f3f87e5f892d2014008 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bgafun_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bgafun_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bgafun_1.36.0.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: 2.0.0 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-2 MD5sum: 9b25e6aac8f01b57728a70b8aa0c8f51 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: Andrea Komljenovic , Frederic Bastian VignetteBuilder: knitr source.ver: src/contrib/BgeeDB_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BgeeDB_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BgeeDB_2.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BgeeDB_2.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R htmlDocs: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html htmlTitles: Vignette Title importsMe: psygenet2r Package: BGmix Version: 1.34.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 8e0f1b9145b7b0f5c2f3bb93fa05b85c 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_1.34.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BGmix_1.34.0.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: 1.40.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: b7513e93cdbd999dd5431b982910f274 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bgx_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bgx_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bgx_1.40.0.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: 1.26.0 License: GPL-3 Archs: i386, x64 MD5sum: 8244635e7dec33e45c91322d6622b2b4 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BHC_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BHC_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BHC_1.26.0.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: 1.32.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 2b21b88138b8c3294c7c285b725365c1 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BicARE_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BicARE_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BicARE_1.32.0.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: BiGGR Version: 1.10.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: 8958225235184927e24b741b4762f5af 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiGGR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiGGR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiGGR_1.10.0.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: 1.0.0 Depends: R (>= 3.3), wateRmelon (>= 1.17.1), gdsfmt (>= 1.0.4), methods, methylumi, minfi, Biobase Imports: stats, utils Suggests: BiocGenerics, BiocStyle, minfiData License: GPL-3 MD5sum: f60cd1404f2bd2fc33c284b8e3ad7990 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 Gorrie-Stone, Ayden Saffari, Karim Malki, Leonard C Schalkwyk Maintainer: Tyler Gorrie-Stone source.ver: src/contrib/bigmelon_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bigmelon_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bigmelon_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bigmelon_1.0.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The \Rpackage{bigmelon} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R Package: bigmemoryExtras Version: 1.20.0 Depends: R (>= 2.12), bigmemory (>= 4.3) Imports: methods Suggests: RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: e8a271efb0bcf9706bf6baa902549408 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_1.20.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bigmemoryExtras_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: bioassayR Version: 1.12.1 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, RefManageR, testthat, ggplot2 License: Artistic-2.0 MD5sum: 42cadec7b8f8ee507cf5239f9cca44a4 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/bioassayR_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/bioassayR_1.12.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bioassayR_1.12.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R htmlDocs: vignettes/bioassayR/inst/doc/bioassayR.html htmlTitles: Introduction and Examples Package: Biobase Version: 2.34.0 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: b761d9241462a4c6403731203c9fbb94 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Biobase_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Biobase_2.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Biobase_2.34.0.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, BrainStars, CAMERA, cancerclass, Cardinal, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, charm, chimera, chroGPS, ClassifyR, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DESeq, DEXSeq, DFP, diggit, doppelgangR, DSS, dualKS, dyebias, EBarrays, EDASeq, edge, EGSEA, eisa, EnrichmentBrowser, 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, GSEAlm, GWASTools, hapFabia, HCsnip, HELP, hopach, HTqPCR, htSeqTools, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, LMGene, lumi, macat, mAPKL, maSigPro, massiR, MEAL, MergeMaid, metagenomeFeatures, metagenomeSeq, MethPed, methyAnalysis, methylumi, Mfuzz, MiChip, MIMOSA, MineICA, minfi, MiRaGE, miRcomp, MLInterfaces, MLSeq, MmPalateMiRNA, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NOISeq, nondetects, normalize450K, NormqPCR, oligo, oneChannelGUI, OrderedList, OTUbase, OutlierD, pandaR, PAnnBuilder, panp, pcaMethods, pcot2, pdInfoBuilder, pdmclass, pepStat, PGSEA, phenoTest, PLPE, plrs, prada, PREDA, pRolocGUI, PROMISE, qpcrNorm, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, reb, RefPlus, rHVDM, Ringo, Risa, Rmagpie, rMAT, RNAinteract, rnaSeqMap, Rnits, Roleswitch, RpsiXML, RTopper, RUVSeq, safe, SCAN.UPC, scater, SeqGSEA, sigaR, SigCheck, siggenes, simpleaffy, simulatorZ, SpeCond, SPEM, spkTools, splicegear, splineTimeR, stepwiseCM, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, tRanslatome, tspair, twilight, UNDO, variancePartition, VegaMC, viper, vsn, wateRmelon, waveTiling, webbioc, xcms, XDE, yarn importsMe: ABarray, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, AnalysisPageServer, annmap, annotate, AnnotationDbi, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, ArrayTools, attract, ballgown, BayesKnockdown, betr, BgeeDB, biobroom, bioCancer, biocViews, BioNet, BioSeqClass, biosigner, biosvd, birte, BiSeq, blima, BrainStars, bsseq, BubbleTree, CAFE, canceR, CGHnormaliter, charm, ChIPpeakAnno, ChIPQC, ChIPXpress, ChromHeatMap, clipper, cn.mops, cogena, ConsensusClusterPlus, crlmm, crossmeta, cummeRbund, cycle, cytofkit, CytoML, ddCt, DESeq2, destiny, diffloop, 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, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GeneSelectMMD, GENESIS, GenomicFeatures, GenomicInteractions, GEOsubmission, gespeR, GGBase, ggbio, GGtools, girafe, globaltest, gmapR, GOFunction, GOstats, gQTLstats, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, IdMappingAnalysis, imageHTS, ImmuneSpaceR, IsoGeneGUI, JunctionSeq, kimod, lapmix, LINC, LiquidAssociation, LVSmiRNA, maanova, makecdfenv, maSigPro, MAST, mBPCR, MCRestimate, MeSHDbi, metaArray, methyAnalysis, MethylAid, methylumi, MiChip, MinimumDistance, MiPP, mmnet, MmPalateMiRNA, mogsa, MoonlightR, MoPS, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, npGSEA, nucleR, OGSA, oligoClasses, oposSOM, oppar, OrderedList, OrganismDbi, PAnnBuilder, panp, Pbase, pbcmc, PCpheno, PharmacoGx, phyloseq, piano, plateCore, plethy, plgem, plier, podkat, ppiStats, prada, prebs, proFIA, pRoloc, PROMISE, ProteomicsAnnotationHubData, PSEA, psygenet2r, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, qpgraph, quantro, QuasR, qusage, randPack, readat, ReadqPCR, RGalaxy, Rmagpie, rMAT, rols, ropls, ROTS, rqubic, Rtreemix, RUVnormalize, SAGx, scran, SeqVarTools, ShortRead, sigsquared, SimBindProfiles, simpleaffy, SLGI, SNPchip, SomaticSignatures, spkTools, splicegear, STATegRa, subSeq, switchde, synapter, TCGAbiolinks, TEQC, TFBSTools, timecourse, ToPASeq, TPP, TSSi, twilight, uSORT, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, wateRmelon, XBSeq, XDE suggestsMe: betr, BiocCaseStudies, BiocCheck, BiocGenerics, BSgenome, CellMapper, cellTree, clustComp, CountClust, DAPAR, DART, epivizr, epivizrStandalone, farms, genefu, GenomicRanges, GlobalAncova, GSAR, Heatplus, interactiveDisplay, kebabs, les, limma, messina, msa, multiClust, nem, OSAT, pkgDepTools, ROC, RTCGA, SeqArray, survcomp, TargetScore, tkWidgets, TypeInfo, vbmp, widgetTools Package: biobroom Version: 1.6.0 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: ad38a2b50d66222735fd3080057714c6 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 source.ver: src/contrib/biobroom_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biobroom_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biobroom_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biobroom_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R htmlDocs: vignettes/biobroom/inst/doc/biobroom_vignette.html htmlTitles: Vignette Title Package: bioCancer Version: 1.2.0 Depends: magrittr (>= 1.5), ggplot2 (>= 1.0.0), lubridate (>= 1.3.3), tidyr (>= 0.3.1), cgdsr, RCurl, XML Imports: dplyr (>= 0.4.3), htmlwidgets, Biobase, geNetClassifier, AnnotationFuncs, org.Hs.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, plyr, grDevices, stats, utils, DiagrammeR(>= 0.7), visNetwork, car, MASS (>= 7.3), gridExtra (>= 2.0.0), AlgDesign (>= 1.1.7.3), psych (>= 1.4.8.11), GPArotation (>= 2014.11.1), wordcloud (>= 2.5), markdown (>= 0.7.4), knitr (>= 1.8), ggdendro (>= 0.1.17), broom (>= 0.3.7), pryr (>= 0.1), shiny (>= 0.13.2), jsonlite (>= 0.9.17), shinyAce (>= 0.1), DT (>= 0.1), readr (>= 0.1.1), data.tree(>= 0.2.1), yaml(>= 2.1.13), scales(>= 0.2.5), curl(>= 0.9.1), covr (>= 1.2.0), stringr (>= 1.0), tibble Suggests: BiocStyle, rmarkdown, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: 7b5503c7f612e6c928d42005a31f1c2e 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bioCancer_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bioCancer_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bioCancer_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R htmlDocs: vignettes/bioCancer/inst/doc/bioCancer.html htmlTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis Package: BiocCaseStudies Version: 1.36.0 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: bb296f75a0b0676e2dfe002e03f7c91a 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocCaseStudies_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocCaseStudies_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiocCaseStudies_1.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.10.1 Depends: R (>= 3.3.0) Imports: biocViews (>= 1.33.7), BiocInstaller, graph, httr, tools, optparse, codetools, methods Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, knitr, devtools (>= 1.4.1) Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: a41b2f9ac73d5f23eb34010b26d73702 NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: 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RNAprobR, rnaseqcomp, RnaSeqSampleSize, Rnits, rols, ropls, rpx, Rsamtools, RTCGAToolbox, RUVcorr, RUVSeq, sangerseqR, sapFinder, scater, scran, segmentSeq, seqPattern, seqplots, SeqVarTools, SGSeq, shinyMethyl, ShortRead, SigCheck, SigFuge, similaRpeak, SIMLR, simulatorZ, sincell, SNPediaR, SNPhood, soGGi, specL, SpidermiR, SPLINTER, SSPA, STAN, StarBioTrek, STATegRa, statTarget, SummarizedExperiment, sva, SVAPLSseq, switchde, synapter, systemPipeR, TCGAbiolinks, TFBSTools, tigre, TPP, tracktables, trackViewer, transcriptR, TRONCO, TurboNorm, TVTB, variancePartition, VariantAnnotation, VariantFiltering, vsn, wavClusteR, XBSeq, xcms, yamss, YAPSA Package: biocViews Version: 1.42.0 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: d6a88cec44b6863829c88591a2863d74 NeedsCompilation: no Title: Categorized views of R package repositories Description: structures for vocabularies and narratives of views biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org/packages/release/BiocViews.html source.ver: src/contrib/biocViews_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biocViews_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biocViews_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biocViews_1.42.0.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: BiocCheck Package: BiocWorkflowTools Version: 1.0.0 Depends: R (>= 3.3) Imports: rmarkdown, tools, stringr, httr, knitr, utils Suggests: BiocStyle License: MIT + file LICENSE MD5sum: 8dcb30527f1bf9752b13538928e2d3e4 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] Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/BiocWorkflowTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocWorkflowTools_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocWorkflowTools_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiocWorkflowTools_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R htmlDocs: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html htmlTitles: Converting Rmarkdown to F1000Research LaTeX Format Package: bioDist Version: 1.46.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: de8934d0577aeda7f1fe6be24022f581 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bioDist_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bioDist_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bioDist_1.46.0.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: 2.30.0 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi Suggests: annotate, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 755c380b1af0d577881bc152fc11418e 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 , Wolfgang Huber Maintainer: Steffen Durinck VignetteBuilder: knitr source.ver: src/contrib/biomaRt_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomaRt_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biomaRt_2.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomaRt_2.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R htmlDocs: vignettes/biomaRt/inst/doc/biomaRt.html htmlTitles: The biomaRt users guide dependsOnMe: chromPlot, coMET, customProDB, dagLogo, domainsignatures, DrugVsDisease, GenomeGraphs, MineICA, PSICQUIC, Roleswitch, VegaMC importsMe: ArrayExpressHTS, BadRegionFinder, ChIPpeakAnno, CHRONOS, cobindR, customProDB, DEXSeq, diffloop, DOQTL, easyRNASeq, EDASeq, EWCE, GenomicFeatures, GenVisR, gespeR, GOexpress, Gviz, HTSanalyzeR, IdMappingRetrieval, isobar, MEDIPS, MetaboSignal, metaseqR, methyAnalysis, MGFR, OncoScore, oposSOM, Pbase, pcaExplorer, PGA, phenoTest, pRoloc, psygenet2r, pwOmics, R453Plus1Toolbox, RCAS, recoup, rgsepd, RNAither, scater, seq2pathway, SeqGSEA, SPLINTER, TCGAbiolinks, yarn suggestsMe: AnnotationForge, bioassayR, BiocCaseStudies, cellTree, chromstaR, DEGreport, GeneAnswers, Genominator, h5vc, LINC, massiR, MineICA, MiRaGE, MutationalPatterns, oligo, oneChannelGUI, OrganismDbi, paxtoolsr, piano, Pigengene, pqsfinder, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, ShortRead, SIM, sincell, systemPipeR, trackViewer Package: biomformat Version: 1.2.0 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: 0a418f48c731375953ff7389b9b6f7a0 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomformat_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biomformat_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomformat_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R htmlDocs: vignettes/biomformat/inst/doc/biomformat.html htmlTitles: The biomformat package Vignette importsMe: phyloseq suggestsMe: metagenomeSeq Package: BioMVCClass Version: 1.42.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 27cfd9f3eb8cb174b80cde9211212662 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioMVCClass_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioMVCClass_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioMVCClass_1.42.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biomvRCNS Version: 1.14.0 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: 2b3735da7e23e679324e43fc0612a136 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomvRCNS_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biomvRCNS_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomvRCNS_1.14.0.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: 1.34.0 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: 496cab983cb666713a0fadd273b18716 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioNet_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioNet_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioNet_1.34.0.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: BioQC Version: 1.2.0 Depends: Rcpp, Biobase Suggests: testthat License: LGPL (>=2) Archs: i386, x64 MD5sum: 744a89ffea606a4d885885ebb4f9461c 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 biocViews: GeneExpression,QualityControl,StatisticalMethod Author: Jitao David Zhang , with inputs from Laura Badi Maintainer: Jitao David Zhang source.ver: src/contrib/BioQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioQC_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioQC_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioQC_1.2.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc.pdf vignetteTitles: BioQC: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioQC/inst/doc/bioqc.R Package: BioSeqClass Version: 1.32.0 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: 0d0fe76b8d487dfd09c3b5c2299f61bb 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioSeqClass_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioSeqClass_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioSeqClass_1.32.0.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: 1.2.4 Imports: methods, e1071, randomForest, ropls, Biobase Suggests: BioMark, RUnit, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, rmarkdown License: CeCILL MD5sum: 243a987abbd4141baf960e82deb3a1b1 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_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/biosigner_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.3/biosigner_1.2.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biosigner_1.2.4.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R Package: Biostrings Version: 2.42.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.6), S4Vectors (>= 0.11.1), IRanges (>= 2.5.27), XVector (>= 0.11.6) Imports: graphics, methods, stats, utils, BiocGenerics, IRanges, XVector 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: f26d6153248ece91637251dd5a7a1da1 NeedsCompilation: yes Title: String objects representing biological sequences, and matching algorithms 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_2.42.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Biostrings_2.42.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Biostrings_2.42.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Biostrings_2.42.1.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, Basic4Cseq, BRAIN, BSgenome, ChIPpeakAnno, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, genphen, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, methVisual, minfi, MotifDb, motifRG, motifStack, msa, muscle, oligo, oneChannelGUI, pcaGoPromoter, PGA, pqsfinder, qrqc, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, SICtools, systemPipeR, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, ArrayExpressHTS, BBCAnalyzer, BCRANK, BEAT, BioSeqClass, biovizBase, BSgenome, charm, ChIPseqR, ChIPsim, CNEr, cobindR, compEpiTools, CrispRVariants, customProDB, dada2, dagLogo, diffHic, DNAshapeR, easyRNASeq, EDASeq, ensemblVEP, eudysbiome, FindMyFriends, FourCSeq, gcrma, genbankr, GeneRegionScan, GenoGAM, genomation, GenomicAlignments, GenomicFeatures, GenVisR, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GUIDEseq, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, IONiseR, KEGGREST, LowMACA, MADSEQ, maftools, MatrixRider, MEDIPS, MEDME, metagenomeFeatures, methVisual, methylPipe, microRNA, MMDiff2, motifbreakR, MotIV, MutationalPatterns, oligoClasses, OTUbase, Pbase, pdInfoBuilder, phyloseq, podkat, polyester, proBAMr, procoil, ProteomicsAnnotationHubData, PureCN, Pviz, qrqc, qsea, QuasR, r3Cseq, RCAS, Rcpi, REDseq, Repitools, rGADEM, RNAprobR, Rqc, rSFFreader, rtracklayer, SeqArray, seqPattern, seqplots, SGSeq, signeR, SNPhood, soGGi, SomaticSignatures, SPLINTER, sscu, synapter, TarSeqQC, TFBSTools, TVTB, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, AnnotationForge, AnnotationHub, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, ggtree, methylumi, microRNA, MiRaGE, rpx, rTRM, XVector Package: biosvd Version: 2.10.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: 4ec9f72f211302ad494ece9cddbef887 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biosvd_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biosvd_2.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biosvd_2.10.0.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: biovizBase Version: 1.22.0 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 (>= 1.3.8) Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 875dbe862dce07f529ac6d38b404a67b 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 source.ver: src/contrib/biovizBase_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biovizBase_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biovizBase_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biovizBase_1.22.0.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, ggbio, Gviz, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinder, derfinderPlot, R3CPET, regionReport Package: BiRewire Version: 3.6.0 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 522862204f5fa13043de906fa165adb2 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_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiRewire_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiRewire_3.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiRewire_3.6.0.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: 1.18.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 1a6fb7e278a5b40ecc8fe4bd48d86bc3 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/birta_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/birta_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/birta_1.18.0.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: 1.10.0 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: f73fdcb9eebc701bddec8d74a3ce4ff1 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/birte_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/birte_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/birte_1.10.0.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: 1.14.0 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: 984d33fefaf5a5f9ceba50c31dc05b11 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiSeq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiSeq_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiSeq_1.14.0.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: 1.18.0 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: dce0c422d52acc8d223f3e2e9a65e3bc 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BitSeq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BitSeq_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BitSeq_1.18.0.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: 1.8.0 Depends: R(>= 3.0.0) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), BiocGenerics, grDevices, stats, graphics Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi License: GPL-3 MD5sum: 4ce94a0cfdbe1ab393f8dc26fec2fc6d NeedsCompilation: no Title: Package 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 Author: Vojtech Kulvait Maintainer: Vojtech Kulvait URL: https://bitbucket.org/kulvait/blima source.ver: src/contrib/blima_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/blima_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/blima_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/blima_1.8.0.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: BPRMeth Version: 1.0.0 Depends: R (>= 3.3.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: cb3a0f973c01ebd83416a0af5c0a015d NeedsCompilation: no Title: Model higher-order methylation profiles Description: BPRMeth package uses the Binomial Probit Regression likelihood to model methylation profiles and extract higher order features. These features quantitate precisely notions of shape of a methylation profile. Using these higher order features across promoter-proximal regions, we construct a powerful predictor of gene expression. Also, these features are used to cluster proximal-promoter regions using the EM algorithm. biocViews: DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr source.ver: src/contrib/BPRMeth_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BPRMeth_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BPRMeth_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BPRMeth_1.0.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.pdf vignetteTitles: An Introduction to the BPR method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R Package: BRAIN Version: 1.20.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 1d682d252effda8da5e6c99491ed103c 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BRAIN_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BRAIN_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BRAIN_1.20.0.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 Package: BrainStars Version: 1.18.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 65bf1f87e989f3e5d46af518648f4f38 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BrainStars_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BrainStars_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BrainStars_1.18.0.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: bridge Version: 1.38.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: e62e414d84ea1948d375d7377cc6ce59 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bridge_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bridge_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bridge_1.38.0.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: 1.8.0 Depends: R (>= 3.3.0), rJava Imports: RCurl Suggests: testthat License: AGPL-3 MD5sum: a72b70f5961994fc82171b9b2bb2c162 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 Author: Christ Leemans , Egon Willighagen , Anwesha Bohler , Lars Eijssen Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDb, https://github.com/BiGCAT-UM/bridgedb-r BugReports: https://github.com/BiGCAT-UM/bridgedb-r/issues source.ver: src/contrib/BridgeDbR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BridgeDbR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BridgeDbR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BridgeDbR_1.8.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.pdf vignetteTitles: tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R Package: BrowserViz Version: 1.6.0 Depends: R (>= 3.2.1), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: f49f37928052af3f4bfe20919241b6e6 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_1.6.0.tar.gz 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, RCyjs Package: BrowserVizDemo Version: 1.6.0 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: ad34f1187c303335438431f0f8ef56af 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_1.6.0.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: 1.42.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.36), IRanges (>= 2.1.33), GenomeInfoDb (>= 1.3.19), GenomicRanges (>= 1.23.15), Biostrings (>= 2.35.3), rtracklayer (>= 1.25.8) 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, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: ee6db342fe767fc8092ce88c0d5d15cc NeedsCompilation: no Title: Infrastructure for Biostrings-based genome data packages and support for efficient SNP representation 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BSgenome_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BSgenome_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BSgenome_1.42.0.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: CAGEr, cleanUpdTSeq, GOTHiC, HelloRanges, htSeqTools, MEDIPS, motifRG, REDseq, regioneR, rGADEM importsMe: AllelicImbalance, BEAT, charm, ChIPpeakAnno, cobindR, CRISPRseek, crisprseekplus, diffHic, genomation, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, MADSEQ, MethylSeekR, MMDiff2, motifbreakR, PING, podkat, qsea, QuasR, R453Plus1Toolbox, regioneR, Repitools, seqplots, signeR, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, chipseq, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, metaseqR, MiRaGE, MutationalPatterns, oneChannelGUI, QDNAseq, recoup, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.10.0 Depends: R (>= 3.3), methods, BiocGenerics, GenomicRanges (>= 1.23.7), SummarizedExperiment (>= 0.1.1), parallel, limma Imports: IRanges (>= 2.5.17), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table, S4Vectors, R.utils (>= 2.0.0), matrixStats (>= 0.50.0), permute Suggests: RUnit, bsseqData, BiocStyle, knitr License: Artistic-2.0 MD5sum: 97479d9c6dffabe933fa057a8cd9c0e7 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 [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues source.ver: src/contrib/bsseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bsseq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bsseq_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bsseq_1.10.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.pdf, vignettes/bsseq/inst/doc/bsseq.pdf vignetteTitles: Analyzing WGBS with bsseq, The 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: DSS Package: BubbleTree Version: 2.4.0 Depends: R (>= 3.2.1), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.7.5), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, rainbow, e1071 Suggests: methods, knitr, rmarkdown License: LGPL (>= 3) MD5sum: 59daaf28e87fbdbf14b4484188941345 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 (Publication Pending). 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BubbleTree_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BubbleTree_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BubbleTree_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R htmlDocs: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html htmlTitles: BubbleTree Tutorial Package: BufferedMatrix Version: 1.38.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 8ca8f7aa73052f605e7991d522982382 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/BufferedMatrix source.ver: src/contrib/BufferedMatrix_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BufferedMatrix_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BufferedMatrix_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BufferedMatrix_1.38.0.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 Package: BufferedMatrixMethods Version: 1.38.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: ae4c0bea5940df6c4f4beef02e0d8e16 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: B. M. Bolstad Maintainer: B. M. Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods source.ver: src/contrib/BufferedMatrixMethods_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BufferedMatrixMethods_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BufferedMatrixMethods_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BufferedMatrixMethods_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bumphunter Version: 1.14.0 Depends: R (>= 2.10), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 90190b1412e78f8f3cd6cb81e3ee51fe 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bumphunter_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bumphunter_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bumphunter_1.14.0.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 suggestsMe: derfinderPlot, epivizrData, regionReport Package: BUS Version: 1.30.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: 323b7ba9e6185e4bcf66742ca26d4712 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BUS_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BUS_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BUS_1.30.0.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: 1.10.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 83524731a059e100a6617716e27b5e8c 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAFE_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAFE_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAFE_1.10.0.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: CAGEr Version: 1.16.0 Depends: methods, R (>= 2.15.0), BSgenome Imports: utils, Rsamtools, GenomicRanges (>= 1.23.16), IRanges (>= 2.5.27), data.table, beanplot, rtracklayer, som, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, FANTOM3and4CAGE Enhances: parallel License: GPL-3 MD5sum: 02b2054c835d58b48c02dded002661da 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, Department of Biology, University of Bergen, Norway Maintainer: Vanja Haberle source.ver: src/contrib/CAGEr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAGEr_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAGEr_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAGEr_1.16.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEr.pdf vignetteTitles: CAGEr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGEr.R suggestsMe: seqPattern Package: CALIB Version: 1.40.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: f4f73cdbdb1211417c14a42fd96e9819 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CALIB_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CALIB_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CALIB_1.40.0.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: 1.30.0 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: aed35cf1c221c691fd10f3b780afa491 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAMERA_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAMERA_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAMERA_1.30.0.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: RMassBank, ropls Package: canceR Version: 1.6.0 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: 2fb494e8b95b676e588be6e6dfcc0427 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/canceR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/canceR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/canceR_1.6.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.pdf vignetteTitles: canceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cancerclass Version: 1.18.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: 95b0c12dcf733504c8121d56ec23dfa3 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cancerclass_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cancerclass_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cancerclass_1.18.0.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: 1.0.0 Depends: Rcpp Imports: methods, grDevices, graphics, stats LinkingTo: Rcpp, testthat, BH Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: c539849498418c8fd835bcfb6d918825 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. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr source.ver: src/contrib/CancerInSilico_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CancerInSilico_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CancerInSilico_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CancerInSilico_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R htmlDocs: vignettes/CancerInSilico/inst/doc/CancerInSilico.html htmlTitles: The CancerInSilico Package Package: CancerMutationAnalysis Version: 1.16.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 9145336eec31622a363ede12599cc95c 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CancerMutationAnalysis_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CancerMutationAnalysis_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CancerMutationAnalysis_1.16.0.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: 1.0.0 Depends: R (>= 3.3), sigclust, NMF Imports: SNFtool, iCluster, survival, cluster, impute, limma, ConsensusClusterPlus, grDevices Suggests: BiocGenerics, knitr, RTCGA.mRNA, RTCGA.clinical License: GPL (>= 2) MD5sum: 76c3f5ba3867a1fd7980bd8d44fe0042 NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on genomic data Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based on the genomic datasets. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu VignetteBuilder: knitr source.ver: src/contrib/CancerSubtypes_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CancerSubtypes_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CancerSubtypes_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CancerSubtypes_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R htmlDocs: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html htmlTitles: CancerSubtypes Package: CAnD Version: 1.6.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: e34d6801e9b60779b42c9a448869dd67 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAnD_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAnD_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAnD_1.6.0.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: 1.4.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: ee1466c53002a1b8b3fdcdb697ef106a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/caOmicsV_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/caOmicsV_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/caOmicsV_1.4.0.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: 1.6.0 Depends: BiocGenerics, Biobase, graphics, methods, stats, ProtGenerics Imports: grid, irlba, lattice, signal, sp, stats4, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a6941f365dd8c52c1acdd5dbd4c60e19 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 source.ver: src/contrib/Cardinal_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Cardinal_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Cardinal_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Cardinal_1.6.0.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 suggestsMe: matter Package: casper Version: 2.8.0 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: 95e9283aa9b61c9a1ddc6e4dc4eaaf9c 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/casper_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/casper_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/casper_2.8.0.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: Category Version: 2.40.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, RSQLite 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: 6041cfb6f7ef99dc72c35353b84f76f1 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: R. Gentleman with contributions from S. Falcon and D.Sarkar Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Category_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Category_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Category_2.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Category_2.40.0.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, GOstats, interactiveDisplay, PCpheno, phenoTest, ppiStats, RDAVIDWebService suggestsMe: BiocCaseStudies, miRLAB, MmPalateMiRNA, qpgraph, RnBeads Package: categoryCompare Version: 1.18.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCytoscape (>= 1.5.11) Suggests: knitr, methods, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: 3e8e4147fabacb4235a24f37d2d80d47 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 (>= 2.8.0) (if used for visualization of results, heavily suggested), CytoscapeRPC plugin (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues source.ver: src/contrib/categoryCompare_1.18.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R htmlDocs: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html htmlTitles: categoryCompare: High-throughput data meta-analysis using gene annotations Package: CausalR Version: 1.6.0 Depends: R (>= 3.2) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 8947f874e95f701ef3989792004d5f14 NeedsCompilation: no Title: Causal Reasoning Methods Description: Causal reasoning methods for biological networks, to enable regulator prediction and reconstruction of regulatory networks from high dimensional data. biocViews: GraphAndNetwork, Network Author: Glyn Bradley, Steven Barrett, David Wiley, Bhushan Bonde, Peter Woollard, Chirag Mistry, David Riley, Mark Pipe Maintainer: Glyn Bradley , Steven Barrett , Bhushan Bonde VignetteBuilder: knitr source.ver: src/contrib/CausalR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CausalR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CausalR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CausalR_1.6.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR : an R Package for causal reasoning on networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R Package: ccmap Version: 1.0.0 Imports: AnnotationDbi (>= 1.34.4), BiocInstaller, ccdata (>= 0.99.4), doParallel (>= 1.0.10), data.table (>= 1.9.6), foreach (>= 1.4.3), parallel (>= 3.3.1), xgboost (>= 0.4.4) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata, License: MIT + file LICENSE MD5sum: b4806f5a1c078180ab4b785489eed2c3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ccmap_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ccmap_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ccmap_1.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R htmlDocs: vignettes/ccmap/inst/doc/ccmap-vignette.html htmlTitles: ccmap vignette Package: CCPROMISE Version: 1.0.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 425b3fe94f625fb550ea87d3c6edb8db 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CCPROMISE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CCPROMISE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CCPROMISE_1.0.0.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: 1.10.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: da4367ca0043c3a52f63df8b2faa9ac1 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ccrepe_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ccrepe_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ccrepe_1.10.0.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: cellGrowth Version: 1.18.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: 43a2e554c0eb496731704ef98e54f72f 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellGrowth_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellGrowth_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellGrowth_1.18.0.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: 2.38.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4 Suggests: ggplot2 License: Artistic-2.0 MD5sum: 32ecdc85d57208a21bfe397ab75e1ccf 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_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellHTS2_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellHTS2_2.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellHTS2_2.38.0.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: coRNAi, imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR, prada Package: cellity Version: 1.2.0 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: 5296a8721dbfb2ec0b7f326304f4316a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellity_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellity_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellity_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R htmlDocs: vignettes/cellity/inst/doc/cellity_vignette.html htmlTitles: An introduction to the cellity package Package: CellMapper Version: 1.0.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 4142471434db4d8ad3a5860d48585bc6 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CellMapper_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CellMapper_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CellMapper_1.0.0.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: 1.20.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML Suggests: RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: b0cbd1696dfd3f4fa515f9e1da2dfbc0 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, TimeCourse Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: T.Cokelaer SystemRequirements: Graphviz version >= 2.2 source.ver: src/contrib/CellNOptR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CellNOptR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CellNOptR_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CellNOptR_1.20.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR Package: cellTree Version: 1.4.0 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: 2020b037bfd38a8d6d2bffebbe906883 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellTree_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellTree_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellTree_1.4.0.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: CexoR Version: 1.12.0 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: e2f3605dc4a329e0565efd6021af7cae 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CexoR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CexoR_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CexoR_1.12.0.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: 1.8.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: dcf02c3606894d7a8b7f455eef489050 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CFAssay_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CFAssay_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CFAssay_1.8.0.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: 3.10.0 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 1dd7f460221ac1ecfca5f62d091c381d 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_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGEN_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGEN_3.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGEN_3.10.0.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: 1.34.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: a262b5d14abc5aade85e7f7f4ad68df2 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHbase_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHbase_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHbase_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.36.0 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: 5f894c4a2fb24794fd149c4c8bcede00 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHcall_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHcall_2.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHcall_2.36.0.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: 1.32.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: a98a12d00a0fd1c65767ec7aad215bc6 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cghMCR_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cghMCR_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cghMCR_1.32.0.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: 1.28.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 3b717cdc24f4c96f27732f4637e1fa12 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHnormaliter_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHnormaliter_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHnormaliter_1.28.0.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: 1.32.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 9f73aa53b4706910c754d4e0ac5cd438 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHregions_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHregions_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHregions_1.32.0.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: 2.6.4 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0), FEM (>= 3.1),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest Imports: sva, IlluminaHumanMethylation450kmanifest, limma,RPMM, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq, GenomicRanges,RefFreeEWAS,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats Suggests: knitr,rmarkdown License: GPL-3 MD5sum: d457219a8550960881440f4a5f309ce8 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 source.ver: src/contrib/ChAMP_2.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChAMP_2.6.4.zip win64.binary.ver: bin/windows64/contrib/3.3/ChAMP_2.6.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChAMP_2.6.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R htmlDocs: vignettes/ChAMP/inst/doc/ChAMP.html htmlTitles: ChAMP: The Chip Analysis Methylation Pipeline Package: charm Version: 2.20.0 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: f07abc95197cfa50d86dd0aa599b2d56 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/charm_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/charm_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/charm_2.20.0.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: 1.12.0 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: 8e57220cd39c256edf683e6a05838080 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChemmineOB_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChemmineOB_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChemmineOB_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R htmlDocs: vignettes/ChemmineOB/inst/doc/ChemmineOB.html htmlTitles: ChemmineOB suggestsMe: ChemmineR Package: ChemmineR Version: 2.26.1 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2 LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineOB (>= 1.3.8), ChemmineDrugs, grid, gridExtra, png,rmarkdown Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 9506b97293cd1a9cff0c684a54d01fb5 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_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChemmineR_2.26.1.zip win64.binary.ver: bin/windows64/contrib/3.3/ChemmineR_2.26.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChemmineR_2.26.1.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R htmlDocs: vignettes/ChemmineR/inst/doc/ChemmineR.html htmlTitles: ChemmineR dependsOnMe: eiR, fmcsR importsMe: bioassayR, eiR, fmcsR, Rchemcpp, Rcpi suggestsMe: ChemmineOB Package: Chicago Version: 1.2.0 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: 78fa42f33d5a730bcb6340309cc56f7e 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Chicago_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Chicago_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Chicago_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R htmlDocs: vignettes/Chicago/inst/doc/Chicago.html htmlTitles: CHiCAGO Vignette Package: chimera Version: 1.16.0 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: 9a515d2e1f720db1a53c7645a3d1823d 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chimera_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chimera_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chimera_1.16.0.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 dependsOnMe: oneChannelGUI Package: ChIPComp Version: 1.4.0 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: adfe34c38f4513f1c4f30d690690aab8 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPComp_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPComp_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPComp_1.4.0.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: 1.12.1 Depends: R (>= 3.1.0) Imports: chipenrich.data, GenomeInfoDb, GenomicRanges, grid, IRanges, lattice, latticeExtra, methods, mgcv, parallel, plyr, rms, S4Vectors, stringr Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: a9dd40d1d755fb8ff62d18857b8fae20 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: Software, Bioinformatics, Enrichment, GeneSetEnrichment Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut, cre], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr source.ver: src/contrib/chipenrich_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/chipenrich_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/chipenrich_1.12.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chipenrich_1.12.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R htmlDocs: vignettes/chipenrich/inst/doc/chipenrich-vignette.html htmlTitles: Vignette Title Package: ChIPpeakAnno Version: 3.8.9 Depends: R (>= 3.2), methods, grid, IRanges (>= 2.5.27), Biostrings, GenomicRanges (>= 1.23.16), S4Vectors (>= 0.9.25), 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, 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, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: 8acc2aac3e0055915667927ce53cb314 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, Herve Pages, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu , Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/ChIPpeakAnno_3.8.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPpeakAnno_3.8.9.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPpeakAnno_3.8.9.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPpeakAnno_3.8.9.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R htmlDocs: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html htmlTitles: ChIPpeakAnno Vignette, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start dependsOnMe: REDseq importsMe: DChIPRep, FunciSNP, GUIDEseq, REDseq suggestsMe: oneChannelGUI, R3CPET, RIPSeeker Package: ChIPQC Version: 1.10.3 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.Hsapiens.UCSC.hg38.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: f6dcae1e75e32adf1ee800ab65b78e62 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 source.ver: src/contrib/ChIPQC_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPQC_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPQC_1.10.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPQC_1.10.3.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: 1.10.3 Depends: R (>= 3.3.0) Imports: AnnotationDbi, BiocGenerics, boot, DOSE, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2 (>= 2.2.0), gplots, graphics, grDevices, grid, gridBase, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors (>= 0.9.25), stats, TxDb.Hsapiens.UCSC.hg19.knownGene, UpSetR, utils Suggests: clusterProfiler, ReactomePA, org.Hs.eg.db, knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 0df4a6b80103b0d51a991328c5028b8b 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 with contributions from Yun Yan, Herve Pages, Michael Kluge and Thomas Schwarzl. Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ChIPseeker/issues source.ver: src/contrib/ChIPseeker_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPseeker_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPseeker_1.10.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPseeker_1.10.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R htmlDocs: vignettes/ChIPseeker/inst/doc/ChIPseeker.html htmlTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization Package: chipseq Version: 1.24.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), IRanges (>= 1.99.1), GenomicRanges (>= 1.17.7), 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: cd6d50d397fdcefc113ea188783347cf 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chipseq_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chipseq_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chipseq_1.24.0.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, oneChannelGUI Package: ChIPseqR Version: 1.28.0 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: 4baef922f50b1886e3ec605bfc80f82b 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPseqR_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPseqR_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPseqR_1.28.0.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: ChIPsim Version: 1.28.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: df47293f700bec0a0ddb202aca780b5c 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPsim_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPsim_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPsim_1.28.0.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: 1.16.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 7cdd1f61616ae752c547d316235eff0e 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_1.16.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPXpress_1.16.0.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: 1.38.0 Depends: R(>= 2.10.0), survival, methods Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 6340cd3f9c4ba39ac1197efd18373497 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chopsticks_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chopsticks_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chopsticks_1.38.0.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: 1.22.0 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: c772488eff7cc15dc83f4a321dd9cffd NeedsCompilation: no Title: chroGPS: 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chroGPS_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chroGPS_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chroGPS_1.22.0.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: 2.4.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 328d1183bde0ca562853e58e3d9cce0a 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chromDraw_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chromDraw_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chromDraw_2.4.0.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: 1.28.0 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: a410691c411d702c7e60249bdddabec5 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChromHeatMap_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChromHeatMap_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChromHeatMap_1.28.0.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: 1.2.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.3.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: ba62f3df45d70d35f819b9afafa44471 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chromPlot_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chromPlot_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chromPlot_1.2.0.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: 1.0.0 Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals Suggests: knitr, BiocStyle, testthat, biomaRt, ggbio License: Artistic-2.0 Archs: i386, x64 MD5sum: 024831787f34f6b379731e4aa74bcd30 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, MultipleComparison Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt VignetteBuilder: knitr source.ver: src/contrib/chromstaR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chromstaR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chromstaR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chromstaR_1.0.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R Package: CHRONOS Version: 1.2.1 Depends: R (>= 3.3) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt Suggests: RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 7014ac33228255020d607fae1e76f756 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 source.ver: src/contrib/CHRONOS_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/CHRONOS_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/CHRONOS_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CHRONOS_1.2.1.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: 1.2.0 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: 6dd48db4ba9d01f2efb03719cde3297a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CINdex_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CINdex_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CINdex_1.2.0.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: 1.14.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: b8fc816559633b363bb232bb2f11d357 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cisPath_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cisPath_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cisPath_1.14.0.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: 1.8.2 Depends: R (>= 3.0.3), methods, Biobase, BiocParallel Imports: locfit, ROCR, grid Suggests: limma, edgeR, car, Rmixmod, ggplot2 (>= 2.0.0), gridExtra (>= 2.0.0), BiocStyle, pamr, sparsediscrim, PoiClaClu, curatedOvarianData, parathyroidSE, knitr, klaR, gtable, scales, e1071, rmarkdown, IRanges License: GPL-3 MD5sum: 55accbcf13d3b5117804ae33f3c02fd7 NeedsCompilation: no Title: A framework for two-class 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 source.ver: src/contrib/ClassifyR_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ClassifyR_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ClassifyR_1.8.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ClassifyR_1.8.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R htmlDocs: vignettes/ClassifyR/inst/doc/ClassifyR.html htmlTitles: An Introduction to the ClassifyR Package Package: cleanUpdTSeq Version: 1.12.0 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: 5e11c1bc1f0404be819934ff03e80d3d 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cleanUpdTSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cleanUpdTSeq_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cleanUpdTSeq_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R htmlDocs: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html htmlTitles: cleanUpdTSeq Vignette importsMe: InPAS Package: cleaver Version: 1.12.0 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: d8fa801e59ded7a600cbe1cf7b0ddd0f 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cleaver_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cleaver_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cleaver_1.12.0.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: 1.24.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: df8b6d42612f3e6d95f4cea4fe5b9a35 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clippda_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clippda_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clippda_1.24.0.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: 1.14.0 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: RCytoscape (>= 1.6.3) License: AGPL-3 MD5sum: 7a35c13c74dc772d7b5565abe0378658 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clipper_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clipper_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clipper_1.14.0.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 importsMe: ToPASeq suggestsMe: graphite Package: Clomial Version: 1.10.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: f1e81444b3ade7591a0951de8064be0b 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Clomial_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Clomial_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Clomial_1.10.0.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: 1.22.0 Depends: R (>= 2.12.2), DNAcopy Imports: DNAcopy, grDevices, graphics, stats, utils Suggests: gdata, DNAcopy License: GPL-3 MD5sum: 20fde8edcecb0e40993abff3cb00ee60 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Clonality_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Clonality_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Clonality_1.22.0.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: 1.12.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: bb0d8d69d831dcd88619b20f617b8513 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clonotypeR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clonotypeR_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clonotypeR_1.12.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R htmlDocs: vignettes/clonotypeR/inst/doc/clonotypeR.html htmlTitles: clonotypeR User's Guide Package: clst Version: 1.22.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: bf42aba98440fe684a60c843beb3ccef 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clst_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clst_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clst_1.22.0.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: 1.22.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 752d7812932bb0e84b1b2da03c09e123 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clstutils_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clstutils_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clstutils_1.22.0.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: 1.2.2 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: beb6505c21083f23ac21c3e6ca3bdd36 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/clustComp_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/clustComp_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clustComp_1.2.2.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: 1.0.0 Depends: R (>= 3.3), methods, SummarizedExperiment Imports: NMF, RColorBrewer, ape, phylobase, cluster, stats, limma, dendextend, howmany, locfdr, matrixStats, graphics, parallel Suggests: BiocStyle, knitr, diagram, testthat, scRNAseq License: Artistic-2.0 MD5sum: e1ff4062bea57e4d4c92aaa76dc3f107 NeedsCompilation: no Title: Compare clusterings for single-cell sequencing Description: This package provides functions for running and comparing many different clusterings of single-cell sequencing data. biocViews: Clustering, RNASeq, Sequencing, Software 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clusterExperiment_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clusterExperiment_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clusterExperiment_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R htmlDocs: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html htmlTitles: clusterExperiment Vignette Package: clusterProfiler Version: 3.2.14 Depends: R (>= 3.3.1), DOSE (>= 3.0.1) Imports: AnnotationDbi, ggplot2, GO.db, GOSemSim (>= 2.0.0), IRanges, magrittr, methods, plyr, qvalue, stats, stats4, tidyr, utils Suggests: AnnotationHub, BiocStyle, GSEABase, KEGG.db, knitr, org.Hs.eg.db, pathview, ReactomePA, testthat, topGO License: Artistic-2.0 MD5sum: 38b8a427cfcdbb328a2aa1284cc11db9 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], Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb] (formula interface of compareCluster) Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues source.ver: src/contrib/clusterProfiler_3.2.14.tar.gz win.binary.ver: bin/windows/contrib/3.3/clusterProfiler_3.2.14.zip win64.binary.ver: bin/windows64/contrib/3.3/clusterProfiler_3.2.14.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clusterProfiler_3.2.14.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R htmlDocs: vignettes/clusterProfiler/inst/doc/clusterProfiler.html htmlTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters importsMe: bioCancer, debrowser, eegc, LINC, MoonlightR, TCGAbiolinks suggestsMe: ChIPseeker, DOSE, GOSemSim, isomiRs, ReactomePA Package: ClusterSignificance Version: 1.2.3 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve, scatterplot3d, RColorBrewer, grDevices, graphics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics License: GPL-3 MD5sum: 57e998961417f061bbd34bfa92b38494 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if clusters have a separation different from random or permuted data Description: The ClusterSignificance package provides tools to assess if clusters have a separation different from random or permuted data. ClusterSignificance investigates clusters of two or more groups by first, 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 and Jesper R. Gadin Maintainer: Jason T Serviss VignetteBuilder: knitr source.ver: src/contrib/ClusterSignificance_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ClusterSignificance_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ClusterSignificance_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ClusterSignificance_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R htmlDocs: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html htmlTitles: ClusterSignificance Vignette Package: clusterStab Version: 1.46.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 0098642e0e1b447ecdae1e02c26f6f59 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clusterStab_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clusterStab_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clusterStab_1.46.0.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: 1.32.0 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: 4fba28d46a1f9581b2afd0d339cfd4d1 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CMA_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CMA_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CMA_1.32.0.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: 1.22.0 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: 4d6c6f3ef4383b4b36b99b3579478155 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cn.farms_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cn.farms_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cn.farms_1.22.0.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: 1.20.1 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: 46e402c66248f84a0315dac5fe8c4b46 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_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/cn.mops_1.20.1.zip win64.binary.ver: bin/windows64/contrib/3.3/cn.mops_1.20.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cn.mops_1.20.1.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 Package: CNAnorm Version: 1.20.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: dfdd8543cd43f24a667624d83b086c81 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNAnorm_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNAnorm_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNAnorm_1.20.0.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: 1.10.2 Depends: R (>= 3.2.2) Imports: Biostrings (>= 2.33.4), 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 (>= 0.9.25), 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: 0e79fade0ec01e8f77a10a82e560f7f2 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_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNEr_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CNEr_1.10.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNEr_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R htmlDocs: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html htmlTitles: CNE identification and visualisation, Pairwise whole genome alignment importsMe: TFBSTools Package: CNORdt Version: 1.16.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 4fc211110081666e2a778760698400e5 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORdt_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORdt_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORdt_1.16.0.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: 1.14.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph License: GPL-3 MD5sum: a32ddd55226df0b674f433c4c247d6c6 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORfeeder_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORfeeder_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORfeeder_1.14.0.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: 1.16.0 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: c13664b6f48addf37dfe7952ba601d92 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORfuzzy_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORfuzzy_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORfuzzy_1.16.0.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: 1.16.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 5de4d1e324f86bd45fba8dcc73b8501a 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORode_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORode_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORode_1.16.0.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: 1.4.0 Depends: GenomicRanges Imports: Rcpp (>= 0.12.1), S4Vectors (>= 0.9.25), matrixStats, RColorBrewer, gtools, combinat, IRanges, GenomeInfoDb, GenomicRanges, methods, BiocGenerics, graphics, stats, coda, SummarizedExperiment LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, VanillaICE (>= 1.31.3), BiocCheck, MASS, oligoClasses, dplyr, tidyr, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 7ebaa8830d51679d10f403949df193b3 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNPBayes_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNPBayes_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNPBayes_1.4.0.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: 1.30.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: a9052ce33c3940b9a33c97051b76ba5e 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNTools_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNTools_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNTools_1.30.0.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: 1.18.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: cf00997ded678ea2533ec1307107cf5a 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cnvGSA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cnvGSA_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cnvGSA_1.18.0.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: 1.4.0 Depends: R (>= 3.2.0), GenomicRanges Imports: S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, openxlsx Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 5ad57f9092eca76a08c61bacb0433a04 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVPanelizer_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVPanelizer_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVPanelizer_1.4.0.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: 1.12.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 59526f3004247ee64b39a4937f0456fc 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVrd2_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVrd2_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVrd2_1.12.0.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: 1.68.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: a30523d9d43b96859cd3d015a0c95748 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_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVtools_1.68.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVtools_1.68.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVtools_1.68.0.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: 1.12.0 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: d28adcba6dd5daa892ed51c56f1767cb 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cobindR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cobindR_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cobindR_1.12.0.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: 1.46.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 566885698b719fc8812aad55e323f00e 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoCiteStats_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CoCiteStats_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoCiteStats_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.42.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 34b8825065f3c4e0a20be3eb9cd41eaa 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/codelink_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/codelink_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/codelink_1.42.0.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: 1.6.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: dbf9171dbab45350c01cd653ff20523e 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CODEX_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CODEX_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CODEX_1.6.0.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 Package: CoGAPS Version: 2.8.0 Depends: R (>= 3.0.1), Rcpp (>= 0.11.2) Imports: RColorBrewer (>= 1.0.5), gplots (>= 2.8.0), graphics, grDevices, methods, stats, utils LinkingTo: Rcpp, BH Suggests: testthat, lintr License: GPL (==2) Archs: i386, x64 MD5sum: 2ff4ba8dee10ce106f9bdaf2d93ca6d4 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: Elana J. Fertig, Michael F. Ochs Maintainer: Elana J. Fertig source.ver: src/contrib/CoGAPS_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoGAPS_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CoGAPS_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoGAPS_2.8.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf vignetteTitles: GAPS/CoGAPS Users Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.R Package: cogena Version: 1.8.0 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: 59baa763c180c819502310a55b93f066 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cogena_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cogena_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cogena_1.8.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf vignetteTitles: 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 htmlDocs: vignettes/cogena/inst/doc/cogena-vignette_html.html htmlTitles: Vignette Title Package: coGPS Version: 1.18.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: ffd8772a69aec26ca01ab7a359cfbfd1 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/coGPS_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/coGPS_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coGPS_1.18.0.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: 1.16.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots License: GPL-3 MD5sum: 6d79339d2efe1c73a932507329ce307c NeedsCompilation: no 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/COHCAP_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/COHCAP_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COHCAP_1.16.0.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: 1.6.0 Depends: R, grid, utils, biomaRt, Gviz, psych, ggbio, trackViewer Imports: colortools, hash, grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, ggplot2, stats, corrplot Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 07b73acaa2ed110ab09a34190ec803f4 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 Author: Tiphaine C. Martin, Thomas Hardiman, Idil Yet, Pei-Chien Tsai, Jordana T. Bell Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr source.ver: src/contrib/coMET_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/coMET_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/coMET_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coMET_1.6.0.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: 1.12.0 Depends: R (>= 3.0.2) Imports: Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.9.66), flowCore, ncdfFlow, shiny, testthat, devtools, Kmisc, flowWorkspaceData License: Artistic-2.0 Archs: i386, x64 MD5sum: 584ec0ee477edcdaf95432968b0bc067 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 source.ver: src/contrib/COMPASS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/COMPASS_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/COMPASS_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COMPASS_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R htmlDocs: vignettes/COMPASS/inst/doc/COMPASS.html htmlTitles: COMPASS Package: compcodeR Version: 1.10.0 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, samr, NBPSeq (>= 0.3.0) Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: d21c0960004e039c8f2c6254994c712e 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 source.ver: src/contrib/compcodeR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/compcodeR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/compcodeR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/compcodeR_1.10.0.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: 1.8.0 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: 8ad65a79d0326cc6a257d3b6538a0c25 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, Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/compEpiTools_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/compEpiTools_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/compEpiTools_1.8.0.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: 1.10.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: deecec9f30a9ee83e710841e59d9948e 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CompGO_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CompGO_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CompGO_1.10.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf, vignettes/CompGO/inst/doc/CompGO-vignette.pdf vignetteTitles: Introduction, CompGO-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R Package: ComplexHeatmap Version: 1.12.0 Depends: R (>= 3.1.2), grid, graphics, stats, grDevices Imports: methods, circlize (>= 0.3.4), GetoptLong, colorspace, RColorBrewer, dendextend (>= 1.0.1), GlobalOptions (>= 0.0.10) Suggests: testthat (>= 0.3), knitr, markdown, cluster, MASS, pvclust, dendsort, HilbertCurve, Cairo, png, jpeg, tiff, fastcluster License: GPL (>= 2) MD5sum: 37d323080bb0f454c83727f4e81a4c59 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 source.ver: src/contrib/ComplexHeatmap_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ComplexHeatmap_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ComplexHeatmap_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ComplexHeatmap_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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 htmlDocs: 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 htmlTitles: 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 dependsOnMe: EnrichedHeatmap, recoup importsMe: EnrichmentBrowser, fCCAC, maftools, TCGAbiolinks, YAPSA suggestsMe: gtrellis, HilbertCurve Package: CONFESS Version: 1.2.2 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,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: 86f89638e3d74e3f3badc51bf680c426 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CONFESS_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CONFESS_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CONFESS_1.2.2.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf vignetteTitles: CONFESS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R htmlDocs: vignettes/CONFESS/inst/doc/vignette.html htmlTitles: CONFESS Package: ConsensusClusterPlus Version: 1.38.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 03732e02d9aa4ab3676a0fd095456c4a 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ConsensusClusterPlus_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ConsensusClusterPlus_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ConsensusClusterPlus_1.38.0.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 dependsOnMe: CVE importsMe: CancerSubtypes, FlowSOM, TCGAbiolinks Package: consensusSeekeR Version: 1.2.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors Suggests: BiocStyle, ggplot2, knitr, RUnit License: Artistic-2.0 MD5sum: 9a6e5e7b1baa46911d4c3c1bf36a51fb 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 Louise Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues source.ver: src/contrib/consensusSeekeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/consensusSeekeR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/consensusSeekeR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/consensusSeekeR_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R htmlDocs: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html htmlTitles: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Package: contiBAIT Version: 1.2.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: 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: feede14611c4b08a2808ea3b1c30dcbc 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/contiBAIT_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/contiBAIT_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/contiBAIT_1.2.0.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: 1.8.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, CopyNumber450kData, RCurl License: GPL (>= 2) MD5sum: 7349b5d553ed43983b26131dd4773b38 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/conumee_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/conumee_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/conumee_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R htmlDocs: vignettes/conumee/inst/doc/conumee.html htmlTitles: conumee Package: convert Version: 1.50.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 43ff2b9dc8c9a3a60b9cf43796db9f36 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 Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html source.ver: src/contrib/convert_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/convert_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/convert_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/convert_1.50.0.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: 1.42.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 1283a2711eb5a6497fbcd435c5279f4e 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/copa_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/copa_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/copa_1.42.0.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: 1.14.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 447fcf9ccd7c90954acbafde47d06502 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/copynumber_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/copynumber_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/copynumber_1.14.0.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: 2.6.0 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: 23fbb9325433ba34157af8b996bc5875 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CopywriteR_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CopywriteR_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CopywriteR_2.6.0.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: 1.10.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 294ee7778380521ef65396ec44a405b4 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoRegNet_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CoRegNet_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoRegNet_1.10.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R htmlDocs: vignettes/CoRegNet/inst/doc/CoRegNet.html htmlTitles: Custom Print Methods Package: Cormotif Version: 1.20.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: d64b0c6d52e66b52f03820e56f243e69 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Cormotif_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Cormotif_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Cormotif_1.20.0.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: 1.16.0 Depends: seqinr,igraph License: GPL-2 MD5sum: b8b04a6db9e8c768a5a34fe72a38809f 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. biocViews: Sequencing Author: Zhenpeng Li, Yang Huang, Yabo Ouyang, Yiming Shao, Liying Ma Maintainer: Zhenpeng Li source.ver: src/contrib/CorMut_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CorMut_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CorMut_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CorMut_1.16.0.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: coRNAi Version: 1.24.0 Depends: R (>= 2.10), cellHTS2, limma, locfit Imports: MASS, gplots, lattice, grDevices, graphics, stats License: Artistic-2.0 MD5sum: 64f6cc27d16eef412da3f3b6e80bf85d NeedsCompilation: no Title: Analysis of co-knock-down RNAi data Description: Analysis of combinatorial cell-based RNAi screens biocViews: CellBasedAssays Author: Elin Axelsson Maintainer: Elin Axelsson SystemRequirements: Graphviz source.ver: src/contrib/coRNAi_1.24.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coRNAi_1.24.0.tgz vignettes: vignettes/coRNAi/inst/doc/coRNAi.pdf vignetteTitles: coRNAi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRNAi/inst/doc/coRNAi.R Package: CORREP Version: 1.40.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 6e496463b36437a946a912256d824447 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CORREP_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CORREP_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CORREP_1.40.0.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: cosmiq Version: 1.8.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 0d22a4b914eb75ec0d43708c2bb78730 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cosmiq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cosmiq_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cosmiq_1.8.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R Package: COSNet Version: 1.8.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: e860018afbdfb721006455c562486ef7 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/COSNet_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/COSNet_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COSNet_1.8.0.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: 1.2.0 Depends: R (>= 3.3.0), ggplot2 (>= 2.1.0) Imports: maptpx, slam, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) MD5sum: 7f7ab6cd265c893d805127a86c64ef47 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CountClust_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CountClust_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CountClust_1.2.0.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.pdf vignetteTitles: Grade of Membership Clustering and Visualization using CountClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R Package: covEB Version: 1.0.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats Suggests: curatedBladderData License: GPL-3 MD5sum: e6d54ff3e3e1306174fa8f7dfa261b71 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/covEB_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/covEB_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/covEB_1.0.0.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: 1.10.0 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: 07253a42b68215a8b242b8e530b884e2 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_1.10.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoverageView_1.10.0.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: 1.0.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 147a85912c1db26e654345060b3ad435 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/covRNA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/covRNA_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/covRNA_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R htmlDocs: vignettes/covRNA/inst/doc/covRNA.html htmlTitles: An Introduction to covRNA Package: cpvSNP Version: 1.6.0 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: 41420532ef02064e33ff3eeb13c89243 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cpvSNP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cpvSNP_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cpvSNP_1.6.0.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: 1.20.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 49cbfe22490f2e1f7f31d4e8a434a7a0 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cqn_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cqn_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cqn_1.20.0.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: 1.22.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 12be6ccebab6a9f91b38d8f142ff6eca 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CRImage_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CRImage_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CRImage_1.22.0.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: 1.14.1 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: 38dae68d37b131540fcddc35fe087479 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, Herve Pages, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/CRISPRseek_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/CRISPRseek_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CRISPRseek_1.14.1.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: 1.0.0 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: aacd4fee06e087c468e877a53469a498 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/crisprseekplus_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/crisprseekplus_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/crisprseekplus_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R htmlDocs: vignettes/crisprseekplus/inst/doc/crisprseekplus.html htmlTitles: DEBrowser Vignette Package: CrispRVariants Version: 1.2.0 Depends: R (>= 3.3), ggplot2 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: f83bde89e69a3e23e444ec83d0b755aa 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CrispRVariants_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CrispRVariants_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CrispRVariants_1.2.0.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: 1.32.0 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: 95b2b71866647ed1ee3667941fdc3194 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/crlmm_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/crlmm_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/crlmm_1.32.0.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: 1.0.1 Depends: R (>= 3.3) Imports: affy (>= 1.50.0), affxparser (>= 1.44.0), AnnotationDbi (>= 1.34.4), Biobase (>= 2.32.0), BiocGenerics (>= 0.18.0), BiocInstaller (>= 1.22.3), DT (>= 0.2), data.table (>= 1.9.6), fdrtool (>= 1.2.15), GEOquery (>= 2.38.4), limma (>= 3.28.17), matrixStats (>= 0.50.2), metaMA (>= 3.1.2), miniUI (>= 0.1.1), oligo (>= 1.36.1), pander (>= 0.6.0), RColorBrewer (>= 1.1.2), rdrop2 (>= 0.7.0), stringr (>= 1.0.0), sva (>= 3.20.0), shiny (>= 0.13.2) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat License: MIT + file LICENSE MD5sum: 7944c749dad11c6a57046dfa4cf7834b 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. A user interface makes it easy to select control and treatment samples for each contrast and study. This input is used for subsequent surrogate variable analysis (models unaccounted sources of variation) and differential expression analysis. Final meta-analysis of differential expression values can include genes measured in only a subset of studies. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr source.ver: src/contrib/crossmeta_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/crossmeta_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/crossmeta_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/crossmeta_1.0.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R htmlDocs: vignettes/crossmeta/inst/doc/crossmeta-vignette.html htmlTitles: crossmeta vignette suggestsMe: ccmap Package: CSAR Version: 1.26.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: c6f1c86b6de8d34902fa15faaabf020a 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CSAR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CSAR_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CSAR_1.26.0.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: 1.8.1 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment (>= 1.1.6), BiocParallel Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, S4Vectors, IRanges, GenomeInfoDb, BiocGenerics, Rhtslib, stats LinkingTo: Rhtslib, zlibbioc Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: e236bd1787215dce64e5cabfda18c4f9 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 source.ver: src/contrib/csaw_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/csaw_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/csaw_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/csaw_1.8.1.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 Package: CSSP Version: 1.12.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: a0d690c55cfa5a2f93ef8cea10d384f4 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CSSP_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CSSP_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CSSP_1.12.0.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: 1.48.0 Depends: amap License: GPL-2 MD5sum: e032e50a1e44babfddb283b28309f514 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ctc_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ctc_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ctc_1.48.0.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: ctsGE Version: 1.0.0 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: da32f305f9242fd842290639bc361469 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ctsGE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ctsGE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ctsGE_1.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R htmlDocs: vignettes/ctsGE/inst/doc/ctsGE.html htmlTitles: ctsGE Package Package: cummeRbund Version: 2.16.0 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: 4563d786aa9a596187f1a6e5e31b2210 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cummeRbund_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cummeRbund_2.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cummeRbund_2.16.0.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: meshr, spliceR suggestsMe: oneChannelGUI Package: customProDB Version: 1.14.1 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.17.13), biomaRt (>= 2.17.1), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AnnotationDbi Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 8259b6afee9e1f73f4570818480f42f3 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search. Description: Generate customized protein sequence database from RNA-Seq data for proteomics search biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing Author: xiaojing wang Maintainer: xiaojing wang source.ver: src/contrib/customProDB_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/customProDB_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/customProDB_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/customProDB_1.14.1.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: 1.0.0 Depends: R (>= 3.3), shiny, ConsensusClusterPlus, RColorBrewer, gplots, plyr, ggplot2, jsonlite, ape, WGCNA Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: d30dc89f90fba8515f9c52cf59a56b77 NeedsCompilation: no Title: Cancer Variant Explorer Description: Shiny app for interactive variant prioritisation in precision cancer medicine. 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CVE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CVE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CVE_1.0.0.tgz 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 htmlDocs: vignettes/CVE/inst/doc/CVE_tutorial.html, vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.html htmlTitles: Cancer Variant Explorer (CVE) tutorial, Weighted gene co-expression network analysis with TCGA RNAseq data Package: cycle Version: 1.28.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 67c8149667740c198a8306e6f7149880 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cycle_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cycle_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cycle_1.28.0.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: cytofkit Version: 1.6.5 Depends: R (>= 2.10.0), ggplot2, plyr Imports: tcltk, stats, Rtsne, e1071, flowCore, gplots, colourpicker, VGAM, reshape2, ggrepel, shiny, vegan, Biobase, doParallel, parallel, pdist, methods, destiny, FlowSOM(>= 1.4.0), igraph(>= 1.0.1), RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 0a55041ee6bf476c537dde4790d54562 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 Maintainer: Jinmiao Chen , Hao Chen VignetteBuilder: knitr source.ver: src/contrib/cytofkit_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/cytofkit_1.6.5.zip win64.binary.ver: bin/windows64/contrib/3.3/cytofkit_1.6.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cytofkit_1.6.5.tgz 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 htmlDocs: vignettes/cytofkit/inst/doc/cytofkit_example.html, vignettes/cytofkit/inst/doc/cytofkit_shinyAPP.html, vignettes/cytofkit/inst/doc/cytofkit_workflow.html htmlTitles: Quick Start, ShinyAPP tutorial, Analysis Pipeline Package: CytoML Version: 1.0.1 Imports: flowCore, flowWorkspace (>= 3.19.24), openCyto (>= 1.11.3), XML, data.table, flowUtils (>= 1.35.7), jsonlite, RBGL, ncdfFlow, Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc Suggests: testthat, flowWorkspaceData, knitr, ggcyto License: Artistic-2.0 MD5sum: 0272bb9787c07223598ac7b46202acec 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 source.ver: src/contrib/CytoML_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/CytoML_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/CytoML_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CytoML_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoML/inst/doc/HowToExportGatingSet.R, vignettes/CytoML/inst/doc/HowToParseGatingML.R htmlDocs: vignettes/CytoML/inst/doc/HowToExportGatingSet.html, vignettes/CytoML/inst/doc/HowToParseGatingML.html htmlTitles: How to export a GatingSet to GatingML, How to parse gatingML into a GatingSet Package: dada2 Version: 1.2.2 Depends: R (>= 3.2.0), Rcpp (>= 0.11.2), methods (>= 3.2.0) Imports: Biostrings (>= 2.32.1), ggplot2 (>= 1.0), data.table (>= 1.9.4), reshape2 (>= 1.4.1), ShortRead (>= 1.24.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0) LinkingTo: Rcpp, RcppParallel Suggests: testthat (>= 0.9.1), BiocStyle, knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: 9813f63e75eecd0298d3d5e7d96a460b NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact sequence variants (SVs) from amplicon data, replacing the commonly used and coarser OTU clustering approach. The dada2 pipeline inputs 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. 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/dada2_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/dada2_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dada2_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R htmlDocs: vignettes/dada2/inst/doc/dada2-intro.html htmlTitles: Introduction to dada2 Package: dagLogo Version: 1.12.0 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, UniProt.ws, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: f988a88059f176e36e55ea4d63a59af3 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dagLogo_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dagLogo_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dagLogo_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R htmlDocs: vignettes/dagLogo/inst/doc/dagLogo.html htmlTitles: dagLogo Vignette Package: daMA Version: 1.46.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 971a7d2303b57120efca741b5aa4588e 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/daMA_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/daMA_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/daMA_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DAPAR Version: 1.6.0 Depends: R (>= 3.3) Imports: MSnbase, RColorBrewer,stats,preprocessCore,Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, imputeLCMD, doParallel, parallel, foreach,grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot Suggests: BiocGenerics, Biobase, testthat, BiocStyle, Prostar License: Artistic-2.0 MD5sum: acaeea42b631a5a957bd9ff347e4bb2b 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, DataImport Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb] Maintainer: Samuel Wieczorek VignetteBuilder: knitr source.ver: src/contrib/DAPAR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DAPAR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DAPAR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DAPAR_1.6.0.tgz vignettes: vignettes/DAPAR/inst/doc/intro.pdf, vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf, vignettes/DAPAR/inst/doc/UPSprotx2.pdf vignetteTitles: DAPAR One Page Introduction, Prostar user manual, UPSprotx2 dataset description hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar Package: DART Version: 1.22.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: ce81c9622ec6961d003f55adac2b5bc4 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DART_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DART_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DART_1.22.0.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: 1.18.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 800cfbbfe37aa9752783710f394470d1 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DBChIP_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DBChIP_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DBChIP_1.18.0.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: 1.2.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 3cfb02d330d9098cdda7888304f855e3 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dcGSA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dcGSA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dcGSA_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DChIPRep Version: 1.4.0 Depends: R (>= 3.3), 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: 83d3bebbcc1677dbef0f55f555acb7ef 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 Author: Bernd Klaus [aut, cre], Christophe Chabbert [aut], Sebastian Gibb [ctb] Maintainer: Bernd Klaus SystemRequirements: Python 2.7, HTSeq (>= 0.6.1), numpy, argparse, sys VignetteBuilder: knitr source.ver: src/contrib/DChIPRep_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DChIPRep_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DChIPRep_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DChIPRep_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DChIPRep/inst/doc/DChIPRepVignette.R htmlDocs: vignettes/DChIPRep/inst/doc/DChIPRepVignette.html htmlTitles: DChIPRepVignette Package: ddCt Version: 1.30.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 0587d4ed5782b3b10da0f28322a3ee5e 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ddCt_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ddCt_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ddCt_1.30.0.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: TRUE 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: ddgraph Version: 1.18.0 Depends: graph, methods, Rcpp Imports: bnlearn (>= 2.8), gtools, pcalg, RColorBrewer, plotrix, MASS LinkingTo: Rcpp Suggests: Rgraphviz, e1071, ROCR, testthat License: GPL-3 Archs: i386, x64 MD5sum: d8639f4ebf3c956e04a130fed60a3683 NeedsCompilation: yes Title: Distinguish direct and indirect interactions with Graphical Modelling Description: Distinguish direct from indirect interactions in gene regulation and infer combinatorial code from highly correlated variables such as transcription factor binding profiles. The package implements the Neighbourhood Consistent PC algorithm (NCPC) and draws Direct Dependence Graphs to represent dependence structure around a target variable. The package also provides a unified interface to other Graphical Modelling (Bayesian Network) packages for distinguishing direct and indirect interactions. biocViews: GraphAndNetwork Author: Robert Stojnic Maintainer: Robert Stojnic source.ver: src/contrib/ddgraph_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ddgraph_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ddgraph_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ddgraph_1.18.0.tgz vignettes: vignettes/ddgraph/inst/doc/ddgraph.pdf vignetteTitles: Overview of the 'ddgraph' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddgraph/inst/doc/ddgraph.R Package: debrowser Version: 1.3.11 Depends: R (>= 3.3.0), shiny, ggvis, jsonlite, shinyjs Imports: DT, ggplot2, RColorBrewer, annotate, gplots, 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, shinydashboard, devtools, RCurl Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 371477cf245d9d9331fe5d79e7d3182a NeedsCompilation: no Title: debrowser: 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 , 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 source.ver: src/contrib/debrowser_1.3.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/debrowser_1.3.11.zip win64.binary.ver: bin/windows64/contrib/3.3/debrowser_1.3.11.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/debrowser_1.3.11.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R htmlDocs: vignettes/debrowser/inst/doc/DEBrowser.html htmlTitles: DEBrowser Vignette Package: DECIPHER Version: 2.2.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.12), RSQLite (>= 1.0.0), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 0f9d68af17d92ba6a8e4cd192764bf43 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DECIPHER_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DECIPHER_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DECIPHER_2.2.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.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, 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/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 Package: DeconRNASeq Version: 1.16.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 41c433788428d5a40231eac1872c210f 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DeconRNASeq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DeconRNASeq_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DeconRNASeq_1.16.0.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: DEDS Version: 1.48.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: a2c0a125be535aef7fe22dec6bdd95a9 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEDS_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEDS_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEDS_1.48.0.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: 1.0.11 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: b978426d83186a3a7c8622cd65ac951b 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_1.0.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/DeepBlueR_1.0.11.zip win64.binary.ver: bin/windows64/contrib/3.3/DeepBlueR_1.0.11.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DeepBlueR_1.0.11.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R htmlDocs: vignettes/DeepBlueR/inst/doc/DeepBlueR.html htmlTitles: The DeepBlue epigenomic data server - R package Package: deepSNV Version: 1.20.0 Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 7aad92032df4ac6702d55a26586dcea0 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/mg14/deepSNV VignetteBuilder: knitr source.ver: src/contrib/deepSNV_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/deepSNV_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/deepSNV_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/deepSNV_1.20.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R suggestsMe: GenomicFiles Package: DEFormats Version: 1.2.0 Imports: checkmate, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: add2e743a8436a75b6df27d7518a3462 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Covert 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEFormats_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEFormats_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEFormats_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R htmlDocs: vignettes/DEFormats/inst/doc/DEFormats.html htmlTitles: Differential gene expression data formats converter importsMe: regionReport Package: DEGraph Version: 1.26.0 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: dbcbe55d28126563ba4819b5d2e00f92 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_1.26.0.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 suggestsMe: ToPASeq Package: DEGreport Version: 1.10.1 Depends: R (>= 3.2.0), quantreg Imports: utils, methods, ggplot2, Nozzle.R1, coda, edgeR, cluster, logging, dplyr, tidyr, reshape, pheatmap, grid, gridExtra, knitr, grDevices, stats Suggests: biomaRt, RUnit, BiocStyle, BiocGenerics, org.Hs.eg.db, DESeq2, AnnotationDbi, BiocParallel License: MIT + file LICENSE MD5sum: 10cf54a93f65e01f42a9a45583e08fe4 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 Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/DEGreport_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEGreport_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/DEGreport_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEGreport_1.10.1.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.pdf vignetteTitles: DEGreport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R Package: DEGseq Version: 1.28.0 Depends: R (>= 2.8.0), qvalue, samr, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 485ada471202bc479e4d8bc7f972ecdd 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 source.ver: src/contrib/DEGseq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEGseq_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEGseq_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEGseq_1.28.0.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: deltaGseg Version: 1.14.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 4670d658d5ef32692faa61ffc9c303f4 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/deltaGseg_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/deltaGseg_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/deltaGseg_1.14.0.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: 1.4.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: e9f26a5c12beb98bebb90c7b25bd0699 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DeMAND_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DeMAND_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DeMAND_1.4.0.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: derfinder Version: 1.8.5 Depends: R(>= 3.2) Imports: 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, biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), limma, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 51135da5b204d8417d4a018779c390e3 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_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinder_1.8.5.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinder_1.8.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinder_1.8.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R htmlDocs: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html htmlTitles: derfinder quick start guide, derfinder users guide importsMe: derfinderPlot, recount, regionReport Package: derfinderHelper Version: 1.8.1 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, rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: f98a084de0873140da58aa1461596abd 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinderHelper_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinderHelper_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinderHelper_1.8.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R htmlDocs: vignettes/derfinderHelper/inst/doc/derfinderHelper.html htmlTitles: Introduction to derfinderHelper importsMe: derfinder Package: derfinderPlot Version: 1.8.1 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, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase, bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle, org.Hs.eg.db, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: f689ad03127b3e1d391feceba0ce0631 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinderPlot_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinderPlot_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinderPlot_1.8.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R htmlDocs: vignettes/derfinderPlot/inst/doc/derfinderPlot.html htmlTitles: Introduction to derfinderPlot suggestsMe: derfinder, regionReport Package: DESeq Version: 1.26.0 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: 192656a424ff0d1e1f60dff8fc5bb28c 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DESeq_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DESeq_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DESeq_1.26.0.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, ToPASeq suggestsMe: BitSeq, compcodeR, dexus, DiffBind, ELBOW, gage, genefilter, oneChannelGUI, regionReport, SSPA, XBSeq Package: DESeq2 Version: 1.14.1 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, BiocStyle, vsn, pheatmap, RColorBrewer, airway, IHW, tximport, tximportData, readr, pasilla (>= 0.2.10) License: LGPL (>= 3) Archs: i386, x64 MD5sum: d1c433056295927a6700046e282bcccd 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, GeneExpression, Transcription Author: Michael Love, Simon Anders, Wolfgang Huber Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/DESeq2_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/DESeq2_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/DESeq2_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DESeq2_1.14.1.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.pdf vignetteTitles: Analyzing RNA-seq data with the "DESeq2" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: ASpli, DChIPRep, DEXSeq, FourCSeq, MLSeq, rgsepd, TCC, XBSeq importsMe: anamiR, debrowser, DEFormats, DEsubs, DiffBind, eegc, EnrichmentBrowser, FourCSeq, GenoGAM, Glimma, HTSFilter, isomiRs, JunctionSeq, pcaExplorer, regionReport, ReportingTools, SNPhood, systemPipeR, ToPASeq suggestsMe: biobroom, BiocGenerics, compcodeR, DEGreport, derfinder, diffloop, gage, GenomicAlignments, GenomicRanges, IHW, oneChannelGUI, phyloseq, recount, RUVSeq, scran, subSeq, tximport, variancePartition Package: destiny Version: 2.0.8 Depends: R (>= 3.2.0) Imports: methods, graphics, grDevices, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, Biobase, BiocGenerics, Hmisc, FNN, VIM, proxy, igraph, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen Suggests: ggplot2, nbconvertR Enhances: rgl License: GPL Archs: i386, x64 MD5sum: dc6410f9206cc0a2cc637f1089e56d1b 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 SystemRequirements: C++11 VignetteBuilder: nbconvertR source.ver: src/contrib/destiny_2.0.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/destiny_2.0.8.zip win64.binary.ver: bin/windows64/contrib/3.3/destiny_2.0.8.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/destiny_2.0.8.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 vignetteTitles: Diffusion-Map-recap.pdf, Diffusion-Maps.pdf, DPT.pdf, Global-Sigma.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: cytofkit suggestsMe: scater Package: DEsubs Version: 1.0.2 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, samr, EBSeq, NBPSeq, DESeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: de640952a51c0358e8a4125a15613dd0 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 and Panos Balomenos VignetteBuilder: knitr source.ver: src/contrib/DEsubs_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEsubs_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DEsubs_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEsubs_1.0.2.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: 1.20.2 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: fafc5f3f3ef32033f11e1347bd318a7f 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_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEXSeq_1.20.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DEXSeq_1.20.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEXSeq_1.20.2.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 dependsOnMe: ASpli suggestsMe: GenomicRanges, oneChannelGUI, subSeq Package: dexus Version: 1.14.0 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 43c91f240daaeddbaaaa1e73bd1d1ffe 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dexus_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dexus_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dexus_1.14.0.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: 1.32.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: fce0da46dd5867be35b6974c21ff981f 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DFP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DFP_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DFP_1.32.0.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: 2.2.12 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, edgeR, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, zlibbioc, lattice, systemPipeR, tools, Rcpp, dplyr, BiocParallel, parallel, S4Vectors, Rsamtools, DESeq2, methods LinkingTo: Rsamtools (>= 1.19.38), Rcpp Suggests: DESeq, BiocStyle, testthat Enhances: rgl, XLConnect License: Artistic-2.0 Archs: i386, x64 MD5sum: d1f4cf8b8642416849460ce9339ccc62 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_2.2.12.tar.gz win.binary.ver: bin/windows/contrib/3.3/DiffBind_2.2.12.zip win64.binary.ver: bin/windows64/contrib/3.3/DiffBind_2.2.12.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DiffBind_2.2.12.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 Package: diffGeneAnalysis Version: 1.56.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: ede0fbda08caa91db7fdff7a11a6ec09 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_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffGeneAnalysis_1.56.0.zip win64.binary.ver: bin/windows64/contrib/3.3/diffGeneAnalysis_1.56.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffGeneAnalysis_1.56.0.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: 1.6.0 Depends: R (>= 3.3.0), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils LinkingTo: Rhtslib, zlibbioc Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix License: GPL-3 Archs: i386, x64 MD5sum: 55b278cd6dc9a970f92a2af67ca98254 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 source.ver: src/contrib/diffHic_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffHic_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/diffHic_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffHic_1.6.0.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 Rfiles: vignettes/diffHic/inst/doc/bam2hdf.R Package: DiffLogo Version: 1.4.0 Depends: R (>= 1.8.0), stats, cba, Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: 4ffaaef49d0bab4b0a9556d1a1c60eaf NeedsCompilation: no Title: DiffLogo: A comparative visualisation of sequence motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization Author: Martin Nettling [aut], Hendrik Treutler [aut, cre], Jan Grau [aut, ctb], Jens Keilwagen [aut, ctb], Stefan Posch [aut], Ivo Grosse [aut] Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ VignetteBuilder: knitr BugReports: https://github.com/mgledi/DiffLogo/issues source.ver: src/contrib/DiffLogo_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DiffLogo_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DiffLogo_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DiffLogo_1.4.0.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: 1.2.2 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: 5e238ccbbd85fb0ad9c1072177e0b7bf 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffloop_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/diffloop_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffloop_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R htmlDocs: vignettes/diffloop/inst/doc/diffloop.html htmlTitles: diffloop: Identifying differential DNA loops from chromatin topology data. Package: diggit Version: 1.6.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: e4edb4a998ecd8bfa453feb3cb29f189 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/diggit_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/diggit_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diggit_1.6.0.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: 1.0.0 Depends: R (>= 3.3) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: 28d0d1717743197875f921fade670f47 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Director_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Director_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Director_1.0.0.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: 1.16.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 9d8db7c2a649f8ff9eee98e549717415 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DirichletMultinomial_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DirichletMultinomial_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DirichletMultinomial_1.16.0.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: dks Version: 1.20.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 45713f07b9a33d076c1b620b72dedc50 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dks_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dks_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dks_1.20.0.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: DMRcaller Version: 1.6.0 Depends: R (>= 3.2), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: a405323789e8943c2b70d6f13953ced9 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 and Jonathan Michael Foonlan Tsang Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr source.ver: src/contrib/DMRcaller_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRcaller_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRcaller_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRcaller_1.6.0.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: 1.10.10 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: 00d6c4e4305d8775b388192e1e6adbe3 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_1.10.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRcate_1.10.10.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRcate_1.10.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRcate_1.10.10.tgz 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: 1.10.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: cd725ba722d7c59dc714da33543ffb0d 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRforPairs_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRforPairs_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRforPairs_1.10.0.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: DNABarcodes Version: 1.4.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 5675217b30b7ef4f36b8a88059050ce3 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNABarcodes_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DNABarcodes_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNABarcodes_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R htmlDocs: vignettes/DNABarcodes/inst/doc/DNABarcodes.html htmlTitles: DNABarcodes Package: DNAcopy Version: 1.48.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: f684816f5cf76f89fd1189842cf63102 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNAcopy_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DNAcopy_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNAcopy_1.48.0.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, Clonality, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH suggestsMe: beadarraySNP, Clonality, cn.mops, fastseg, genoset Package: DNAshapeR Version: 1.2.0 Depends: R (>= 3.3), 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: e8e11921254ffe5a800d1a971a46ca2a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNAshapeR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DNAshapeR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNAshapeR_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R htmlDocs: vignettes/DNAshapeR/inst/doc/DNAshapeR.html htmlTitles: DNAshapeR Package: domainsignatures Version: 1.34.0 Depends: R (>= 2.4.0), KEGG.db, prada, biomaRt, methods Imports: AnnotationDbi License: Artistic-2.0 MD5sum: a64dbdcb1e23c0651893f1c97bcda567 NeedsCompilation: no Title: Geneset enrichment based on InterPro domain signatures Description: Find significantly enriched gene classifications in a list of functionally undescribed genes based on their InterPro domain structure. biocViews: Annotation, Pathways, GeneSetEnrichment Author: Florian Hahne, Tim Beissbarth Maintainer: Florian Hahne source.ver: src/contrib/domainsignatures_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/domainsignatures_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/domainsignatures_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/domainsignatures_1.34.0.tgz vignettes: vignettes/domainsignatures/inst/doc/domainenrichment.pdf vignetteTitles: Gene set enrichment using InterPro domain signatures hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/domainsignatures/inst/doc/domainenrichment.R Package: doppelgangR Version: 1.2.0 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: 20a7081c36500da4e8d80c56b0faa4d8 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/doppelgangR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/doppelgangR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/doppelgangR_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R htmlDocs: vignettes/doppelgangR/inst/doc/doppelgangR.html htmlTitles: doppelgangR vignette Package: DOQTL Version: 1.10.0 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: 53260bc0199adac1f4e168250924293b 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 source.ver: src/contrib/DOQTL_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DOQTL_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DOQTL_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DOQTL_1.10.0.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: DOSE Version: 3.0.10 Depends: R (>= 3.3.1) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), graphics, grDevices, grid, igraph, methods, qvalue, reshape2, S4Vectors, scales, stats, stats4, utils Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, testthat, UpSetR License: Artistic-2.0 MD5sum: e914514c03ca7fc63aab000c231d2d8f 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 with contributions from Li-Gen Wang, Vladislav Petyuk and Giovanni Dall'Olio. Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues source.ver: src/contrib/DOSE_3.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/DOSE_3.0.10.zip win64.binary.ver: bin/windows64/contrib/3.3/DOSE_3.0.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DOSE_3.0.10.tgz 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 htmlDocs: vignettes/DOSE/inst/doc/DOSE.html, vignettes/DOSE/inst/doc/enrichmentAnalysis.html, vignettes/DOSE/inst/doc/GSEA.html, vignettes/DOSE/inst/doc/semanticAnalysis.html htmlTitles: 00 DOSE introduction, 02 Disease enrichment analysis, 03 Disease GSEA, 01 DOSE semantic similarity analysis dependsOnMe: clusterProfiler, meshes, ReactomePA importsMe: bioCancer, ChIPseeker, debrowser, eegc, facopy, LINC, MoonlightR suggestsMe: GOSemSim Package: DRIMSeq Version: 1.2.0 Depends: R (>= 3.3.0) Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, edgeR, utils, stats, grDevices, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: 4413845b7ab7e186ae1e9c4f93e35347 NeedsCompilation: no Title: Differential splicing and sQTL analyses with Dirichlet-multinomial model in RNA-Seq Description: The package provides two frameworks. One for the differential splicing analysis between different conditions and one for the sQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts, exons or exonic bins) with 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DRIMSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DRIMSeq_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DRIMSeq_1.2.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential splicing and sQTL analyses in RNA-seq with 'DRIMSeq' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R Package: DriverNet Version: 1.14.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 1783e50d1693efd554878992938987a0 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DriverNet_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DriverNet_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DriverNet_1.14.0.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: DrugVsDisease Version: 2.14.0 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: af0592b06a8fe326f46d31376fef442a 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DrugVsDisease_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DrugVsDisease_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DrugVsDisease_2.14.0.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: 1.0.0 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: bdb1c946272689a45ffa117c5577111b 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dSimer_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dSimer_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dSimer_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dSimer/inst/doc/dSimer.R htmlDocs: vignettes/dSimer/inst/doc/dSimer.html htmlTitles: Integration of Disease Similarity Methods Package: DSS Version: 2.14.0 Depends: Biobase, bsseq, splines, methods Suggests: BiocStyle License: GPL Archs: i386, x64 MD5sum: 20ce023ae7de59c82dfe6a2a0ff34ea6 NeedsCompilation: yes Title: Dispersion shrinakge 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DSS_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DSS_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DSS_2.14.0.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 Package: DTA Version: 2.20.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 907645b2c13a568952e490c92f360a8f 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DTA_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DTA_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DTA_2.20.0.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: 1.34.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: a73afaebab46c4a1c72a7bf2917c09ad 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dualKS_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dualKS_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dualKS_1.34.0.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: 1.12.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: 4e4c5d5944217c1be86aaabcc2a9ac85 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DupChecker_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DupChecker_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DupChecker_1.12.0.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: 1.4.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: bf81a5534f2a465e7c7a792b18c7633f 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_1.4.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dupRadar_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R htmlDocs: vignettes/dupRadar/inst/doc/dupRadar.html htmlTitles: Using dupRadar Package: dyebias Version: 1.34.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: e7b37d0c7785502b9c6e6747e5dc51b3 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dyebias_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dyebias_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dyebias_1.34.0.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: 1.52.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: db212af5bb3f57c14dc9af6663f02221 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DynDoc_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DynDoc_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DynDoc_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.16.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: 8de8a87b9756fd05eeb02588a97413ff 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EasyqpcR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EasyqpcR_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EasyqpcR_1.16.0.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: 2.10.0 Imports: Biobase (>= 2.31.3), BiocGenerics (>= 0.17.2), BiocParallel (>= 1.5.1), biomaRt (>= 2.27.2), Biostrings (>= 2.39.3), DESeq (>= 1.23.0), edgeR (>= 3.13.4), GenomeInfoDb (>= 1.7.3), genomeIntervals (>= 1.27.0), GenomicAlignments (>= 1.7.3), GenomicRanges (>= 1.23.16), SummarizedExperiment (>= 1.1.11), graphics, IRanges (>= 2.5.27), LSD (>= 3.0), locfit, methods, parallel, Rsamtools (>= 1.23.1), S4Vectors (>= 0.9.38), ShortRead (>= 1.29.1), utils Suggests: BiocStyle (>= 1.9.2), BSgenome (>= 1.39.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, GenomicFeatures (>= 1.23.15), knitr, rmarkdown, RnaSeqTutorial (>= 0.9.0), RUnit (>= 0.4.31) License: Artistic-2.0 MD5sum: e4970184d486f5a07a984d1f7716eb05 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/easyRNASeq_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/easyRNASeq_2.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/easyRNASeq_2.10.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf vignetteTitles: easyRNASeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R htmlDocs: vignettes/easyRNASeq/inst/doc/simpleRNASeq.html htmlTitles: geneNetworkR suggestsMe: SeqGSEA Package: EBarrays Version: 2.38.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: dc143c0344fff2436c6ea5698735297d 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_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBarrays_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBarrays_2.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBarrays_2.38.0.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: 1.18.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: bcbc591f9986b6a061fcaec84d3ccf91 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBcoexpress_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBcoexpress_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBcoexpress_1.18.0.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: 4.16.0 Imports: BiocGenerics (>= 0.7.1), methods, graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils Suggests: BiocStyle, digest, knitr, rmarkdown License: LGPL Archs: i386, x64 MD5sum: 8346f2f8de5f8d9890dcc2cfa0f544c0 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 source.ver: src/contrib/EBImage_4.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBImage_4.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBImage_4.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBImage_4.16.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R htmlDocs: vignettes/EBImage/inst/doc/EBImage-introduction.html htmlTitles: Introduction to EBImage dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: flowCHIC, yamss suggestsMe: ggtree, HilbertVis, tofsims Package: EBSEA Version: 1.2.0 Imports: edgeR, limma, gtools, graphics, stats License: GPL-2 MD5sum: ff9fd33ec92646d5286c476bacdcee3b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSEA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSEA_1.2.0.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: 1.14.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: fcb77b346a9d0b02139bd2f2b095807e 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSeq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSeq_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSeq_1.14.0.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 suggestsMe: compcodeR Package: EBSeqHMM Version: 1.8.0 Depends: EBSeq License: Artistic-2.0 MD5sum: f0cff910dac70e72449d79e017c55085 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSeqHMM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSeqHMM_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSeqHMM_1.8.0.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: 1.46.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 92c0850fe72ba6503abed258d0825b65 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ecolitk_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ecolitk_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ecolitk_1.46.0.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: 2.8.0 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: 66cf53c66753ebcd237607aeed9b7b22 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 source.ver: src/contrib/EDASeq_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EDASeq_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EDASeq_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EDASeq_2.8.0.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: EnrichmentBrowser, TCGAbiolinks suggestsMe: HTSFilter, oneChannelGUI Package: EDDA Version: 1.12.0 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: 5221bcd28dabf657ab24d3ab39105248 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EDDA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EDDA_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EDDA_1.12.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: edge Version: 2.6.0 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: 2a58fdb50b8f4690454eb84701725701 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 source.ver: src/contrib/edge_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/edge_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/edge_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/edge_2.6.0.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: 3.16.5 Depends: R (>= 2.15.0), limma Imports: graphics, stats, utils, methods, locfit Suggests: MASS, statmod, splines, KernSmooth License: GPL (>=2) Archs: i386, x64 MD5sum: 7fa050082478fb13173ee758342a6861 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, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression, TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect, MultipleComparison, Normalization, QualityControl 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 source.ver: src/contrib/edgeR_3.16.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/edgeR_3.16.5.zip win64.binary.ver: bin/windows64/contrib/3.3/edgeR_3.16.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/edgeR_3.16.5.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: DBChIP, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome importsMe: affycoretools, ampliQueso, ArrayExpressHTS, baySeq, compcodeR, csaw, debrowser, DEFormats, DEGreport, DEsubs, DiffBind, diffHic, diffloop, DRIMSeq, easyRNASeq, EBSEA, EDDA, eegc, EGSEA, EnrichmentBrowser, erccdashboard, Glimma, HTSFilter, MEDIPS, metaseqR, msmsTests, PROPER, regsplice, Repitools, rnaSeqMap, scater, scde, scran, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, ToPASeq, tweeDEseq, yarn suggestsMe: biobroom, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, missMethyl, oneChannelGUI, regionReport, SSPA, subSeq, tximport, variancePartition Package: eegc Version: 1.0.0 Depends: R (>= 3.3.0) 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: 5bb152aa0332698179a2bebb9bada0bb 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/eegc_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/eegc_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eegc_1.0.0.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: 1.2.0 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: 7ae2bb13243ba5b828a22938095e8708 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EGAD_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EGAD_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EGAD_1.2.0.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: 1.2.0 Depends: R (>= 3.3), 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 Suggests: BiocStyle, knitr, testthat License: GPL-2 MD5sum: 8142ada223aa3d0cb80a60b12a3b3ca5 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 Author: Monther Alhamdoosh, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr source.ver: src/contrib/EGSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EGSEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EGSEA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EGSEA_1.2.0.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: 1.14.1 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics LinkingTo: BH Suggests: RCurl, snow, BiocStyle, knitcitations, knitr, knitrBootstrap License: Artistic-2.0 MD5sum: a443353df0c6469aac5b516de63d6b75 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 SystemRequirements: GSL (>=1.14) http://www.gnu.org/software/gsl/ VignetteBuilder: knitr source.ver: src/contrib/eiR_1.14.1.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eiR_1.14.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R htmlDocs: vignettes/eiR/inst/doc/eiR.html htmlTitles: eiR Package: eisa Version: 1.26.0 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: a4b012c00d3bf9c469ae31db7cf7218f 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/eisa_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/eisa_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eisa_1.26.0.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: 1.10.0 Depends: R (>= 2.15.0) Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: f644fa05be5f0c1889cea6adc23df955 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ELBOW_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ELBOW_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ELBOW_1.10.0.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: 1.4.1 Depends: R (>= 3.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, Homo.sapiens, ELMER.data Imports: methods,BiocGenerics,S4Vectors,IRanges,GenomeInfoDb,GenomicRanges,ggplot2,reshape,grid,gridExtra,minfi,GenomicFeatures Suggests: parallel, snow, BiocStyle, knitr, R.utils, downloader License: GPL-3 MD5sum: b63a5fe0d5ecb731ee8456100298d3ff 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 Author: Lijing Yao [aut], Ben Berman [aut], Peggy Farnham [aut], Hui Shen [ctb], Peter Laird [ctb], Simon Coetzee [cre, ctb] Maintainer: Simon Coetzee VignetteBuilder: knitr source.ver: src/contrib/ELMER_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/ELMER_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/ELMER_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ELMER_1.4.1.tgz vignettes: vignettes/ELMER/inst/doc/vignettes.pdf vignetteTitles: ELMER: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Methylomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/vignettes.R Package: EMDomics Version: 2.4.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: f23af14934fd7e1961bec0dcc38ce02a 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EMDomics_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EMDomics_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EMDomics_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R htmlDocs: vignettes/EMDomics/inst/doc/EMDomics.html htmlTitles: EMDomics Vignette Package: EmpiricalBrownsMethod Version: 1.2.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: c8f49bd24ec89c908e785885eb4472f4 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EmpiricalBrownsMethod_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EmpiricalBrownsMethod_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EmpiricalBrownsMethod_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R htmlDocs: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html htmlTitles: Empirical Browns Method Package: ENCODExplorer Version: 2.0.6 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: 2201a50cc000505366ddb53d2b023e13 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 [aut, cre], Audrey Lemacon [aut], Arnaud Droit [aut], Louis Gendron [ctb], Astrid-Louise Deschenes [ctb] Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues source.ver: src/contrib/ENCODExplorer_2.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENCODExplorer_2.0.6.zip win64.binary.ver: bin/windows64/contrib/3.3/ENCODExplorer_2.0.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENCODExplorer_2.0.6.tgz 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 htmlDocs: vignettes/ENCODExplorer/inst/doc/DataUpdate.html, vignettes/ENCODExplorer/inst/doc/DBmodel.html, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html htmlTitles: Data update, Database model, Introduction to ENCODExplorer Package: ENmix Version: 1.10.0 Depends: minfi,parallel,doParallel,Biobase (>= 2.17.8),foreach Imports: MASS,preprocessCore,wateRmelon,sva,geneplotter,impute,grDevices,graphics,stats Suggests: minfiData (>= 0.4.1), RPMM, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2c44baf7cc05c58e1968b113c16f69ec NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip Description: Illumina Methylation BeadChip array measurements have intrinsic levels of background noise that degrade methylation measurement. The ENmix package provides an efficient data pre-processing tool designed to reduce background noise and improve signal for DNA methylation estimation. Several efficient novel methods were incorporated in the package: ENmix is a model based background correction method that can significantly improve accuracy and reproducibility of methylation measures; RCP taking advantage of the high spatial correlation of DNA methylation levels between nearby type I and II probe pairs to reduce probe type bias and improve data quality on type II probe measures.The data structure used by the ENmix package is compatible with several other related R packages, such as minfi, wateRmelon and ChAMP, providing straightforward integration of ENmix-corrected datasets for subsequent data analysis. The software is designed to support large scale data analysis, and provides multi-processor parallel computing wrappers for some commonly used but computation intensive data preprocessing methods. In addition ENmix package has selectable complementary functions for efficient data visualization (such as data distribution plotting), quality control (identification and filtering of low quality data points, samples, probes, and outliers, along with imputation of missing values), inter-array normalization (3 different quantile normalizations), identification of probes with multimodal distributions due to SNPs and other factors, and exploration of data variance structure using principal component regression analysis plots. Together these provide a set of flexible and transparent tools for preprocessing of EWAS data in a computationally-efficient and user-friendly package. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu source.ver: src/contrib/ENmix_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENmix_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ENmix_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENmix_1.10.0.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: 1.4.0 Depends: R (>= 3.1.2), grid, ComplexHeatmap (>= 1.11.1), GenomicRanges, IRanges, locfit Imports: methods, matrixStats, stats, GetoptLong Suggests: testthat (>= 0.3), knitr, markdown, circlize (>= 0.3.1) License: GPL (>= 2) MD5sum: e5278b5981542a99689b539ba0801b26 NeedsCompilation: no 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 source.ver: src/contrib/EnrichedHeatmap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EnrichedHeatmap_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EnrichedHeatmap_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EnrichedHeatmap_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R htmlDocs: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html htmlTitles: Make Enriched Heatmaps Package: EnrichmentBrowser Version: 2.4.6 Depends: R(>= 3.0.0), Biobase, GSEABase, pathview Imports: AnnotationDbi, ComplexHeatmap, DESeq2, EDASeq, GO.db, KEGGREST, KEGGgraph, MASS, ReportingTools, Rgraphviz, S4Vectors, SPIA, SummarizedExperiment, biocGraph, edgeR, geneplotter, graph, hwriter, limma, methods, safe, topGO Suggests: ALL, BiocStyle, airway, hgu95av2.db License: Artistic-2.0 MD5sum: 6688b6e75a81d148b8cc5245c495c765 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, Gergely Csaba, Ralf Zimmer Maintainer: Ludwig Geistlinger source.ver: src/contrib/EnrichmentBrowser_2.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/EnrichmentBrowser_2.4.6.zip win64.binary.ver: bin/windows64/contrib/3.3/EnrichmentBrowser_2.4.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EnrichmentBrowser_2.4.6.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 Package: ensembldb Version: 1.6.2 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.23.21), GenomicFeatures (>= 1.23.18) Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges Suggests: BiocStyle, knitr, rmarkdown, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit, shiny, Gviz, BSgenome.Hsapiens.UCSC.hg19 Enhances: RMySQL License: LGPL MD5sum: f7642a87638f6d4373cfabae899aaae0 NeedsCompilation: no Title: Utilities to create and use an Ensembl based annotation database 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, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer , Tim Triche Maintainer: Johannes Rainer URL: https://github.com/jotsetung/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jotsetung/ensembldb/issues source.ver: src/contrib/ensembldb_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ensembldb_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ensembldb_1.6.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ensembldb_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R htmlDocs: vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html htmlTitles: Generating an using Ensembl based annotation packages, Using a MySQL server backend importsMe: biovizBase, ChIPpeakAnno, ggbio, TVTB suggestsMe: alpine Package: ensemblVEP Version: 1.14.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb Suggests: RUnit License: Artistic-2.0 MD5sum: 335bdb275bf79dbaf7960d0762df511b 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 Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 86) and the Perl package DBD::mysql must be installed. See the package README and Ensembl web site, http://www.ensembl.org/info/docs/tools/vep/index.html for installation instructions. source.ver: src/contrib/ensemblVEP_1.14.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ensemblVEP_1.14.0.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf vignetteTitles: ensemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R importsMe: TVTB Package: ENVISIONQuery Version: 1.22.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 3337c90cae18975b5c7df7a3368874aa 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENVISIONQuery_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ENVISIONQuery_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENVISIONQuery_1.22.0.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: epigenomix Version: 1.14.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 6f93379a1d79d6a5758ab7048fa80df7 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/epigenomix_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/epigenomix_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epigenomix_1.14.0.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: epivizr Version: 2.4.1 Depends: R (>= 3.3), methods, Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.1.1), GenomicRanges, S4Vectors, IRanges Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle License: Artistic-2.0 MD5sum: 3dfe1f592f6a7ce307441c6fd8a4e2b2 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 Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizr_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizr_2.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizr_2.4.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R htmlDocs: vignettes/epivizr/inst/doc/IntroToEpivizr.html htmlTitles: Introduction to epivizr dependsOnMe: epivizrStandalone Package: epivizrData Version: 1.2.0 Depends: R (>= 3.3), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 28177ed75cfab06dce7bd4dd388c5124 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrData_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrData_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrData_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R htmlDocs: vignettes/epivizrData/inst/doc/epivizrData.html htmlTitles: Vignette Title importsMe: epivizr Package: epivizrServer Version: 1.2.0 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: 7de5394a791dafcacdb90f1088818aad 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrServer_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrServer_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrServer_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/epivizrServer/inst/doc/epivizrServer.html htmlTitles: epivizrServer Usage dependsOnMe: epivizrData importsMe: epivizr, epivizrStandalone Package: epivizrStandalone Version: 1.2.0 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: 6e074ee44572522fa87bbd7ba2a6097b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrStandalone_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrStandalone_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrStandalone_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html htmlTitles: Introduction to epivizrStandalone Package: erccdashboard Version: 1.8.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, QuasiSeq, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: daabce5759d62d35fd5a0e4fe3521ed7 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: http://www.nist.gov/mml/bbd/erccdashboard.cfm, https://github.com/usnistgov/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/usnistgov/erccdashboard/issues source.ver: src/contrib/erccdashboard_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/erccdashboard_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/erccdashboard_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/erccdashboard_1.8.0.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: 0.6.0 Depends: R (>= 3.1), methods, Homo.sapiens Imports: GenomicFiles (>= 1.5.2), rtracklayer, S4Vectors, BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, Biobase, shiny, foreach, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, BiocParallel, png, DT, doParallel License: Artistic-2.0 MD5sum: 1d3c5967beb4771d03d2c24ed574d77c 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_0.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/erma_0.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/erma_0.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/erma_0.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R htmlDocs: vignettes/erma/inst/doc/erma.html htmlTitles: ermaInteractive suggestsMe: gQTLBase Package: esetVis Version: 1.0.1 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: 3f5ba26e9d42db3d3549f6797d1efdce 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 source.ver: src/contrib/esetVis_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/esetVis_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/esetVis_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/esetVis_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R htmlDocs: vignettes/esetVis/inst/doc/esetVis-vignette.html htmlTitles: esetVis package Package: eudysbiome Version: 1.4.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: f2fcd93acf10b8835c75848cc14b7383 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/eudysbiome_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/eudysbiome_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eudysbiome_1.4.0.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: EWCE Version: 1.2.0 Depends: R(>= 3.3) Imports: ggplot2, reshape2, biomaRt Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 5702e4b3d8621d5f9661acfe861284e5 NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics Author: Dr Nathan Skene Maintainer: Nathan Skene VignetteBuilder: knitr source.ver: src/contrib/EWCE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EWCE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EWCE_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EWCE_1.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R htmlDocs: vignettes/EWCE/inst/doc/EWCE.html htmlTitles: Expression Weighted Celltype Enrichment with EWCE Package: ExiMiR Version: 2.16.0 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: d73f52efcc95a43f0924dffa2d88313c 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExiMiR_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExiMiR_2.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExiMiR_2.16.0.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: 1.20.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: cc3c723a6966a33db0aabac02af35312 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/exomeCopy_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/exomeCopy_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/exomeCopy_1.20.0.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: 2.8.0 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer, GenomicAlignments License: GPL-2 MD5sum: 3e9971570ce4deaf96a0d368ecff51eb 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/exomePeak_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/exomePeak_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/exomePeak_2.8.0.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: 1.0.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.5.9) Imports: utils, S4Vectors, BiocInstaller Suggests: knitr, BiocStyle Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: b1a59f56f9cb0156ca7516ac2ddb751d 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 source.ver: src/contrib/ExperimentHub_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExperimentHub_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExperimentHub_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExperimentHub_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ExperimentHubData suggestsMe: CellMapper Package: ExperimentHubData Version: 1.0.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.1.4) Imports: methods, ExperimentHub, BiocInstaller, DBI, BiocCheck, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle License: Artistic-2.0 MD5sum: 31330cd6b319d9051fdb8608cdc475da 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExperimentHubData_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExperimentHubData_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExperimentHubData_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.R htmlDocs: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html htmlTitles: Introduction to ExperimentHubData Package: explorase Version: 1.38.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 3d97bc0825a3f97b1da20c2e6a20b7ca 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/explorase_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/explorase_1.38.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ExpressionAtlas Version: 1.2.0 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: cc1be402c2ab2735350f55ee79d5e6b4 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExpressionAtlas_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExpressionAtlas_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExpressionAtlas_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R htmlDocs: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html htmlTitles: ExpressionAtlas Package: ExpressionView Version: 1.26.0 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: fc36131ee3ed7b1c4bd8f3275f559d1b 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExpressionView_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExpressionView_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExpressionView_1.26.0.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: 2.20.0 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 9f75b316a6e38d4639b884b001d2185e 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fabia_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fabia_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fabia_2.20.0.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: 1.8.0 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: a86a495e4adfceeb783d8eadd55be2f5 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/facopy_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/facopy_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/facopy_1.8.0.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: 1.50.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 0214c3677d83a601b85498f1853a6f5a 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/factDesign_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/factDesign_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/factDesign_1.50.0.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: 1.2.1 Depends: methods, kinship2, igraph Imports: gap, BiocGenerics, Matrix, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: ea1ee5251e169264893ff9d7413f99fb 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/jotsetung/FamAgg VignetteBuilder: knitr BugReports: https://github.com/jotsetung/FamAgg/issues source.ver: src/contrib/FamAgg_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/FamAgg_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/FamAgg_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FamAgg_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R htmlDocs: vignettes/FamAgg/inst/doc/FamAgg.html htmlTitles: Pedigree Analysis and Familial Aggregation Package: farms Version: 1.26.0 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: 7b01082a566e30bc63042a115f446cc5 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/farms_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/farms_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/farms_1.26.0.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: 1.10.0 Depends: methods, LiquidAssociation, parallel, stats, Hmisc Imports: WGCNA Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 53bac77200ac684c2d1f688a59e7a8ed 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fastLiquidAssociation_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fastLiquidAssociation_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fastLiquidAssociation_1.10.0.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: 1.20.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 1840f449546b1d89983e95a6280006c1 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fastseg_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fastseg_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fastseg_1.20.0.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: 1.0.0 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: 0b12dd3da4695f8fe789cb6a98ff916c 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fCCAC_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fCCAC_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fCCAC_1.0.0.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: 1.4.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 0ce525981c4c70fe28af6e03260c98cb 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fCI_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fCI_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fCI_1.4.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R htmlDocs: vignettes/fCI/inst/doc/fCI.html htmlTitles: fCI Package: fdrame Version: 1.46.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 305abb43c81017ba726d6672167bc766 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fdrame_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fdrame_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fdrame_1.46.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FEM Version: 3.2.0 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db,graph,BiocGenerics Imports: graph License: GPL (>=2) MD5sum: ebd681f5e1e6243814f1445ba398c9a3 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_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FEM_3.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FEM_3.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FEM_3.2.0.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: 1.18.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: e508953bf1aaec46be32f5ba3ce00f32 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ffpe_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ffpe_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ffpe_1.18.0.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: 3.8.0 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: eeb6efac0d95333b2fa171570c19279e 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_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FGNet_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FGNet_3.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FGNet_3.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R htmlDocs: vignettes/FGNet/inst/doc/FGNet.html htmlTitles: FGNet Package: fgsea Version: 1.0.2 Depends: R (>= 3.3), Rcpp Imports: data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel License: MIT + file LICENCE Archs: i386, x64 MD5sum: 7cf097058e524d43558cb17ea0a11ff1 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/fgsea_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/fgsea_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fgsea_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R htmlDocs: vignettes/fgsea/inst/doc/fgsea-tutorial.html htmlTitles: Using fgsea package importsMe: DOSE, piano suggestsMe: Pi Package: FindMyFriends Version: 1.4.0 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: b8d665f06288ab3d194516ca5927cda7 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FindMyFriends_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FindMyFriends_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FindMyFriends_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R htmlDocs: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html htmlTitles: Creating pangenomes using FindMyFriends importsMe: PanVizGenerator Package: FISHalyseR Version: 1.8.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: f11d5e038bdb2e51b224d11e827ba244 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FISHalyseR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FISHalyseR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FISHalyseR_1.8.0.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: 1.0.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 78e48b7349cb444b86114cc74fbf2605 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FitHiC_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FitHiC_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FitHiC_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R htmlDocs: vignettes/FitHiC/inst/doc/fithic.html htmlTitles: Vignette Title Package: flagme Version: 1.30.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: bd6ce5caa90ffe121e43c18924269ba7 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flagme_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flagme_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flagme_1.30.0.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: 1.12.0 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 Archs: i386, x64 MD5sum: b16a4c45e7ae0224f48853ed7a4a1a53 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flipflop_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flipflop_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flipflop_1.12.0.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: 1.2.10 Depends: R (>= 3.2) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales Suggests: testthat, shiny, rmarkdown License: GPL MD5sum: c67305383cecfff3b8735dd481ef3ec9 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 source.ver: src/contrib/flowAI_1.2.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowAI_1.2.10.zip win64.binary.ver: bin/windows64/contrib/3.3/flowAI_1.2.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowAI_1.2.10.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R htmlDocs: vignettes/flowAI/inst/doc/flowAI.html htmlTitles: Automatic and GUI methods to do quality control on Flow cytometry Data Package: flowBeads Version: 1.12.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 607ab174f6e7104db20cf9163876554e 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowBeads_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowBeads_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowBeads_1.12.0.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: 1.10.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 20fb9a4abd154ba8c2c2625724d6043c 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowBin_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowBin_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowBin_1.10.0.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: 1.8.0 Depends: R (>= 2.10), methods, EBImage Imports: rgl, colorRamps, abind, BiocParallel Suggests: BiocStyle, knitr, shiny License: BSD_3_clause + file LICENSE MD5sum: 98e792b93a10a90d97284774320614a1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowcatchR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowcatchR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowcatchR_1.8.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchR-vignette.pdf 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: 1.8.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 8f10364ad27d4c3e6417775eddfc8b0c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCHIC_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCHIC_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCHIC_1.8.0.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: 1.12.0 Depends: R (>= 3.3), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 69d1d0781cd2736953dea840ad2e6283 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCL_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCL_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCL_1.12.0.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: 1.12.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 0f267495060d2cd82700e2c3dbc5bb15 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowClean_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowClean_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowClean_1.12.0.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: 3.12.2 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: e037fad976c28c846cd7515b9f22faba 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_3.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowClust_3.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowClust_3.12.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowClust_3.12.2.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: 1.40.6 Depends: R (>= 2.10.0) Imports: Biobase, BiocGenerics (>= 0.1.14), graph, graphics, methods, rrcov, stats, utils, stats4, corpcor, Rcpp, matrixStats LinkingTo: Rcpp, BH(>= 1.62.0-1) Suggests: Rgraphviz, flowViz, flowStats, testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: i386, x64 MD5sum: 142b8bcf038f87af68921d2a6a8f5d1c 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 source.ver: src/contrib/flowCore_1.40.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCore_1.40.6.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCore_1.40.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCore_1.40.6.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowFP, flowMatch, flowStats, flowTrans, flowViz, flowVS, ggcyto, immunoClust, ncdfFlow, plateCore importsMe: cytofkit, CytoML, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowFit, flowMeans, flowPloidy, flowQ, flowQB, FlowSOM, flowStats, flowTrans, flowType, flowUtils, flowViz, plateCore suggestsMe: COMPASS, FlowRepositoryR, RchyOptimyx Package: flowCyBar Version: 1.10.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 285eab7db4e97e5aeb061fb28dd6c6e0 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCyBar_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCyBar_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCyBar_1.10.0.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: 1.8.0 Depends: R (>= 2.10.0), methods Imports: flowCore, graphics, car, gplots, RFOC, GEOmap, methods, grDevices License: Artistic-2.0 MD5sum: 8f583b7d26d941c1f23d3e7115c9cc8e 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: M. Jafar Taghiyar, Mehrnoush Malek Maintainer: Mehrnoush Malek source.ver: src/contrib/flowDensity_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowDensity_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowDensity_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowDensity_1.8.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensityVignette.pdf vignetteTitles: Automated alternative to the current manual gating practice hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensityVignette.R Package: flowFit Version: 1.12.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: 83d510c6c7d8bd30c4670b31feb76fca 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowFit_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowFit_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowFit_1.12.0.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: 1.32.0 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: d8e46632b4b5340c9d4ed6ac601cdcab 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowFP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowFP_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowFP_1.32.0.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: 1.12.0 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: 7af14450faf6f98d232298679e4d27f9 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMap_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMap_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMap_1.12.0.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: 1.10.0 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: f1403f10a87fd56eacfd3f017fc2b6f5 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMatch_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMatch_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMatch_1.10.0.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: 1.34.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: 9bf65c78e53f6264bc3ddd9f1857f4c7 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMeans_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMeans_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMeans_1.34.0.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: 2.22.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 098c8f84436a2ededbf2ad93420e7bdc 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMerge_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMerge_2.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMerge_2.22.0.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: 1.18.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 43ece343eb31544dcabda42d80c0a849 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowPeaks_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowPeaks_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowPeaks_1.18.0.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 Package: flowPloidy Version: 1.0.0 Depends: R (>= 3.3) Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData License: GPL-3 MD5sum: e7918ab27eeb3ced0bbbfcce2d87875b 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowPloidy_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowPloidy_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowPloidy_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-overview.R htmlDocs: vignettes/flowPloidy/inst/doc/flowPloidy-overview.html htmlTitles: flowPloidy: Overview Package: flowPlots Version: 1.22.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 3914e96cbb043838f50dd769dc1a87dd 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowPlots_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowPlots_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowPlots_1.22.0.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: 1.34.0 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: 88669985820b5eaba8a9b4d94a3422c0 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_1.34.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowQ_1.34.0.tgz 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: 2.2.0 Imports: methods, flowCore (>= 1.32.0), stats, extremevalues Suggests: flowQBData, FlowRepositoryR, xlsx, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 8abcfec155937a720bc6fd5cef91fc1b 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowQB_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowQB_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowQB_2.2.0.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: 1.6.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: 6569a1e55a47a17638e49293303f29e8 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FlowRepositoryR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FlowRepositoryR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FlowRepositoryR_1.6.0.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: 1.6.0 Depends: R (>= 3.2), igraph Imports: flowCore, ConsensusClusterPlus, BiocGenerics, tsne, flowUtils, XML Suggests: BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 663ae3bdf4b442cc6bd31e343c5c4d73 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FlowSOM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FlowSOM_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FlowSOM_1.6.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: Using SOMs for visualization of cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: cytofkit Package: flowStats Version: 3.32.0 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: 20c2ace027d69fbf773dc4e486301c01 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_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowStats_3.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowStats_3.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowStats_3.32.0.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: flowCore, flowQ, ggcyto Package: flowTrans Version: 1.26.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 23365c76903716916f37e2d44d19d0c0 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowTrans_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowTrans_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowTrans_1.26.0.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: 2.12.0 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: 98635b1620c744db66fc1f34995546a7 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowType_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowType_2.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowType_2.12.0.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: 1.38.0 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: f7173dce195bcc31576538e13fe668eb 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowUtils_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowUtils_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowUtils_1.38.0.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 Package: flowViz Version: 1.38.0 Depends: R (>= 2.7.0), flowCore, 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: ca001789da5bf4d21a63d222d69255c7 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowViz_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowViz_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowViz_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R htmlDocs: vignettes/flowViz/inst/doc/filters.html htmlTitles: Visualizing Gates with Flow Cytometry Data dependsOnMe: flowFP, flowQ, flowVS, plateCore importsMe: flowClust, flowFit, flowQ, flowStats, flowTrans, flowWorkspace suggestsMe: flowBeads, flowClean, flowCore, ggcyto Package: flowVS Version: 1.6.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: a4ad62ab4911bb10b5ae196a66f83d17 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowVS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowVS_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowVS_1.6.0.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: 3.20.5 Depends: R (>= 2.16.0),flowCore(>= 1.39.9),ncdfFlow(>= 2.19.5) Imports: Biobase, BiocGenerics, graph, graphics, lattice, methods, stats, stats4, utils, RBGL, XML, tools, gridExtra, Rgraphviz, data.table, dplyr, latticeExtra, Rcpp, RColorBrewer, stringr, scales, flowViz LinkingTo: Rcpp, BH(>= 1.62.0-1) Suggests: testthat, flowWorkspaceData, RSVGTipsDevice, knitr, ggcyto License: Artistic-2.0 Archs: i386, x64 MD5sum: 6edfea0f1de577f5ba464c9c33153dce 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 VignetteBuilder: knitr source.ver: src/contrib/flowWorkspace_3.20.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowWorkspace_3.20.5.zip win64.binary.ver: bin/windows64/contrib/3.3/flowWorkspace_3.20.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowWorkspace_3.20.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R htmlDocs: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html htmlTitles: How to merge GatingSets, How to plot gated data dependsOnMe: flowStats, ggcyto, openCyto importsMe: CytoML suggestsMe: COMPASS, flowClust, flowCore Package: fmcsR Version: 1.16.0 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: 5bd457e200849f15b56c9a22e9f0d688 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fmcsR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fmcsR_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fmcsR_1.16.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R htmlDocs: vignettes/fmcsR/inst/doc/fmcsR.html htmlTitles: fmcsR importsMe: Rcpi suggestsMe: ChemmineR Package: focalCall Version: 1.8.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 51c3ec942b621a5c54b262712a6ae94f 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/focalCall_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/focalCall_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/focalCall_1.8.0.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: 1.8.0 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: 85adbe4417919cbf51b7a7b6edcce543 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FourCSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FourCSeq_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FourCSeq_1.8.0.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: 1.10.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 9c5f90b2cb02627e572886074b87c13d 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FRGEpistasis_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FRGEpistasis_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FRGEpistasis_1.10.0.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: 1.26.0 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: 2a965933dae0acd8efe0587be791d407 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/frma_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/frma_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/frma_1.26.0.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: 1.26.0 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: ee49579f3e48b86f5dc3825359263dc8 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/frmaTools_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/frmaTools_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/frmaTools_1.26.0.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: 1.0.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats LinkingTo: Rcpp License: Artistic-2.0 Archs: i386, x64 MD5sum: 765c670db9c931a57a48858a35204be1 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FunChIP_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FunChIP_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FunChIP_1.0.0.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: 1.18.0 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: a751ed9df193c00549a20e766f0139ee 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FunciSNP_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FunciSNP_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FunciSNP_1.18.0.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: gaga Version: 2.20.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 99871317f2326ff4bf53cda843b028a1 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaga_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaga_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaga_2.20.0.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: 2.24.0 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: 5de8a0bf2b15968d72996a10c722941a 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_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gage_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gage_2.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gage_2.24.0.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 suggestsMe: FGNet, pathview Package: gaggle Version: 1.42.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: f28dd77356d82beeb360aa5e1e6ea13b 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaggle_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaggle_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaggle_1.42.0.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: 2.18.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: cec0ada4060f4bfc9f2afe2b39ae8a7d 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaia_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaia_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaia_2.18.0.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: 1.0.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 9fa2206c1762a7dc05a8c3584be397db 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GAprediction_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GAprediction_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GAprediction_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R htmlDocs: vignettes/GAprediction/inst/doc/GAprediction.html htmlTitles: GAprediction Package: garfield Version: 1.2.0 Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 3e794e6a29b152e90f298fdeeb2a6cc5 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/garfield_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/garfield_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/garfield_1.2.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gaucho Version: 1.10.0 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: 175d31df64eb152af31d62b5ba17bddb 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaucho_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaucho_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaucho_1.10.0.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: gcatest Version: 1.4.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 95ad2edb8ea807e6eeba9a66ecdc6916 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gcatest_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gcatest_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gcatest_1.4.0.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: 1.18.0 Depends: GSEABase, limma (>= 3.20.0) Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa Enhances: bigmemory, bigmemoryExtras (>= 1.1.2) License: Artistic-2.0 MD5sum: 033cafeefb2625f08148e66b105acfaf 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gCMAP_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gCMAP_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gCMAP_1.18.0.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: 1.14.0 Depends: Biobase, gCMAP (>= 1.3.0), methods, R (>= 3.3.0), Rook Imports: brew, BiocGenerics, annotate, AnnotationDbi, grDevices, GSEABase, hwriter, parallel, 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 MD5sum: 115446e32af2f57b719f35302b93f4ab 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 Author: Thomas Sandmann Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAPWeb_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gCMAPWeb_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gCMAPWeb_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gCMAPWeb_1.14.0.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: 1.0.0 Depends: R (>= 3.3) Imports: Biobase, limma, RobustRankAggreg, ggplot2, parallel, PANTHER.db, BiocParallel, rmarkdown, grDevices, graphics, stats, utils Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db License: Artistic-2.0 MD5sum: 619761c4784729d34f8c194cde055d4e 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, Pete Haverty, Steve Lianoglou Maintainer: Russell Bainer VignetteBuilder: knitr source.ver: src/contrib/gCrisprTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gCrisprTools_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gCrisprTools_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gCrisprTools_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R htmlDocs: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html htmlTitles: gCrisprTools_Vignette, gCrisprTools_Vignette Package: gcrma Version: 2.46.0 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: f4dcc2f63a74049cab8c8f4ba00a3a4d 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_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gcrma_2.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gcrma_2.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gcrma_2.46.0.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, limmaGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp Package: gdsfmt Version: 1.10.1 Depends: R (>= 2.15.0) Imports: methods Suggests: parallel, digest, crayon, RUnit, knitr, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: 712765fe2a9bc321908d3b4bbf5c780b 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/gdsfmt_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/gdsfmt_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gdsfmt_1.10.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.R htmlDocs: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.html htmlTitles: Introduction to GDS Format dependsOnMe: bigmelon, SeqArray, SNPRelate importsMe: GENESIS, GWASTools, SeqVarTools suggestsMe: HIBAG Package: geecc Version: 1.8.0 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: da7eb4a8df95b7e8e6a6631ddd6bad83 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geecc_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geecc_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geecc_1.8.0.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: 1.0.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 MD5sum: 3cd2be88710dff507a818bbf70677748 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEM_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEM_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R htmlDocs: vignettes/GEM/inst/doc/user_guide.html htmlTitles: The GEM User's Guide Package: genArise Version: 1.50.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 2aacab464c4b73150b235b5d602aa9da 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genArise_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genArise_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genArise_1.50.0.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: 1.2.1 Depends: methods Imports: BiocGenerics, IRanges, GenomicRanges(>= 1.23.24), GenomicFeatures, Biostrings, VariantAnnotation, rtracklayer, S4Vectors, GenomeInfoDb, Biobase Suggests: RUnit, rentrez, rmarkdown, knitr, BiocStyle License: Artistic-2.0 MD5sum: 95fa6d34cfd72605bc00a486dd37258e 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/genbankr_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/genbankr_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genbankr_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R htmlDocs: vignettes/genbankr/inst/doc/genbankr.html htmlTitles: genbankr Package: GENE.E Version: 1.14.0 Depends: R (>= 2.7.0), rhdf5 (>= 2.8.0), RCurl (>= 1.6-6) Imports: rhdf5, RCurl Suggests: RUnit, BiocGenerics, knitr, golubEsets (>= 1.0) License: GPL-2 MD5sum: dbb560cc71453a43919a31cbba095647 NeedsCompilation: no Title: Interact with GENE-E from R Description: Interactive exploration of matrices in GENE-E. biocViews: ThirdPartyClient Author: Joshua Gould Maintainer: Joshua Gould URL: http://www.broadinstitute.org/cancer/software/GENE-E SystemRequirements: GENE-E software. VignetteBuilder: knitr source.ver: src/contrib/GENE.E_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GENE.E_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GENE.E_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GENE.E_1.14.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENE.E/inst/doc/GENE.E-vignette.R htmlDocs: vignettes/GENE.E/inst/doc/GENE.E-vignette.html htmlTitles: GENE.E Overview Package: GeneAnswers Version: 2.16.0 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: 947b2dcd80145b1152640d6a7bf9f9a4 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneAnswers_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneAnswers_2.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneAnswers_2.16.0.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 Package: geneAttribution Version: 1.0.1 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: fddf4b37ee5c134362649465ce16639c 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneAttribution_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/geneAttribution_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneAttribution_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/geneAttribution/inst/doc/geneAttribution.html htmlTitles: Vignette Title Package: GeneBreak Version: 1.4.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: dad705c997102ffb7f8c9bb3c5d19f00 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneBreak_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneBreak_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneBreak_1.4.0.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: GeneExpressionSignature Version: 1.20.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 829aab889c27167c3db2494668a5cdfd 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneExpressionSignature_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneExpressionSignature_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneExpressionSignature_1.20.0.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: 1.56.0 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: 919e4deb451ca4e9dbea1e125e15cf98 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_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genefilter_1.56.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genefilter_1.56.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genefilter_1.56.0.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, covRNA, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GSRI, JunctionSeq, methyAnalysis, methylumi, minfi, MLInterfaces, mogsa, pcaExplorer, PECA, phenoTest, Ringo, simpleaffy, TCGAbiolinks, tilingArray, XDE suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, categoryCompare, clusterStab, codelink, compcodeR, factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array, logicFS, lumi, MCRestimate, npGSEA, oligo, oneChannelGUI, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO, XDE Package: genefu Version: 2.6.0 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: 345fda72b11c54d0edaa2ba34fc962b0 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genefu_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genefu_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genefu_2.6.0.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 Package: GeneGA Version: 1.24.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 630008973a05590fc4459b2cacd94ea6 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_1.24.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneGA_1.24.0.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: 1.0.0 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: 2c5dfe955fc42c0cd021453143c3735a 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneGeneInteR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneGeneInteR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneGeneInteR_1.0.0.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: 1.46.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 5fb0add9baf68495202dbc728ce799c0 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneMeta_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneMeta_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneMeta_1.46.0.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 suggestsMe: genefu, XDE Package: GeneNetworkBuilder Version: 1.16.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, Rgraphviz, XML, RCytoscape, RBGL, knitr License: GPL (>= 2) MD5sum: 010715945e9bcbcbe6f5fe4507882754 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_1.16.0.tar.gz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.pdf vignetteTitles: GeneNetworkBuilder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.R Package: GeneOverlap Version: 1.10.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: c2a2f860a087a5328a98c4e699971424 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneOverlap_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneOverlap_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneOverlap_1.10.0.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: 1.0.0 Depends: R (>= 3.3), methods Imports: snow,ape,grDevices,graphics,stats,utils Suggests: BiocStyle, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 05f87dddd5213e0aa544e55c5d76afa0 NeedsCompilation: no Title: Evolutionary and plasticity analysis based on orthologous groups distribution 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 source.ver: src/contrib/geneplast_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneplast_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneplast_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneplast_1.0.0.tgz vignettes: vignettes/geneplast/inst/doc/geneplast.pdf vignetteTitles: Geneplast main vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast.R Package: geneplotter Version: 1.52.0 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: e59231f27b28e16b2bd7bbca79e44251 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneplotter_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneplotter_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneplotter_1.52.0.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, IsoGeneGUI, JunctionSeq, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.46.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 6217fd1b2dc66683835035b01618dd06 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneRecommender_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneRecommender_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneRecommender_1.46.0.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: 1.30.0 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: 69583c1ef58680956d2f8fbff8bc4ea3 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneRegionScan_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneRegionScan_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneRegionScan_1.30.0.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: 1.10.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 2acd25338bf9efacfdb2098f23026480 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneRxCluster_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneRxCluster_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneRxCluster_1.10.0.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: 2.18.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 3c11e0a539e9d0d0c158fea49fa3ae53 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneSelectMMD_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneSelectMMD_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneSelectMMD_2.18.0.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: 2.24.0 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: ea9e1a0c2485dc4b682e53f43ff4710f 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_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneSelector_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneSelector_2.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneSelector_2.24.0.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: 2.4.0 Imports: GWASTools, SeqArray, SeqVarTools, Biobase, gdsfmt, graph, grDevices, graphics, stats, utils Suggests: SNPRelate, logistf, survey, CompQuadForm, RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: ebb93ea22487353f9470ce5c99f9a843 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 and Timothy Thornton Maintainer: Matthew P. Conomos VignetteBuilder: knitr source.ver: src/contrib/GENESIS_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GENESIS_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GENESIS_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GENESIS_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R htmlDocs: vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html htmlTitles: Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package Package: geNetClassifier Version: 1.14.0 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: b51eae76057668b5903b89ace6d20e3e 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geNetClassifier_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geNetClassifier_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geNetClassifier_1.14.0.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: 1.42.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: daf518f823a525929b7455e034bfa94a 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneticsDesign_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneticsDesign_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneticsDesign_1.42.0.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: 1.36.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: f62998bb6a3a4c890727e60d223ab683 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneticsPed_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneticsPed_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneticsPed_1.36.0.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: 1.0.0 Depends: rtracklayer, R (>= 3.3.1) Imports: data.table, dplyr, graphics, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 3) Archs: i386, x64 MD5sum: 6e50a4302bdfec86597a67a2feb95182 NeedsCompilation: yes Title: Optimal Gene Extensions From Histone Modification ChIP-seq Data Description: geneXtendeR is designed to optimally annotate a histone modification ChIP-seq peak input file with functionally important genomic features (e.g., genes associated with peaks) based on optimization calculations. geneXtendeR optimally extends the boundaries of every gene in a genome by some genomic distance (in DNA base pairs) for the purpose of flexibly incorporating cis-regulatory elements (CREs), such as enhancers and promoters, as well as downstream elements that are important to the function of the gene relative to an epigenetic histone modification ChIP-seq dataset. geneXtender computes optimal gene extensions tailored to the broadness of the specific epigenetic mark (e.g., H3K9me1, H3K27me3), as determined by a user-supplied ChIP-seq peak input file. As such, geneXtender maximizes the signal-to-noise ratio of locating genes closest to and directly under peaks. By performing a computational expansion of this nature, ChIP-seq reads that would initially not map strictly to a specific gene can now be optimally mapped to the regulatory regions of the gene, thereby implicating the gene as a potential candidate, and thereby making the ChIP-seq experiment more successful. Such an approach becomes particularly important when working with epigenetic histone modifications that have inherently broad peaks. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneXtendeR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneXtendeR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneXtendeR_1.0.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR Vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneXtendeR/inst/doc/geneXtendeR.R Package: genoCN Version: 1.26.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: d32c8dbe85e93e334c1b537a585f00db 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genoCN_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genoCN_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genoCN_1.26.0.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: 1.2.1 Depends: R (>= 3.3), Rsamtools (>= 1.18.2), SummarizedExperiment (>= 1.1.19), GenomicRanges (>= 1.23.16), 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 (>= 2.5.30), 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: fb22833fa452c241050e5eb78692ed36 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenoGAM_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GenoGAM_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenoGAM_1.2.1.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: 1.6.0 Depends: R (>= 3.0.0),grid Imports: Biostrings, BSgenome, data.table, GenomeInfoDb, GenomicRanges (>= 1.23.26), GenomicAlignments, S4Vectors (>= 0.9.25), ggplot2, gridBase, impute, IRanges, matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools, seqPattern, rtracklayer, Rcpp, RUnit LinkingTo: Rcpp, Rhtslib Suggests: BiocGenerics, genomationData, knitr, knitrBootstrap, RColorBrewer, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: ceb645ad51f99c5b9ef09491c942d06a 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] Maintainer: Altuna Akalin , Vedran Franke URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues source.ver: src/contrib/genomation_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomation_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genomation_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomation_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R htmlDocs: vignettes/genomation/inst/doc/GenomationManual.html htmlTitles: genomation importsMe: CexoR, fCCAC, RCAS suggestsMe: methylKit Package: GenomeGraphs Version: 1.34.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: 630ed529e9d3d2159a21db50352cb233 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomeGraphs_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomeGraphs_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomeGraphs_1.34.0.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: 1.10.3 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.25), IRanges (>= 1.99.26) Imports: stats, stats4, utils, RCurl 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: 88376d3b96da905887491c9b6ef28339 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_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomeInfoDb_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomeInfoDb_1.10.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomeInfoDb_1.10.3.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, TitanCNA, VariantAnnotation importsMe: AllelicImbalance, alpine, AneuFinder, AnnotationHubData, annotatr, BaalChIP, ballgown, biovizBase, BiSeq, BSgenome, bsseq, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, chromstaR, cn.mops, CNEr, CNPBayes, compEpiTools, consensusSeekeR, conumee, CopywriteR, CrispRVariants, csaw, customProDB, DeepBlueR, derfinder, derfinderPlot, diffHic, diffloop, easyRNASeq, ensembldb, ensemblVEP, epigenomix, epivizrData, epivizrStandalone, exomeCopy, FunChIP, genbankr, geneAttribution, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, genoset, genotypeeval, ggbio, GGtools, GoogleGenomics, gQTLstats, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, InPAS, InteractionSet, IVAS, MADSEQ, metagene, methylKit, methylPipe, methylumi, minfi, MinimumDistance, mosaics, motifbreakR, MutationalPatterns, myvariant, NarrowPeaks, normr, Pi, podkat, prebs, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RareVariantVis, Rariant, Rcade, RCAS, recount, regioneR, regionReport, Repitools, RiboProfiling, riboSeqR, roar, rtracklayer, segmentSeq, SeqArray, seqplots, SGSeq, ShortRead, SNPchip, SNPhood, soGGi, SomaticSignatures, SplicingGraphs, SPLINTER, STAN, SummarizedExperiment, TarSeqQC, TFBSTools, transcriptR, TVTB, VanillaICE, VariantFiltering, VariantTools, YAPSA suggestsMe: AnnotationForge, AnnotationHub, ExperimentHubData, gQTLBase, QDNAseq, recoup Package: genomeIntervals Version: 1.30.1 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: 8f8b3c02b89d243d34128d5cab4b3795 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_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomeIntervals_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.3/genomeIntervals_1.30.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomeIntervals_1.30.1.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: 3.4.0 Depends: readr, curl License: GPL-3 MD5sum: 16a7ab73705d538e885c756258b750c7 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_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomes_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genomes_3.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomes_3.4.0.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: 1.10.1 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.9.40), IRanges (>= 2.5.36), GenomeInfoDb (>= 1.1.20), GenomicRanges (>= 1.25.6), SummarizedExperiment (>= 0.3.1), Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4) 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: a05adb11949aa2b15fbac07597bd35ec 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicAlignments_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicAlignments_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicAlignments_1.10.1.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, ASpli, Basic4Cseq, chimera, exomePeak, GoogleGenomics, groHMM, Guitar, HelloRanges, hiReadsProcessor, prebs, recoup, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: alpine, AneuFinder, BaalChIP, biovizBase, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, contiBAIT, CopywriteR, CoverageView, CrispRVariants, customProDB, derfinder, DiffBind, easyRNASeq, FourCSeq, FunChIP, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, INSPEcT, MADSEQ, metagene, methylPipe, mosaics, PICS, QuasR, Rcade, Repitools, RiboProfiling, RNAprobR, roar, Rqc, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, SPLINTER, TarSeqQC, trackViewer, transcriptR suggestsMe: BiocParallel, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, oneChannelGUI, Rsamtools, Streamer Package: GenomicFeatures Version: 1.26.4 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.47), IRanges (>= 2.3.21), GenomeInfoDb (>= 1.5.16), GenomicRanges (>= 1.21.32), AnnotationDbi (>= 1.33.15) Imports: methods, utils, stats, tools, DBI, RSQLite, RCurl, XVector, Biostrings (>= 2.23.3), rtracklayer (>= 1.29.24), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: 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.dbSNP141.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 275a00ad50b7a5ec5ca5389b136491ca 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 Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/GenomicFeatures_1.26.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicFeatures_1.26.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicFeatures_1.26.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicFeatures_1.26.4.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, Guitar, HelloRanges, InPAS, OrganismDbi, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, biovizBase, bumphunter, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO, crisprseekplus, csaw, customProDB, derfinder, derfinderPlot, EDASeq, epivizrData, epivizrStandalone, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gQTLstats, Gviz, gwascat, HTSeqGenie, INSPEcT, lumi, metagene, methyAnalysis, PGA, proBAMr, qpgraph, QuasR, RCAS, RiboProfiling, SGSeq, SplicingGraphs, SPLINTER, systemPipeR, trackViewer, transcriptR, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: AnnotationHub, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, cummeRbund, DEXSeq, easyRNASeq, flipflop, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, IRanges, MiRaGE, recount, RIPSeeker, Rsamtools, ShortRead, SummarizedExperiment Package: GenomicFiles Version: 1.10.3 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), GenomicRanges (>= 1.23.21), 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: a39b2cdb90b498eb102ed5bf100bd7d0 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_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicFiles_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicFiles_1.10.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicFiles_1.10.3.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 importsMe: contiBAIT, derfinder, erma, gQTLBase, gQTLstats, QuasR, Rqc suggestsMe: MultiAssayExperiment Package: GenomicInteractions Version: 1.8.1 Depends: R (>= 3.3), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges, IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors, dplyr, Gviz, Biobase, graphics, stats, utils Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: 46e2691d7c57a7802bec445b1fc0a12a 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: Malcolm Perry , Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr source.ver: src/contrib/GenomicInteractions_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicInteractions_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicInteractions_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicInteractions_1.8.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R htmlDocs: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html htmlTitles: chiapet_vignette.html, GenomicInteractions-HiC suggestsMe: Chicago Package: GenomicRanges Version: 1.26.4 Depends: R (>= 2.10), methods, stats4, BiocGenerics (>= 0.17.5), S4Vectors (>= 0.9.47), IRanges (>= 2.7.8), GenomeInfoDb (>= 1.1.20) Imports: utils, stats, XVector 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: 86c7d60ee21a86abc9f48b6527877bf4 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 source.ver: src/contrib/GenomicRanges_1.26.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicRanges_1.26.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicRanges_1.26.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicRanges_1.26.4.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: Extending GenomicRanges, GenomicRanges HOWTOs, An Introduction to the GenomicRanges Package, A quick introduction to GRanges and GRangesList objects (slides), 10 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, BPRMeth, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, casper, chimera, ChIPpeakAnno, ChIPQC, chipseq, chroGPS, chromPlot, chromstaR, CINdex, cleanUpdTSeq, cn.mops, CNPBayes, cnvGSA, CNVPanelizer, compEpiTools, consensusSeekeR, CSAR, csaw, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, DMRcaller, DMRforPairs, DNAshapeR, DOQTL, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, exomeCopy, fastseg, fCCAC, FourCSeq, FunChIP, GeneBreak, GenoGAM, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicTuples, genoset, gmapR, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HilbertCurve, HiTC, htSeqTools, IdeoViz, InPAS, intansv, InteractionSet, isomiRs, MBASED, metagene, methyAnalysis, methylKit, methylPipe, minfi, MutationalPatterns, PGA, PING, podkat, QuasR, r3Cseq, Rariant, Rcade, recoup, regioneR, rfPred, rGREAT, riboSeqR, RIPSeeker, RnBeads, Rsamtools, RSVSim, rtracklayer, segmentSeq, seqbias, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, SummarizedExperiment, TarSeqQC, TitanCNA, trackViewer, TransView, VanillaICE, VariantAnnotation, VariantTools, vtpnet, wavClusteR, YAPSA importsMe: ALDEx2, alpine, annotatr, ArrayExpressHTS, BadRegionFinder, ballgown, bamsignals, BBCAnalyzer, beadarray, BEAT, biovizBase, BiSeq, BSgenome, CAGEr, CexoR, chipenrich, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, CNEr, CNPBayes, coMET, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, crisprseekplus, CrispRVariants, customProDB, DChIPRep, debrowser, DeepBlueR, DEFormats, derfinder, derfinderPlot, diffloop, DMRcate, DRIMSeq, easyRNASeq, EDASeq, epivizr, epivizrData, erma, flipflop, FourCSeq, FunciSNP, genbankr, geneAttribution, GeneGeneInteR, genomation, genomeIntervals, GenomicAlignments, GenomicInteractions, genotypeeval, GenVisR, GGBase, ggbio, GGtools, GoogleGenomics, gQTLBase, gQTLstats, gwascat, h5vc, hiReadsProcessor, HTSeqGenie, INSPEcT, IVAS, JunctionSeq, LOLA, lumi, M3D, MADSEQ, MEAL, MEDIPS, methyAnalysis, MethylSeekR, methylumi, MinimumDistance, MMDiff2, mosaics, motifbreakR, MultiAssayExperiment, MultiDataSet, NarrowPeaks, normr, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pepStat, Pi, PICS, pqsfinder, prebs, proBAMr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, recount, regioneR, regionReport, Repitools, RiboProfiling, RNAprobR, rnaSeqMap, roar, seq2pathway, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, signeR, simulatorZ, SNPchip, soGGi, spliceR, SplicingGraphs, SPLINTER, STAN, SVM2CRM, systemPipeR, TCGAbiolinks, TFBSTools, tracktables, transcriptR, triplex, TVTB, VariantFiltering, waveTiling suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, Chicago, cummeRbund, GenomeInfoDb, HDF5Array, interactiveDisplay, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, RTCGA, S4Vectors, SeqGSEA Package: GenomicTuples Version: 1.8.3 Depends: R (>= 3.3.0), GenomicRanges (>= 1.23.15), GenomeInfoDb (>= 1.7.2), S4Vectors (>= 0.9.38) Imports: methods, BiocGenerics (>= 0.17.0), Rcpp (>= 0.11.2), IRanges (>= 2.5.26), data.table LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: b247a7c67d4da07148a012640ff4ad24 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 Herve Pages. Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues source.ver: src/contrib/GenomicTuples_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicTuples_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicTuples_1.8.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicTuples_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R htmlDocs: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html htmlTitles: GenomicTuplesIntroduction Package: Genominator Version: 1.28.0 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: a826b9c64b3b012afaacd7ebc0992bd9 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Genominator_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Genominator_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Genominator_1.28.0.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 suggestsMe: oneChannelGUI Package: genoset Version: 1.30.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.9.25), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: RUnit, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: b0a5cf43df0dec2be600bd743b22010c 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genoset_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genoset_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genoset_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R htmlDocs: vignettes/genoset/inst/doc/genoset.html htmlTitles: genoset importsMe: methyAnalysis, VegaMC Package: genotypeeval Version: 1.4.0 Depends: R (>= 3.2.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel Suggests: knitr, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 76f2436d4b20d65b26768e7b37cb4c36 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: knitr source.ver: src/contrib/genotypeeval_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genotypeeval_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genotypeeval_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genotypeeval_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.R htmlDocs: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html htmlTitles: genotypeeval_vignette Package: genphen Version: 1.2.0 Depends: R(>= 3.3), randomForest, e1071, ggplot2, effsize, Biostrings, rjags License: GPL (>= 2) MD5sum: 678696ea4f386b7c12e05b789ef53ef9 NeedsCompilation: no Title: A tool for quantification of associations between genotypes and phenotypes with statistical learning techniques such as random forests and support vector machines as well as with Bayesian inference using hierarchical models Description: Genetic association studies have become an essential tool for studying the relationship between genotypes and phenotypes. They are necessary for the discovery of disease-causing genetic variants. Here we provide a tool for conducting genetic association studies, which uses statistical learning techniques such as random forests and support vector machines, as well as using Bayesian inference with Bayesian hierarchical models. These techniques are superior to the commonly used (frequentist) statistical approaches, alleviating the multiple hypothesis problems and the need for P value corrections, which often lead to massive numbers of false negatives. Thus, with genphen we provide a framework to compare the results obtained using frequentist methods with those obtained using the more sophisticated methods provided by this tool. The tool also provides a few visualization functions which enable the user to inspect the results of such genetic association study and conveniently select the genotypes which have the highest strength of association with the phenotype. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski Maintainer: Simo Kitanovski source.ver: src/contrib/genphen_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genphen_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genphen_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genphen_1.2.0.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: 1.2.0 Depends: R (>= 3.2.3) Imports: matrixStats, reshape2, survcomp Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 8d7b8dd6547e31d15cf47b30ab5f4795 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenRank_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GenRank_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenRank_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenRank/inst/doc/GenRank_Vignette.R htmlDocs: vignettes/GenRank/inst/doc/GenRank_Vignette.html htmlTitles: Introduction to GenRank Package Package: GenVisR Version: 1.4.1 Depends: R (>= 3.3.0) Imports: AnnotationDbi, biomaRt, BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges, ggplot2 (>= 2.1.0), grid, gridExtra, gtable, gtools, IRanges, plyr (>= 1.8.3), reshape2, Rsamtools, scales, stats, utils, viridis Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown License: GPL-3 + file LICENSE MD5sum: dfdee7d5758b6a1bee7cb2d437f2f23c 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenVisR_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GenVisR_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenVisR_1.4.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/GenVisR_intro.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R htmlDocs: vignettes/GenVisR/inst/doc/GenVisR_intro.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html htmlTitles: GenVisR: An introduction, waterfall: function introduction Package: GEOmetadb Version: 1.34.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: bc80206762269b6761eaf30e9e618324 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOmetadb_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOmetadb_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOmetadb_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R htmlDocs: vignettes/GEOmetadb/inst/doc/GEOmetadb.html htmlTitles: GEOmetadb Package: GEOquery Version: 2.40.0 Depends: methods, Biobase Imports: XML, RCurl, httr Suggests: limma, knitr, rmarkdown, RUnit, BiocGenerics License: GPL-2 MD5sum: 5eeafa5f4ce40f37e208b1d2f026bd98 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_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOquery_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOquery_2.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOquery_2.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R htmlDocs: vignettes/GEOquery/inst/doc/GEOquery.html htmlTitles: Using GEOquery dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: AnnotationHubData, ChIPXpress, crossmeta, EGAD, minfi, MoonlightR, recount, SRAdb suggestsMe: ctsGE, dyebias, ELBOW, multiClust, MultiDataSet, PGSEA, RGSEA, RnBeads, Rnits, skewr, TargetScore Package: GEOsearch Version: 1.0.0 Depends: R(>= 3.2) Imports: RCurl, org.Hs.eg.db, org.Mm.eg.db Suggests: knitr, shiny, DT, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL(>=2) MD5sum: fb1e7fc4fc9f1ec74bd29d2e66223247 NeedsCompilation: no Title: GEOsearch Description: GEOsearch is an extendable search engine for NCBI GEO (Gene Expression Omnibus). Instead of directly searching the term, GEOsearch can find all the gene names contained in the search term and search all the alias of the gene names simultaneously in GEO database. GEOsearch also provides other functions such as summarizing common biology keywords in the search results. biocViews: GUI,Software Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/GEOsearch_1.0.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOsearch_1.0.0.tgz vignettes: vignettes/GEOsearch/inst/doc/GEOsearch.pdf vignetteTitles: GEOsearch: Extendable Search Engine for Gene Expression Omnibus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsearch/inst/doc/GEOsearch.R Package: GEOsubmission Version: 1.26.1 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: ec504c8092c836d337cf6994e9416941 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_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOsubmission_1.26.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOsubmission_1.26.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOsubmission_1.26.1.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: gespeR Version: 1.6.1 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: e2b06649ed00a8e41511299339757004 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/gespeR_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/gespeR_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gespeR_1.6.1.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: 1.18.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 289edebfc239869679fd4231d61a81ab 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEWIST_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEWIST_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEWIST_1.18.0.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: 3.36.0 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: 4721ca464e13048412abfbc6413f1075 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_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GGBase_3.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GGBase_3.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GGBase_3.36.0.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: 1.22.4 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.19.1), Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.21.10), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.17.13), OrganismDbi, GGally, ensembldb (>= 1.3.8), AnnotationDbi 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 License: Artistic-2.0 MD5sum: d09a2ba6e92a5c8676cf4edc5133bfe8 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 source.ver: src/contrib/ggbio_1.22.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggbio_1.22.4.zip win64.binary.ver: bin/windows64/contrib/3.3/ggbio_1.22.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggbio_1.22.4.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, coMET, intansv importsMe: derfinderPlot, FourCSeq, gwascat, Pi, R3CPET, Rariant, ReportingTools, RiboProfiling, SomaticSignatures suggestsMe: beadarray, chromstaR, GoogleGenomics, gQTLstats, interactiveDisplay, regionReport, RnBeads Package: ggcyto Version: 1.2.3 Depends: methods, ggplot2(>= 2.0.0), flowCore, ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.17.24) Imports: plyr, scales, data.table, RColorBrewer, gridExtra Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz License: Artistic-2.0 MD5sum: 830310cab9e42d2ecdb921d31115cce8 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fority 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 source.ver: src/contrib/ggcyto_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggcyto_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ggcyto_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggcyto_1.2.3.tgz 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 htmlDocs: 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 htmlTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto suggestsMe: CytoML, flowCore, flowWorkspace Package: GGtools Version: 5.10.1 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel, Homo.sapiens Imports: methods, utils, stats, BiocGenerics, snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, 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: 738e902ea03a36f6b7ace070ab502ebb 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_5.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GGtools_5.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GGtools_5.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GGtools_5.10.1.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, gwascat Package: ggtree Version: 1.6.11 Depends: R (>= 3.3.1), ggplot2 (>= 2.2.0) Imports: ape, grDevices, grid, jsonlite, magrittr, methods, stats4, tidyr, utils Suggests: Biostrings, colorspace, EBImage, emojifont, knitr, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: cd28978bdef28a35035a38952835438d 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 and Tommy Tsan-Yuk Lam Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/ggtree VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ggtree/issues source.ver: src/contrib/ggtree_1.6.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggtree_1.6.11.zip win64.binary.ver: bin/windows64/contrib/3.3/ggtree_1.6.11.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggtree_1.6.11.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/advanceTreeAnnotation.R, vignettes/ggtree/inst/doc/ggtree.R, vignettes/ggtree/inst/doc/ggtreeUtilities.R, vignettes/ggtree/inst/doc/treeAnnotation.R, vignettes/ggtree/inst/doc/treeImport.R, vignettes/ggtree/inst/doc/treeManipulation.R, vignettes/ggtree/inst/doc/treeVisualization.R htmlDocs: vignettes/ggtree/inst/doc/advanceTreeAnnotation.html, vignettes/ggtree/inst/doc/ggtree.html, vignettes/ggtree/inst/doc/ggtreeUtilities.html, vignettes/ggtree/inst/doc/treeAnnotation.html, vignettes/ggtree/inst/doc/treeImport.html, vignettes/ggtree/inst/doc/treeManipulation.html, vignettes/ggtree/inst/doc/treeVisualization.html htmlTitles: 05 Advance Tree Annotation, 00 ggtree introduction, 06 ggtree utilities, 04 Tree Annotation, 01 Tree Data Import, 03 Tree Manipulation, 02 Tree Visualization importsMe: LINC, philr Package: girafe Version: 1.26.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.7.21), Rsamtools, intervals (>= 0.13.1), ShortRead (>= 1.3.21), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings, graphics, grDevices, stats, utils, IRanges (>= 2.3.23) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: 0a08248c25d610277f077b57f1d75a3a 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/girafe_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/girafe_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/girafe_1.26.0.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: GLAD Version: 2.38.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: 4ea9e2c27e13e1cf5e4a9cd9d9e8a5f3 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_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GLAD_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GLAD_2.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GLAD_2.38.0.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 Package: Glimma Version: 1.2.1 Depends: R (>= 3.3.0) Imports: DESeq2, edgeR, grDevices, methods, stats, utils Suggests: BiocStyle, limma License: GPL-3 | file LICENSE MD5sum: 806b341f8255e00cfce5061745ac1e09 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, ReportWriting, RNASeq, Visualization Author: Shian Su, Matt Ritchie, Charity Law Maintainer: Shian Su URL: https://github.com/Shians/Glimma BugReports: https://github.com/Shians/Glimma/issues source.ver: src/contrib/Glimma_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Glimma_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Glimma_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Glimma_1.2.1.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 Package: GlobalAncova Version: 3.42.0 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: 8277af66056416236f46e1422f8aec97 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_3.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GlobalAncova_3.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GlobalAncova_3.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GlobalAncova_3.42.0.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: 1.2.0 Depends: R(>= 3.3.0) Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: f79ecdfeccd122c023141c9d7bc19e01 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 Maintainer: Armin Rauschenberger VignetteBuilder: knitr source.ver: src/contrib/globalSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/globalSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/globalSeq_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/globalSeq_1.2.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf vignetteTitles: globalSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R Package: globaltest Version: 5.28.0 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: aa33575d25d437d991c602f7ceefc37e 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 source.ver: src/contrib/globaltest_5.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/globaltest_5.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/globaltest_5.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/globaltest_5.28.0.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: 1.16.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12), Rsamtools (>= 1.17.8) Imports: S4Vectors (>= 0.9.25), IRanges, rtracklayer (>= 1.31.2), GenomicFeatures (>= 1.17.13), Biostrings, VariantAnnotation (>= 1.11.4), tools, Biobase, BSgenome, GenomicAlignments (>= 1.1.9), 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: 29669b353d07892536e0c3d1767acb02 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_1.16.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gmapR_1.16.0.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 importsMe: VariantTools Package: GMRP Version: 1.2.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics, VariantAnnotation License: GPL (>= 2) MD5sum: fb47ec3eb0b11e8fb6c07d3748d70d24 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 and Dajiang Liu Maintainer: Yuan-De Tan source.ver: src/contrib/GMRP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GMRP_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GMRP_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GMRP_1.2.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: 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: 1.8.1 Depends: R (>= 3.0.2), 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), VennDiagram (>= 1.6.5), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: 92a64baa2d22da131b7a60ba8b39e255 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOexpress_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GOexpress_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOexpress_1.8.1.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: GOFunction Version: 1.22.0 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: b1f9011104c7d78cfd15bf79e1e6f73b 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOFunction_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOFunction_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOFunction_1.22.0.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: 1.6.0 Depends: R (>= 3.1.0), GenomicAlignments (>= 1.0.1), VariantAnnotation Imports: Biostrings, GenomeInfoDb, GenomicRanges, IRanges, httr, rjson, Rsamtools, S4Vectors (>= 0.9.25) Suggests: BiocStyle, 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 MD5sum: d5286fc47ed324eb83aeaea675835cea NeedsCompilation: no Title: R Client for Google Genomics API Description: Provides an R package to interact with the Google Genomics API. biocViews: DataImport, ThirdPartyClient Author: Cassie Doll [aut], Nicole Deflaux [aut], Siddhartha Bagaria [aut, cre] Maintainer: Siddhartha Bagaria URL: https://cloud.google.com/genomics/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GoogleGenomics/issues source.ver: src/contrib/GoogleGenomics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GoogleGenomics_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GoogleGenomics_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GoogleGenomics_1.6.0.tgz 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 htmlDocs: vignettes/GoogleGenomics/inst/doc/AnnotatingVariants.html, vignettes/GoogleGenomics/inst/doc/PlottingAlignments.html, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.html htmlTitles: Annotating Variants, Plotting Alignments, Reproducing Variant Annotation Results Package: GOpro Version: 1.0.0 Depends: R (>= 3.3) 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: 5e34a8c22244f66c3d82a31ae36a737f 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOpro_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOpro_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOpro_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R htmlDocs: vignettes/GOpro/inst/doc/GOpro_vignette.html htmlTitles: GOpro: Determine groups of genes and find their characteristic GO term Package: goProfiles Version: 1.36.0 Depends: Biobase, AnnotationDbi, GO.db Suggests: org.Hs.eg.db License: GPL-2 MD5sum: c4527712d8440b349bdf7aef10075e1b 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goProfiles_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goProfiles_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goProfiles_1.36.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R Package: GOSemSim Version: 2.0.4 Depends: R (>= 3.3.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocInstaller, BiocStyle, clusterProfiler, DOSE, knitr, org.Hs.eg.db, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 141cefa151503670e62bfdf3a8353a4b 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 with contributions from Alexey Stukalov and Chuanle Xiao. Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/GOSemSim/issues source.ver: src/contrib/GOSemSim_2.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOSemSim_2.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GOSemSim_2.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOSemSim_2.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R htmlDocs: vignettes/GOSemSim/inst/doc/GOSemSim.html htmlTitles: An introduction to GOSemSim dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, meshes, Rcpi suggestsMe: SemDist Package: goseq Version: 1.26.0 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: da46e940cc1cf1f624a06c908f07f910 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goseq_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goseq_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goseq_1.26.0.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: SMITE suggestsMe: oneChannelGUI Package: GOSim Version: 1.12.0 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: 3e935c538ec173ca4827f44e1e6699ef 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOSim_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOSim_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOSim_1.12.0.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: GOstats Version: 2.40.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.3.26), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), Biobase (>= 1.15.29), Category (>= 2.3.26), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), graph (>= 1.15.15), AnnotationForge Suggests: hgu95av2.db (>= 1.13.0), ALL, GO.db (>= 1.13.0), annotate, multtest, genefilter, RColorBrewer, Rgraphviz, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c9f869cf6389715d51a9918250d226f5 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: R. Gentleman and S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GOstats_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOstats_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOstats_2.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOstats_2.40.0.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, mvGST, pcaExplorer, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, miRLAB, MLP, MmPalateMiRNA, oneChannelGUI, phenoDist, qpgraph, RnBeads, safe Package: GOsummaries Version: 2.8.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: 72d456c9ba0c8e1f1c4d5cec104b05e4 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOsummaries_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOsummaries_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOsummaries_2.8.0.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: 1.10.0 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: 869e35d51d80e3fd407a67971be42c10 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOTHiC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOTHiC_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOTHiC_1.10.0.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: 1.48.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: ea446de381d743a87c7c79590c26854e 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goTools_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goTools_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goTools_1.48.0.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: 1.46.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 6e7dab804c99bc5ba50a34aa869f39a4 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gpls_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gpls_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gpls_1.46.0.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: 1.18.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 6774b553a69b0a2fe23b24e350196c50 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gprege_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gprege_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gprege_1.18.0.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: 1.6.0 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: 3a4fb5ce044ead87367a07b23eb5e5a9 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gQTLBase_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gQTLBase_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gQTLBase_1.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R htmlDocs: vignettes/gQTLBase/inst/doc/gQTLBase.html htmlTitles: gQTLBase infrastructure for eQTL archives importsMe: gQTLstats Package: gQTLstats Version: 1.6.0 Depends: R (>= 3.1.0) 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 Suggests: geuvPack, geuvStore2, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, Homo.sapiens, RUnit, multtest License: Artistic-2.0 MD5sum: 44166e79333f176ad5dee09c14dd8375 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gQTLstats_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gQTLstats_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gQTLstats_1.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R htmlDocs: vignettes/gQTLstats/inst/doc/gQTLstats.html htmlTitles: gQTLstats: statistics for genetics of genomic features importsMe: gwascat Package: graph Version: 1.52.0 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: a2f2cf1b4b83208e9746770bd2f5b925 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 source.ver: src/contrib/graph_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/graph_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/graph_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/graph_1.52.0.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, CellNOptR, clipper, CNORfeeder, ddgraph, gaggle, gaucho, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, NCIgraph, NetSAM, pathRender, Pigengene, pkgDepTools, RbcBook1, RBGL, RBioinf, RCy3, RCyjs, RCytoscape, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, ToPASeq, topGO, vtpnet importsMe: alpine, AnalysisPageServer, BgeeDB, BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, ChIPpeakAnno, CHRONOS, CytoML, DEGraph, DEsubs, EnrichmentBrowser, FEM, flowCL, flowClust, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GENESIS, GOFunction, GOSim, GOstats, GraphAT, graphite, gwascat, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, mvGST, NCIgraph, nem, netresponse, OncoSimulR, OrganismDbi, pathview, PCpheno, pkgDepTools, ppiStats, qpgraph, RchyOptimyx, RGraph2js, rsbml, Rtreemix, SplicingGraphs, Streamer, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, DEGraph, EBcoexpress, ecolitk, GeneAnswers, KEGGlincs, mmnet, MmPalateMiRNA, netbenchmark, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, VariantTools Package: GraphAlignment Version: 1.38.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 67111cf057ef90d879092c030247a170 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphAlignment_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphAlignment_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphAlignment_1.38.0.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: 1.46.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: d1aef590af9bcce1c647737a27d93c11 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphAT_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphAT_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphAT_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.20.1 Depends: R (>= 2.10), methods Imports: AnnotationDbi, graph, stats, utils, rappdirs Suggests: BiocStyle, hgu133plus2.db, org.Hs.eg.db, SPIA (>= 2.2), topologyGSA (>= 1.4.0), clipper, ALL, testthat Enhances: DEGraph, RCytoscape, RCy3 License: AGPL-3 MD5sum: e4b11c02b3a48deb27010df060bad612 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 Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales source.ver: src/contrib/graphite_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/graphite_1.20.1.zip win64.binary.ver: bin/windows64/contrib/3.3/graphite_1.20.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/graphite_1.20.1.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: ToPASeq importsMe: facopy, mogsa, ReactomePA suggestsMe: clipper Package: GraphPAC Version: 1.16.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 49bba41d38362739facbe258f3d42b7a 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 source.ver: src/contrib/GraphPAC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphPAC_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphPAC_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphPAC_1.16.0.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: 1.26.0 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: 0b7ce1ee89c32fb37eef16ea576c920f 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GRENITS_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GRENITS_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GRENITS_1.26.0.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: 1.6.0 Depends: R (>= 3.1), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 64341fba467a524d659cd37bc0408888 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GreyListChIP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GreyListChIP_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GreyListChIP_1.6.0.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: 1.0.0 Depends: R (>= 3.3), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 86b0296bc0a36825ddc5c3c728c58e69 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 URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues source.ver: src/contrib/GRmetrics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GRmetrics_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GRmetrics_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GRmetrics_1.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R htmlDocs: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html htmlTitles: GRmetrics: an R package for calculation and visualization of Growth Rate Metrics Package: groHMM Version: 1.8.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.9.25), IRanges (>= 2.5.27), GenomeInfoDb, GenomicRanges (>= 1.23.16), GenomicAlignments, rtracklayer Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 6837136b45f0b8fed4d0c0f2b4388929 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 , Venkat Malladi , 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/groHMM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/groHMM_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/groHMM_1.8.0.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: GSALightning Version: 1.2.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: ebbcd6587fa1d37ee13ce2a5916514a4 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSALightning_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSALightning_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSALightning_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R htmlDocs: vignettes/GSALightning/inst/doc/vignette.html htmlTitles: Vignette Title Package: GSAR Version: 1.8.0 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: 36ed86c5072a25cc452e83e3aeae400c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSAR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSAR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSAR_1.8.0.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: 2.4.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 21731e41436c02df1da418a9df132042 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSCA_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSCA_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSCA_2.4.0.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: 1.36.0 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 License: Artistic-2.0 MD5sum: 03945b4d71555e5429e2c6db1ee90e2d 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSEABase_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSEABase_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSEABase_1.36.0.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, EnrichmentBrowser, gCMAP, npGSEA, PROMISE, splineTimeR importsMe: canceR, Category, categoryCompare, cellHTS2, gCMAPWeb, GSRI, GSVA, HTSanalyzeR, mogsa, oppar, PCpheno, phenoTest, PROMISE, ReportingTools suggestsMe: BiocCaseStudies, clusterProfiler, gage, GlobalAncova, globaltest, GOstats, GSAR, MAST, PGSEA, phenoTest Package: GSEAlm Version: 1.34.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: d4685402971c72caaf2358d87d9cd557 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSEAlm_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSEAlm_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSEAlm_1.34.0.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: GSReg Version: 1.8.0 Depends: R (>= 2.13.1) Suggests: GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: b611fa56229887e67da759ffbc4c9301 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari source.ver: src/contrib/GSReg_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSReg_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSReg_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSReg_1.8.0.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: 2.22.0 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: 688bdb18d93a451b94d84be3ae9b6e8c 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSRI_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSRI_2.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSRI_2.22.0.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: 1.22.4 Depends: R (>= 2.13.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4) Suggests: limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) Archs: i386, x64 MD5sum: a6193234fa6b2d0c809801fd230c5b07 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] Maintainer: Justin Guinney URL: http://www.sagebase.org source.ver: src/contrib/GSVA_1.22.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSVA_1.22.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GSVA_1.22.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSVA_1.22.4.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: EGSEA, oppar Package: gtrellis Version: 1.6.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.3.3), GetoptLong Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.9.7) License: GPL (>= 2) MD5sum: 65429167b161555d18a312d0a8e3e970 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 source.ver: src/contrib/gtrellis_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gtrellis_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gtrellis_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gtrellis_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R htmlDocs: vignettes/gtrellis/inst/doc/gtrellis.html htmlTitles: Make Genome-level Trellis Graph importsMe: YAPSA Package: GUIDEseq Version: 1.4.1 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: c1789bce9552edc3c4f8208609290b59 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, Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/GUIDEseq_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GUIDEseq_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GUIDEseq_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GUIDEseq_1.4.1.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: 1.12.0 Depends: Rsamtools, GenomicFeatures, rtracklayer, GenomicAlignments, GenomicRanges, ggplot2, grid, IRanges License: GPL-2 MD5sum: 8eb26e4117ef823e8ae6fee48f2077a7 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Guitar_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Guitar_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Guitar_1.12.0.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: 1.18.2 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: 2da6170d79446a59331e61ecca534366 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: Florian Hahne source.ver: src/contrib/Gviz_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Gviz_1.18.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Gviz_1.18.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Gviz_1.18.2.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: ASpli, biomvRCNS, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, DMRcate, GenomicInteractions, GGtools, gwascat, InPAS, methyAnalysis, methylPipe, motifbreakR, PING, SPLINTER, STAN, trackViewer, VariantFiltering suggestsMe: annmap, CNEr, DeepBlueR, ensembldb, GenomicRanges, interactiveDisplay, Pi, pqsfinder, QuasR, RnBeads, SplicingGraphs Package: gwascat Version: 2.6.0 Depends: R (>= 3.0.0), Homo.sapiens Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, snpStats, Biostrings, Rsamtools, rtracklayer, gQTLstats, Gviz, VariantAnnotation, AnnotationHub, AnnotationDbi, GenomicFeatures, graph, ggbio, ggplot2, SummarizedExperiment Suggests: DO.db, DT, utils, knitr, RBGL, RUnit, GGtools Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: 943b79820156dcfac9fa437599a1dcaa 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gwascat_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gwascat_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gwascat_2.6.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf vignetteTitles: gwascat -- exploring NHGRI GWAS catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R htmlDocs: vignettes/gwascat/inst/doc/gwascatOnt.html htmlTitles: gwascat: exploring GWAS results using the experimental factor ontology dependsOnMe: vtpnet suggestsMe: gQTLBase Package: GWASTools Version: 1.20.0 Depends: Biobase Imports: graphics, stats, utils, methods, ncdf4, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation License: Artistic-2.0 MD5sum: 7f1f46b997bad9cc1f71cc25ec932923 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, 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GWASTools_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GWASTools_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GWASTools_1.20.0.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: 2.8.1 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, bit64, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) Archs: i386, x64 MD5sum: 1a8a09aca75c6b87b51a24ede112d17e 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_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/h5vc_2.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/h5vc_2.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/h5vc_2.8.1.tgz 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 htmlDocs: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html htmlTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour Package: hapFabia Version: 1.16.1 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 9b195c8fc08115bac0276696bd67b936 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_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/hapFabia_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.3/hapFabia_1.16.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hapFabia_1.16.1.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: 1.2.0 Depends: R (>= 3.3) 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: b4e4e20eae02a2608867bf24eb5a9c5a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Harman_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Harman_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Harman_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R htmlDocs: vignettes/Harman/inst/doc/IntroductionToHarman.html htmlTitles: IntroductionToHarman Package: Harshlight Version: 1.46.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: ce697e0dcc10841f23aaafcfa938f24e 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Harshlight_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Harshlight_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Harshlight_1.46.0.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: HCsnip Version: 1.14.0 Depends: R(>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase License: GPL (>= 2) MD5sum: 9b42c8612245e00b32ad0e87c59195b7 NeedsCompilation: no Title: Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree Description: Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy. biocViews: Microarray, aCGH, GeneExpression, Clustering Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/HCsnip_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HCsnip_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HCsnip_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HCsnip_1.14.0.tgz vignettes: vignettes/HCsnip/inst/doc/HCsnip.pdf vignetteTitles: HCsnip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCsnip/inst/doc/HCsnip.R Package: HDF5Array Version: 1.2.1 Depends: R (>= 3.2), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.9.43), IRanges (>= 2.7.6) Imports: stats, rhdf5 Suggests: Matrix, h5vcData, SummarizedExperiment, GenomicRanges, genefilter, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: a67f7ad704e51911ef42008f1bc9c90b NeedsCompilation: no Title: An array-like container for convenient access and manipulation of HDF5 datasets Description: This package implements the HDF5Array class for convenient access and manipulation of HDF5 datasets. In order to reduce memory usage and optimize performance, operations on an HDF5Array object are either delayed or executed using a block processing mechanism. The delaying and block processing mechanisms are independent of the on-disk backend and implemented via the DelayedArray class. They even work on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, or ordinary arrays or data frames, where they can improve performance. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Hervé Pagès Maintainer: Hervé Pagès VignetteBuilder: knitr source.ver: src/contrib/HDF5Array_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/HDF5Array_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/HDF5Array_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HDF5Array_1.2.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiAssayExperiment Package: HDTD Version: 1.8.0 Imports: stats License: GPL-3 MD5sum: 796b3dc7d5ab557a1251fdeea6d5bb77 NeedsCompilation: no 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, John C. Marioni and Simon Tavare Maintainer: Anestis Touloumis source.ver: src/contrib/HDTD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HDTD_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HDTD_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HDTD_1.8.0.tgz vignettes: vignettes/HDTD/inst/doc/Manual.pdf vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/Manual.R Package: Heatplus Version: 2.20.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: c84e65ba23f9a2b3cf7d0f34725f658a 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Heatplus_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Heatplus_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Heatplus_2.20.0.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: 1.0.1 Depends: methods, BiocGenerics, S4Vectors (>= 0.11.6), IRanges (>= 2.7.11), GenomicRanges (>= 1.25.5), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures, VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments, rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: 6689febeb821d20de4576a720aa77931 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/HelloRanges_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/HelloRanges_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HelloRanges_1.0.1.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 Package: HELP Version: 1.32.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 35ca16894100a0064ae8925d73d2fbd7 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HELP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HELP_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HELP_1.32.0.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: 1.46.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 5164796080bbb9cdf4222c6d26b1056f 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HEM_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HEM_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HEM_1.46.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hiAnnotator Version: 1.8.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: bd5c170f0b3b9267b251a477c1fa01fb 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hiAnnotator_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hiAnnotator_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hiAnnotator_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R htmlDocs: vignettes/hiAnnotator/inst/doc/Intro.html htmlTitles: Using hiAnnotator dependsOnMe: hiReadsProcessor Package: HIBAG Version: 1.10.0 Depends: R (>= 3.2.0) Imports: methods Suggests: parallel, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6) License: GPL-3 Archs: i386, x64 MD5sum: 94b1ac500f4eeda379a484afa39b2ef3 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://www.biostat.washington.edu/~bsweir/HIBAG/, http://github.com/zhengxwen/HIBAG VignetteBuilder: knitr source.ver: src/contrib/HIBAG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HIBAG_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HIBAG_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HIBAG_1.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.R htmlDocs: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.html htmlTitles: HIBAG vignette html Package: hierGWAS Version: 1.4.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: e1593d1bdbad8f903f051a40ce86a916 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hierGWAS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hierGWAS_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hierGWAS_1.4.0.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: 1.4.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: methods, HilbertVis, png, grDevices, circlize (>= 0.3.3) Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.7.0), markdown, RColorBrewer, RCurl, GetoptLong License: GPL (>= 2) MD5sum: 7be961f57373ae1aefc4dcca7207fc15 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 source.ver: src/contrib/HilbertCurve_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HilbertCurve_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HilbertCurve_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HilbertCurve_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R htmlDocs: vignettes/HilbertCurve/inst/doc/HilbertCurve.html htmlTitles: Making 2D Hilbert Curve suggestsMe: ComplexHeatmap Package: HilbertVis Version: 1.32.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 803d540500055892e10c5fd6b07d6df2 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HilbertVis_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HilbertVis_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HilbertVis_1.32.0.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: 1.32.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) Archs: x64 MD5sum: 6abbc8634e045ddfd26778f875bbee3d 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HilbertVisGUI_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HilbertVisGUI_1.32.0.zip vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Package: hiReadsProcessor Version: 1.10.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, rSFFreader, readxl, methods Suggests: knitr, testthat License: GPL-3 MD5sum: 4f725b25fe3fd0c2a5b79a68486a2f00 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_1.10.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hiReadsProcessor_1.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R htmlDocs: vignettes/hiReadsProcessor/inst/doc/Tutorial.html htmlTitles: Using hiReadsProcessor Package: HiTC Version: 1.18.1 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: 411b0f20a6b7702d4d4d3844aa57a8ab 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_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/HiTC_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.3/HiTC_1.18.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HiTC_1.18.1.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: HMMcopy Version: 1.16.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: cd703f3645c7103379a27a4fbd6c066f 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HMMcopy_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HMMcopy_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HMMcopy_1.16.0.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: 2.34.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: a3bcd3bcedd3cd04ce3d5540f1ac09aa 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hopach_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hopach_2.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hopach_2.34.0.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: 1.16.0 Depends: R (>= 2.15) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 7f967a4c1fd908a6192563234f8dcb04 NeedsCompilation: no Title: Human Protein Atlas in R Description: A simple 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hpar_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hpar_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hpar_1.16.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.pdf 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: 1.28.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 668530254fddff06883696d3d8c0a4dd 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTqPCR_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HTqPCR_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTqPCR_1.28.0.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: 2.26.0 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: db8c7727d8ba0df0259becf54b78013e 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTSanalyzeR_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HTSanalyzeR_2.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTSanalyzeR_2.26.0.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 suggestsMe: RTN Package: HTSeqGenie Version: 4.4.0 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: 4640e411b3b0a73ee919d1f6002a8928 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_4.4.0.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: 1.22.0 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: bef4c86b41f22732b6b2593d7dbf1f5a 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 source.ver: src/contrib/htSeqTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/htSeqTools_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/htSeqTools_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/htSeqTools_1.22.0.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: 1.14.1 Depends: R (>= 3.3.0) 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: 3ef7e88ce9f1b822658037bea8516896 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_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTSFilter_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/HTSFilter_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTSFilter_1.14.1.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 Package: HybridMTest Version: 1.18.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: de98780fb7d82d3d033b7ee86e18b59d 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HybridMTest_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HybridMTest_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HybridMTest_1.18.0.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: 1.26.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 96c6013709e49ba6c33f5feabd2303ef 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hyperdraw_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hyperdraw_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hyperdraw_1.26.0.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: 1.46.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: c2c9eeebc0832c49766dc64240f1c7e6 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hypergraph_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hypergraph_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hypergraph_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.18.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 763db46158609062d7746a0cbb14b313 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iASeq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iASeq_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iASeq_1.18.0.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: 1.18.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: d9dae998bfa315a88d76936623f39afb 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iBBiG_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iBBiG_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iBBiG_1.18.0.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: 1.22.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: de4bf6706a1f768141f1e597e0c3135b 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ibh_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ibh_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ibh_1.22.0.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: 1.14.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 180dd106f164e0376f1c57c46ae7aa50 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iBMQ_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iBMQ_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iBMQ_1.14.0.tgz 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: 1.2.0 Depends: R (>= 3.3.0) Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 2ee124d7caae93264cb760f9d9297a75 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCARE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iCARE_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCARE_1.2.0.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: 1.46.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 5c69079601cbf75d9946bfdad136ad20 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Icens_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Icens_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Icens_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iCheck Version: 1.4.0 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: f5cab90d675af377c7eb2ecad86f4a92 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCheck_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iCheck_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCheck_1.4.0.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: 1.28.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: f63254bfde2287eeb0d9abcfb4a2b756 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iChip_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iChip_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iChip_1.28.0.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: 1.10.0 Depends: R (>= 2.15.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: c1bbde654697c37a46be579a829f7215 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iClusterPlus_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iClusterPlus_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iClusterPlus_1.10.0.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: iCOBRA Version: 1.2.0 Depends: R (>= 3.2) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2, scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, testthat License: GPL (>=2) MD5sum: 90f18c3263a754b0aed1987950eb0483 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCOBRA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iCOBRA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCOBRA_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R htmlDocs: vignettes/iCOBRA/inst/doc/iCOBRA.html htmlTitles: iCOBRA User Guide Package: IdeoViz Version: 1.8.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 671bdf012378ddb1e8a405efad0daefb 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdeoViz_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdeoViz_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdeoViz_1.8.0.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: FALSE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R Package: idiogram Version: 1.50.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 7dcfa34b812d0fadbf9667c57b26ba6a 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/idiogram_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/idiogram_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/idiogram_1.50.0.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: 1.18.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: d8e789187ef25c77e37d2ee5a4374230 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdMappingAnalysis_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdMappingAnalysis_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdMappingAnalysis_1.18.0.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: 1.22.0 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: b396d68428c45ced39530ebb00b596a7 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdMappingRetrieval_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdMappingRetrieval_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdMappingRetrieval_1.22.0.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: 1.4.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 8c9a33b71dae00921c63cff4f43f3b95 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iGC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iGC_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iGC_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R htmlDocs: vignettes/iGC/inst/doc/Introduction.html htmlTitles: Introduction to iGC Package: IHW Version: 1.2.0 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: 7b22b1eb1f83cb428545d7a4fb63728b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IHW_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IHW_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IHW_1.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R htmlDocs: vignettes/IHW/inst/doc/introduction_to_ihw.html htmlTitles: "Introduction to IHW" suggestsMe: DESeq2 Package: illuminaio Version: 0.16.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: ea2ba7f88b654cb2de32d367dcf96361 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_0.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/illuminaio_0.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/illuminaio_0.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/illuminaio_0.16.0.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: 1.24.0 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: d8efba2650c252b0069d091108cfed1a 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/imageHTS_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/imageHTS_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/imageHTS_1.24.0.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: Imetagene Version: 1.4.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 2bd1a37bf371d78f83cacf3c8f36dbe9 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Imetagene_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Imetagene_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Imetagene_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Imetagene/inst/doc/imetagene.R htmlDocs: vignettes/Imetagene/inst/doc/imetagene.html htmlTitles: Presentation of Imetagene Package: ImmuneSpaceR Version: 1.2.0 Imports: methods, data.table, RCurl, Rlabkey (>= 2.1.127), Biobase, pheatmap, ggplot2, scales, stats, gtools, gplots, reshape2 Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 246acf946eff5e57783e763e07c5d32d 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, Renan Sauteraud, Mike Jiang, Gil Guday Maintainer: Renan Sauteraud URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues source.ver: src/contrib/ImmuneSpaceR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ImmuneSpaceR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ImmuneSpaceR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ImmuneSpaceR_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/arrays.R, vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R, vignettes/ImmuneSpaceR/inst/doc/Using_RImmuneSpace.R htmlDocs: vignettes/ImmuneSpaceR/inst/doc/arrays.html, vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html, vignettes/ImmuneSpaceR/inst/doc/Using_RImmuneSpace.html htmlTitles: Handling expression matrices with ImmuneSpaceR, Downloading tables with getDataset, Reproducing an online report using ImmuneSpaceR: Correlation of HAI/virus neutralizition titer and cell counts in SDY144, Reproducing an online report using ImmuneSpaceR: Plasmablast abundance in SDY180, Reproducing an online report using ImmuneSpaceR: Correlation between HAI and flow cytometry in SDY269, An introduction to the ImmuneSpaceR package Package: immunoClust Version: 1.6.0 Depends: R(>= 3.2), methods, stats, graphics, grid, lattice, flowCore Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: dca737288ff20cf40c218481ae19bded 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/immunoClust_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/immunoClust_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/immunoClust_1.6.0.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: 1.8.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 58dcf9a8b75382c5a67e570fd959d22e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IMPCdata_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IMPCdata_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IMPCdata_1.8.0.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: 1.0.0 Depends: graphics, grDevices, stats, utils, parallel, compiler, R (>= 3.2.3) Imports: amap, boot Suggests: longitudinal, knitr License: GPL-3 MD5sum: b5f17c32a94378a87cfde9dde67e6585 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ImpulseDE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ImpulseDE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ImpulseDE_1.0.0.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: impute Version: 1.48.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: b8495a77bd675a86228a5bb5800a84c8 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/impute_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/impute_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/impute_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, HCsnip, TIN importsMe: CancerSubtypes, doppelgangR, EGAD, genomation, MethylMix, miRLAB, MSnbase, Pigengene, Rnits suggestsMe: BioNet, MethPed, RnBeads Package: InPAS Version: 1.6.0 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: f680317730aa7b8cac800ba14511df32 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 VignetteBuilder: knitr source.ver: src/contrib/InPAS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/InPAS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/InPAS_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/InPAS_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R htmlDocs: vignettes/InPAS/inst/doc/InPAS.html htmlTitles: InPAS Vignette Package: INPower Version: 1.10.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 18f67a60a7d49c60380ee8e0d4a5b7ee 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/INPower_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/INPower_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/INPower_1.10.0.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: 1.4.0 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: eee6d249ff01b04bebb202bd7efe69df 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/INSPEcT_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/INSPEcT_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/INSPEcT_1.4.0.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: intansv Version: 1.12.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: 4458219cdd5b991277ebee710b3daa65 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 source.ver: src/contrib/intansv_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/intansv_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/intansv_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/intansv_1.12.0.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: 1.2.1 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: IRanges, S4Vectors (>= 0.9.24), GenomeInfoDb, BiocGenerics, methods, Matrix Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 59056cefab8b25f581e3bdcaebd8b338 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 VignetteBuilder: knitr source.ver: src/contrib/InteractionSet_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/InteractionSet_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/InteractionSet_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/InteractionSet_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R htmlDocs: vignettes/InteractionSet/inst/doc/interactions.html htmlTitles: Interacting with InteractionSet classes for genomic interaction data dependsOnMe: diffHic, GenomicInteractions Package: interactiveDisplay Version: 1.12.0 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: 7d39ed6f5fd0ac195bbbca114197c566 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/interactiveDisplay_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/interactiveDisplay_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/interactiveDisplay_1.12.0.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: 1.12.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: bb3c434214362f3c289245a49ff67801 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/interactiveDisplayBase_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/interactiveDisplayBase_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/interactiveDisplayBase_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R htmlDocs: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html htmlTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification importsMe: AnnotationHub, interactiveDisplay Package: inveRsion Version: 1.22.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 3322d8d459531250dc027150f9aedd8f 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/inveRsion_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/inveRsion_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/inveRsion_1.22.0.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: 1.4.4 Depends: R (>= 3.3) Imports: rhdf5, dplyr, magrittr, tidyr, data.table, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, stats, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 2cf4593c555d91b5448983a1ae4b7868 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_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/IONiseR_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.3/IONiseR_1.4.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IONiseR_1.4.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R htmlDocs: vignettes/IONiseR/inst/doc/IONiseR.html htmlTitles: Quality assessment tools for nanopore data Package: iontree Version: 1.20.0 Depends: methods, rJava, RSQLite, XML Suggests: iontreeData License: GPL-2 MD5sum: f2e85093d36ac7803854a75410172e16 NeedsCompilation: no Title: Data management and analysis of ion trees from ion-trap mass spectrometry Description: Ion fragmentation provides structural information for metabolite identification. This package provides utility functions to manage and analyse MS2/MS3 fragmentation data from ion trap mass spectrometry. It was designed for high throughput metabolomics data with many biological samples and a large numer of ion trees collected. Tests have been done with data from low-resolution mass spectrometry but could be readily extended to precursor ion based fragmentation data from high resoultion mass spectrometry. biocViews: Metabolomics, MassSpectrometry Author: Mingshu Cao Maintainer: Mingshu Cao source.ver: src/contrib/iontree_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iontree_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iontree_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iontree_1.20.0.tgz vignettes: vignettes/iontree/inst/doc/iontree_doc.pdf vignetteTitles: MSn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iontree/inst/doc/iontree_doc.R Package: iPAC Version: 1.18.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: ee7538c4b6a2d412c1b68e39aa96dbb8 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 source.ver: src/contrib/iPAC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iPAC_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iPAC_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iPAC_1.18.0.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: 1.0.0 Depends: xcms, rsm, CAMERA, grDevices, graphics, stats, utils Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: 4968825d7c43cd96c6ad7e8e6e775d10 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 Maintainer: Thomas Riebenbauer URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new source.ver: src/contrib/IPO_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IPO_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IPO_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IPO_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R htmlDocs: vignettes/IPO/inst/doc/IPO.html htmlTitles: XCMS Parameter Optimization with IPO Package: IPPD Version: 1.22.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 6cfc9d6eaac933cd825d46bbbf38da2b 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IPPD_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IPPD_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IPPD_1.22.0.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: 2.8.2 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.19.1), S4Vectors (>= 0.11.19) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, GenomicFeatures, GenomicAlignments, BSgenome.Celegans.UCSC.ce2, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: ae1e6b78c6b84e4df6bec44e98e2136c 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 source.ver: src/contrib/IRanges_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/IRanges_2.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/IRanges_2.8.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IRanges_2.8.2.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Introduction to IRanges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, ASpli, BaalChIP, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, ChIPpeakAnno, chipseq, CODEX, consensusSeekeR, CSAR, customProDB, deepSNV, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, EnrichedHeatmap, epigenomix, exomeCopy, fCCAC, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, groHMM, gtrellis, Guitar, Gviz, HDF5Array, HelloRanges, HilbertCurve, HiTC, HMMcopy, htSeqTools, IdeoViz, isomiRs, methyAnalysis, MotifDb, motifRG, oneChannelGUI, OTUbase, pepStat, PGA, PING, proBAMr, PSICQUIC, RefNet, rfPred, rGADEM, rGREAT, RIPSeeker, rMAT, scsR, SGSeq, SICtools, TEQC, TitanCNA, triform, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, alpine, AneuFinder, annmap, AnnotationDbi, annotatr, ArrayExpressHTS, ballgown, bamsignals, BayesPeak, BBCAnalyzer, beadarray, Biostrings, biovizBase, BiSeq, BitSeq, BPRMeth, BSgenome, bsseq, CAGEr, charm, chipenrich, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, chromstaR, CINdex, cleaver, clusterProfiler, cn.mops, CNEr, CNPBayes, CNVPanelizer, CNVrd2, cobindR, coMET, compEpiTools, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, customProDB, debrowser, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, diffloop, DMRcate, DOQTL, DRIMSeq, easyRNASeq, EDASeq, ensembldb, epivizr, epivizrData, facopy, fastseg, FindMyFriends, flipflop, flowQ, FunciSNP, genbankr, geneAttribution, GeneGeneInteR, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, GenomicTuples, genoset, genotypeeval, GenVisR, GGBase, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GOpro, GOTHiC, gQTLstats, GUIDEseq, gwascat, h5vc, HTSeqGenie, InPAS, INSPEcT, intansv, InteractionSet, IVAS, JunctionSeq, LOLA, M3D, MADSEQ, MatrixRider, MEAL, MEDIPS, metagene, methVisual, methyAnalysis, methylKit, methylPipe, MethylSeekR, methylumi, minfi, MinimumDistance, mosaics, motifbreakR, MotIV, msa, MSnbase, MultiAssayExperiment, MultiDataSet, MutationalPatterns, NarrowPeaks, normr, nucleoSim, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, pqsfinder, prebs, PureCN, Pviz, QDNAseq, qpgraph, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RareVariantVis, Rariant, Rcade, recount, REDseq, regioneR, Repitools, ReportingTools, rGADEM, RiboProfiling, riboSeqR, rMAT, rnaSeqMap, RnBeads, roar, Rqc, Rsamtools, rSFFreader, RSVSim, RTN, rtracklayer, SCAN.UPC, segmentSeq, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, simulatorZ, skewr, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, spliceR, SplicingGraphs, SPLINTER, STAN, SummarizedExperiment, SVM2CRM, TarSeqQC, TCGAbiolinks, TFBSTools, tracktables, trackViewer, transcriptR, TransView, triform, TSSi, TVTB, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, XVector, yamss suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, gQTLBase, HilbertVis, HilbertVisGUI, MiRaGE, regionReport, RTCGA, S4Vectors Package: iSeq Version: 1.26.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: d1331363ee5a8a926c6cecf6fac165bc 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iSeq_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iSeq_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iSeq_1.26.0.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: 1.20.0 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: cdaa4c3983602ce4ec8cbeecd56eed2e 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/isobar_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/isobar_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/isobar_1.20.0.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: IsoGeneGUI Version: 2.10.0 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: c9c69e1ac53eb3180d28ca4e3d04c206 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IsoGeneGUI_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IsoGeneGUI_2.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IsoGeneGUI_2.10.0.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: 1.2.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: i386, x64 MD5sum: d8ee91dd636a7bd6c4d787f3c2f7948a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ISoLDE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ISoLDE_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ISoLDE_1.2.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: isomiRs Version: 1.2.0 Depends: R (>= 3.2), DiscriMiner, IRanges, S4Vectors (>= 0.9.25), GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), DESeq2, reshape, tidyr, plyr, readr, dplyr, RColorBrewer, gplots, methods, ggplot2, gtools, gridExtra, grid, GGally Suggests: knitr, RUnit, org.Mm.eg.db, cluster, clusterProfiler, BiocStyle License: MIT + file LICENSE MD5sum: 4ac35c8e16846c2f26e88c5fa2254780 NeedsCompilation: no 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, Georgia Escaramis Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/isomiRs_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/isomiRs_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/isomiRs_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/isomiRs_1.2.0.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs-intro.pdf vignetteTitles: isomiRs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs-intro.R Package: ITALICS Version: 2.34.0 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: b7ff91941c700463d2ff5a2f30239b61 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ITALICS_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ITALICS_2.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ITALICS_2.34.0.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: 1.32.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 8782da00c5e7cd46515f741c2aaa15ce 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iterativeBMA_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iterativeBMA_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iterativeBMA_1.32.0.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: 1.32.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 37bda6802cd067eabc2eca36a3eb4400 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iterativeBMAsurv_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iterativeBMAsurv_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iterativeBMAsurv_1.32.0.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: IVAS Version: 1.6.0 Depends: R (> 3.0.0),GenomicFeatures Imports: doParallel, lme4, Matrix, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb Suggests: BiocStyle License: GPL-2 MD5sum: a923c93d3c6a3845cf92704bf8161efc 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IVAS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IVAS_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IVAS_1.6.0.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 Package: joda Version: 1.22.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: a0ff1d5f557e10c2f54aa5a1e6befd8a 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/joda_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/joda_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/joda_1.22.0.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: 1.4.0 Depends: R (>= 3.2.2), methods, SummarizedExperiment (>= 0.2.0) Imports: DESeq2 (>= 1.10.0), statmod, Hmisc, plotrix, stringr, Biobase (>= 2.30.0), locfit, BiocGenerics (>= 0.7.5), BiocParallel, genefilter, geneplotter, S4Vectors, IRanges, GenomicRanges Suggests: MASS, knitr, JctSeqData, BiocStyle Enhances: Cairo, pryr License: file LICENSE MD5sum: 4e467dc06ce264cec3aa6d02a6ad3378 NeedsCompilation: no 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/JunctionSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/JunctionSeq_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/JunctionSeq_1.4.0.tgz vignettes: vignettes/JunctionSeq/inst/doc/JunctionSeq.pdf vignetteTitles: JunctionSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: KCsmart Version: 2.32.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 6a2722f2c19b0b41d1caac1a7969318c 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KCsmart_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KCsmart_2.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KCsmart_2.32.0.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: 1.8.1 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: a6e580b74551375fbd8d975eb5904c0e 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/kebabs_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/kebabs_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/kebabs_1.8.1.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: 1.32.0 Imports: methods, XML (>= 2.3-0), graph Suggests: Rgraphviz, RBGL, RUnit, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 926673052cba27aecc7b78cc704f6cd2 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGgraph_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGgraph_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGgraph_1.32.0.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, NCIgraph, pathview, ToPASeq suggestsMe: DEGraph, GenomicRanges Package: KEGGlincs Version: 1.0.0 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 Suggests: BiocInstaller (>= 1.20.3), knitr, rmarkdown, graph License: GPL-3 MD5sum: 47aab1ef63f41876cb315a1abef4e345 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 SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr source.ver: src/contrib/KEGGlincs_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGlincs_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGlincs_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGlincs_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Cytoscape_formatting.R, vignettes/KEGGlincs/inst/doc/Example-workflow.R htmlDocs: vignettes/KEGGlincs/inst/doc/Cytoscape_formatting.html, vignettes/KEGGlincs/inst/doc/Example-workflow.html htmlTitles: KEGGlincs Workflows, KEGGlincs Workflows Package: keggorthology Version: 2.26.0 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: 8ce2401ca7a502f69851102a947a870d 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/keggorthology_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/keggorthology_2.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/keggorthology_2.26.0.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: 1.16.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: 87d72a2848f516a623dde51fb2ab8dcb 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGprofile_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGprofile_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGprofile_1.16.0.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 Package: KEGGREST Version: 1.14.1 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 430ce1c753d7414e848b5dcc4ae890b6 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 source.ver: src/contrib/KEGGREST_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGREST_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGREST_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGREST_1.14.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R htmlDocs: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html htmlTitles: Accessing the KEGG REST API dependsOnMe: PAPi, ROntoTools importsMe: attract, CNEr, EnrichmentBrowser, gage, MetaboSignal, mmnet, pathview, StarBioTrek, YAPSA Package: kimod Version: 1.2.0 Depends: R(>= 3.3),methods Imports: cluster, graphics, Biobase License: GPL (>=2) MD5sum: e4cf0f765c47f4e5630ad39f90e4cb1d 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/kimod_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/kimod_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/kimod_1.2.0.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: lapmix Version: 1.40.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: 6975f98d251be69204926e8024a5cef2 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lapmix_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lapmix_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lapmix_1.40.0.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: 1.42.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: c41651298945d31cc81e171012329ecf 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LBE_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LBE_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LBE_1.42.0.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: 1.4.0 Depends: R (>= 3.1), methods Imports: Matrix, snpStats Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 4864508e836fb879e216b4700b2edd2a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ldblock_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ldblock_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ldblock_1.4.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.pdf vignetteTitles: LD block import and manipulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R Package: LEA Version: 1.6.0 Depends: R (>= 3.0.2), methods, stats, utils License: GPL-3 Archs: i386, x64 MD5sum: c1b2d2ab3c441afe2f5684ada64974fb 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 genome scans for local adaptation. It includes statistical methods for estimating ancestry coefficients from large genotypic matrices and evaluating the number of ancestral populations (snmf, pca); and identifying genetic polymorphisms that exhibit high correlation with some environmental gradient or with the variables used as proxies for ecological pressures (lfmm), and controlling the false discovery rate. LEA is mainly based on optimized C programs that can scale with the dimension of very large data sets. biocViews: Software, StatisticalMethod, Clustering, Regression Author: Eric Frichot , Olivier Francois Maintainer: Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html source.ver: src/contrib/LEA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LEA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LEA_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LEA_1.6.0.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: 1.8.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 63d73fad92b1ecec1c6c521c26ad01a7 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 source.ver: src/contrib/LedPred_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LedPred_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LedPred_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LedPred_1.8.0.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: 1.24.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: a99cc9918824579b3921c430a1ca8f04 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/les_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/les_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/les_1.24.0.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: 1.4.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: f815be98ee16854a33ff9a332dee1869 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lfa_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lfa_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lfa_1.4.0.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: 3.30.13 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: 6505fdccd7ae146652224377cd104e6a 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, 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 source.ver: src/contrib/limma_3.30.13.tar.gz win.binary.ver: bin/windows/contrib/3.3/limma_3.30.13.zip win64.binary.ver: bin/windows64/contrib/3.3/limma_3.30.13.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/limma_3.30.13.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, bsseq, CALIB, cghMCR, codelink, convert, Cormotif, coRNAi, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, gCMAP, HTqPCR, maigesPack, marray, metagenomeSeq, metaseqR, MLSeq, MmPalateMiRNA, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, snapCGH, splineTimeR, SRGnet, SSPA, tRanslatome, TurboNorm, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, anamiR, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, attract, ballgown, BatchQC, beadarray, betr, birte, BubbleTree, bumphunter, CALIB, CancerMutationAnalysis, CancerSubtypes, casper, charm, ChIPpeakAnno, clusterExperiment, compcodeR, CountClust, crlmm, crossmeta, csaw, ctsGE, debrowser, derfinderPlot, DEsubs, DiffBind, diffHic, diffloop, DMRcate, EBSEA, eegc, EGAD, EGSEA, EnrichmentBrowser, erccdashboard, explorase, flowBin, gCrisprTools, GeneSelectMMD, GeneSelector, GGBase, GOsummaries, gQTLstats, GUIDEseq, HTqPCR, iCheck, iChip, iCOBRA, InPAS, limmaGUI, Linnorm, lmdme, LVSmiRNA, mAPKL, MEAL, methylKit, MethylMix, minfi, miRLAB, missMethyl, MmPalateMiRNA, monocle, MoonlightR, MSstats, nem, nethet, nondetects, OGSA, OLIN, PAA, PADOG, PathoStat, pbcmc, pcaExplorer, PECA, pepStat, phenoTest, polyester, qsea, regsplice, Ringo, RNAinteract, RNAither, RTN, RTopper, scater, sigaR, SimBindProfiles, snapCGH, STATegRa, SVAPLSseq, systemPipeR, TCGAbiolinks, timecourse, ToPASeq, TPP, tweeDEseq, variancePartition, vsn, yamss, yarn suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, derfinder, dyebias, ELBOW, gage, GeneSelector, GEOquery, Glimma, GSRI, GSVA, Harman, Heatplus, isobar, les, lumi, MAST, mdgsa, methylumi, MLP, npGSEA, oligo, oneChannelGUI, oppar, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, scran, subSeq, sva, tximport Package: limmaGUI Version: 1.50.0 Imports: limma, tcltk, BiocInstaller, tkrplot, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: 2adfc18428326cce04544559a8dcc7ed 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], Yifang Hu [ctb] Maintainer: Yifang Hu , Gordon Smyth , Keith Satterley URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/limmaGUI_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/limmaGUI_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/limmaGUI_1.50.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R htmlDocs: 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 htmlTitles: about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html Package: LINC Version: 1.2.0 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: 686abae2d8fcad037ec9f84da1af2e00 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LINC_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LINC_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LINC_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LINC/inst/doc/LINC.R htmlDocs: vignettes/LINC/inst/doc/LINC.html htmlTitles: "LINC - Co-Expression Analysis of lincRNAs" Package: Linnorm Version: 1.2.11 Depends: R(>= 3.3) Imports: Rcpp (>= 0.12.2), RcppArmadillo, fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, gplots, RColorBrewer License: MIT + file LICENSE Archs: i386, x64 MD5sum: 9cc3e531e7e8fa23ae0b7841b1827878 NeedsCompilation: yes Title: Linear model and normality based transformation method (Linnorm) Description: Please note that significant updates to Linnorm are available in version 1.99.x +, we strongly suggest using the newest version. 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, the following pipelines are implemented: 1. Cell subpopluation analysis and visualization using PCA clustering, 2. Differential expression analysis or differential peak detection using limma, 3. Highly variable gene discovery and visualization, 4. Gene correlation network analysis and visualization. 5. Hierarchical clustering and plotting. Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, Linnorm provides the RnaXSim function for the simulation of RNA-seq raw counts for the evaluation of differential expression analysis methods. RnaXSim can simulate RNA-seq dataset in Gamma, Log Normal, Negative Binomial or Poisson distributions. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network 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_1.2.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/Linnorm_1.2.11.zip win64.binary.ver: bin/windows64/contrib/3.3/Linnorm_1.2.11.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Linnorm_1.2.11.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: 1.28.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: bd2a60adb367c140ae5d1f5f954a38b6 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LiquidAssociation_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LiquidAssociation_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LiquidAssociation_1.28.0.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: 1.16.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: c9d7c886b275142dd8230c0298af9273 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lmdme_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lmdme_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lmdme_1.16.0.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: 2.30.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 6cf301e8162659abcc3cf0b56405e49b 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LMGene_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LMGene_2.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LMGene_2.30.0.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: 1.0.0 Depends: R (>= 3.3), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: bc0a9ca607fc5afba59f56266307ffce 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LOBSTAHS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LOBSTAHS_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LOBSTAHS_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R htmlDocs: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html htmlTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS Package: logicFS Version: 1.44.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 74ac874eff6653311565aa0e754ef999 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/logicFS_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/logicFS_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/logicFS_1.44.0.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: 1.32.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 859580e68700c2c60c9c52d353f855b1 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/logitT_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/logitT_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/logitT_1.32.0.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: lol Version: 1.22.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: a57875361554bbba7b392efe98ef69b8 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lol_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lol_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lol_1.22.0.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: 1.4.0 Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table Suggests: knitr, parallel, testthat Enhances: simpleCache, qvalue License: GPL-3 MD5sum: 98bf205426d236511f7f28b7813cbe55 NeedsCompilation: no Title: Location 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 make is 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 [cre] Maintainer: Nathan Sheffield URL: http://databio.org/lola VignetteBuilder: knitr BugReports: http://github.com/sheffien/LOLA source.ver: src/contrib/LOLA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LOLA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LOLA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LOLA_1.4.0.tgz 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 htmlDocs: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html htmlTitles: Choosing a LOLA Universe, Getting Started with LOLA, Using LOLA Core suggestsMe: DeepBlueR Package: LowMACA Version: 1.6.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: ebcaa667175a02113c1f084af2ba0a1a 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LowMACA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LowMACA_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LowMACA_1.6.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.pdf vignetteTitles: LowMACA hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R Package: LPE Version: 1.48.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 665e951c364ac57d3e8a51c94da4ed1b 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LPE_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LPE_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LPE_1.48.0.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: 1.34.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 5db73ba9ad9b9bfe6ffe2f65396d060b 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LPEadj_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LPEadj_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LPEadj_1.34.0.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: 2.6.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: 3ca5539e4e47efc1d30e09c54840f735 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lpNet_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lpNet_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lpNet_2.6.0.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: 1.2.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr Enhances: slam License: EPL Archs: i386, x64 MD5sum: 4e6cd258c1f33d1dc6e6bab97199a60c 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lpsymphony_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lpsymphony_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lpsymphony_1.2.0.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: 2.26.4 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: 1391b28459029139f5f45de6f6d1f419 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_2.26.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/lumi_2.26.4.zip win64.binary.ver: bin/windows64/contrib/3.3/lumi_2.26.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lumi_2.26.4.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: 1.24.0 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: cf6e7984e727838608ab3dbfa0672466 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LVSmiRNA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LVSmiRNA_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LVSmiRNA_1.24.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf vignetteTitles: LVSmiRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R Package: LymphoSeq Version: 1.2.0 Depends: R (>= 3.2), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats Suggests: knitr, pheatmap, wordcloud License: Artistic-2.0 MD5sum: 64209ccd5bb6e22432fef0b89dc6dd1b 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 Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr source.ver: src/contrib/LymphoSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LymphoSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LymphoSeq_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LymphoSeq_1.2.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.pdf vignetteTitles: Analyze 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: M3D Version: 1.8.2 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: db50962edfce0906522cb98f0a7532b5 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_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/M3D_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/M3D_1.8.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/M3D_1.8.2.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: 1.0.0 Depends: R (>= 3.3), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats Suggests: ROCR, knitr, M3DExampleData License: GPL (>=2) MD5sum: 1ed30b4e672c2f4136980569c3d45e9a 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/M3Drop_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/M3Drop_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/M3Drop_1.0.0.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: 1.44.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 58d4ead9cf7d0a5901e84a20334f6af1 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maanova_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maanova_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maanova_1.44.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: macat Version: 1.48.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: e1d4622fdd7d83cfd57e70e89c7a0710 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/macat_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/macat_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/macat_1.48.0.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: 1.44.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: ce53804ebbda2c11525f09f9227f79da 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maCorrPlot_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maCorrPlot_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maCorrPlot_1.44.0.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: made4 Version: 1.48.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: dacb004d45112dfc8e00843cfe3a9aef 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/made4_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/made4_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/made4_1.48.0.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: 1.0.0 Depends: R(>= 3.3), 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 Suggests: knitr License: GPL(>=2) MD5sum: 0cea24e6075190f31f8dffa411f34d92 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MADSEQ_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MADSEQ_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MADSEQ_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R htmlDocs: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html htmlTitles: R Package MADSEQ Package: maftools Version: 1.0.55 Depends: R (>= 3.3) Imports: data.table, ggplot2(>= 2.0), cowplot, cometExactTest, RColorBrewer, NMF, ggrepel, methods, ComplexHeatmap, mclust, VariantAnnotation, Biostrings, Rsamtools, rjson, grid, DPpackage, wordcloud, grDevices, changepoint Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 5043b6ba1c1b5aa16048b29c89f8226c 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 Author: Anand Mayakonda Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues source.ver: src/contrib/maftools_1.0.55.tar.gz win.binary.ver: bin/windows/contrib/3.3/maftools_1.0.55.zip win64.binary.ver: bin/windows64/contrib/3.3/maftools_1.0.55.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maftools_1.0.55.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/maftools.R htmlDocs: vignettes/maftools/inst/doc/maftools.html htmlTitles: Summarize,, Analyze and Visualize MAF files Package: maigesPack Version: 1.38.0 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: fdbd7734d3893aa5e8e2279e8b790b7e 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maigesPack_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maigesPack_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maigesPack_1.38.0.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: 1.8.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Enhances: rgl License: GPL-2 MD5sum: 4f72328059315d70b85da4faeb34e0ad 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: Francesc Fernandez-Albert source.ver: src/contrib/MAIT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MAIT_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MAIT_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MAIT_1.8.0.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: 1.50.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 6d7e641274e9339c7ab7c76e01cd8e66 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/makecdfenv_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/makecdfenv_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/makecdfenv_1.50.0.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: 1.46.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 9dacd59ace2ce31acba4c994403dbcea 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MANOR_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MANOR_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MANOR_1.46.0.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: 1.20.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 46ede51b1ba70d9f5bbcb276cc9cd060 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/manta_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/manta_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/manta_1.20.0.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: 1.44.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 4ae18141c59c213a3bd454f71b37e1c6 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MantelCorr_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MantelCorr_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MantelCorr_1.44.0.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: 1.6.0 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: 3d32c96346887c1a5e205ca676952f92 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mAPKL_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mAPKL_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mAPKL_1.6.0.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: 1.12.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: c04d6c0e7a622aeeb84a86e209af0a37 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maPredictDSC_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maPredictDSC_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maPredictDSC_1.12.0.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: marray Version: 1.52.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: a95fc584433fff8804ca37aea6c750a1 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/marray_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/marray_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/marray_1.52.0.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: maSigPro Version: 1.46.0 Depends: R (>= 2.3.1), stats, Biobase, MASS Imports: Biobase, graphics, grDevices, venn, mclust, stats, utils, MASS License: GPL (>= 2) MD5sum: 4d71b5a900624cd2425471920c472fd9 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maSigPro_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maSigPro_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maSigPro_1.46.0.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 suggestsMe: oneChannelGUI Package: maskBAD Version: 1.18.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe License: GPL version 2 or newer MD5sum: 37aa44aa577f7eb19226f0896713faa2 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maskBAD_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maskBAD_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maskBAD_1.18.0.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: 1.26.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: 7832c4eb835cf0118d0a3015b1dc5cc9 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MassArray_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MassArray_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MassArray_1.26.0.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: 1.10.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: caafc1bef52666a5753666f2d5b1badc 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/massiR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/massiR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/massiR_1.10.0.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: 1.40.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: a3577977a3072ba9d366987e0c34925d 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MassSpecWavelet_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MassSpecWavelet_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MassSpecWavelet_1.40.0.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: 1.0.5 Depends: SummarizedExperiment, R(>= 3.3) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils 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 License: GPL(>= 2) MD5sum: 9577efb1bcc66a386297a6322bfda9c6 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 , Greg Finak , Masanao Yajima Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues source.ver: src/contrib/MAST_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/MAST_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.3/MAST_1.0.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MAST_1.0.5.tgz vignettes: vignettes/MAST/inst/doc/MAST-intro.pdf vignetteTitles: MAST-intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-intro.R htmlDocs: vignettes/MAST/inst/doc/MAITAnalysis.html htmlTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells Package: matchBox Version: 1.16.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 5ac1ed2f8031c380e1915f41520273ef 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/matchBox_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/matchBox_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/matchBox_1.16.0.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: 1.6.0 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: e8b692442ebefb1be71cc9fb7bbb1e9d 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MatrixRider_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MatrixRider_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MatrixRider_1.6.0.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: 1.0.1 Depends: methods, stats, biglm Imports: BiocGenerics, irlba, S4Vectors, utils Suggests: BiocStyle, Cardinal, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 82e303d9c664192310dac06b5f96e3a4 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, and arrays. This package is designed to be used as a back-end for Cardinal for working with high-resolution mass spectrometry imaging data. biocViews: Software, Infrastructure Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org source.ver: src/contrib/matter_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/matter_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/matter_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/matter_1.0.1.tgz vignettes: vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter.R Package: MBAmethyl Version: 1.8.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 256ff6e0ec6bc03f761fffd49df1c168 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBAmethyl_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBAmethyl_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBAmethyl_1.8.0.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: 1.8.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: ec1845b980f5be3cbda918e302eec371 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBASED_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBASED_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBASED_1.8.0.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: 1.28.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: c30fa3c12e3b17a480c46f3d1352a21b 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBCB_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBCB_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBCB_1.28.0.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: 1.28.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: bbadd79cc987fc2a66e7af7b9641c746 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mBPCR_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mBPCR_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mBPCR_1.28.0.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: 1.2.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: fe322dddfb74f2bf991f9d0971c5077a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBttest_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBttest_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBttest_1.2.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: 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: 1.22.0 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: 28060ae4faf7c2635b46553a387b0a1a 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 source.ver: src/contrib/mcaGUI_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mcaGUI_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mcaGUI_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mcaGUI_1.22.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCRestimate Version: 2.30.0 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: e8589b02fa040b5efe11f07de0cf1575 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MCRestimate_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MCRestimate_2.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MCRestimate_2.30.0.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: mdgsa Version: 1.6.0 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: bd342bda7b062210ce1c6c47f1795716 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mdgsa_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mdgsa_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mdgsa_1.6.0.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: mdqc Version: 1.36.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 79700baf6d2a0a2ed138571b4b66d0d8 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mdqc_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mdqc_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mdqc_1.36.0.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: MEAL Version: 1.4.2 Depends: R (>= 3.2.0), Biobase, MultiDataSet Imports: GenomicRanges, SNPassoc, limma, DMRcate, snpStats, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, doParallel, parallel, ggplot2 (>= 2.0.0), sva, permute Suggests: testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, MEALData, BiocStyle License: Artistic-2.0 MD5sum: 9d59fc1b70271822dfd344d0baa7c996 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 [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzlez [aut] Maintainer: Carlos Ruiz VignetteBuilder: knitr source.ver: src/contrib/MEAL_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEAL_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MEAL_1.4.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEAL_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R htmlDocs: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html htmlTitles: MEAL case example, Introduction to MEAL Package: MeasurementError.cor Version: 1.46.0 License: LGPL MD5sum: a100d2075ad6b0058decc06ccd0b5087 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeasurementError.cor_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeasurementError.cor_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeasurementError.cor_1.46.0.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: 1.24.0 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: ca9c2c2e0e55e3b6287348640bf1625b 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEDIPS_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEDIPS_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEDIPS_1.24.0.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: 1.34.0 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: f8721d11eab04b4ccd4b51615e7df925 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEDME_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEDME_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEDME_1.34.0.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: 1.8.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: 2c5759e18dc5df03c406897604648f9a 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEIGOR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEIGOR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEIGOR_1.8.0.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: 2.46.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: 18b5f319a8040247d9d1720163e0ee19 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_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MergeMaid_2.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MergeMaid_2.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MergeMaid_2.46.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: metaArray, XDE suggestsMe: oneChannelGUI Package: Mergeomics Version: 1.2.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: a1758eb0dca181503798175abfa63eaf 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mergeomics_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mergeomics_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mergeomics_1.2.0.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: 1.10.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: c2134a253b11cce3a9c6c53a999d400d 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeSHDbi_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeSHDbi_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeSHDbi_1.10.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: meshr Package: meshes Version: 1.0.0 Depends: R (>= 3.3.1), DOSE (>= 2.11.7) Imports: AnnotationDbi, GOSemSim (>= 1.99.3), MeSH.db, methods Suggests: BiocStyle, knitr, MeSH.Cel.eg.db, MeSH.Hsa.eg.db License: Artistic-2.0 MD5sum: 7130cf77aa64ac358021d15c8283588f 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/meshes VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues source.ver: src/contrib/meshes_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/meshes_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/meshes_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/meshes_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R htmlDocs: vignettes/meshes/inst/doc/meshes.html htmlTitles: An introduction to meshes Package: meshr Version: 1.10.0 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: 6267f13eb86451afde270ce285ab41e8 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/meshr_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/meshr_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/meshr_1.10.0.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: MeSHSim Version: 1.6.0 Depends: R(>= 3.0.0) Imports: XML, RCurl License: GPL-2 MD5sum: ce2f8fec17d022815b35237223aa6d3f NeedsCompilation: no Title: MeSH(Medical Subject Headings) Semantic Similarity Measures Description: Provide for measuring semantic similarity over MeSH headings and MEDLINE documents biocViews: Clustering, Software Author: Jing Zhou, Yuxuan Shui Maintainer: Jing ZHou <12210240050@fudan.edu.cn> source.ver: src/contrib/MeSHSim_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeSHSim_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeSHSim_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeSHSim_1.6.0.tgz vignettes: vignettes/MeSHSim/inst/doc/MeSHSim.pdf vignetteTitles: MeSHSim Quick Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeSHSim/inst/doc/MeSHSim.R Package: messina Version: 1.10.0 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: e28d1d371758884c705464f05be7695a 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/messina_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/messina_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/messina_1.10.0.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: 1.52.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: 0f48270c0707213f8f069fd74595704f 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaArray_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaArray_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaArray_1.52.0.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 suggestsMe: oneChannelGUI Package: Metab Version: 1.8.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 2050e8937931656f3984b4f35565c35b 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Metab_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Metab_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Metab_1.8.0.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: 1.8.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest Suggests: xtable, ggplot2, reshape2 License: GPL-2 MD5sum: 5ee408a32409c948adc46a3b74730a9c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metabomxtr_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metabomxtr_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metabomxtr_1.8.0.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: 1.4.0 Depends: R(>= 3.3) Imports: KEGGgraph, mygene, hpar, org.Hs.eg.db, igraph, RCurl, biomaRt, KEGGREST, AnnotationDbi, stats, graphics, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 66b06b7b1fd50aa1a2c7f5b0678ec10c 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 , Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr source.ver: src/contrib/MetaboSignal_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MetaboSignal_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MetaboSignal_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MetaboSignal_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R htmlDocs: vignettes/MetaboSignal/inst/doc/MetaboSignal.html htmlTitles: MetaboSignal Package: metaCCA Version: 1.2.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 58022616c4ba146770e151a129d25368 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaCCA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaCCA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaCCA_1.2.0.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: metagene Version: 2.6.1 Depends: R (>= 3.2.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP, matrixStats Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: 550ff65e1a476ae5e8cb11ecd6a6b5ce 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 , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues source.ver: src/contrib/metagene_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagene_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/metagene_2.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagene_2.6.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene.R htmlDocs: vignettes/metagene/inst/doc/metagene.html htmlTitles: Introduction to metagene dependsOnMe: Imetagene Package: metagenomeFeatures Version: 1.4.0 Depends: R (>= 3.3), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), ShortRead (>= 1.26.0), dplyr (>= 0.4.3), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>= 3.3.1), lattice (>= 0.20.33), metagenomeSeq (>= 1.14.2), ape (>= 3.5), purrr (>= 0.2.2) Suggests: knitr (>= 1.11), msd16s (>= 0.102.0), testthat (>= 0.10.0), rmarkdown License: Artistic-2.0 MD5sum: 43066a04b263b0a481dbd02adca73a4d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagenomeFeatures_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metagenomeFeatures_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagenomeFeatures_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeFeatures/inst/doc/Example_16S_Annotation_Workflow.R, vignettes/metagenomeFeatures/inst/doc/Exploring_a_MgDb.R htmlDocs: vignettes/metagenomeFeatures/inst/doc/Example_16S_Annotation_Workflow.html, vignettes/metagenomeFeatures/inst/doc/Exploring_a_MgDb.html htmlTitles: Example 16S Annotation Workflow, Vignette Title Package: metagenomeSeq Version: 1.16.0 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: 72b34587afb800c1a50edd047389d565 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, 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagenomeSeq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metagenomeSeq_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagenomeSeq_1.16.0.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 importsMe: metagenomeFeatures suggestsMe: interactiveDisplay, phyloseq Package: metahdep Version: 1.32.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: e5bb065bdf00e6864fc4a5cc489e86d6 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metahdep_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metahdep_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metahdep_1.32.0.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: 1.10.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: e17fd878c86fafb0269e9f1ade4a6ff8 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 source.ver: src/contrib/metaMS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaMS_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaMS_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaMS_1.10.0.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: metaSeq Version: 1.14.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 6b3ce46dd5d64e8a22126d8bc4f526e7 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaSeq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaSeq_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaSeq_1.14.0.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: 1.14.0 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: 81bb4a2a37ccd83e44d7a5882ca2f28a 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaseqR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaseqR_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaseqR_1.14.0.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: MetCirc Version: 1.0.1 Depends: R (>= 3.3), amap (>= 0.8), circlize (>= 0.3.5), graphics (>= 3.3), grDevices (>= 3.3), methods (>= 3.3), scales (>= 0.3.0), shiny (>= 0.13.1), stats (>= 3.3) Suggests: BiocGenerics, knitr (>= 1.11) License: GPL-2 MD5sum: 08536f8d1ab597c1976c37e556b4fc78 NeedsCompilation: no Title: A workflow to analyse and visualise metabolomics data Description: MetCirc comprises a workflow to interactively explore metabolomics data: create MSP, bin m/z values, calculate similarity between precursors and visualise similarities. biocViews: Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr source.ver: src/contrib/MetCirc_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/MetCirc_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/MetCirc_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MetCirc_1.0.1.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: MethPed Version: 1.2.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: 7538f0853a474304acd0e81f75039c18 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethPed_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethPed_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethPed_1.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R htmlDocs: vignettes/MethPed/inst/doc/MethPed-vignette.html htmlTitles: MethPed User Guide Package: MethTargetedNGS Version: 1.6.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 7057679944ea4f7c8c8e72c8cc2359bd 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethTargetedNGS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethTargetedNGS_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethTargetedNGS_1.6.0.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: 1.26.0 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: 001e1d1602567803b59894ee90b286ff 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methVisual_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methVisual_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methVisual_1.26.0.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: 1.16.1 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: aae7a006f6ae32169f62e72375a0468f 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_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/methyAnalysis_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.3/methyAnalysis_1.16.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methyAnalysis_1.16.1.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: 1.8.0 Depends: R (>= 3.0) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.17.9), methods, RColorBrewer, shiny, stats, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, RUnit License: GPL (>= 2) MD5sum: e9a512dc0bc856a09bb1b9524c4e81e7 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 URL: https://github.com/mvaniterson/methylaid VignetteBuilder: knitr BugReports: https://github.com/mvaniterson/methylaid/issues source.ver: src/contrib/MethylAid_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylAid_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylAid_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylAid_1.8.0.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: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R Package: methylKit Version: 1.0.0 Depends: R (>= 3.3.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors, GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: testthat,knitr, rmarkdown, genomation License: Artistic-2.0 Archs: i386, x64 MD5sum: 167ee8c3e92bf61827bcc749da042cb9 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 URL: http://code.google.com/p/methylkit/ VignetteBuilder: knitr source.ver: src/contrib/methylKit_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylKit_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methylKit_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylKit_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R htmlDocs: vignettes/methylKit/inst/doc/methylKit.html htmlTitles: Vignette Title Package: MethylMix Version: 2.0.0 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: 79f91783cfb0d95a4d2198e80afc475f 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 0. 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. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr source.ver: src/contrib/MethylMix_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylMix_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylMix_2.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylMix_2.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R htmlDocs: vignettes/MethylMix/inst/doc/vignettes.html htmlTitles: MethylMix Package: methylMnM Version: 1.12.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: 786c164acc776d35a6c5df5483e767a6 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylMnM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methylMnM_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylMnM_1.12.0.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: 1.8.0 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: 20d19f8aeb09cd4e2c369cc58cb180f0 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylPipe_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methylPipe_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylPipe_1.8.0.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: 1.14.0 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: fb7b4d34bcc4a2983f2c3e8c0e84d559 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylSeekR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylSeekR_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylSeekR_1.14.0.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: 2.20.0 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: 90dc0b456677fd01b5f599931519c508 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylumi_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methylumi_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylumi_2.20.0.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 suggestsMe: MultiDataSet Package: Mfuzz Version: 2.34.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 5248ce12576ecb219c469f4e20c03815 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mfuzz_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mfuzz_2.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mfuzz_2.34.0.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: 1.8.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: eecb581e053c5d93cf12fd871f6f24fe 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MGFM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MGFM_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MGFM_1.8.0.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 Package: MGFR Version: 1.0.0 Depends: R (>= 3.3) Imports: biomaRt, annotate License: GPL-3 MD5sum: f625289c0f88941f31691fcc8002c39e 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MGFR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MGFR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MGFR_1.0.0.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 Package: mgsa Version: 1.22.0 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: 6ad4614087c4c1e8538c1907f38da120 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mgsa_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mgsa_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mgsa_1.22.0.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: 1.28.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: e9709ff4169ccbc396d062f2b369ca35 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiChip_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiChip_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiChip_1.28.0.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: microRNA Version: 1.32.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) Suggests: Biostrings (>= 2.11.32) Enhances: Rlibstree License: Artistic-2.0 MD5sum: 00f1d98d0c33b4312b1e095d9299c496 NeedsCompilation: no 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/microRNA_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/microRNA_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/microRNA_1.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIMOSA Version: 1.12.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, Kmisc LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: cf1cc1b0fba67e10696ae457ce99f747 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MIMOSA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MIMOSA_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MIMOSA_1.12.0.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: 1.14.0 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: f214c4fcf50ed39e8733e4d589dd5474 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MineICA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MineICA_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MineICA_1.14.0.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: 3.32.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 461b66fe520ca3ece85ec86c339c443e 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_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/minet_3.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/minet_3.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/minet_3.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: netbenchmark, RTN, TCGAbiolinks suggestsMe: CNORfeeder, predictionet Package: minfi Version: 1.20.2 Depends: methods, BiocGenerics (>= 0.15.3), Biobase (>= 2.17.8), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio, matrixStats (>= 0.50.0), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils 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 License: Artistic-2.0 MD5sum: ec6fa2a876104c771314e2cf6310b003 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] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/minfi VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/minfi/issues source.ver: src/contrib/minfi_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/minfi_1.20.2.zip win64.binary.ver: bin/windows64/contrib/3.3/minfi_1.20.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/minfi_1.20.2.tgz vignettes: vignettes/minfi/inst/doc/minfi.pdf 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, shinyMethyl importsMe: MEAL, MethylAid, methylumi, missMethyl, MultiDataSet, quantro, skewr suggestsMe: Harman, RnBeads Package: MinimumDistance Version: 1.18.0 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: 88cbccae04baa8c08c19264100b21068 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MinimumDistance_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MinimumDistance_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MinimumDistance_1.18.0.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: 1.46.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: d7e6c1362d9d4723d3cb9136e4d17710 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiPP_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiPP_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiPP_1.46.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MiRaGE Version: 1.16.0 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: a63d73ca3d01270e829d4ee288c9afe1 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiRaGE_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiRaGE_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiRaGE_1.16.0.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: miRcomp Version: 1.4.0 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: 68cd4dfdd9b90a0086b3ed979c4e8556 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRcomp_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRcomp_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRcomp_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R htmlDocs: vignettes/miRcomp/inst/doc/miRcomp.html htmlTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) Package: mirIntegrator Version: 1.4.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: d1936acbdbc31a183516bdb2f61e7a72 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mirIntegrator_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mirIntegrator_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mirIntegrator_1.4.0.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: 1.4.0 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: c21dee35648355aae97a84540cbc4326 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRLAB_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRLAB_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRLAB_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R htmlDocs: vignettes/miRLAB/inst/doc/miRLAB-vignette.html htmlTitles: miRLAB Package: miRNAmeConverter Version: 1.2.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5755c243694e958e82a6beacdbefd38f NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Package containing an S4 class for 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNAmeConverter_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNAmeConverter_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNAmeConverter_1.2.0.tgz vignettes: vignettes/miRNAmeConverter/inst/doc/miRNAmeConverter-vignette.pdf vignetteTitles: miRNAmeConverter-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAmeConverter/inst/doc/miRNAmeConverter-vignette.R Package: miRNApath Version: 1.34.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 0f7f9b21088520d78fda7cc79d3c06d1 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNApath_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNApath_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNApath_1.34.0.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 suggestsMe: oneChannelGUI Package: miRNAtap Version: 1.8.0 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: cdd3a56fcd98e3c95b18a77a488cc8ce 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNAtap_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNAtap_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNAtap_1.8.0.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 suggestsMe: oneChannelGUI Package: Mirsynergy Version: 1.10.0 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: 560b02cf2e88b03aec216e20800006fb 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mirsynergy_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mirsynergy_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mirsynergy_1.10.0.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: 1.8.0 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: d5a9e1e516df9f0367f159c17b5723e8 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 VignetteBuilder: knitr source.ver: src/contrib/missMethyl_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/missMethyl_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/missMethyl_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/missMethyl_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R htmlDocs: vignettes/missMethyl/inst/doc/missMethyl.html htmlTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data importsMe: DMRcate Package: mitoODE Version: 1.12.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: 814bef9e1f0c8fd1ca049305d0fdb691 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mitoODE_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mitoODE_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mitoODE_1.12.0.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: 1.54.0 Depends: R (>= 2.9), 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 Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, rgl, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party Enhances: parallel License: LGPL MD5sum: e15331452d85bde5cfb0bcbe70cf671b 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 source.ver: src/contrib/MLInterfaces_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLInterfaces_1.54.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MLInterfaces_1.54.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLInterfaces_1.54.0.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: 1.22.0 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: c96771e0e2523b8713a23a22eb60d0bf 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLP_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MLP_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLP_1.22.0.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: 1.14.1 Depends: R (>= 3.0.0), caret, DESeq2, Biobase, limma, randomForest, edgeR Imports: methods Suggests: knitr, e1071, kernlab, earth, ellipse, fastICA, gam, ipred, klaR, MASS, mda, mgcv, mlbench, nnet, party, pls, pROC, proxy, RANN, spls, affy License: GPL(>=2) MD5sum: 6a0b30b91ffc2dec1509e80172405319 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, Turgay Unver, Ahmet Ozturk Maintainer: Gokmen Zararsiz VignetteBuilder: knitr source.ver: src/contrib/MLSeq_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLSeq_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/MLSeq_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLSeq_1.14.1.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: MLSeq hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R Package: MMDiff2 Version: 1.2.0 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: d756c6accc009072a0707b264667f82b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MMDiff2_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MMDiff2_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MMDiff2_1.2.0.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: mmnet Version: 1.12.0 Depends: R (>= 2.14), igraph, biom Imports: Biobase, RJSONIO, stringr, reshape2, ggplot2, KEGGREST, plyr, XML, RCurl, flexmix, Matrix, methods, tools Suggests: RCytoscape, graph, knitr License: GPL (>= 2) MD5sum: 3f5999b26286a9214d74e2114f16a578 NeedsCompilation: no Title: A metagenomic pipeline for systems biology Description: This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome. biocViews: GraphsAndNetwork, Sequencing, Pathways, Microbiome, SystemsBiology Author: Yang Cao, Fei Li Maintainer: Yang Cao , Fei Li VignetteBuilder: knitr source.ver: src/contrib/mmnet_1.12.0.tar.gz vignettes: vignettes/mmnet/inst/doc/mmnet.pdf vignetteTitles: mmnet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mmnet/inst/doc/mmnet.R Package: MmPalateMiRNA Version: 1.24.0 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: 97fda516753bcb8a99e8e0a6500a3db5 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MmPalateMiRNA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MmPalateMiRNA_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MmPalateMiRNA_1.24.0.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: 1.0.0 Depends: R (>= 3.1.0) Imports: WGCNA,dynamicTreeCut,igraph Suggests: BiocStyle, knitr License: GPL (>= 2) MD5sum: 46b58bb6676829b2f262a355f185d664 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 VignetteBuilder: knitr source.ver: src/contrib/MODA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MODA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MODA_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MODA_1.0.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.pdf vignetteTitles: Usage of MODA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MODA/inst/doc/MODA.R Package: mogsa Version: 1.8.0 Depends: R (>= 3.2.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster Suggests: BiocStyle, knitr License: GPL-2 MD5sum: f625facfd92912ef8f8a6cf79c77670c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mogsa_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mogsa_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mogsa_1.8.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster.pdf, vignettes/mogsa/inst/doc/mogsa.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.R, vignettes/mogsa/inst/doc/mogsa.R Package: monocle Version: 2.2.0 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, MASS, reshape2, limma, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, stats Suggests: knitr, Hmisc, testthat License: Artistic-2.0 MD5sum: d255175648c8414f2a4d9a68ebbb2225 NeedsCompilation: no 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/monocle_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/monocle_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/monocle_2.2.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Differential expression and time-series analysis for single-cell RNA-Seq and qPCR 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: 1.0.0 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: 0996852bbf0e3a40ea2a3f26a3febb43 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MoonlightR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MoonlightR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MoonlightR_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R htmlDocs: vignettes/MoonlightR/inst/doc/Moonlight.html htmlTitles: Vignette Title Package: MoPS Version: 1.8.0 Imports: Biobase License: GPL-3 MD5sum: 97bf812dbcfa0fcc93406b63673178f9 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MoPS_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MoPS_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MoPS_1.8.0.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: 2.12.0 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: d106f71ca1592632e2e33af7df76e0bf 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mosaics_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mosaics_2.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mosaics_2.12.0.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: 1.4.0 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: 16954bdc8c0b19ab0ad699a0c8188093 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifbreakR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/motifbreakR_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifbreakR_1.4.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R htmlDocs: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html htmlTitles: motifbreakR: an Introduction Package: MotifDb Version: 1.16.1 Depends: R (>= 2.15.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer Suggests: RUnit, seqLogo, MotIV License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 96376396f929770a263a868d772ca417 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 2000 annotated position frequency matrices from nine public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/MotifDb_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/MotifDb_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.3/MotifDb_1.16.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MotifDb_1.16.1.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 importsMe: rTRMui suggestsMe: DiffLogo, MMDiff2, motifStack, profileScoreDist, PWMEnrich, rTRM, vtpnet Package: motifRG Version: 1.18.0 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: 2f1dab8afccdc3077109dcb514945f3b 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifRG_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/motifRG_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifRG_1.18.0.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 Package: motifStack Version: 1.18.0 Depends: R (>= 2.15.1), methods, grImport, grid, MotIV, ade4, Biostrings Imports: XML, scales Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr License: GPL (>= 2) MD5sum: fb52e98375eb1312dde0addcc160f9cd 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 source.ver: src/contrib/motifStack_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifStack_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/motifStack_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifStack_1.18.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack.pdf vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R, vignettes/motifStack/inst/doc/motifStack.R htmlDocs: vignettes/motifStack/inst/doc/motifStack_HTML.html htmlTitles: motifStack Vignette dependsOnMe: dagLogo importsMe: LowMACA, motifbreakR suggestsMe: ChIPpeakAnno Package: MotIV Version: 1.30.0 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: 19da23b2900e7e9b334084a7bd78c17c 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MotIV_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MotIV_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MotIV_1.30.0.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: 1.10.0 License: GPL (>= 3) MD5sum: 1b795cc05ca344cb18d8ac2df0f760be 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MPFE_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MPFE_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MPFE_1.10.0.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: mQTL.NMR Version: 1.8.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: 589c3e1693656fab52c360dc18f9986c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mQTL.NMR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mQTL.NMR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mQTL.NMR_1.8.0.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: 1.6.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.30.0) 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: 204b75a17af6d47f00949ebee7e59bac 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msa_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msa_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msa_1.6.0.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: odseq Package: MSGFgui Version: 1.8.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 18c27296ba0ccfe557683439db749292 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSGFgui_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MSGFgui_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSGFgui_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R htmlDocs: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html htmlTitles: Using MSGFgui Package: MSGFplus Version: 1.8.0 Depends: methods Imports: mzID Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: a83a3a6788149aee6c95da78921c87c2 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using MS-GF+ biocViews: MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr source.ver: src/contrib/MSGFplus_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSGFplus_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MSGFplus_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSGFplus_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R htmlDocs: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html htmlTitles: Using MSGFgui importsMe: MSGFgui Package: msmsEDA Version: 1.12.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: ee9d44031a61a44e2ceb5bc9c8cbb0a1 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msmsEDA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msmsEDA_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msmsEDA_1.12.0.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: 1.12.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 0a347937dcb34a8d0186ccd49bab31eb 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msmsTests_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msmsTests_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msmsTests_1.12.0.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: 2.0.2 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.7.6), BiocParallel, ProtGenerics (>= 1.5.1) Imports: plyr, IRanges, preprocessCore, vsn, grid, reshape2, stats4, affy, impute, pcaMethods, mzID (>= 1.5.2), MALDIquant (>= 1.12), digest, lattice, ggplot2, S4Vectors, XML, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.12.2), roxygen2, rgl, rpx, AnnotationHub, BiocStyle, rmarkdown, imputeLCMD, norm, gplots, shiny License: Artistic-2.0 Archs: i386, x64 MD5sum: 48077adae90435849f2965a460edde35 NeedsCompilation: yes Title: Base Functions and Classes for MS-based Proteomics Description: Basic plotting, data manipulation and processing of MS-based Proteomics data. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Martina Fisher, Johannes Rainer and Sebastian Gibb. Maintainer: Laurent Gatto URL: https://github.com/lgatto/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues source.ver: src/contrib/MSnbase_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSnbase_2.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MSnbase_2.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSnbase_2.0.2.tgz vignettes: vignettes/MSnbase/inst/doc/MSnbase-demo.pdf, vignettes/MSnbase/inst/doc/MSnbase-development.pdf, vignettes/MSnbase/inst/doc/MSnbase-io.pdf vignetteTitles: Base Functions and Classes for MS-based Proteomics, 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-demo.R, vignettes/MSnbase/inst/doc/MSnbase-development.R, vignettes/MSnbase/inst/doc/MSnbase-io.R htmlDocs: vignettes/MSnbase/inst/doc/benchmarking.html htmlTitles: MSnbase benchmarking dependsOnMe: msmsEDA, msmsTests, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter importsMe: DAPAR, MSnID, MSstats, Pbase, ProteomicsAnnotationHubData suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, qcmetrics, readat, rpx Package: MSnID Version: 1.8.0 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: ee98cf361322eefba6e20d462ca419ea 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSnID_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MSnID_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSnID_1.8.0.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: 1.0.0 Depends: Rcpp Imports: plyr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, sapa Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData License: GPL (>= 2) MD5sum: e954cb89bbc62f65500cfe6cdf483193 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. 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msPurity_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msPurity_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msPurity_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msPurity/inst/doc/msPurity-vignette.R htmlDocs: vignettes/msPurity/inst/doc/msPurity-vignette.html htmlTitles: msPurity Package: MSstats Version: 3.6.0 Depends: R (>= 3.2) Imports: Rcpp, lme4, marray, limma, gplots, ggplot2, methods, grid, ggrepel, preprocessCore, data.table, MSnbase, reshape, reshape2, survival, minpack.lm, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 41e260d885ec79a2df74b6268a23cc19 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. Author: Meena Choi [aut, cre], Cyril Galitzine [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 source.ver: src/contrib/MSstats_3.6.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSstats_3.6.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats-manual.pdf vignetteTitles: MSstats-manual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R htmlDocs: vignettes/MSstats/inst/doc/MSstats.html htmlTitles: Vignette Title Package: Mulcom Version: 1.24.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: 091b500f721508faf1687b9e58ae91f7 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mulcom_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mulcom_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mulcom_1.24.0.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: 1.0.1 Depends: R (>= 3.3.0) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, SummarizedExperiment (>= 1.3.81), S4Vectors, IRanges, Biobase, shinydashboard, shiny, utils Suggests: BiocStyle, testthat, knitr, GenomicFiles, HDF5Array, rmarkdown License: Artistic-2.0 MD5sum: f60f0cf5f277ba895554b515a52e089a NeedsCompilation: no Title: Create Classes and Functions for Managing Multiple Assays on Sets of Samples Description: Develop an integrative environment where multiple assays are managed and preprocessed for genomic data analysis. biocViews: Infrastructure, DataRepresentation Author: MultiAssay SIG Maintainer: Marcel Ramos VignetteBuilder: knitr source.ver: src/contrib/MultiAssayExperiment_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/MultiAssayExperiment_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/MultiAssayExperiment_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MultiAssayExperiment_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/pradMAEO-RTCGAToolbox.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R htmlDocs: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/pradMAEO-RTCGAToolbox.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html htmlTitles: Coordinating Analysis of Multi-Assay Experiments, MultiAssayExperiment: Prostate Cancer Data, HDF5Array and MultiAssayExperiment importsMe: GOpro Package: multiClust Version: 1.4.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: ce5492526ef0b3cd5404ace01c5aa790 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/multiClust_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/multiClust_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multiClust_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R htmlDocs: vignettes/multiClust/inst/doc/multiClust.html htmlTitles: "A Guide to multiClust" Package: MultiDataSet Version: 1.2.0 Depends: R (>= 3.3), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, minfi, S4Vectors, SummarizedExperiment, methods, IlluminaHumanMethylation450kanno.ilmn12.hg19, utils Suggests: MEALData, minfiData, knitr, rmarkdown, testthat, methylumi, omicade4, iClusterPlus, GEOquery License: file LICENSE MD5sum: e00a6d480c9aac826dcd7ba68b247b7b NeedsCompilation: no Title: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet. MultiDataSet is designed for integrating multi omics data sets and MethylationSet to contain normalized methylation data. These package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzlez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr source.ver: src/contrib/MultiDataSet_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MultiDataSet_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MultiDataSet_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MultiDataSet_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet.R htmlDocs: vignettes/MultiDataSet/inst/doc/MultiDataSet.html htmlTitles: Introduction to MultiDataSet dependsOnMe: MEAL Package: MultiMed Version: 1.8.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 6ec5e807c156adb17e079959a7b21af6 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements permutation method with joint correction for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Joshua N. Sampson Maintainer: Simina M. Boca source.ver: src/contrib/MultiMed_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MultiMed_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MultiMed_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MultiMed_1.8.0.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: multiscan Version: 1.34.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 0713a788854e15b2494ebef85da797bc 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/multiscan_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/multiscan_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multiscan_1.34.0.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: 2.30.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: 5b86c1a043a80ee2b95a111b660985b6 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/multtest_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/multtest_2.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multtest_2.30.0.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, anota, ChIPpeakAnno, GeneSelector, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, REDseq, RTopper, synapter, webbioc, xcms suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GeneSelector, GGtools, GOstats, gQTLstats, GSEAlm, maigesPack, MmPalateMiRNA, oneChannelGUI, pcot2, ropls, topGO Package: muscle Version: 3.16.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: bfb04caf19a5ef16a3a827b6a6e5e787 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_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/muscle_3.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/muscle_3.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/muscle_3.16.0.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: 1.0.0 Depends: R (>= 3.3.0), NMF (>= 0.20.6), GenomicRanges (>= 1.24.0) Imports: stats, 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 (>= 1.8.1), Biostrings (>= 2.40.0), gridExtra (>= 2.2.1) Suggests: BSgenome (>= 1.40.0), BiocStyle (>= 2.0.3), biomaRt (>= 2.28.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), rtracklayer (>= 1.32.2) License: MIT + file LICENSE MD5sum: 9aa7b140c97dfbb6722a31e98bb64029 NeedsCompilation: no Title: Studying patterns in base substitution catalogues Description: An extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution data. biocViews: Genetics, SomaticMutation Author: Francis Blokzijl, Roel Janssen, Ruben van Boxtel, Edwin Cuppen Maintainer: Francis Blokzijl , Roel Janssen URL: https://github.com/CuppenResearch/MutationalPatterns source.ver: src/contrib/MutationalPatterns_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MutationalPatterns_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MutationalPatterns_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MutationalPatterns_1.0.0.tgz vignettes: vignettes/MutationalPatterns/inst/doc/MutationalPatterns.pdf vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/MutationalPatterns.R Package: MVCClass Version: 1.48.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 1b538f84a68d674a5184e4aa51a87b54 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MVCClass_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MVCClass_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MVCClass_1.48.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass Package: mvGST Version: 1.8.0 Depends: R(>= 2.10.0), GO.db, Rgraphviz Imports: gProfileR, stringr, topGO, GOstats, annotate, AnnotationDbi, graph Suggests: hgu133plus2.db, org.Hs.eg.db License: GPL-3 MD5sum: 71e2a8b7ce42e7cf5b05a98793c2f0cd NeedsCompilation: no Title: Multivariate and directional gene set testing Description: mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile. biocViews: Microarray, OneChannel, RNASeq, DifferentialExpression, GO, Pathways, GeneSetEnrichment, GraphAndNetwork Author: John R. Stevens and Dennis S. Mecham Maintainer: John R. Stevens source.ver: src/contrib/mvGST_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mvGST_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mvGST_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mvGST_1.8.0.tgz vignettes: vignettes/mvGST/inst/doc/mvGST.pdf vignetteTitles: mvGST Tutorial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mvGST/inst/doc/mvGST.R Package: mygene Version: 1.10.0 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: 5b157f7c8fe98417f3aa32c3e859a798 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 source.ver: src/contrib/mygene_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mygene_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mygene_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mygene_1.10.0.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: 1.4.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: a28109d92982772ee94eb6da3a32c349 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/myvariant_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/myvariant_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/myvariant_1.4.0.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: 1.12.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 1548319d721b5ff7f6cf02dc6fe90f46 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mzID_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mzID_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mzID_1.12.0.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: 2.8.1 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics LinkingTo: Rcpp, zlibbioc Suggests: msdata (>= 0.3.5), RUnit, mzID, BiocStyle, knitr, XML License: Artistic-2.0 Archs: i386, x64 MD5sum: af4274f5dde013bc5a2302fce2a05b16 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 Maintainer: Bernd Fischer , 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_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/mzR_2.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/mzR_2.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mzR_2.8.1.tgz vignettes: vignettes/mzR/inst/doc/mzR.pdf 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, xcms importsMe: MSnID, msPurity, Pbase, ProteomicsAnnotationHubData, SIMAT, yamss suggestsMe: AnnotationHub, qcmetrics Package: NanoStringDiff Version: 1.4.0 Depends: Biobase Imports: matrixStats, methods Suggests: testthat, BiocStyle License: GPL MD5sum: 88dd4de8daa08155f34844fbf58f51e4 NeedsCompilation: no 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 , chi wang Maintainer: hong wang source.ver: src/contrib/NanoStringDiff_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NanoStringDiff_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NanoStringDiff_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NanoStringDiff_1.4.0.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: 1.6.0 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 License: Artistic-2.0 MD5sum: aab57d0551f829bd8ebca222add98fca 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 source.ver: src/contrib/NanoStringQCPro_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NanoStringQCPro_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NanoStringQCPro_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NanoStringQCPro_1.6.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: NanoStringQCPro overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.R Package: NarrowPeaks Version: 1.18.0 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: d881870084ec1fcd822a4d44e908597e 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NarrowPeaks_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NarrowPeaks_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NarrowPeaks_1.18.0.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: 2.20.2 Depends: R (>= 2.14.0), flowCore(>= 1.37.15), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,flowViz,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH Suggests: testthat,parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 8d1c9a1a6d0f08c2081f35894ae82b60 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 SystemRequirements: hdf5 (>= 1.8.0) source.ver: src/contrib/ncdfFlow_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ncdfFlow_2.20.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ncdfFlow_2.20.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ncdfFlow_2.20.2.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 Package: NCIgraph Version: 1.22.0 Depends: graph, R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCytoscape, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: e601cb487a84715395f89e886fb45c8c 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_1.22.0.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: nem Version: 2.48.0 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: 211076adeab231209e168e3ea98e9d87 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_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nem_2.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nem_2.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nem_2.48.0.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 suggestsMe: rBiopaxParser Package: netbenchmark Version: 1.6.0 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, randomForest, 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: e2365f1a3b187b27ec013b85dbeebda2 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, with contributions from Alexandre Irrthum Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr source.ver: src/contrib/netbenchmark_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/netbenchmark_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/netbenchmark_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netbenchmark_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbenchmark/inst/doc/netbenchmark.R htmlDocs: vignettes/netbenchmark/inst/doc/netbenchmark.html htmlTitles: Netbenchmark Package: netbiov Version: 1.8.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 815385f90799073212b203893c22c786 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/netbiov_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/netbiov_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netbiov_1.8.0.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: 1.6.0 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: 2bd48b232c418fc1e4adc0a81977420e 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nethet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nethet_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nethet_1.6.0.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: 1.10.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph License: GPL (>= 2) Archs: i386, x64 MD5sum: 2e9a9f465134f957a666ae8506ff64d0 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NetPathMiner_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NetPathMiner_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NetPathMiner_1.10.0.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: 1.0.0 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: e61f9cbc5bee818dc86b3355359dd413 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/netprioR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/netprioR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netprioR_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R htmlDocs: vignettes/netprioR/inst/doc/netprioR.html htmlTitles: netprioR Vignette Package: netresponse Version: 1.34.0 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: 972f9f024706a81617c85ab7284a935b 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/netresponse_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/netresponse_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netresponse_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NetSAM Version: 1.14.0 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: 702881ffe6e23baf54938da4441236c2 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NetSAM_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NetSAM_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NetSAM_1.14.0.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: networkBMA Version: 2.12.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1) LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) MD5sum: 3cc64ad9398849a6a28c4012c6a63ca3 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 source.ver: src/contrib/networkBMA_2.12.0.tar.gz 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: 1.8.0 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: e3aaa4877488b30ce248386f6452d51f 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NGScopy_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NGScopy_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NGScopy_1.8.0.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: 2.38.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: f083aec7899451513af949882fa0d3f5 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_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nnNorm_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nnNorm_2.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nnNorm_2.38.0.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: 2.18.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: d74de38f0369b58c3e203f29e8a64f3c 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NOISeq_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NOISeq_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NOISeq_2.18.0.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: 2.4.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, HTqPCR (>= 1.16.0) Suggests: BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: a0dd707178bca5579400a4a3b0af32d2 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 source.ver: src/contrib/nondetects_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nondetects_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nondetects_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nondetects_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: normalize450K Version: 1.2.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 4a11fd0b3a0feffa237df435400eca1a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/normalize450K_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/normalize450K_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/normalize450K_1.2.0.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: 1.20.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 2422b3e8a7da417f13d9532676e24268 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NormqPCR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NormqPCR_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NormqPCR_1.20.0.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: 1.0.0 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: bb3f586b5a97d3cde4839706d878d355 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/normr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/normr_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/normr_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R htmlDocs: vignettes/normr/inst/doc/normr.html htmlTitles: Introduction to the normR package Package: npGSEA Version: 1.10.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 1bc54a59fa447bb81c9499800da6b45c 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/npGSEA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/npGSEA_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/npGSEA_1.10.0.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: 1.24.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 4a957b7450c2c4166ce8de58d61b0745 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NTW_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NTW_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NTW_1.24.0.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: 1.2.0 Imports: stats, IRanges, S4Vectors, graphics Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 30881db014ec8bcc16dc816aa51c59c6 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nucleoSim_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nucleoSim_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nucleoSim_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R htmlDocs: vignettes/nucleoSim/inst/doc/nucleoSim.html htmlTitles: Generate synthetic nucleosome maps Package: nucleR Version: 2.6.0 Depends: ShortRead Imports: methods, BiocGenerics, S4Vectors (>= 0.9.39), IRanges (>= 2.5.27), Biobase, GenomicRanges (>= 1.23.16), Rsamtools, stats, graphics, parallel Suggests: Starr License: LGPL (>= 3) MD5sum: d5065004a00f49775237a4041db50e5c NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: ChIPSeq, Microarray, Sequencing, Genetics Author: Oscar Flores, Ricard Illa Maintainer: Ricard Illa source.ver: src/contrib/nucleR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nucleR_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nucleR_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nucleR_2.6.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: nucleR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R Package: nudge Version: 1.40.0 Imports: stats License: GPL-2 MD5sum: 09a6719e5a95a294725d3c7057edd15a 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nudge_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nudge_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nudge_1.40.0.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: 1.24.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 8ae3986a78c250dfd442961ee0f45d58 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NuPoP_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NuPoP_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NuPoP_1.24.0.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: 1.34.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: dcd88a730e874e456818967f18b7771e 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/occugene_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/occugene_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/occugene_1.34.0.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: 1.48.2 Depends: R (>= 2.1.0), akima Imports: multtest (>= 1.7.3), graphics, grDevices, stats License: LGPL MD5sum: c3c381158ee35512a0a4947f9dd9ff31 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_1.48.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/OCplus_1.48.2.zip win64.binary.ver: bin/windows64/contrib/3.3/OCplus_1.48.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OCplus_1.48.2.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: 1.2.0 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: ef7b17e2ae4198437bc263c7af48d2fa 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/odseq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/odseq_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/odseq_1.2.0.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: 1.4.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) MD5sum: 7a3f82125a43ffbaa3fb01ef42574e61 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OGSA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OGSA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OGSA_1.4.0.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: 1.38.0 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: e5c5c9ec8777a6ec8a232fc63fc7d9b5 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oligo_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oligo_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oligo_1.38.0.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 suggestsMe: BiocGenerics, fastseg, frmaTools Package: oligoClasses Version: 1.36.0 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 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: 306c4d806fb6fbc85474acfe596f14c1 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oligoClasses_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oligoClasses_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oligoClasses_1.36.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, charm, frma, ITALICS, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics, CNPBayes Package: OLIN Version: 1.52.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 91a3ed371c1c76e4d5ff9d87376766af 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OLIN_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OLIN_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OLIN_1.52.0.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: 1.48.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 9bd60e2725736b58994d689c4c42ff79 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OLINgui_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OLINgui_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OLINgui_1.48.0.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: omicade4 Version: 1.14.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: 31d1e0136fd2fe22784a68e923a0b7dd 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/omicade4_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/omicade4_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/omicade4_1.14.0.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 suggestsMe: MultiDataSet Package: OmicCircos Version: 1.12.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: ffc850f142ad5b42d9ac72014da4ccf5 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OmicCircos_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OmicCircos_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OmicCircos_1.12.0.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: OmicsMarkeR Version: 1.8.1 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: 5a1599983afe8f368abcce76dffe7f33 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/OmicsMarkeR_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/OmicsMarkeR_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OmicsMarkeR_1.8.1.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: OncoScore Version: 1.2.1 Depends: R (>= 3.3), Imports: biomaRt, grDevices, graphics, utils, Suggests: BiocGenerics, BiocStyle, testthat, License: GPL-3 MD5sum: b879c00ac1f1274994e0c770d1306efb 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 BugReports: https://github.com/danro9685/OncoScore source.ver: src/contrib/OncoScore_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/OncoScore_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/OncoScore_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OncoScore_1.2.1.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoScore/inst/doc/vignette.R Package: OncoSimulR Version: 2.4.0 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 LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown License: GPL (>= 3) Archs: i386, x64 MD5sum: fa3208c2a5147d17634390e98b9b691d 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OncoSimulR_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OncoSimulR_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OncoSimulR_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R htmlDocs: vignettes/OncoSimulR/inst/doc/OncoSimulR.html htmlTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. Package: oneChannelGUI Version: 1.40.0 Depends: Biobase, affylmGUI, tkrplot, tkWidgets, IRanges, Rsamtools (>= 1.13.1), Biostrings, siggenes, chimera Suggests: annotate, genefilter, maSigPro, pamr, pdmclass, ChIPpeakAnno, chipseq, BSgenome, Rgraphviz, affy ,annaffy, affyPLM, multtest, ssize, sizepower, RankProd, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, edgeR, metaArray, MergeMaid, biomaRt, GenomeGraphs,AffyCompatible, rtracklayer, Genominator, EDASeq, limma, DESeq, DEXSeq, goseq, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, ragene10sttranscriptcluster.db, GOstats, AnnotationDbi, preprocessCore, baySeq, HuExExonProbesetLocation, MoExExonProbesetLocation, RaExExonProbesetLocation, snow, RmiR, RmiR.Hs.miRNA, BSgenome.Hsapiens.UCSC.hg19, R.utils, cummeRbund, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn4, DESeq2, GenomicAlignments, BiocParallel, KEGG.db, miRNApath, miRNAtap, miRNAtap.db License: Artistic-2.0 MD5sum: 5b80a16c6b0d3be5701614982537de08 NeedsCompilation: no Title: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops Description: This package was developed to simplify the use of Bioconductor tools for beginners having limited or no experience in writing R code. This library provides a graphical interface for microarray gene and exon level analysis as well as miRNA/mRNA-seq data analysis biocViews: Sequencing, RNASeq, Microarray, OneChannel, DataImport, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, GUI, MultipleComparison Author: Raffale A Calogero, Bioinformatics and Genomics Unit, Molecular Biotechnology Center, Torino (Italy) Maintainer: Raffaele A Calogero URL: http://www.bioinformatica.unito.it/oneChannelGUI/ source.ver: src/contrib/oneChannelGUI_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oneChannelGUI_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oneChannelGUI_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oneChannelGUI_1.40.0.tgz vignettes: vignettes/oneChannelGUI/inst/doc/Exon-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/gene-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/install.pdf, vignettes/oneChannelGUI/inst/doc/RNAseq.pdf, vignettes/oneChannelGUI/inst/doc/standAloneFunctions.pdf vignetteTitles: oneChannelGUI microarray exon-level data analysis overview, oneChannelGUI microarray gene-level data analysis overview, oneChannelGUI Installation, oneChannelGUI miRNA and RNA-seq data analysis overview, oneChannelGUI Stand Alone Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oneChannelGUI/inst/doc/install.R Package: ontoCAT Version: 1.26.0 Depends: rJava, methods License: Apache License 2.0 MD5sum: 69d92d9b652e11ea1558254dbadb5e62 NeedsCompilation: no Title: Ontology traversal and search Description: The ontoCAT R package provides a simple interface to ontologies described in widely used standard formats, stored locally in the filesystem or accessible online. biocViews: Classification, DataRepresentation Author: Natalja Kurbatova, Tomasz Adamusiak, Pavel Kurnosov, Morris Swertz, Misha Kapushevsky Maintainer: Natalja Kurbatova source.ver: src/contrib/ontoCAT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ontoCAT_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ontoCAT_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ontoCAT_1.26.0.tgz vignettes: vignettes/ontoCAT/inst/doc/ontoCAT.pdf vignetteTitles: ontoCAT package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ontoCAT/inst/doc/ontoCAT.R suggestsMe: RMassBank Package: openCyto Version: 1.12.1 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 License: Artistic-2.0 Archs: i386, x64 MD5sum: 19038794693962e3a065bdb763cdbb90 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/openCyto_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/openCyto_1.12.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/openCyto_1.12.1.tgz 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 htmlDocs: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html htmlTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package importsMe: CytoML suggestsMe: flowCore, ggcyto Package: OperaMate Version: 1.6.0 Depends: R (>= 3.2.0),stats,methods,grDevices Imports: pheatmap,grid,ggplot2,fBasics,gProfileR,gridExtra,reshape2,stabledist Suggests: BiocStyle License: GPL (>= 3) MD5sum: 605a838103e2ebaa68df28c8ba83dc21 NeedsCompilation: no Title: An R package of Data Importing, Processing and Analysis for Opera High Content Screening System Description: OperaMate is a flexible R package dealing with the data generated by PerkinElmer's Opera High Content Screening System. The functions include the data importing, normalization and quality control, hit detection and function analysis. biocViews: Preprocessing, CellBasedAssays, Normalization, QualityControl Author: Chenglin Liu Maintainer: Chenglin Liu source.ver: src/contrib/OperaMate_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OperaMate_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OperaMate_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OperaMate_1.6.0.tgz vignettes: vignettes/OperaMate/inst/doc/OperaMate-manual.pdf, vignettes/OperaMate/inst/doc/OperaMate-vignette.pdf vignetteTitles: OperaMate-manual.pdf, An introduction to OperaMate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OperaMate/inst/doc/OperaMate-vignette.R Package: oposSOM Version: 1.12.0 Depends: R (>= 3.0), igraph (>= 1.0.0) Imports: som, fastICA, scatterplot3d, pixmap, fdrtool, ape, KernSmooth, biomaRt, Biobase License: GPL (>=2) MD5sum: 1bbea93fa6f84990bf82c41eddf7e047 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oposSOM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oposSOM_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oposSOM_1.12.0.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: 1.2.0 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: 9f99a7a3d2550306ff4303239e8434e3 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oppar_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oppar_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oppar_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R htmlDocs: vignettes/oppar/inst/doc/oppar.html htmlTitles: OPPAR: Outlier Profile and Pathway Analysis in R Package: OrderedList Version: 1.46.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: 8b89f476835e033e98935f1516f8fa3a 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OrderedList_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OrderedList_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OrderedList_1.46.0.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: OrganismDbi Version: 1.16.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.23.31) Imports: Biobase, BiocInstaller, GenomicRanges, graph, IRanges, RBGL, RSQLite, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit License: Artistic-2.0 MD5sum: 56c816b5526cf923170b83c8334b28fc 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, Herve Pages, Martin Morgan, Valerie Obenchain Maintainer: Biocore Data Team source.ver: src/contrib/OrganismDbi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OrganismDbi_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OrganismDbi_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OrganismDbi_1.16.0.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: 1.22.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 32d68aad8ac54c742b747fbe1ed6fd8b 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OSAT_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OSAT_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OSAT_1.22.0.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: 1.4.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 677fae951b5630250496fa60847a8acd 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Oscope_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Oscope_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Oscope_1.4.0.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: 1.24.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 91181dc3b3d58b2beaa0aa9b46c1d203 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OTUbase_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OTUbase_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OTUbase_1.24.0.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: 1.38.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: 7a00b158c4c454d97c7d56d14e5a25e2 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OutlierD_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OutlierD_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OutlierD_1.38.0.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: 1.8.0 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: 54ce624153f265a0c2c2d51d048d5e17 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PAA_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAA_1.8.0.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: 1.16.0 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: c345375fb3fc69c949d5856f10ab5ade 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PADOG_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PADOG_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PADOG_1.16.0.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 importsMe: EGSEA Package: paircompviz Version: 1.12.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: dac32e61b14dfa4ab867143552cbda3e 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/paircompviz_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/paircompviz_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/paircompviz_1.12.0.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: 1.6.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: rmarkdown License: GPL-2 MD5sum: eab545fbb991e200fb3298802d2352da 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: rmarkdown source.ver: src/contrib/pandaR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pandaR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pandaR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pandaR_1.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PAnnBuilder Version: 1.38.0 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: a1ac929cd54e0c51eb47ef711a4e42e5 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAnnBuilder_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PAnnBuilder_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAnnBuilder_1.38.0.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: 1.44.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: db365d15dde61939384c18f19f60fa58 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/panp_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/panp_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/panp_1.44.0.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: 1.20.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 2fb333f9ec4e736e1e1448d1afb1560e 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PANR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PANR_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PANR_1.20.0.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 suggestsMe: RedeR Package: PanVizGenerator Version: 1.2.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: c7b125493013e88170cbb7d1bb1420d6 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PanVizGenerator_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PanVizGenerator_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PanVizGenerator_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R htmlDocs: vignettes/PanVizGenerator/inst/doc/panviz_howto.html htmlTitles: Creating PanViz visualizations with PanVizGenerator Package: PAPi Version: 1.14.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: 4e7bc9701fd574ce06479bbb29a47871 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAPi_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PAPi_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAPi_1.14.0.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: 1.6.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS License: Artistic-2.0 MD5sum: 51898ed6f74a55c8120cf939007bca5f 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/parglms_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/parglms_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/parglms_1.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: parody Version: 1.32.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: 4d00d845e4c1ec93a7e3e541c603f6b1 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/parody_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/parody_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/parody_1.32.0.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: 1.4.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 7b32f0f1f4b75278afe5ac51c0805cfa 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Path2PPI_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Path2PPI_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Path2PPI_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R htmlDocs: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html htmlTitles: Path2PPI - A brief tutorial Package: pathifier Version: 1.12.0 Imports: R.oo, princurve License: Artistic-1.0 MD5sum: 49ffb4a63eaa6c342f1f750e122c8f5d 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 source.ver: src/contrib/pathifier_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathifier_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathifier_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathifier_1.12.0.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: 1.14.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: d5015e0ed885989e1252a561858a1121 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PathNet_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PathNet_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PathNet_1.14.0.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: 1.0.0 Depends: R (>= 3.3.1) Imports: MCMCpack, limma, corpcor, rmarkdown, knitr, pander, matrixStats, reshape2, scales, ggplot2, rentrez, BatchQC, DT, gtools, tidyr, plyr, dplyr, ape, phyloseq, shiny, grDevices, stats, methods, XML, graphics, utils, alluvial, BiocStyle Suggests: testthat License: GPL (>= 2) MD5sum: 9fe8f20484ea870710157b5e0bf532de 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 , W. Evan Johnson Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/PathoStat SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues source.ver: src/contrib/PathoStat_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PathoStat_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PathoStat_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PathoStat_1.0.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStatUserManual.pdf vignetteTitles: PathoStatUserManual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStatAdvanced.R, vignettes/PathoStat/inst/doc/PathoStatUserManual.R htmlDocs: vignettes/PathoStat/inst/doc/PathoStatAdvanced.html, vignettes/PathoStat/inst/doc/PathoStatIntro.html htmlTitles: PathoStatAdvanced, PathoStatIntro Package: pathRender Version: 1.42.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: cdd69b69be2ec05a0840200e6fc771c4 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathRender_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathRender_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathRender_1.42.0.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: 1.4.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 8338070aa7ed7c751ef930fe3fab6767 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathVar_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathVar_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathVar_1.4.0.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: 1.14.0 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: fc905814ccfd8ef17491c88a857b13b6 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: http://pathview.uncc.edu/ source.ver: src/contrib/pathview_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathview_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathview_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathview_1.14.0.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: EGSEA, EnrichmentBrowser importsMe: CompGO, TCGAbiolinks suggestsMe: clusterProfiler, gage Package: paxtoolsr Version: 1.8.0 Depends: R (>= 3.2), rJava (>= 0.9-4), XML Imports: httr, igraph, plyr, rjson, R.utils, data.table Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma, foreach, doSNOW, parallel License: LGPL-3 MD5sum: 46a3efc59b0b21c22f1a7de5286e1b66 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/paxtoolsr_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/paxtoolsr_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/paxtoolsr_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R htmlDocs: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html htmlTitles: Using PaxtoolsR suggestsMe: RCy3 Package: Pbase Version: 0.14.0 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings, IRanges, S4Vectors, mzID, mzR (>= 1.99.1), MSnbase (>= 1.15.5), Pviz, biomaRt, GenomicRanges, rtracklayer Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationHub, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 53ea5a9082b6154943596ecba646587f 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_0.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pbase_0.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Pbase_0.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pbase_0.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/ensucsc.R, vignettes/Pbase/inst/doc/mapping.R, vignettes/Pbase/inst/doc/Pbase-data.R htmlDocs: vignettes/Pbase/inst/doc/ensucsc.html, vignettes/Pbase/inst/doc/mapping.html, vignettes/Pbase/inst/doc/Pbase-data.html htmlTitles: Ensembl and UCSC mapping, Mapping experimental MS data to genomic coordinates, Pbase data Package: pbcmc Version: 1.2.2 Depends: R (>= 3.3.0), 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: 6f45013332f989dd2e25a15ba762590e 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pbcmc_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pbcmc_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pbcmc_1.2.2.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 Package: pcaExplorer Version: 2.0.0 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: 9d7acdaf5b4b5e02604016edbb5edf23 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_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaExplorer_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaExplorer_2.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaExplorer_2.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R htmlDocs: vignettes/pcaExplorer/inst/doc/pcaExplorer.html htmlTitles: pcaExplorer User Guide Package: pcaGoPromoter Version: 1.18.0 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: 73ab312d5b0fe6b1bd750aeec6bb67d5 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaGoPromoter_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaGoPromoter_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaGoPromoter_1.18.0.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: 1.66.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) Archs: i386, x64 MD5sum: 9667690b43d6c9cda9fc171bf138c322 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_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaMethods_1.66.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaMethods_1.66.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaMethods_1.66.0.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: 1.2.1 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 34195cd634bdcc2761ba1d16eacd69cd 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/PCAN_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/PCAN_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PCAN_1.2.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R htmlDocs: vignettes/PCAN/inst/doc/PCAN.html htmlTitles: Assessing gene relevance for a set of phenotypes Package: pcot2 Version: 1.42.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: 17d1990503b89346e8db299e8b23fc22 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcot2_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcot2_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcot2_1.42.0.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: 1.36.0 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: 3be6cde384aa23fe731833611e0cc12e 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PCpheno_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PCpheno_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PCpheno_1.36.0.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: pdInfoBuilder Version: 1.38.0 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: 8ea4b76fca1ddd05f6a9531b8e03b0c7 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pdInfoBuilder_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pdInfoBuilder_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pdInfoBuilder_1.38.0.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: pdmclass Version: 1.46.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), fibroEset, mda License: Artistic-2.0 MD5sum: bb36974366c54708bf205d3ddcb60e75 NeedsCompilation: no Title: Classification of Microarray Samples using Penalized Discriminant Methods Description: This package can be used to classify microarray data using one of three penalized regression methods; partial least squares, principal components regression, or ridge regression. biocViews: Classification Author: James W. MacDonald, Debashis Ghosh, based in part on pls code of Mike Denham Maintainer: James W. MacDonald source.ver: src/contrib/pdmclass_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pdmclass_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pdmclass_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pdmclass_1.46.0.tgz vignettes: vignettes/pdmclass/inst/doc/pdmclass.pdf vignetteTitles: pdmclass Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdmclass/inst/doc/pdmclass.R suggestsMe: oneChannelGUI Package: PECA Version: 1.10.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: dc33f865eabf38ec70e84162a6fde333 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PECA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PECA_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PECA_1.10.0.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 Package: pepStat Version: 1.8.0 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: b5624791925b22884287b8501e4ff05c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pepStat_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pepStat_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pepStat_1.8.0.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: 1.8.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 9a77d9eb50479aae904e420fb057a210 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pepXMLTab_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pepXMLTab_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pepXMLTab_1.8.0.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: PGA Version: 1.4.0 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: 61632d04bb70cbcc5896b1a519655453 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PGA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PGA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PGA_1.4.0.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: PGSEA Version: 1.48.0 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: ceef171413c103c5a0db022fc49d5c03 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PGSEA_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PGSEA_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PGSEA_1.48.0.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: PharmacoGx Version: 1.4.3 Depends: R (>= 3.3) Imports: Biobase, piano, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, lsa Suggests: xtable License: Artistic-2.0 MD5sum: 9e79c63ea7e7f0ac582379f5629e5655 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 source.ver: src/contrib/PharmacoGx_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/PharmacoGx_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/PharmacoGx_1.4.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PharmacoGx_1.4.3.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: 1.22.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: c3a421f54a98d02e01306227e72cb79f 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/phenoDist_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/phenoDist_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phenoDist_1.22.0.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: phenoTest Version: 1.23.1 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: afdc03e516f4ba27d1b78f5fa000fc0e 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_1.23.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/phenoTest_1.23.1.zip win64.binary.ver: bin/windows64/contrib/3.3/phenoTest_1.23.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phenoTest_1.23.1.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: 2.8.0 Depends: R (>= 2.3.0) Imports: methods, car, nlme, nortest, MASS, logistf Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: d4ccfbb0ba958bbdcac4544f61d56bd5 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason Maintainer: Jeremy Mason source.ver: src/contrib/PhenStat_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PhenStat_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PhenStat_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PhenStat_2.8.0.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: 1.0.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet, dplyr License: GPL-3 MD5sum: 9dc4564b613c254061e8257e9733f0a3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/philr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/philr_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/philr_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R htmlDocs: vignettes/philr/inst/doc/philr-intro.html htmlTitles: Introduction to PhILR Package: phyloseq Version: 1.19.1 Depends: R (>= 3.3.0) Imports: BiocGenerics (>= 0.18.0), ade4 (>= 1.7.4), ape (>= 3.4), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.9.6), 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 (>= 2.3.5), Biobase Suggests: BiocStyle (>= 2.0.0), DESeq2 (>= 1.12.0), genefilter (>= 1.54), testthat (>= 1.0.2), knitr (>= 1.13), metagenomeSeq (>= 1.14), rmarkdown (>= 0.9.6) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: 5bb158f1bbeeaae4ca3e2cb1ca08fbfd 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 source.ver: src/contrib/phyloseq_1.19.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/phyloseq_1.19.1.zip win64.binary.ver: bin/windows64/contrib/3.3/phyloseq_1.19.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phyloseq_1.19.1.tgz 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 htmlDocs: 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 htmlTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data dependsOnMe: RPA importsMe: PathoStat suggestsMe: philr Package: Pi Version: 1.2.1 Depends: XGR, igraph, dnet, ggplot2, graphics, stats Imports: Matrix, MASS, ggbio, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, fgsea, Gviz License: GPL-3 MD5sum: 2f385cbb161e8c5738165935d88c8596 NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene, Pathway and Network 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pi_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Pi_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pi_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R htmlDocs: vignettes/Pi/inst/doc/Pi_vignettes.html htmlTitles: Pi User Manual (R/Bioconductor package) Package: piano Version: 1.14.5 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: b37c89e39ba51747590ee29b17849cbf 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 source.ver: src/contrib/piano_1.14.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/piano_1.14.5.zip win64.binary.ver: bin/windows64/contrib/3.3/piano_1.14.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/piano_1.14.5.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: 1.46.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 1f63bf4d9ecedb0404960af25171a28e 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pickgene_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pickgene_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pickgene_1.46.0.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: 2.18.0 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: a9e89d61c911cce929a1bb9af6ee48cc 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PICS_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PICS_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PICS_2.18.0.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: 1.0.0 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, knitr, BiocStyle, AnnotationDbi, energy License: GPL (>=2) MD5sum: 16db224e9ed35abbc2e71a2ecd2948ae 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pigengene_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Pigengene_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pigengene_1.0.0.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: 2.18.0 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: 722ebb1db63b9d95e195c6a4b773abe0 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PING_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PING_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PING_2.18.0.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: 1.24.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: dcd0cd52f60231b19c4e4cbf2f33f99c 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pint_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pint_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pint_1.24.0.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: 1.40.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: 61931171f9a3dbd32973fc415e9af5b1 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 Maintainer: Seth Falcon source.ver: src/contrib/pkgDepTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pkgDepTools_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pkgDepTools_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pkgDepTools_1.40.0.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: 1.32.0 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: 5ab7a0f594f51185f03741a0c6a44d63 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plateCore_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plateCore_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plateCore_1.32.0.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: 1.12.1 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: 73160d4476f8513039ffd1a27df95e95 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/plethy_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/plethy_1.12.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plethy_1.12.1.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: 1.46.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: d99e7b8e2be2268c4c094a8481994248 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plgem_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plgem_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plgem_1.46.0.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: 1.44.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 46f970f4a815369de3447be693451093 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plier_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plier_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plier_1.44.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.34.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 5e676f41d9f90a2ebd7d7e06074d26ad 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PLPE_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PLPE_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PLPE_1.34.0.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: 1.14.0 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: 843c8e751ed3a2db2ca6e106a8234ad8 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plrs_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plrs_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plrs_1.14.0.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: 1.34.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: d6dc7c2aa320db11005ee2f2b3c03f9f 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plw_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plw_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plw_1.34.0.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: pmm Version: 1.6.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: 6c10b4be42be312b89c1b563525af2c3 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pmm_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pmm_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pmm_1.6.0.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: 1.6.1 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: 8ed332d10bf0e214f007218e23de094c 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/podkat_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/podkat_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/podkat_1.6.1.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: polyester Version: 1.10.1 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 22e2005512a110d95d96302016001c25 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/polyester_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/polyester_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/polyester_1.10.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R htmlDocs: vignettes/polyester/inst/doc/polyester.html htmlTitles: The Polyester package for simulating RNA-seq reads Package: Polyfit Version: 1.8.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: 76897427a0356e85f57e740ff000bfe1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Polyfit_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Polyfit_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Polyfit_1.8.0.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: ppiStats Version: 1.40.0 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: 1f2875df8544ed6e0df06a77808c999d 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ppiStats_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ppiStats_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ppiStats_1.40.0.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: 1.2.3 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.62.0) Suggests: BiocStyle, knitr, Gviz, rtracklayer, biomaRt, BSgenome.Hsapiens.UCSC.hg38 License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: b8223c55a6ae8850440c03c0c3a2cb58 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: The main functionality of the 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 VignetteBuilder: knitr source.ver: src/contrib/pqsfinder_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/pqsfinder_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/pqsfinder_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pqsfinder_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R htmlDocs: vignettes/pqsfinder/inst/doc/pqsfinder.html htmlTitles: pqsfinder: User Guide Package: prada Version: 1.50.0 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: 93ffa121f9f35bba9f7556c42c3a02ce 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prada_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prada_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prada_1.50.0.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: domainsignatures, RNAither importsMe: cellHTS2 Package: prebs Version: 1.14.0 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: 01512cd5c7eec27c870012ba0c2fbd9e 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prebs_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prebs_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prebs_1.14.0.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: 1.20.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, samr, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 97e07380fe989e0545cb8c0d56c6c908 NeedsCompilation: no Title: Position RElated Data Anlysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari source.ver: src/contrib/PREDA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PREDA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PREDA_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PREDA_1.20.0.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: 1.20.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 56cb8706cdcea59b0bc7e2d29aa78303 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_1.20.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/predictionet_1.20.0.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: 1.36.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: e3a70a55cc19fe3a2d03cce03e542cd5 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines biocViews: Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/preprocessCore source.ver: src/contrib/preprocessCore_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/preprocessCore_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/preprocessCore_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/preprocessCore_1.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus importsMe: affy, charm, cn.farms, EMDomics, ExiMiR, frma, frmaTools, iCheck, InPAS, INSPEcT, lumi, MADSEQ, MBCB, MEDIPS, minfi, MSnbase, MSstats, oligo, PECA, Pigengene, soGGi, waveTiling, yarn suggestsMe: multiClust, oneChannelGUI Package: Prize Version: 1.5.1 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c0948c4b701108051fdc254f1db2f983 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_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Prize_1.5.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Prize_1.5.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Prize_1.5.1.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: 1.8.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 5b9cc37d4789fab36d00046ae06b13e2 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/proBAMr_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/proBAMr_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proBAMr_1.8.0.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: 1.50.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 11da68b25b84e6997d1a0f178aeb9037 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROcess_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PROcess_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROcess_1.50.0.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: 2.2.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 6d10a3b95c22bf375415796f4be30360 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/procoil_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/procoil_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/procoil_2.2.0.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: 1.12.0 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: 6741542723a57071852680af2bcb663a 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProCoNA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ProCoNA_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProCoNA_1.12.0.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: 1.0.10 Depends: R (>= 3.3.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, FNN, minpack.lm, BiocParallel Suggests: ropls, BiocGenerics, plasFIA, knitr, License: CeCILL Archs: i386, x64 MD5sum: a6c8b7b75231be47a6dfeaa1c6fe54b1 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_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/proFIA_1.0.10.zip win64.binary.ver: bin/windows64/contrib/3.3/proFIA_1.0.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proFIA_1.0.10.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R htmlDocs: vignettes/proFIA/inst/doc/proFIA-vignette.html htmlTitles: Vignette Title Package: profileScoreDist Version: 1.2.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: i386, x64 MD5sum: a73b33115304384657fbb4a059727895 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/profileScoreDist_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/profileScoreDist_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/profileScoreDist_1.2.0.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: pRoloc Version: 1.14.6 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, 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, tsne, hexbin, rgl, BiocStyle, hpar (>= 1.15.3), dplyr, GO.db, AnnotationDbi License: GPL-2 Archs: i386, x64 MD5sum: 3644eb381c08e7a07a4d141c6b8a35ff NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: This package implements pattern recognition techniques on quantitiative mass spectrometry data to 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 source.ver: src/contrib/pRoloc_1.14.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/pRoloc_1.14.6.zip win64.binary.ver: bin/windows64/contrib/3.3/pRoloc_1.14.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pRoloc_1.14.6.tgz vignettes: vignettes/pRoloc/inst/doc/HUPO_2011_poster.pdf, vignettes/pRoloc/inst/doc/HUPO_2014_poster.pdf, vignettes/pRoloc/inst/doc/pRoloc-ml.pdf, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.pdf, vignettes/pRoloc/inst/doc/pRoloc-tutorial.pdf vignetteTitles: HUPO 2011 poster: pRoloc -- A unifying bioinformatics framework for organelle proteomics, HUPO 2014 poster: A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data, Machine learning techniques available in pRoloc, A transfer learning algorithm for spatial proteomics, pRoloc tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/HUPO_2011_poster.R, vignettes/pRoloc/inst/doc/HUPO_2014_poster.R, vignettes/pRoloc/inst/doc/pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.R htmlDocs: vignettes/pRoloc/inst/doc/pRoloc-goannotations.html htmlTitles: Annotating spatial proteomics data dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.8.2 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.11.1), Biobase, MSnbase (>= 1.13.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils Suggests: pRolocdata, knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: edc1da4fbd1883396030d54f86298c26 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_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pRolocGUI_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pRolocGUI_1.8.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pRolocGUI_1.8.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R htmlDocs: vignettes/pRolocGUI/inst/doc/pRolocGUI.html htmlTitles: pRolocGUI - Interactive visualisation of spatial proteomics data Package: PROMISE Version: 1.26.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: f85282293e35af5f2ffd4c4b00783b30 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROMISE_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PROMISE_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROMISE_1.26.0.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: 1.6.0 Depends: R (>= 2.10) Imports: edgeR Suggests: BiocStyle,DESeq,DSS,knitr License: GPL MD5sum: 0c6e19340cf2dc2c38c8c48fd1c2520d 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROPER_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PROPER_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROPER_1.6.0.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: Prostar Version: 1.6.1 Depends: R (>= 3.3) Imports: DAPAR (>= 1.5.5), DAPARdata, rhandsontable, data.table, shinyjs, DT, shiny, shinyAce, sm Suggests: BiocStyle License: Artistic-2.0 MD5sum: b4807311e7b1f374d6a3080b3e9c94b4 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: MassSpectrometry, Normalization, Preprocessing, Proteomics, GUI Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek source.ver: src/contrib/Prostar_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Prostar_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Prostar_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Prostar_1.6.1.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_Tutorial.pdf, vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar tutorial, Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_Tutorial.R, vignettes/Prostar/inst/doc/Prostar_UserManual.R suggestsMe: DAPAR Package: prot2D Version: 1.12.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: 17c792808721534caf471327bba77c1a 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prot2D_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prot2D_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prot2D_1.12.0.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: 1.14.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 385415fdf0182c8bfb963b44d9516e89 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/proteinProfiles_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/proteinProfiles_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proteinProfiles_1.14.0.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: 1.4.0 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: 5757f856e51ffff53bfb0985f22936eb 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProteomicsAnnotationHubData_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ProteomicsAnnotationHubData_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProteomicsAnnotationHubData_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R htmlDocs: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html htmlTitles: Proteomics Data in Annotation Hub Package: proteoQC Version: 1.10.0 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, Rcpp (>= 0.11.1) LinkingTo: Rcpp Suggests: RforProteomics, knitr, BiocStyle, rpx, R.utils, RUnit,BiocGenerics License: LGPL-2 Archs: i386, x64 MD5sum: cbaf351b3dd8427899314c04526bd8a9 NeedsCompilation: yes Title: An R package for proteomics data quality control Description: This package creates a 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 VignetteBuilder: knitr source.ver: src/contrib/proteoQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/proteoQC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/proteoQC_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proteoQC_1.10.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.pdf vignetteTitles: proteoQC tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R Package: ProtGenerics Version: 1.6.0 Depends: methods License: Artistic-2.0 MD5sum: adf7d84821b4444336ab9f2d1c4b9eaf 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProtGenerics_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ProtGenerics_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProtGenerics_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, tofsims, xcms importsMe: MSnID, mzID, mzR Package: PSEA Version: 1.8.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 28c3d9f8685b9630bfd1c96e5e4072de 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PSEA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PSEA_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PSEA_1.8.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA.R Package: psichomics Version: 1.0.8 Depends: R (>= 3.3), shiny (>= 1.0.0), shinyBS Imports: AnnotationHub, data.table, digest, dplyr, DT (>= 0.2), fastmatch, highcharter (>= 0.5.0), httr, jsonlite, miscTools, plyr, R.utils, shinyjs, stringr, stats, survival, Sushi, tools, utils, XML, methods Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car License: MIT + file LICENSE MD5sum: 60d9adba9d0577bf6635020265ab27a5 NeedsCompilation: no Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Package with a Shiny-based graphical interface for the integrated analysis of alternative splicing data from The Cancer Genome Atlas (TCGA). This tool interactively performs survival, principal components and differential splicing analyses with direct incorporation of clinical features (such as tumour stage or survival) associated with TCGA samples. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, Visualization, MultipleComparison Author: Nuno Saraiva-Agostinho [aut, cre], Nuno Barbosa-Morais [aut, ths], André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://github.com/nuno-agostinho/psichomics VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues source.ver: src/contrib/psichomics_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/psichomics_1.0.8.zip win64.binary.ver: bin/windows64/contrib/3.3/psichomics_1.0.8.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/psichomics_1.0.8.tgz 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/GUI_tutorial.R htmlDocs: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/GUI_tutorial.html htmlTitles: Custom alternative splicing annotation, Command-line interface tutorial, Visual interface tutorial Package: PSICQUIC Version: 1.12.1 Depends: R (>= 3.2.2), methods, IRanges, biomaRt, BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 12405efd78449a271409e030c03594d8 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 source.ver: src/contrib/PSICQUIC_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/PSICQUIC_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/PSICQUIC_1.12.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PSICQUIC_1.12.1.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: 1.7.4 Depends: R (>= 3.3) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, BiocInstaller, Biobase, labeling Suggests: testthat License: MIT + file LICENSE MD5sum: 3be5b8d9237bc83345d0f30159a902bd 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], Carles Hernandez-Ferrer [cre] Maintainer: Alba Gutierrez-Sacristan source.ver: src/contrib/psygenet2r_1.7.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/psygenet2r_1.7.4.zip win64.binary.ver: bin/windows64/contrib/3.3/psygenet2r_1.7.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/psygenet2r_1.7.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: puma Version: 3.16.0 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: 150bbadf51c141d316443176d2f418d8 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_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/puma_3.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/puma_3.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/puma_3.16.0.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: 1.2.3 Depends: R (>= 3.2), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, Rsamtools, Biostrings Suggests: PSCBS, RUnit, BiocStyle, BiocGenerics, knitr, getopt, rtracklayer License: Artistic-2.0 MD5sum: 84b782885d2826800c640c337db20aa9 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of single nucleotide variants (SNVs). PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage Author: Markus Riester Maintainer: Markus Riester VignetteBuilder: knitr source.ver: src/contrib/PureCN_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/PureCN_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/PureCN_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PureCN_1.2.3.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf vignetteTitles: PureCN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R Package: pvac Version: 1.22.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: b4cab8e6891992b647d4d75c36379198 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pvac_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pvac_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pvac_1.22.0.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: 1.14.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: b89b9f5cb80fc42babf4206aa92a78d0 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pvca_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pvca_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pvca_1.14.0.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: 1.8.0 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: 828e93705911e491e5e41a0b85333193 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pviz_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Pviz_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pviz_1.8.0.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: 4.10.0 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: a32de99322e0c96aaf713bf16f190c35 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_4.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PWMEnrich_4.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PWMEnrich_4.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PWMEnrich_4.10.0.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: 1.6.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: cbc8f9b59e593b153ea0aff791b381b8 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 proteins. A separate downstream analysis of proteomic data including pathway identification and enrichment analysis, 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 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. biocViews: SystemsBiology, Transcription, GeneTarget Author: Astrid Wachter Maintainer: Astrid Wachter source.ver: src/contrib/pwOmics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pwOmics_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pwOmics_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pwOmics_1.6.0.tgz vignettes: vignettes/pwOmics/inst/doc/pwOmics.pdf vignetteTitles: pwOmics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwOmics/inst/doc/pwOmics.R Package: qcmetrics Version: 1.12.0 Depends: R (>= 2.10) 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: af32fe996d5aceef163d49637657ffbd 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 Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr source.ver: src/contrib/qcmetrics_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qcmetrics_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qcmetrics_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qcmetrics_1.12.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R Package: QDNAseq Version: 1.10.0 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), 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: d1b2ca2f3d90d46a8bf799fd2541de5a 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QDNAseq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QDNAseq_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QDNAseq_1.10.0.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 Package: qpcrNorm Version: 1.32.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: d2898866eadc6b3d46a32f3c08a4825e 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qpcrNorm_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qpcrNorm_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qpcrNorm_1.32.0.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: 2.8.3 Depends: R (>= 3.0.0) 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: cea07f3dc76aa306619dbb28d9c0eb00 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: http://functionalgenomics.upf.edu/qpgraph source.ver: src/contrib/qpgraph_2.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/qpgraph_2.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/qpgraph_2.8.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qpgraph_2.8.3.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, ToPASeq Package: qrqc Version: 1.28.0 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: 0d154b99b219d3b18c1fe3c4636aacf6 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qrqc_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qrqc_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qrqc_1.28.0.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: 1.0.3 Depends: R (>= 3.3) 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: 9096a800258065725b76a6bf474d4c2d 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_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/qsea_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/qsea_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qsea_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R htmlDocs: vignettes/qsea/inst/doc/qsea_tutorial.html htmlTitles: qsea Package: QUALIFIER Version: 1.18.0 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: 1220c1616d03d87a291775dd49f27ba0 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QUALIFIER_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QUALIFIER_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QUALIFIER_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: quantro Version: 1.8.0 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: a462268dac719fd90b12f970715e24f1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/quantro_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/quantro_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/quantro_1.8.0.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: 1.40.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 4406cd2d33564b4cadbea71459564905 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/quantsmooth_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/quantsmooth_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/quantsmooth_1.40.0.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: 1.6.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: d16a2be6286d3bc8a01963201153ba9e 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 source.ver: src/contrib/QuartPAC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuartPAC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QuartPAC_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuartPAC_1.6.0.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: 1.14.0 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 License: GPL-2 Archs: x64 MD5sum: 16224a42105d200de34c86eb5912e1fd 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 source.ver: src/contrib/QuasR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuasR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QuasR_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuasR_1.14.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.pdf vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R Package: QuaternaryProd Version: 1.2.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3) LinkingTo: Rcpp Suggests: readr, org.Hs.eg.db, dplyr, stringr, knitr, fdrtool License: GPL (>=3) Archs: i386, x64 MD5sum: 96afcc8c00756a9d6f511aa3986b1907 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 String-db. QuaternaryProd is a free alternative to commercial products such as Quiagen and 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. 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 String-db. 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuaternaryProd_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QuaternaryProd_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuaternaryProd_1.2.0.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: 1.2.1 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: ee366ceae5a7a4d1d49d2c3b96a44b3a 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/QUBIC_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/QUBIC_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QUBIC_1.2.1.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 Package: qusage Version: 2.6.1 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, lsmeans License: GPL (>= 2) MD5sum: faf01cb6cfd29c9923a8b00f299da13b 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_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/qusage_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/qusage_2.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qusage_2.6.1.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: 2.6.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: be8b7972cb37667edd96948c9b56ce89 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qvalue_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qvalue_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qvalue_2.6.0.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, netresponse, normr, Rnits, sights, sRAP, subSeq, synapter, trigger, webbioc suggestsMe: biobroom, LBE, maanova, PREDA Package: R3CPET Version: 1.6.0 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, GenomeInfoDb, GenomicRanges, 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: 93d4b83cef6e0c1600563d8eb7d33074 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 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/R3CPET_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/R3CPET_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R3CPET_1.6.0.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: 1.20.0 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: 01a3a820eb604116269a05195b8ac69c 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/r3Cseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/r3Cseq_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/r3Cseq_1.20.0.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: 1.24.0 Depends: R (>= 2.12.0), methods, VariantAnnotation, Biostrings, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, XVector, GenomicRanges, SummarizedExperiment, biomaRt, BSgenome, Rsamtools, ShortRead Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: 25c40b05775534afe4e0cdbd9c96edb1 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/R453Plus1Toolbox_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/R453Plus1Toolbox_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R453Plus1Toolbox_1.24.0.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: 1.2.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: a09d285a6408229838a9610529defb80 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/R4RNA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/R4RNA_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R4RNA_1.2.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf vignetteTitles: R4RNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R Package: rain Version: 1.8.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: f2401b4582ae13efbe4530633ae5472c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rain_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rain_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rain_1.8.0.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: 1.48.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: f96341350b20129944f4d7c009999002 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rama_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rama_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rama_1.48.0.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: 1.20.0 Depends: gsubfn,methods Imports: igraph,RCurl,png,RCytoscape,graph License: Artistic-2.0 MD5sum: 6656f5a3a7979ce8b90db5fb72da0895 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. 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_1.20.0.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: randPack Version: 1.20.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: ce62a6ada68ebc617d2ae2a33b19b074 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/randPack_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/randPack_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/randPack_1.20.0.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: 3.0.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 95c9215478783018eeafc7aae084f3fb 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 Janckevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore source.ver: src/contrib/RankProd_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RankProd_3.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RankProd_3.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RankProd_3.0.0.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 suggestsMe: oneChannelGUI Package: RareVariantVis Version: 1.8.0 Depends: BiocGenerics, VariantAnnotation, googleVis Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Suggests: knitr, AshkenazimSonChr21 License: Artistic-2.0 MD5sum: 0ba655ca0daca9a939c4bb6709a7d481 NeedsCompilation: no Title: Visualization of rare variants in whole genome sequencing data Description: Genomic variants can be analyzed and visualized using many tools. Unfortunately, number of tools for global interrogation of variants is limited. Package RareVariantVis aims to present genomic variants (especially rare ones) in a global, per chromosome way. Visualization 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. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr source.ver: src/contrib/RareVariantVis_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RareVariantVis_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RareVariantVis_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RareVariantVis_1.8.0.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: 1.10.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: methods, 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: bf4c6d416960c946b2592a054e2840c1 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rariant_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rariant_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rariant_1.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R htmlDocs: vignettes/Rariant/inst/doc/Rariant-vignette.html htmlTitles: Rariant Package: RbcBook1 Version: 1.42.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 829cafd37333007c39e358263172afdf 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RbcBook1_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RbcBook1_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RbcBook1_1.42.0.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: 1.50.0 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 5fae5f13886bd33c5a8cfcc356f00f72 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBGL_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RBGL_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBGL_1.50.0.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, flowClust, flowWorkspace, GeneAnswers, GOSim, GOstats, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, Streamer, ToPASeq, VariantFiltering suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools Package: RBioinf Version: 1.34.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: cdc0c45a05a1c69fbf8905c92db2a930 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBioinf_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RBioinf_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBioinf_1.34.0.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: 2.14.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL, igraph License: GPL (>= 2) MD5sum: 2066a75f41184b1c051d306e3726f158 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rBiopaxParser_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rBiopaxParser_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rBiopaxParser_2.14.0.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: 1.6.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 8e3ec8c0de87aa041ff0471dc2099144 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RBM_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBM_1.6.0.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: 1.14.0 Suggests: parallel License: Artistic-1.0 | file LICENSE Archs: x64 MD5sum: 00aa734da52f38f40b00a785631e27ae 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 source.ver: src/contrib/Rbowtie_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rbowtie_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rbowtie_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rbowtie_1.14.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.pdf vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR Package: rbsurv Version: 2.32.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: ea341c5bbf705091ca14b750cd3e3d28 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rbsurv_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rbsurv_2.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rbsurv_2.32.0.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: 1.16.0 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: d842e9941643c8931f97b872001368fc 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rcade_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rcade_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rcade_1.16.0.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: 1.0.2 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, Biostrings, rtracklayer, org.Hs.eg.db, GenomicFeatures, genomation (>= 1.5.5), rmarkdown (>= 0.9.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, stats, Suggests: 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 License: Artistic-2.0 MD5sum: e3e76d4a4773f4548410b2566ac1dc7f 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RCAS_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RCAS_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RCAS_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.vignette.R htmlDocs: vignettes/RCAS/inst/doc/RCAS.vignette.html htmlTitles: Vignette Title Package: RCASPAR Version: 1.20.0 License: GPL (>=3) MD5sum: 36f46ae2593b54e7030e6fe29b3a791b 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RCASPAR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RCASPAR_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RCASPAR_1.20.0.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: 1.6.0 Depends: R (>= 3.2), Biobase, rcdk, fingerprint, rcellminerData Imports: stringr, gplots, methods, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite License: LGPL-3 MD5sum: 6a59277dd33259fab3ee97ec4e5c6029 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 URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr source.ver: src/contrib/rcellminer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rcellminer_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rcellminer_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rcellminer_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R htmlDocs: vignettes/rcellminer/inst/doc/rcellminerUsage.html htmlTitles: Using rcellminer Package: rCGH Version: 1.4.0 Depends: R (>= 3.2.1),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: 7204ba931b5948aca2d3cb8e8215a58d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rCGH_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rCGH_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rCGH_1.4.0.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: 2.12.0 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: 958437b738a492a9f6b9046bea04b8ae 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rchemcpp_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rchemcpp_2.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rchemcpp_2.12.0.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: 2.14.0 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: 996cb1417bb385922cb504aba08ec0d9 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RchyOptimyx_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RchyOptimyx_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RchyOptimyx_2.14.0.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: Rcpi Version: 1.10.6 Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 60f319ad35b6bdae905f8af6ca4b1786 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], Dongsheng Cao [aut], Qingsong Xu [aut] Maintainer: Nan Xiao URL: http://nanx.me/Rcpi/, https://github.com/road2stat/Rcpi BugReports: https://github.com/road2stat/Rcpi/issues source.ver: src/contrib/Rcpi_1.10.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rcpi_1.10.6.zip win64.binary.ver: bin/windows64/contrib/3.3/Rcpi_1.10.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rcpi_1.10.6.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.pdf, vignettes/Rcpi/inst/doc/Rcpi.pdf vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcpi/inst/doc/Rcpi-quickref.R, vignettes/Rcpi/inst/doc/Rcpi.R Package: RCy3 Version: 1.5.2 Depends: R (>= 3.2), graph (>= 1.48.0) Imports: httr, methods, RCurl, RJSONIO Suggests: BiocGenerics, RUnit, knitr, igraph, RColorBrewer, paxtoolsr, rmarkdown License: Artistic-2.0 MD5sum: be6aabbf550d576a67c56f6b7b38a50d NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape >= 3.3.0 Description: Vizualize, analyze and explore graphs, connecting R to Cytoscape (>= 3.3.0). biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Tanja Muetze, Georgi Kolishovski, Paul Shannon Maintainer: Tanja Muetze , Georgi Kolishovski , Paul Shannon URL: https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape SystemRequirements: Cytoscape (>= 3.3.0), CyREST (>= 3.3.7), Java (>=8) VignetteBuilder: knitr BugReports: https://github.com/tmuetze/Bioconductor_RCy3_the_new_RCytoscape/issues source.ver: src/contrib/RCy3_1.5.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RCy3_1.5.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RCy3_1.5.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RCy3_1.5.2.tgz vignettes: vignettes/RCy3/inst/doc/RCy3.pdf vignetteTitles: RCy3 Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCy3/inst/doc/RCy3.R Package: RCyjs Version: 1.6.0 Depends: R (>= 3.2.0), BrowserViz (>= 1.1.7), graph (>= 1.44.0) Imports: methods, httpuv (>= 1.3.2), Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), BiocGenerics, igraph Suggests: RUnit, BiocStyle, RefNet License: GPL-2 MD5sum: 6f99b96c0c4ab12726c163767b2bb8a6 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCyjs_1.6.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RCyjs_1.6.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.pdf vignetteTitles: RCyjs hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R Package: RCytoscape Version: 1.24.1 Depends: R (>= 2.14.0), graph (>= 1.31.0), XMLRPC (>= 0.2.4) Imports: methods, XMLRPC, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: c64e5f3441ecfc646d175d4eaeb2550e NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape Description: Interactvive viewing and exploration of graphs, connecting R to Cytoscape. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCytoscape_1.24.1.tar.gz vignettes: vignettes/RCytoscape/inst/doc/RCytoscape.pdf vignetteTitles: RCytoscape Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCytoscape/inst/doc/RCytoscape.R importsMe: categoryCompare, NCIgraph suggestsMe: GeneNetworkBuilder, mmnet Package: RDAVIDWebService Version: 1.12.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: 46bc74a1c1fbbcf8e7d637435562e71f 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RDAVIDWebService_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RDAVIDWebService_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RDAVIDWebService_1.12.0.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 Package: rDGIdb Version: 1.0.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 00d6482aad00afcc5fb04f4eded295bd 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://dgidb.genome.wustl.edu/). 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rDGIdb_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rDGIdb_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rDGIdb_1.0.0.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: 1.34.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: 24d11bb4555056c907be4709ee83c275 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rdisop_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rdisop_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rdisop_1.34.0.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: 1.24.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: d2b56ecc31d75084b182c536b8ff7021 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RDRToolbox_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RDRToolbox_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RDRToolbox_1.24.0.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: 1.18.1 Depends: R (>= 3.3.0), DOSE (>= 2.11.12) Imports: AnnotationDbi, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, testthat License: GPL-2 MD5sum: 763197e1b81292a893d301d454c4dd4a 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 with contributions from Vladislav Petyuk Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/ReactomePA VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues source.ver: src/contrib/ReactomePA_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReactomePA_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.3/ReactomePA_1.18.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReactomePA_1.18.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R htmlDocs: vignettes/ReactomePA/inst/doc/ReactomePA.html htmlTitles: An R package for Reactome Pathway Analysis importsMe: bioCancer, LINC suggestsMe: ChIPseeker, CINdex, clusterProfiler Package: readat Version: 1.0.0 Depends: R (>= 3.3.1) Imports: assertive.base (>= 0.0-6), assertive.files (>= 0.0-2), assertive.numbers (>= 0.0-2), assertive.properties (>= 0.0-3), assertive.sets (>= 0.0-2), assertive.types (>= 0.0-2), Biobase (>= 2.32.0), data.table (>= 1.9.6), dplyr (>= 0.5.0), magrittr (>= 1.5), openxlsx (>= 3.0.0), pathological (>= 0.1-1), reshape2 (>= 1.4.1), stats, stringi (>= 1.1.1), SummarizedExperiment (>= 1.2.3), testthat (>= 1.0.2), tidyr (>= 0.6.0), utils Suggests: knitr, MSnbase, rmarkdown, withr License: GPL-3 MD5sum: 8bbea6f118afed62ed487c21ed6a902c 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/readat_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/readat_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/readat_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/readat/inst/doc/introduction.R htmlDocs: vignettes/readat/inst/doc/introduction.html htmlTitles: Introduction Package: ReadqPCR Version: 1.20.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: a298d6c50e2ee56397e561d7dee9e5b8 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReadqPCR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ReadqPCR_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReadqPCR_1.20.0.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: 1.52.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: f9ac71aafd5c4e6188ff7a51f0b6bb17 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/reb_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/reb_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/reb_1.52.0.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: 1.0.17 Depends: R (>= 3.3.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.34.2), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocStyle, DESeq2, devtools (>= 1.6), EnsDb.Hsapiens.v79, GenomicFeatures, knitcitations, knitr (>= 1.6), org.Hs.eg.db, regionReport, rmarkdown (>= 0.9.5), testthat License: Artistic-2.0 MD5sum: d49fcc3fb7b0b4059b5dc18ddd2ea65e 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://biorxiv.org/content/early/2016/08/08/068478. 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/ source.ver: src/contrib/recount_1.0.17.tar.gz win.binary.ver: bin/windows/contrib/3.3/recount_1.0.17.zip win64.binary.ver: bin/windows64/contrib/3.3/recount_1.0.17.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/recount_1.0.17.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R htmlDocs: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html htmlTitles: recount quick start guide, Basic DESeq2 results exploration Package: recoup Version: 1.2.0 Depends: GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, circlize, rtracklayer, plyr Suggests: grid, GenomeInfoDb, Rsamtools, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocInstaller, BSgenome, RSQLite, RMySQL Enhances: parallel License: GPL (>= 3) MD5sum: f13eeac3e3b2cf1eec9f345ce3fbad1d 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/recoup_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/recoup_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/recoup_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R htmlDocs: vignettes/recoup/inst/doc/recoup_intro.html htmlTitles: Introduction to the recoup package Package: RedeR Version: 1.22.0 Depends: R (>= 2.15), methods, igraph Imports: RCurl, XML, pvclust Suggests: PANR License: GPL (>= 2) MD5sum: b5ddad64ae237134b4ec88baa8d0047c 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) source.ver: src/contrib/RedeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RedeR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RedeR_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RedeR_1.22.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.pdf vignetteTitles: Main vignette: interactive visualization and manipulation of nested networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: SRGnet importsMe: PANR, RTN Package: REDseq Version: 1.20.0 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: 508ca179c45db20d55afb12218c90f55 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/REDseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/REDseq_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/REDseq_1.20.0.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: 1.10.1 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: 0bdee431d4bb8ea9cfdd83b7ab8da867 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RefNet_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RefNet_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RefNet_1.10.1.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: 1.44.0 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: 7c71bc55e413af2dc37da4d5badbed54 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RefPlus_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RefPlus_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RefPlus_1.44.0.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: 1.6.2 Depends: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Imports: memoise, GenomicRanges, BSgenome, rtracklayer, parallel, graphics, stats, utils, GenomeInfoDb, IRanges Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 93ecdf4d04f690580c0f1681bddc1246 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/regioneR_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/regioneR_1.6.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/regioneR_1.6.2.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 importsMe: annotatr, ChIPpeakAnno Package: regionReport Version: 1.8.2 Depends: R(>= 3.2) Imports: 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: BiocStyle, biovizBase, bumphunter (>= 1.7.6), Cairo, 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: cdf7a0fdbe7c76133c91cdbfc5bde865 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/ source.ver: src/contrib/regionReport_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/regionReport_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/regionReport_1.8.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/regionReport_1.8.2.tgz 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 htmlDocs: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/bumphunterExampleOutput.html, vignettes/regionReport/inst/doc/regionReport.html htmlTitles: Example report using bumphunter results, Basic genomic regions exploration, Introduction to regionReport suggestsMe: recount Package: regsplice Version: 1.0.0 Imports: glmnet, SummarizedExperiment, S4Vectors, BiocParallel, limma, edgeR, stats, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: a791f63171e2bd5c3c70774aded62f2b NeedsCompilation: no Title: Lasso-Based Methods for Detection of Differential Exon Usage Description: Statistical methods for detection of differential exon usage in RNA-seq and exon microarray data sets, 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/regsplice_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/regsplice_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/regsplice_1.0.0.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: Repitools Version: 1.20.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.9.25), IRanges (>= 1.20.0), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome, 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: 3f27f4e075425ed0b3a9bb47bcaf8aaf 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Repitools_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Repitools_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Repitools_1.20.0.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: 2.14.0 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, License: Artistic-2.0 MD5sum: d40177df43301a9808e7027eda095e9c 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReportingTools_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ReportingTools_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReportingTools_2.14.0.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 vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny 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 htmlDocs: vignettes/ReportingTools/inst/doc/knitr.html htmlTitles: Knitr and ReportingTools importsMe: affycoretools, EnrichmentBrowser suggestsMe: cpvSNP, GSEABase, npGSEA Package: ReQON Version: 1.20.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: c81fb414fa67f48cb9db3208b79b30f5 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReQON_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ReQON_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReQON_1.20.0.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: rfPred Version: 1.12.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: 8145be3ffc6276a0b31a4ed3ccb60e35 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rfPred_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rfPred_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rfPred_1.12.0.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: 2.22.0 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: 68855b9cecf4cb029027b6df0a0b6b2d 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rGADEM_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rGADEM_2.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rGADEM_2.22.0.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: 1.18.0 Depends: XML, methods, tools, optparse, digest, Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: 924e98c9cd983008ebe01df4c5fb6e2f 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGalaxy_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RGalaxy_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGalaxy_1.18.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R htmlDocs: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html htmlTitles: Introduction to RGalaxy Package: RGraph2js Version: 1.2.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 049c4cadda87991cce3113703164e6ae 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGraph2js_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RGraph2js_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGraph2js_1.2.0.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: 2.18.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: 0c55f4c53a311f1e5ba9028d865afd09 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rgraphviz_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rgraphviz_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rgraphviz_2.18.0.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, mvGST, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, ToPASeq importsMe: apComplex, biocGraph, CompGO, CytoML, DEGraph, EnrichmentBrowser, facopy, flowWorkspace, GOFunction, hyperdraw, mirIntegrator, nem, OncoSimulR, paircompviz, pathview, Pigengene, qpgraph, RchyOptimyx, SplicingGraphs, TRONCO suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, ddgraph, DEGraph, flowCore, GeneNetworkBuilder, geneplotter, GlobalAncova, globaltest, GOstats, GSEABase, KEGGgraph, MLP, NCIgraph, oneChannelGUI, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, vtpnet Package: rGREAT Version: 1.6.0 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils Suggests: testthat (>= 0.3), knitr, circlize License: GPL (>= 2) MD5sum: 4f42ca21eed8e6df481444f21781d209 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 VignetteBuilder: knitr source.ver: src/contrib/rGREAT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rGREAT_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rGREAT_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rGREAT_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R htmlDocs: vignettes/rGREAT/inst/doc/rGREAT.html htmlTitles: Analyze with GREAT Package: RGSEA Version: 1.8.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 0c0f7d4d03f8bfa58b2fface148dbccb 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGSEA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RGSEA_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGSEA_1.8.0.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: 1.6.0 Depends: R (>= 3.3.0), DESeq2, goseq (>= 1.17) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, RUnit, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 7145a4be5879b2c25aa49eae5a3d16e0 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rgsepd_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rgsepd_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rgsepd_1.6.0.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: 2.18.0 Depends: methods Imports: zlibbioc Suggests: bit64,BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: f7838670e2bf8646a5ad47ee381b5f2b NeedsCompilation: yes Title: HDF5 interface to R Description: This R/Bioconductor 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, Gregoire Pau Maintainer: Bernd Fischer SystemRequirements: GNU make source.ver: src/contrib/rhdf5_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rhdf5_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rhdf5_2.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rhdf5_2.18.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.pdf vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GENE.E, GSCA importsMe: biomformat, diffHic, DOQTL, GENE.E, h5vc, HDF5Array, IONiseR, scater suggestsMe: SummarizedExperiment Package: Rhtslib Version: 1.6.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 55d3eb8ebd9762b87f145c33b8e72373 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.1 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 [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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rhtslib_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rhtslib_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rhtslib_1.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R htmlDocs: vignettes/Rhtslib/inst/doc/Rhtslib.html htmlTitles: Motivation and Use of Rhtslib dependsOnMe: deepSNV importsMe: csaw, deepSNV, diffHic Package: rHVDM Version: 1.40.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: 4a95a4327bad2b6d6330f86de848208f 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rHVDM_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rHVDM_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rHVDM_1.40.0.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: 1.4.1 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: e90c754d3cfccdce9a1d9848ce9b053d 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RiboProfiling_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RiboProfiling_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RiboProfiling_1.4.1.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: 1.8.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: d140e8e95aeb56770375e802d716924c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/riboSeqR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/riboSeqR_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/riboSeqR_1.8.0.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: 1.2.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 2873ef43c7ffb34c1d524b7518810999 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: Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr source.ver: src/contrib/RImmPort_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RImmPort_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RImmPort_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RImmPort_1.2.0.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: 1.38.0 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: 17a1b47d16f09dd4525f26b272c48539 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Ringo_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Ringo_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Ringo_1.38.0.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: 1.14.0 Depends: R (>= 2.15), methods, S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: bfabcde873a440e12f7ff2aa6ec47975 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RIPSeeker_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RIPSeeker_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RIPSeeker_1.14.0.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: 1.16.0 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: 6d8fff3bc2f11dce9a8317dfa554b0be 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Risa_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Risa_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Risa_1.16.0.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: RLMM Version: 1.36.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: f3f6e78a8806d182fd77a1600f15208b 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RLMM_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RLMM_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RLMM_1.36.0.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: 1.30.0 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: ba5d2b5782d177919878aff332638eae 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rmagpie_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rmagpie_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rmagpie_1.30.0.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: 2.2.1 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: bed8cefcc3db7231fcdf5ee5af258d44 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_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RMassBank_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RMassBank_2.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RMassBank_2.2.1.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: 3.24.0 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: bc6d34187eca473eee385005a998b651 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_3.24.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rMAT_3.24.0.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: 1.30.0 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: 14e17ae2c910f0c1b5551743304c91ee 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RmiR_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RmiR_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RmiR_1.30.0.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 suggestsMe: oneChannelGUI Package: RNAinteract Version: 1.22.0 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: 8a0f17fb3f9284b21545720f27ae2290 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAinteract_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAinteract_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAinteract_1.22.0.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: 2.22.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 29cd60f4554987551067434ffb71715a 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAither_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAither_2.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAither_2.22.0.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: 1.6.0 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), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12) Suggests: BiocStyle License: GPL (>=2) MD5sum: 259ce29e97cefb8569fdddbfce2cb510 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAprobR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAprobR_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAprobR_1.6.0.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: 1.4.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: b0b383f3b8f4f425afc3ec8cd7032a3f 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rnaseqcomp_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rnaseqcomp_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rnaseqcomp_1.4.0.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 Package: rnaSeqMap Version: 2.32.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: d264449b52509dc1fd80df8808d37e2f 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rnaSeqMap_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rnaSeqMap_2.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rnaSeqMap_2.32.0.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: 1.14.0 License: LGPL (>=2) MD5sum: 685017f2ad8309d3c16958b74dc947c6 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNASeqPower_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNASeqPower_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNASeqPower_1.14.0.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: 1.6.0 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: 8dffc306e07720525d95bdec11f75cfa 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RnaSeqSampleSize_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RnaSeqSampleSize_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RnaSeqSampleSize_1.6.0.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: 1.6.1 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, RColorBrewer, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GEOquery, 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, sva, wateRmelon, wordcloud, argparse, glmnet, impute License: GPL-3 MD5sum: c305e40da0de671c295e52360904bbb2 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], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller source.ver: src/contrib/RnBeads_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RnBeads_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RnBeads_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RnBeads_1.6.1.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: 1.8.0 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: 579f3d6bd1ca54c7fd8576b46a34d152 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rnits_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rnits_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rnits_1.8.0.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: 1.10.0 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: 92865bf8ceb4c85c7e934b4e00a92a51 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/roar_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/roar_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/roar_1.10.0.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: 1.50.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 365d2b3224174fc2e405a7cd5334ab9b 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROC_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROC_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROC_1.50.0.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: 1.12.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: ed14309b2d303faea5d1c9d3cd938388 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Roleswitch_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Roleswitch_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Roleswitch_1.12.0.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: 2.2.5 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase Suggests: GO.db, knitr (>= 1.1.0), BiocStyle, testthat, lubridate, DT, rmarkdown License: GPL-2 MD5sum: 71371ef54d2b74ec773a0447266be541 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: 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 source.ver: src/contrib/rols_2.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/rols_2.2.5.zip win64.binary.ver: bin/windows64/contrib/3.3/rols_2.2.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rols_2.2.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R htmlDocs: vignettes/rols/inst/doc/rols.html htmlTitles: The rols interface to the Ontology Lookup Service suggestsMe: MSnbase Package: ROntoTools Version: 2.2.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: dc6003bfe9be7bcd476adce52eb50e64 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROntoTools_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROntoTools_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROntoTools_2.2.0.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 Package: ropls Version: 1.6.2 Imports: Biobase, methods Suggests: BiocGenerics, BiocStyle, CAMERA, faahKO, knitr, multtest, rmarkdown, RUnit, xcms License: CeCILL MD5sum: 2fb5a979001b540f29ac634a73b46e31 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ropls_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ropls_1.6.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ropls_1.6.2.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: biosigner suggestsMe: proFIA Package: ROTS Version: 1.2.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 8c70dc276838e5fffe5f20f388e34401 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROTS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROTS_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROTS_1.2.0.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 Package: RPA Version: 1.30.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods, phyloseq Suggests: affydata, parallel License: BSD_2_clause + file LICENSE MD5sum: 6510539053459f44732d4705802c8ccf 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. Lahti et al. "Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays", TCBB/IEEE, 2011. http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.38 biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA BugReports: https://github.com/antagomir/RPA source.ver: src/contrib/RPA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RPA_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RPA_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RPA_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs Package: RpsiXML Version: 2.16.0 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: a165ca5863cd8c0237d348cc8cddd190 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RpsiXML_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RpsiXML_2.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RpsiXML_2.16.0.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: 1.10.2 Depends: methods Imports: XML, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, testthat, knitr License: GPL-2 MD5sum: 6ad203643070cc6b2504e7869ca5ca24 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: This 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_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rpx_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rpx_1.10.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rpx_1.10.2.tgz vignettes: vignettes/rpx/inst/doc/rpx.pdf vignetteTitles: An interface to proteomics data repositories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MSnbase, proteoQC Package: Rqc Version: 1.8.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics, 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: 82465dc0ed9fdc82684e11c477b6f4cc 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 source.ver: src/contrib/Rqc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rqc_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rqc_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rqc_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R htmlDocs: vignettes/Rqc/inst/doc/Rqc.html htmlTitles: Using Rqc Package: rqubic Version: 1.20.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: b186c4b98bed632104f4cf2506227dbe 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rqubic_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rqubic_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rqubic_1.20.0.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: 1.8.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: b392f51cc676ab4f350ab70691667869 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rRDP_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rRDP_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rRDP_1.8.0.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: 1.14.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 5f4e0c293bd7e6125cc7ed49f0405f87 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RRHO_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RRHO_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RRHO_1.14.0.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: 1.26.2 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.21.6), Biostrings (>= 2.37.1) Imports: utils, BiocGenerics (>= 0.1.3), S4Vectors (>= 0.7.11), IRanges (>= 2.3.7), XVector (>= 0.9.1), 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, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 57f272df193b7252d90860170efb74b5 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 source.ver: src/contrib/Rsamtools_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rsamtools_1.26.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Rsamtools_1.26.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rsamtools_1.26.2.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, exomeCopy, exomePeak, GenoGAM, GenomicAlignments, GenomicFiles, girafe, gmapR, Guitar, HelloRanges, MEDIPS, methylPipe, MMDiff2, oneChannelGUI, podkat, qrqc, r3Cseq, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, TitanCNA, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, ArrayExpressHTS, BadRegionFinder, BBCAnalyzer, biovizBase, BSgenome, CAGEr, casper, CexoR, ChIPpeakAnno, ChIPQC, chromstaR, cn.mops, CNVPanelizer, CNVrd2, compEpiTools, CopywriteR, CrispRVariants, csaw, customProDB, derfinder, DEXSeq, DiffBind, diffHic, DOQTL, easyRNASeq, EDASeq, ensembldb, epigenomix, eudysbiome, FourCSeq, FunChIP, FunciSNP, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, GGtools, GoogleGenomics, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HTSeqGenie, INSPEcT, MADSEQ, maftools, metagene, methylKit, mosaics, nucleR, PGA, PICS, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, Rariant, Repitools, RiboProfiling, riboSeqR, RNAprobR, Rqc, rtracklayer, segmentSeq, similaRpeak, soGGi, SplicingGraphs, tracktables, trackViewer, transcriptR, TransView, TVTB, VariantFiltering, VariantTools suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocParallel, biomvRCNS, Chicago, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, gQTLstats, metaseqR, recoup, seqbias, SigFuge, Streamer Package: rsbml Version: 2.32.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 6554f15f6563d0fd65b37c9359098dde 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rsbml_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rsbml_2.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rsbml_2.32.0.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: rSFFreader Version: 0.22.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: a7b2fe2b7cd14a16be2b051a5371f001 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_0.22.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rSFFreader_0.22.0.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: 1.24.2 License: GPL-3 MD5sum: 5e2485502268daa7404b1d8b9d2443bb NeedsCompilation: yes Title: Subread sequence alignment for R Description: Provides powerful and easy-to-use tools for analyzing next-gen sequencing read data. Includes quality assessment of sequence reads, read alignment, read summarization, exon-exon junction detection, fusion detection, detection of short and long indels, absolute expression calling and SNP calling. Can be used with reads generated from any of the major sequencing platforms including Illumina GA/HiSeq/MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM/Proton sequencers. 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 Smyth, Jenny Dai and Timothy Triche, Jr. Maintainer: Wei Shi URL: http://bioconductor.org/packages/release/bioc/html/Rsubread.html source.ver: src/contrib/Rsubread_1.24.2.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rsubread_1.24.2.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf vignetteTitles: Rsubread Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R importsMe: dupRadar Package: RSVSim Version: 1.14.0 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: 08651d08ec44da9eb93bd2da01f4489d 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RSVSim_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RSVSim_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RSVSim_1.14.0.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: 1.14.2 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: 0ec069add992e318de75437b7ff59aef 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_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTANDEM_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rTANDEM_1.14.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTANDEM_1.14.2.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: 1.26.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 9fbd86d7388dcd65a2e22895249e4ff1 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCA_1.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCA_1.26.0.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: 1.4.0 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: bed45d2ee1409741f737e313b6d34455 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCGA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCGA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCGA_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html htmlTitles: Integrating TCGA Data - RTCGA Workflow Package: RTCGAToolbox Version: 2.4.0 Depends: R (>= 3.2.0) Imports: methods,XML,limma (>= 3.18),survival,RCircos,data.table (>= 1.9.4),RCurl,RJSONIO Suggests: BiocStyle, knitr, rmarkdown, Homo.sapiens License: GPL (>= 2) MD5sum: e03f9e9c1fbc9f7090a259f942b21d15 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: Sequencing, DifferentialExpression, GeneExpression Author: Mehmet Kemal Samur Maintainer: Mehmet Kemal Samur VignetteBuilder: knitr source.ver: src/contrib/RTCGAToolbox_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCGAToolbox_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCGAToolbox_2.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCGAToolbox_2.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R htmlDocs: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html htmlTitles: Vignette Title Package: RTN Version: 1.12.0 Depends: R (>= 2.15), methods, igraph Imports: RedeR, minet, snow, limma, data.table, ff, car, IRanges Suggests: HTSanalyzeR, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 8396a5cba75ec897b08670b58448703e 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, NetworkAnalysis, 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 source.ver: src/contrib/RTN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTN_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTN_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTN_1.12.0.tgz vignettes: vignettes/RTN/inst/doc/RTN.pdf vignetteTitles: Main vignette: reconstruction and analysis of transcriptional networks in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R Package: RTopper Version: 1.20.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 598f72d6a8a7defbbb1d2775c0cf7d5e 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTopper_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTopper_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTopper_1.20.0.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: 1.34.2 Depends: R (>= 3.3), methods, GenomicRanges (>= 1.21.20) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.33), IRanges (>= 2.3.7), XVector (>= 0.9.4), GenomeInfoDb (>= 1.3.14), Biostrings (>= 2.37.1), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.17.8), GenomicAlignments (>= 1.5.4), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: f631b7060bd3b397e5e938bb0af94f22 NeedsCompilation: yes Title: R interface to genome browsers and their annotation tracks 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 source.ver: src/contrib/rtracklayer_1.34.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rtracklayer_1.34.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rtracklayer_1.34.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rtracklayer_1.34.2.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CoverageView, cummeRbund, exomePeak, geneXtendeR, GenomicFiles, groHMM, Guitar, HelloRanges, MethylSeekR, r3Cseq, regioneR, RIPSeeker, spliceR importsMe: AnnotationHubData, annotatr, ballgown, BiSeq, BSgenome, CAGEr, casper, CexoR, ChIPseeker, ChromHeatMap, CNEr, coMET, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, DeepBlueR, derfinder, diffloop, ensembldb, erma, FourCSeq, FunciSNP, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, GGtools, gmapR, GOTHiC, gQTLBase, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, MADSEQ, MEDIPS, metagene, methyAnalysis, methylKit, motifbreakR, MotifDb, normr, Pbase, PGA, proBAMr, qsea, QuasR, RCAS, recount, recoup, regioneR, Repitools, RiboProfiling, RNAprobR, roar, seqplots, SGSeq, similaRpeak, soGGi, TFBSTools, trackViewer, transcriptR, VariantAnnotation, VariantTools, wavClusteR suggestsMe: alpine, AnnotationHub, biovizBase, ChIPpeakAnno, CINdex, compEpiTools, CrispRVariants, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, MotIV, MutationalPatterns, NarrowPeaks, oneChannelGUI, OrganismDbi, PICS, PING, pqsfinder, PureCN, R453Plus1Toolbox, Ringo, rMAT, RnBeads, RSVSim, signeR, triplex, TSSi, TVTB Package: Rtreemix Version: 1.36.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: a683e28989c5abdb2d044f64bac7cebe 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rtreemix_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rtreemix_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rtreemix_1.36.0.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: 1.12.0 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: 7ec063219a9b776dc48b5b6b9b0bb3bc 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTRM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rTRM_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTRM_1.12.0.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: 1.12.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 92e4c1c86212a1497e4a4b999e309349 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTRMui_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rTRMui_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTRMui_1.12.0.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: RUVcorr Version: 1.6.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 MD5sum: 923f0a218fba54fb99935aedc813510a 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVcorr_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVcorr_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVcorr_1.6.0.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: 1.8.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 30b5ecfe60231cbf74e06dc13f9691a2 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVnormalize_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVnormalize_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVnormalize_1.8.0.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: 1.8.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 41142c2fbb6a7ac6de80ab8b93646595 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVSeq_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVSeq_1.8.0.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 Package: S4Vectors Version: 0.12.2 Depends: R (>= 3.3.0), methods, utils, stats, stats4, BiocGenerics (>= 0.15.10) Suggests: IRanges, GenomicRanges, Matrix, ShortRead, graph, data.table, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: df48bd8a07a23fc72a0401173d2a8167 NeedsCompilation: yes Title: S4 implementation of vectors and lists 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_0.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/S4Vectors_0.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/S4Vectors_0.12.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/S4Vectors_0.12.2.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ASpli, Biostrings, BiSeq, BSgenome, bumphunter, CellMapper, CexoR, ChIPpeakAnno, chipseq, ChIPseqR, CODEX, CSAR, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, epigenomix, ExperimentHubData, ExpressionAtlas, fCCAC, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, girafe, groHMM, Gviz, HDF5Array, HelloRanges, htSeqTools, InPAS, IRanges, isomiRs, meshr, MotifDb, OTUbase, plethy, RIPSeeker, RnBeads, segmentSeq, triplex, VariantTools, XVector importsMe: affycoretools, ALDEx2, AllelicImbalance, alpine, AneuFinder, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, ArrayTV, BadRegionFinder, ballgown, biovizBase, BiSeq, BitSeq, BPRMeth, BSgenome, bsseq, casper, chipenrich, ChIPQC, ChIPseeker, chromstaR, cleaver, cn.mops, CNEr, CNPBayes, CNVPanelizer, coMET, compEpiTools, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, cummeRbund, customProDB, DChIPRep, debrowser, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, diffloop, DMRcate, DOSE, DRIMSeq, easyRNASeq, eegc, EnrichmentBrowser, ensembldb, ensemblVEP, epivizr, epivizrData, epivizrStandalone, erma, ExperimentHub, facopy, fastseg, FindMyFriends, FunciSNP, genbankr, genefilter, GeneRegionScan, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, genoset, GGBase, ggbio, GGtools, gmapR, GoogleGenomics, GOpro, GOTHiC, gQTLBase, gQTLstats, GRmetrics, GUIDEseq, gwascat, h5vc, HTSeqGenie, INSPEcT, InteractionSet, IVAS, JunctionSeq, kebabs, LOLA, M3D, MADSEQ, MAST, matter, MEAL, methylKit, methylPipe, methylumi, minfi, MinimumDistance, MiRaGE, MMDiff2, mosaics, motifbreakR, MotIV, msa, MSnbase, MultiAssayExperiment, MultiDataSet, mygene, myvariant, NarrowPeaks, nucleoSim, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pdInfoBuilder, PGA, PICS, PING, polyester, pqsfinder, prebs, procoil, PureCN, qcmetrics, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RareVariantVis, Rariant, Rcade, RCAS, recount, regionReport, regsplice, Repitools, RiboProfiling, roar, Rqc, Rsamtools, rtracklayer, SeqArray, seqplots, SeqVarTools, sevenbridges, SGSeq, ShortRead, simulatorZ, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, SplicingGraphs, SPLINTER, STAN, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TFBSTools, trackViewer, transcriptR, TransView, TSSi, TVTB, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, xcms, XVector, yamss suggestsMe: BiocGenerics, RTCGA, scran Package: safe Version: 3.14.0 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: 7ee04c60f66ea6ca4c366afed81c23bc 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_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/safe_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/safe_3.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/safe_3.14.0.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: 1.44.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: 491d572d8e20c56f56a101cb9529a3c6 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sagenhaft_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sagenhaft_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sagenhaft_1.44.0.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: 1.48.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: b6f34e285fa324da6f36612ef81c6e24 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SAGx_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SAGx_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SAGx_1.48.0.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: SamSPECTRAL Version: 1.28.0 Depends: R (>= 2.10) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 615a3b87f79fba9b83c9a305bcc21d3f 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SamSPECTRAL_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SamSPECTRAL_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SamSPECTRAL_1.28.0.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 Package: sangerseqR Version: 1.10.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 3c03310759fa77654a54dea43750cadf 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sangerseqR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sangerseqR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sangerseqR_1.10.0.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: 2.12.0 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: 7b6f6134362efb79a4a0f325ffec499b 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SANTA_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SANTA_2.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SANTA_2.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R htmlDocs: vignettes/SANTA/inst/doc/SANTA-vignette.html htmlTitles: Introduction to SANTA Package: sapFinder Version: 1.12.0 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: 06483162261e357163b2f4a2f0036862 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sapFinder_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sapFinder_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sapFinder_1.12.0.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: 1.12.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: cd7bd7dc9e5f9d7ce6ccc9af7121e5cc 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/savR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/savR_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/savR_1.12.0.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: 1.70.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: af7bc56c4aff2e98edabdd08931c866d 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_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SBMLR_1.70.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SBMLR_1.70.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SBMLR_1.70.0.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: 1.3.18 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), scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, mclust License: GPL-3 Archs: i386, x64 MD5sum: c5a36c1213c87f64d6c440c7601f7fe3 NeedsCompilation: no 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_1.3.18.tar.gz win.binary.ver: bin/windows/contrib/3.3/SC3_1.3.18.zip win64.binary.ver: bin/windows64/contrib/3.3/SC3_1.3.18.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SC3_1.3.18.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SC3/inst/doc/my-vignette.R htmlDocs: vignettes/SC3/inst/doc/my-vignette.html htmlTitles: SC3 package manual Package: SCAN.UPC Version: 2.16.0 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: 35243a75ca33443c3741b3def392bead 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SCAN.UPC_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SCAN.UPC_2.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SCAN.UPC_2.16.0.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: 1.2.0 Depends: R (>= 3.3), Biobase, ggplot2, methods Imports: biomaRt, BiocGenerics, data.table, dplyr, edgeR, ggbeeswarm, grid, limma, matrixStats, parallel, plyr, reshape2, rhdf5, rjson, shiny, shinydashboard, stats, tximport, viridis Suggests: BiocStyle, cowplot, cluster, destiny, knitr, monocle, mvoutlier, rmarkdown, Rtsne, testthat, magrittr License: GPL (>= 2) Archs: i386, x64 MD5sum: 3b3a617010a6cf8b129040fc01812e20 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 Author: Davis McCarthy Maintainer: Davis McCarthy URL: https://github.com/davismcc/scater VignetteBuilder: knitr BugReports: https://github.com/davismcc/scater/issues source.ver: src/contrib/scater_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/scater_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/scater_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scater_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/vignette.R htmlDocs: vignettes/scater/inst/doc/vignette.html htmlTitles: An introduction to the scater package dependsOnMe: scran importsMe: SC3 suggestsMe: switchde Package: scde Version: 2.2.0 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: f812f348edd4868eab6f9d3f2d7514df 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/scde_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/scde_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scde_2.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScISI Version: 1.46.0 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: 31e8ff5011f1adae4e0adbab0699d1d7 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ScISI_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ScISI_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ScISI_1.46.0.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: scran Version: 1.2.2 Depends: R (>= 3.3.0), BiocParallel, scater Imports: dynamicTreeCut, zoo, edgeR, stats, BiocGenerics, methods, Biobase, utils, Matrix, shiny, graphics, grDevices, statmod Suggests: limSolve, limma, testthat, knitr, BiocStyle, org.Mm.eg.db, DESeq2, monocle, S4Vectors, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 1b2e2a58ae36171a12ba289c2143f431 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes. biocViews: Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr source.ver: src/contrib/scran_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/scran_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/scran_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scran_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R htmlDocs: vignettes/scran/inst/doc/scran.html htmlTitles: Using scran to perform basic analyses of single-cell RNA-seq data Package: scsR Version: 1.10.0 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: 78c07fb85efcae4507a9564e11c4d861 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/scsR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/scsR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scsR_1.10.0.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: segmentSeq Version: 2.8.0 Depends: R (>= 2.3.0), methods, baySeq (>= 1.99.0), S4Vectors, parallel, GenomicRanges, ShortRead Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: b90281e6a4c16ac38e665c9fec01a00e 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/segmentSeq_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/segmentSeq_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/segmentSeq_2.8.0.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: 1.6.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: 2aaeca6f6c75ac4e6251767a771d6821 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SELEX_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SELEX_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SELEX_1.6.0.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: 1.8.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: a425f53c9317f9ba19d949ee9de9d5e1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SemDist_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SemDist_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SemDist_1.8.0.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: SEPA Version: 1.4.0 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: 79a06a4599df557f9e4217dc3dc97a2f 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SEPA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SEPA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SEPA_1.4.0.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: seq2pathway Version: 1.6.0 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 1ee0ec8a9cbee0b144d12312cb0bd1ca 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seq2pathway_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seq2pathway_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seq2pathway_1.6.0.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: 1.14.1 Depends: R (>= 3.3.0), gdsfmt (>= 1.10.1) Imports: methods, parallel, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, SummarizedExperiment, Biostrings, VariantAnnotation LinkingTo: gdsfmt Suggests: BiocParallel, digest, crayon, RUnit, Biobase, BiocGenerics, knitr, Rcpp, SNPRelate License: GPL-3 Archs: i386, x64 MD5sum: b8523fe4ac2431e52ee3a11006cc9157 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 source.ver: src/contrib/SeqArray_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqArray_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqArray_1.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqArray_1.14.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/R_Integration.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R htmlDocs: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/R_Integration.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html htmlTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access dependsOnMe: SeqVarTools importsMe: GENESIS Package: seqbias Version: 1.22.0 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: 849001ed43f2489d1b843d0ef578311c 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqbias_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqbias_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqbias_1.22.0.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: seqCNA Version: 1.20.0 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: 44fb143b689ad761a83a5b0b52668da1 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqCNA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqCNA_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqCNA_1.20.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf vignetteTitles: seqCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R Package: SeqGSEA Version: 1.14.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: d5f1c9747089cda26d28b7aac210df3c 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqGSEA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqGSEA_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqGSEA_1.14.0.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: 1.40.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: 4fd759c2257243dea8f0c89c5329710b 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqLogo_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqLogo_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqLogo_1.40.0.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: PWMEnrich, rGADEM, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, MotifDb Package: seqPattern Version: 1.6.0 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: b0f51e76f8e30b7bcd2969f97622d83c 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqPattern_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqPattern_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqPattern_1.6.0.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: 1.12.0 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 Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 500c15544ad7da18d01ae140659ec078 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 Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues source.ver: src/contrib/seqplots_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqplots_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqplots_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqplots_1.12.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R htmlDocs: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html htmlTitles: Vignette Title, Vignette Title Package: seqTools Version: 1.8.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 642d01b86c01479c4372acff32cd0383 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqTools_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqTools_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqTools_1.8.0.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: 1.12.0 Depends: SeqArray (>= 1.11.17) Imports: grDevices, graphics, stats, methods, stringr, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, VariantAnnotation, Biobase, logistf Suggests: BiocGenerics, BiocStyle, RUnit License: GPL-3 MD5sum: 7c09d4e63dc9350516782dd00a23ca90 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 , Xiuwen Zheng , Adrienne Stilp source.ver: src/contrib/SeqVarTools_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqVarTools_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqVarTools_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqVarTools_1.12.0.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: 1.4.9 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, liftr, 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: 46d6996a0d923b034f31ffbad17db69e 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], Dusan Randjelovic [aut], Emile Young [ctb], Tengfei Yin [aut], Seven Bridges Genomics [cph] 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 source.ver: src/contrib/sevenbridges_1.4.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/sevenbridges_1.4.9.zip win64.binary.ver: bin/windows64/contrib/3.3/sevenbridges_1.4.9.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sevenbridges_1.4.9.tgz 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 htmlDocs: 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 htmlTitles: 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 Package: SGSeq Version: 1.8.1 Depends: IRanges, GenomicRanges (>= 1.23.21), Rsamtools, SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, RUnit, S4Vectors (>= 0.9.39), grDevices, graphics, igraph, parallel, rtracklayer, stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: 28c53d733f974c453d07fa15966d5fdf 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 sequence reads mapped to a reference genome in BAM format. Genes are represented as a genome-wide splice graph, which can be obtained from existing annotation or can be predicted from the data. 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 package 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SGSeq_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SGSeq_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SGSeq_1.8.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R htmlDocs: vignettes/SGSeq/inst/doc/SGSeq.html htmlTitles: SGSeq Package: shinyMethyl Version: 1.10.0 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: 93e712471cf35f44fc54b3a2fa0055a2 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/shinyMethyl_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/shinyMethyl_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/shinyMethyl_1.10.0.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: 1.12.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: 574e993c0f0aee7736f16eabd3c433fe 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/shinyTANDEM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/shinyTANDEM_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/shinyTANDEM_1.12.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ShortRead Version: 1.32.1 Depends: BiocGenerics (>= 0.11.3), BiocParallel, Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4), GenomicAlignments (>= 1.5.4) Imports: Biobase, S4Vectors (>= 0.7.1), IRanges (>= 2.3.7), GenomeInfoDb (>= 1.1.19), GenomicRanges (>= 1.21.6), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: 471dc8d60988ff8de17e0c4c4d60ecfa 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_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/ShortRead_1.32.1.zip win64.binary.ver: bin/windows64/contrib/3.3/ShortRead_1.32.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ShortRead_1.32.1.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, girafe, HTSeqGenie, nucleR, OTUbase, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: ArrayExpressHTS, BEAT, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, GOTHiC, IONiseR, metagenomeFeatures, QuasR, R453Plus1Toolbox, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools, S4Vectors Package: SICtools Version: 1.4.0 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: 3febcbd21bc0517c778c660326b277ff 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_1.4.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SICtools_1.4.0.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: 1.22.0 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: 29779a98b5a8da70ddcb0f37a8473680 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sigaR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sigaR_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigaR_1.22.0.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 dependsOnMe: HCsnip Package: SigCheck Version: 2.6.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 21fd89535278cd85d37f726ef6349a95 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SigCheck_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SigCheck_2.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SigCheck_2.6.0.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: 1.12.0 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: b35369ad06032b23aa8876a59ee89743 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SigFuge_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SigFuge_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SigFuge_1.12.0.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: 1.48.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: 768b519560144ba058352bb58747f426 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/siggenes_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/siggenes_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/siggenes_1.48.0.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, oneChannelGUI importsMe: charm, GeneSelector, minfi suggestsMe: GeneSelector, logicFS, trio, XDE Package: sights Version: 1.0.0 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: 0369de5c1689e91a55f1a4bd0d5d8592 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sights_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sights_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sights_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R htmlDocs: vignettes/sights/inst/doc/sights.html htmlTitles: Using **SIGHTS** R-package Package: signeR Version: 1.0.1 Imports: BiocGenerics, Biostrings, BSgenome (>= 1.36.3), class, graphics, grDevices, GenomicRanges, nloptr, methods, NMF, stats, utils, VariantAnnotation, PMCMR LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 Archs: i386, x64 MD5sum: edc96646e9c9e8f88a0a147f4662095b 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 SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/signeR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/signeR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/signeR_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/signeR_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R htmlDocs: vignettes/signeR/inst/doc/signeR-vignette.html htmlTitles: signeR Package: sigPathway Version: 1.42.0 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: 0a6d6d8eb343e207938bc1bc0d36ba81 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sigPathway_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sigPathway_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigPathway_1.42.0.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: 1.6.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 4aca0ec23f094667ba526e06ef09d0b1 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sigsquared_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sigsquared_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigsquared_1.6.0.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: 1.44.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: e79051e298b28ff3a6779dde94b220a1 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SIM_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SIM_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SIM_1.44.0.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: 1.6.1 Depends: R (>= 3.3.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2 Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 4124f0d02af587eb447beefb34c8e0f0 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: Mo R. Nezami Ranjbar Maintainer: Mo R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html source.ver: src/contrib/SIMAT_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SIMAT_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SIMAT_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SIMAT_1.6.1.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: 1.12.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: 6a34b39e22b970be8e68d14dcbb8e262 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SimBindProfiles_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SimBindProfiles_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SimBindProfiles_1.12.0.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: 1.6.0 Depends: R6 (>= 2.0) Imports: rtracklayer, GenomicAlignments, Rsamtools, stats Suggests: RUnit, BiocGenerics, knitr, BiocStyle License: Artistic-2.0 MD5sum: 7bf9a042deba74909e28ce5485ccc3b7 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 Louise Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues source.ver: src/contrib/similaRpeak_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/similaRpeak_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/similaRpeak_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/similaRpeak_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R htmlDocs: vignettes/similaRpeak/inst/doc/similaRpeak.html htmlTitles: Similarity between two ChIP-Seq profiles suggestsMe: metagene Package: SIMLR Version: 1.0.1 Depends: R (>= 3.3), Imports: parallel, Matrix, stats, methods, Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph, scran, License: file LICENSE Archs: i386, x64 MD5sum: 97454275a744a1a434694fbf73e4fc64 NeedsCompilation: yes Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing dimension reduction methods. Additionally, SIMLR demonstrates high sensitivity and accuracy on high-throughput peripheral blood mononuclear cells (PBMC) data sets generated by the GemCode single-cell technology from 10x Genomics. biocViews: Clustering, GeneExpression, Sequencing, SingleCell Author: Bo Wang [aut], Daniele Ramazzotti [aut, cre], Luca De Sano [aut], Junjie Zhu [ctb], Emma Pierson [ctb], 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SIMLR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SIMLR_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SIMLR_1.0.1.tgz vignettes: vignettes/SIMLR/inst/doc/SIMLR.pdf vignetteTitles: An R Package for todo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/SIMLR.R Package: simpleaffy Version: 2.50.0 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: 128f6616c02f98c586bbf70d1ad1ce26 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_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/simpleaffy_2.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/simpleaffy_2.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/simpleaffy_2.50.0.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: 1.8.0 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: 52eff5823b7012165f556e7582ab84c4 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/simulatorZ_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/simulatorZ_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/simulatorZ_1.8.0.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: 1.6.0 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: 232d71c7695eddcf1def9bd5c831f56f 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sincell_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sincell_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sincell_1.6.0.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: SISPA Version: 1.4.0 Depends: R (>= 3.2),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: 3fd230d7cf47f0d638d8872c67d299f2 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SISPA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SISPA_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SISPA_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sizepower Version: 1.44.0 Depends: stats License: LGPL MD5sum: a99d6780ebc4de677b8de589dcbd24f4 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sizepower_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sizepower_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sizepower_1.44.0.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 suggestsMe: oneChannelGUI Package: skewr Version: 1.6.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, IRanges, RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: 5dcc49d863b92c01e61725360d6f204f 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/skewr_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/skewr_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/skewr_1.6.0.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: SLGI Version: 1.34.0 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: f4cdb630f665cd1048023dfe2bfc22c6 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SLGI_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SLGI_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SLGI_1.34.0.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: 1.40.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 5747c5078125bb1ae0968e709b95a754 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SLqPCR_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SLqPCR_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SLqPCR_1.40.0.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: 1.38.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 417f091f9fc8c877ffda4fea4557887a 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SMAP_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SMAP_1.38.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SMAP_1.38.0.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: 1.2.0 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: 54f540cd89ffdf18c957849910531797 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SMITE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SMITE_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SMITE_1.2.0.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: 1.14.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: c7a436033676da94295191c1e18349a9 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNAGEE_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNAGEE_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNAGEE_1.14.0.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: 1.44.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 7497133fff5332ed6667af3a414da862 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snapCGH_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snapCGH_1.44.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snapCGH_1.44.0.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: 1.22.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 24390ae2ada6cb97cbd0ba08171a04f7 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snm_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snm_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snm_1.22.0.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: 2.20.0 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: 3ad715bc239969c6ff4e690a71c58303 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPchip_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPchip_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPchip_2.20.0.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: 1.0.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: ba259fc9dd83f44322c53489a8f25116 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPediaR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPediaR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPediaR_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R htmlDocs: vignettes/SNPediaR/inst/doc/SNPediaR.html htmlTitles: Vignette Title Package: SNPhood Version: 1.4.1 Depends: R (>= 3.2), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices,gridExtra,stats,grid,utils, graphics, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, SNPhoodData, corrplot, pryr License: LGPL (>= 3) MD5sum: 30c261d226f92dc14e50003ab0abaa17 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 VignetteBuilder: knitr BugReports: christian.arnold@embl.de source.ver: src/contrib/SNPhood_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPhood_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPhood_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPhood_1.4.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R htmlDocs: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html htmlTitles: Introduction and Methodological Details, Workflow example Package: SNPRelate Version: 1.8.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) LinkingTo: gdsfmt Suggests: parallel, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray (>= 1.11.12) License: GPL-3 Archs: i386, x64 MD5sum: 7d88f57e0ae809251645fe408a8f797e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPRelate_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPRelate_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPRelate_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.R htmlDocs: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html htmlTitles: SNPRelate Tutorial suggestsMe: GENESIS, GWASTools, HIBAG, SeqArray Package: snpStats Version: 1.24.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: db0102d0889a6f6a3cfca4c17538baa5 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snpStats_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snpStats_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snpStats_1.24.0.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, MEAL suggestsMe: crlmm, GWASTools, VariantAnnotation Package: soGGi Version: 1.6.1 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: fd00a859b27b40c298bc34f179f78c55 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/soGGi_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/soGGi_1.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/soGGi_1.6.1.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: 2.10.0 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges 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: e32c9e80fc4423bda8da51aca1372212 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SomaticSignatures_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SomaticSignatures_2.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SomaticSignatures_2.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R htmlDocs: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html htmlTitles: SomaticSignatures importsMe: Rariant, YAPSA Package: SpacePAC Version: 1.12.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 6ff3d15b079f13977266acb882c7115a 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 source.ver: src/contrib/SpacePAC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpacePAC_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SpacePAC_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpacePAC_1.12.0.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: specL Version: 1.8.0 Depends: R (>= 3.3), methods, DBI, RSQLite, seqinr, protViz (>= 0.2.15), LinkingTo: Rcpp (>= 0.12.4) Suggests: RUnit, BiocGenerics, BiocStyle, plotrix, knitr, msqc1 (>= 1.0.0) License: GPL-3 Archs: i386, x64 MD5sum: 41aac89afedb9a283537e594d1e37dec NeedsCompilation: yes Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: specL 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 , Christian Panse , Jonas Grossmann , Witold E. Wolski 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/specL_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/specL_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/specL_1.8.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf vignetteTitles: Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/cdsw.R, vignettes/specL/inst/doc/specL.R, vignettes/specL/inst/doc/ssrc.R htmlDocs: vignettes/specL/inst/doc/cdsw.html, vignettes/specL/inst/doc/ssrc.html htmlTitles: Computing Dynamic SWATH Windows, Retention Time Prediction using the ssrc Method Package: SpeCond Version: 1.28.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 36ff570ce85171954428ef0c6b014559 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpeCond_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SpeCond_1.28.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpeCond_1.28.0.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: 1.14.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 07ed1aad3fb29ec74956568acfde6776 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SPEM_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SPEM_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SPEM_1.14.0.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 Package: SPIA Version: 2.26.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: ce40875578a8618b956b54bddff8f321 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SPIA_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SPIA_2.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SPIA_2.26.0.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: 1.4.8 Depends: R (>= 3.0.0) Imports: networkD3, httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 3e3a368193712d70e7cbc03b042fc2dc 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, 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_1.4.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpidermiR_1.4.8.zip win64.binary.ver: bin/windows64/contrib/3.3/SpidermiR_1.4.8.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpidermiR_1.4.8.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiRcasestudy.pdf vignetteTitles: SpidermiR examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R, vignettes/SpidermiR/inst/doc/SpidermiRcasestudy.R htmlDocs: vignettes/SpidermiR/inst/doc/SpidermiR.html htmlTitles: Working with SpidermiR package importsMe: StarBioTrek Package: spikeLI Version: 2.34.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 664949ebd5c905b1de0a43e1e5f55d0d 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spikeLI_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spikeLI_2.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spikeLI_2.34.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spkTools Version: 1.30.0 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: c0a1bc60070188895da00ba701d72417 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spkTools_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spkTools_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spkTools_1.30.0.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: splicegear Version: 1.46.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: 46f1d8bc68b582f3edb680c30e55e585 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/splicegear_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/splicegear_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splicegear_1.46.0.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: 1.16.0 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: 3f64e08d4801b6aa92f969aa5ca5d7e8 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 source.ver: src/contrib/spliceR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spliceR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spliceR_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spliceR_1.16.0.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: 1.22.1 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: 9198a7f031e0da2fcdecf103d1d820b7 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_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/spliceSites_1.22.1.zip win64.binary.ver: bin/windows64/contrib/3.3/spliceSites_1.22.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spliceSites_1.22.1.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: 1.14.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.9.25), 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: 6a847ba065626289bca019f8858dc822 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SplicingGraphs_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SplicingGraphs_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SplicingGraphs_1.14.0.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: 1.2.0 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: 166c7babc3a5260a03c98def28ba1f9e 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/splineTimeR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/splineTimeR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splineTimeR_1.2.0.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: 1.0.0 Depends: R (>= 3.3.0), 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: 8979e82fb0591e0fb0f0e68a4c134d08 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SPLINTER_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SPLINTER_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SPLINTER_1.0.0.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: 1.40.0 Imports: grid, RColorBrewer License: LGPL MD5sum: 71416bb7165374d5553b1397e1d4287e 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/splots_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/splots_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splots_1.40.0.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: spotSegmentation Version: 1.48.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 2d13e762a6e1876820e188f8b685a510 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spotSegmentation_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spotSegmentation_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spotSegmentation_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.24.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: aee67954815ba1bca5b76fc0a9c05801 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SQUADD_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SQUADD_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SQUADD_1.24.0.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: 1.32.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: c71a15ded5e740dc953073bdfcfc6821 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_1.32.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SRAdb_1.32.0.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: 1.14.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 21b249c77e1de7d3885d2e0d64c642eb 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sRAP_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sRAP_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sRAP_1.14.0.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: 1.0.0 Depends: R (>= 3.2.2), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), RedeR, gRain (>= 1.2-5), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats Suggests: knitr, rmarkdown License: GPL-2 MD5sum: e39165e17188c31855093a80da07a349 NeedsCompilation: no Title: SRGnet An R package for studying synergistic response genes from transcriptomics data Description: We developed SRMnet 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SRGnet_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SRGnet_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SRGnet_1.0.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SRGnet/inst/doc/vignette.html htmlTitles: SRGnet: An R package for studying synergistic response genes from transcriptomics data Package: sscore Version: 1.46.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 68e03cd96c6047fd8a5404fd93a25d49 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sscore_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sscore_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sscore_1.46.0.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: 2.2.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: e637e1abed9702ae6ec1addb14ab3b66 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package can calculate 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) Translational accuracy selection can be inferred from 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 biased used in the highly expressed genes), 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 proportion_index in the package. The function focus on the proportion of optimal codon against its corresponding non-optimal codons for the the four and six codon boxes. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr source.ver: src/contrib/sscu_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sscu_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sscu_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sscu_2.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sSeq Version: 1.12.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: a19688133bb3882b57969c67272cd425 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sSeq_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sSeq_1.12.0.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 Package: ssize Version: 1.48.0 Depends: gdata, xtable License: LGPL MD5sum: a640a7189f0bc5ee9f1403582810dcd0 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ssize_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ssize_1.48.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ssize_1.48.0.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 suggestsMe: oneChannelGUI Package: SSPA Version: 2.14.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: 776bc39b0f187432c29b8ecf60027220 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SSPA_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SSPA_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SSPA_2.14.0.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: 1.8.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer Suggests: knitr License: GPL-2 MD5sum: 5f091a5fd980c60b7c3143e9927ba8f5 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ssviz_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ssviz_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ssviz_1.8.0.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: STAN Version: 2.2.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 18a938c1ef2f4617506b63ce18c2ff3c 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: Benedikt Zacher VignetteBuilder: knitr source.ver: src/contrib/STAN_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/STAN_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/STAN_2.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STAN_2.2.0.tgz vignettes: vignettes/STAN/inst/doc/STAN.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN.R Package: staRank Version: 1.16.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: cb125e7bc9e33d3ba700680d18416e12 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/staRank_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/staRank_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/staRank_1.16.0.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: 1.0.3 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: 0941f2d2af5bd1bf484486b58d21690d 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_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/StarBioTrek_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/StarBioTrek_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/StarBioTrek_1.0.3.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek_Application_Examples.pdf vignetteTitles: StarBioTrek:Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek_Application_Examples.R, vignettes/StarBioTrek/inst/doc/StarBioTrek.R htmlDocs: vignettes/StarBioTrek/inst/doc/StarBioTrek.html htmlTitles: Vignette Title Package: Starr Version: 1.30.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: c15622ca6873530483644d694b87ce00 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Starr_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Starr_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Starr_1.30.0.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: 1.8.0 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: 093ca6bb9b0db52684d532ef1ca90bbb 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 , Patricia Sebastián-León , Gordon Ball VignetteBuilder: knitr source.ver: src/contrib/STATegRa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/STATegRa_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/STATegRa_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STATegRa_1.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R htmlDocs: vignettes/STATegRa/inst/doc/STATegRa.html htmlTitles: STATegRa User's Guide Package: statTarget Version: 1.4.12 Depends: R (>= 3.3.0) Imports: randomForest,plyr,pdist,pROC,utils,grDevices,graphics,rrcov,stats, pls,impute,gWidgets2,gWidgets2RGtk2 Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: b801513abe4a0438c3aeca2c3e979e14 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, Normalization, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, GUI, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://github.com/13479776/statTarget VignetteBuilder: knitr source.ver: src/contrib/statTarget_1.4.12.tar.gz win.binary.ver: bin/windows/contrib/3.3/statTarget_1.4.12.zip win64.binary.ver: bin/windows64/contrib/3.3/statTarget_1.4.12.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/statTarget_1.4.12.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/statTarget.R htmlDocs: vignettes/statTarget/inst/doc/statTarget.html htmlTitles: statTargetIntroduction Package: stepNorm Version: 1.46.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: f1ca8d3cb8357c883dfe6215dbbb7ec8 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/stepNorm_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/stepNorm_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/stepNorm_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: stepwiseCM Version: 1.20.0 Depends: R (>= 2.14), randomForest, MAclinical, tspair, pamr, snowfall, glmpath, penalized, e1071, Biobase License: GPL (>2) MD5sum: 88fe5664bd08eec3e0d34ccbd5d39dc2 NeedsCompilation: no Title: Stepwise Classification of Cancer Samples using High-dimensional Data Sets Description: Stepwise classification of cancer samples using multiple data sets. This package implements the classification strategy using two heterogeneous data sets without actually combining them. Package uses the data type for which full measurements are available at the first stage, and the data type for which only partial measurements are available at the second stage. For incoming new samples package quantifies how much improvement will be obtained if covariates of new samples for the data types at the second stage are measured. This packages suits for the application where study goal is not only obtain high classification accuracy, but also requires economically cheap classifier. biocViews: Classification, Microarray Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/stepwiseCM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/stepwiseCM_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/stepwiseCM_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/stepwiseCM_1.20.0.tgz vignettes: vignettes/stepwiseCM/inst/doc/stepwiseCM.pdf vignetteTitles: stepwiseCM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stepwiseCM/inst/doc/stepwiseCM.R Package: Streamer Version: 1.20.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: a050bb74c17abbfd5fe7643b91efae04 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Streamer_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Streamer_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Streamer_1.20.0.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: 1.14.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 0d8a03028b3e70f5bc712135be7c17ff 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: Alexander Roth source.ver: src/contrib/STRINGdb_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/STRINGdb_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/STRINGdb_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STRINGdb_1.14.0.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: scsR importsMe: pwOmics suggestsMe: PCAN Package: subSeq Version: 1.4.0 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: beb617ccaa7563d31418c1a3be9c3ba2 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/subSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/subSeq_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/subSeq_1.4.0.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: SummarizedExperiment Version: 1.4.0 Depends: R (>= 3.2), methods, GenomicRanges (>= 1.23.15), Biobase Imports: utils, stats, Matrix, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.11.7), IRanges (>= 2.7.2), GenomeInfoDb Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown, digest, jsonlite, rhdf5, airway, RUnit License: Artistic-2.0 MD5sum: 35159a339981efa22e96db639dadc850 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SummarizedExperiment_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SummarizedExperiment_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SummarizedExperiment_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R htmlDocs: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html htmlTitles: SummarizedExperiment dependsOnMe: AllelicImbalance, anamiR, BiSeq, bsseq, clusterExperiment, csaw, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, epigenomix, ExpressionAtlas, GenoGAM, GenomicAlignments, GenomicFiles, genoset, GRmetrics, HelloRanges, InteractionSet, isomiRs, JunctionSeq, MAST, MBASED, methylPipe, minfi, recount, RIPSeeker, SGSeq, simulatorZ, soGGi, VanillaICE, VariantAnnotation, yamss importsMe: ALDEx2, alpine, anamiR, BBCAnalyzer, biovizBase, BiSeq, ChIPpeakAnno, CNPBayes, DChIPRep, debrowser, DEFormats, easyRNASeq, EnrichmentBrowser, ensemblVEP, epivizrData, erma, FourCSeq, GGBase, ggbio, gQTLBase, gQTLstats, GreyListChIP, gwascat, HTSeqGenie, M3D, MADSEQ, methyAnalysis, methylumi, MinimumDistance, MoonlightR, MultiAssayExperiment, MultiDataSet, MutationalPatterns, oligoClasses, pcaExplorer, PureCN, R453Plus1Toolbox, readat, regionReport, regsplice, rgsepd, roar, SeqArray, SNPchip, SNPhood, SVAPLSseq, systemPipeR, TCGAbiolinks, ToPASeq, TVTB, VariantFiltering suggestsMe: AnnotationHub, biobroom, epivizr, esetVis, GenomicRanges, globalSeq, HDF5Array, interactiveDisplay, podkat, RiboProfiling Package: supraHex Version: 1.12.0 Depends: R (>= 3.0.2), hexbin Imports: ape, MASS License: GPL-2 MD5sum: 54fe52f84e30678a2071db727a1fd66f 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/supraHex_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/supraHex_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/supraHex_1.12.0.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, TCGAbiolinks Package: survcomp Version: 1.24.0 Depends: survival, prodlim, R (>= 2.10) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta Suggests: Hmisc, CPE, clinfun, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 2eb9284a8369312d6fcc837494f80a63 NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: R package providing functions to assess and to compare the performance of risk prediction (survival) models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/survcomp_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/survcomp_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/survcomp_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/survcomp_1.24.0.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: 1.12.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: f1c0321bd94b6749fcd1d4437d9b2a79 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Sushi_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Sushi_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Sushi_1.12.0.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, psichomics Package: sva Version: 3.22.0 Depends: R (>= 2.8), mgcv, genefilter Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 21715175f74be20918e8499b3e427ba4 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 Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson source.ver: src/contrib/sva_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sva_3.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sva_3.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sva_3.22.0.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: ballgown, BatchQC, ChAMP, charm, crossmeta, debrowser, doppelgangR, edge, LINC, MEAL, PAA, trigger suggestsMe: Harman, RnBeads, SomaticSignatures Package: SVAPLSseq Version: 1.0.0 Depends: R (>= 3.3) Imports: methods, stats, SummarizedExperiment, edgeR, ggplot2, limma, lmtest, parallel, pls Suggests: BiocStyle License: GPL-3 MD5sum: d5b2ab50175945af859bd09eba0ef0d7 NeedsCompilation: no Title: SVAPLSseq-An R package to adjust for the hidden factors of variability in differential gene expression studies based on RNAseq data Description: The package contains functions that are intended for the identification of differentially expressed genes between two groups of samples from RNAseq data after adjusting for various hidden biological and technical factors of variability. biocViews: GeneExpression, RNASeq, Normalization, BatchEffect Author: Sutirtha Chakraborty Maintainer: Sutirtha Chakraborty source.ver: src/contrib/SVAPLSseq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SVAPLSseq_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SVAPLSseq_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SVAPLSseq_1.0.0.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: 1.6.0 Depends: R (>= 3.2.0), LiblineaR, SVM2CRMdata Imports: AnnotationDbi, mclust, GenomicRanges, IRanges, zoo, squash, pls,rtracklayer,ROCR,verification License: GPL-3 MD5sum: b96bb7923165b6b1eec3e6503045c034 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SVM2CRM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SVM2CRM_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SVM2CRM_1.6.0.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: 1.4.1 Depends: R(>= 2.10.0) Imports: data.table, reshape2, grid, ggplot2, stats, grDevices, graphics, utils Suggests: testthat, aLFQ, knitr Enhances: imsbInfer, MSstats License: GPL-3 MD5sum: ef242b9c95003f5f9acbc5ae504a2b65 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 source.ver: src/contrib/SWATH2stats_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SWATH2stats_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SWATH2stats_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SWATH2stats_1.4.1.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: 1.2.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 00dbc03735f0135ab56112bc51e8ccf5 NeedsCompilation: no Title: SWATH extended library generation and satistical data analysis Description: It 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SwathXtend_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SwathXtend_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SwathXtend_1.2.0.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: SwimR Version: 1.12.0 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: 4bbe1f3403595ef76a139d78e43bee21 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SwimR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SwimR_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SwimR_1.12.0.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: 1.10.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 Archs: i386, x64 MD5sum: 3320c3c14013341f2023d9e3fa4cc649 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/switchBox_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/switchBox_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/switchBox_1.10.0.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: 1.0.0 Depends: R (>= 3.3) Imports: Biobase, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr, scater License: GPL (>= 2) MD5sum: bfaedd3a185c5768aeb801e169d3f579 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 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/switchde_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/switchde_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/switchde_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R htmlDocs: vignettes/switchde/inst/doc/switchde_vignette.html htmlTitles: An overview of the switchde package Package: synapter Version: 1.16.0 Depends: R (>= 2.15), methods, MSnbase Imports: hwriter, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel Suggests: synapterdata, xtable, tcltk, BiocStyle License: GPL-2 MD5sum: 608b8548a532019258b5a00d65809123 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, GUI Author: Laurent Gatto, Nick J. Bond and Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: http://lgatto.github.com/synapter/ VignetteBuilder: knitr source.ver: src/contrib/synapter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/synapter_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/synapter_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/synapter_1.16.0.tgz vignettes: vignettes/synapter/inst/doc/synapter.pdf vignetteTitles: Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/synapter.R suggestsMe: pRoloc Package: synergyfinder Version: 1.0.0 Imports: drc (>= 2.5-12), reshape2 (>= 1.4.1), kriging (>= 1.1), 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: Artistic License 2.0 MD5sum: 852030bc8660e6d8da014dcb0d6c20dd 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/synergyfinder_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/synergyfinder_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/synergyfinder_1.0.0.tgz vignettes: vignettes/synergyfinder/inst/doc/synergyfinder.pdf vignetteTitles: synergyfinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/synergyfinder.R Package: synlet Version: 1.4.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: c08d5273d398b7eb9693a868f48c87db 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/synlet_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/synlet_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/synlet_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R htmlDocs: vignettes/synlet/inst/doc/synlet-vignette.html htmlTitles: A working Demo for synlet Package: systemPipeR Version: 1.8.1 Depends: Rsamtools, Biostrings, ShortRead, methods Imports: BiocGenerics, GenomicRanges, GenomicFeatures, SummarizedExperiment, VariantAnnotation, 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: e9b8c86f1ba8ebc82dc2e88a664ca6f9 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: https://github.com/tgirke/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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/systemPipeR_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/systemPipeR_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/systemPipeR_1.8.1.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 vignetteTitles: ChIP-Seq Workflow Template, Ribo-Seq Workflow Template, RNA-Seq Workflow Template, VAR-Seq Workflow Template 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 htmlDocs: vignettes/systemPipeR/inst/doc/systemPipeR.html htmlTitles: Overview Vignette importsMe: DiffBind Package: TargetScore Version: 1.12.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 6cd2503dda7cd6fec7f82826aa868987 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TargetScore_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TargetScore_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TargetScore_1.12.0.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: 1.30.0 Depends: ncdf4 Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 2dc7b0ad63c4b70f3eb6e5152ebfd665 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 source.ver: src/contrib/TargetSearch_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TargetSearch_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TargetSearch_1.30.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TargetSearch_1.30.0.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: 1.4.1 Depends: R (>= 3.2.1), 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: db86c32097a196c620ee80ce3e56bf99 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/TarSeqQC_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/TarSeqQC_1.4.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TarSeqQC_1.4.1.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: 1.14.0 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports: samr Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: d5c172f62e76240ffe1d649c054ec4ac 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 source.ver: src/contrib/TCC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TCC_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TCC_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TCC_1.14.0.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: 2.2.10 Depends: R (>= 3.2) Imports: downloader (>= 0.4), survminer, grDevices, gridExtra, graphics, tibble, GenomicRanges, XML (>= 3.98.0), Biobase, affy, xtable, data.table, EDASeq (>= 2.0.0), edgeR (>= 3.0.0), jsonlite (>= 1.0.0), plyr, c3net, minet, knitr, methods, biomaRt, gplots, ggplot2, ggthemes, survival, stringr (>= 1.0.0), IRanges, scales, rvest (>= 0.3.0), stats, utils, dnet, igraph, selectr, supraHex, S4Vectors, ComplexHeatmap (>= 1.10.2), R.utils, SummarizedExperiment (>= 1.4.0), limma, genefilter, ConsensusClusterPlus, readr, RColorBrewer, doParallel, dplyr, clusterProfiler, pathview, parallel, xml2, httr (>= 1.2.1), parmigene, matlab, circlize, ggrepel (>= 0.6.3) Suggests: testthat, png, BiocStyle, rmarkdown, devtools License: GPL (>= 3) MD5sum: 0e3d4f1018ae2d8eeb3d30048534aa28 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with TCGA data Description: The aim of TCGAbiolinks is : i) facilitate the TCGA 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) allow the user to download a specific version of the data and thus 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 source.ver: src/contrib/TCGAbiolinks_2.2.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/TCGAbiolinks_2.2.10.zip win64.binary.ver: bin/windows64/contrib/3.3/TCGAbiolinks_2.2.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TCGAbiolinks_2.2.10.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.R htmlDocs: vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html htmlTitles: "TCGAbiolinks: Clinical data", "TCGAbiolinks: Downloading and preparing files for analysis", "Introduction", "TCGAbiolinks: Mutation data", "TCGAbiolinks: Searching GDC database", Working with TCGAbiolinks package importsMe: MoonlightR, SpidermiR Package: TDARACNE Version: 1.24.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 0b37553d793cc271172706dbb0ce3a3a 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TDARACNE_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TDARACNE_1.24.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TDARACNE_1.24.0.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: TEQC Version: 3.14.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 457a51c9cd4767c02315d7acd7be3b69 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_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TEQC_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TEQC_3.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TEQC_3.14.0.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: 1.18.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 64d4ab58e4aeed86ebff75e26062d251 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ternarynet_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ternarynet_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ternarynet_1.18.0.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: TFBSTools Version: 1.12.2 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, 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) Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0) License: GPL-2 Archs: i386, x64 MD5sum: 66b2600a18baef085dea3d6fa25ad684 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_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/TFBSTools_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/TFBSTools_1.12.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TFBSTools_1.12.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R htmlDocs: vignettes/TFBSTools/inst/doc/TFBSTools.html htmlTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package importsMe: MatrixRider Package: tigre Version: 1.28.1 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle License: AGPL-3 Archs: i386, x64 MD5sum: fbe346e19cc6b8e86686bb21352ada55 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_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/tigre_1.28.1.zip win64.binary.ver: bin/windows64/contrib/3.3/tigre_1.28.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tigre_1.28.1.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: 1.52.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 95caedcd19de18a38f526b357d66c00a 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tilingArray_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tilingArray_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tilingArray_1.52.0.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: 1.46.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 631555dff4cc9a6bc14267e4187cc533 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/timecourse_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/timecourse_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/timecourse_1.46.0.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: TIN Version: 1.6.0 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: 07de3229b35dddac0f3766b7b11f8069 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TIN_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TIN_1.6.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TIN_1.6.0.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: TitanCNA Version: 1.12.0 Depends: R (>= 3.2.0), foreach (>= 1.4.2), IRanges (>= 2.2.4), GenomicRanges (>= 1.20.5), Rsamtools (>= 1.20.4), GenomeInfoDb (>= 1.4.0) License: GPL-3 Archs: i386, x64 MD5sum: 97095648ce826ef6d7c01c9d014f4459 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TitanCNA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TitanCNA_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TitanCNA_1.12.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf vignetteTitles: TitanCNA hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R Package: tkWidgets Version: 1.52.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 760213ef99136478dcfdd497e6431094 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tkWidgets_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tkWidgets_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tkWidgets_1.52.0.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 dependsOnMe: oneChannelGUI importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: tofsims Version: 1.2.0 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: 35da698acd9aeb58e1cd77452484b5ea 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 VignetteBuilder: knitr source.ver: src/contrib/tofsims_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tofsims_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tofsims_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tofsims_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R htmlDocs: vignettes/tofsims/inst/doc/workflow.html htmlTitles: Workflow with the `tofsims` package Package: ToPASeq Version: 1.8.0 Depends: graphite (>= 1.16), gRbase, graph, locfit, Rgraphviz Imports: R.utils, methods, Biobase, parallel, edgeR, DESeq2, SummarizedExperiment, RBGL, DESeq, fields, limma, TeachingDemos, KEGGgraph, qpgraph, clipper, AnnotationDbi, doParallel LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, gageData, DEGraph, plotrix, org.Hs.eg.db License: AGPL-3 MD5sum: 14a72a3acfd08b945927623687072475 NeedsCompilation: yes Title: Package for Topology-based Pathway Analysis of RNASeq data Description: Implementation of seven methods for topology-based pathway analysis of both RNASeq and microarray data: SPIA, DEGraph, TopologyGSA, TAPPA, PRS, PWEA and a visualization tool for a single pathway. biocViews: Software, GeneExpression, NetworkEnrichment, GraphAndNetwork, RNASeq, Visualization, Microarray, Pathways, DifferentialExpression, Author: Ivana Ihnatova, Eva Budinska Maintainer: Ivana Ihnatova source.ver: src/contrib/ToPASeq_1.8.0.tar.gz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq.pdf vignetteTitles: An R Package for topology-based pathway analysis of microaray and RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq.R Package: topGO Version: 2.26.0 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: 7604873b748d0fa2c2825e814eec46bf 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/topGO_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/topGO_2.26.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/topGO_2.26.0.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, RCAS, RNAither, tRanslatome importsMe: cellity, EnrichmentBrowser, GOSim, mvGST, pcaExplorer, psygenet2r, SEPA suggestsMe: clusterProfiler, FGNet, miRNAtap, Ringo Package: TPP Version: 3.0.3 Depends: R (>= 3.3), dplyr, magrittr, tidyr Imports: Biobase, doParallel, foreach, ggplot2, gridExtra, grid, limma, MASS, nls2, openxlsx (>= 2.4.0), parallel, plyr, RColorBrewer, RCurl, reshape2, sme, splines, VennDiagram, VGAM, knitr, rmarkdown, grDevices, stats, utils Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: d49d19771b802b96b5d00b3aae42da26 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 source.ver: src/contrib/TPP_3.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/TPP_3.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/TPP_3.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TPP_3.0.3.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, 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: 1.8.1 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 10ca3cfe2ebdebc931bd518e1433f3a2 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/tracktables_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/tracktables_1.8.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tracktables_1.8.1.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 htmlDocs: vignettes/tracktables/inst/doc/EBFGI.html, vignettes/tracktables/inst/doc/IGV_Example.html, vignettes/tracktables/inst/doc/IGVEx3.html, vignettes/tracktables/inst/doc/index.html, vignettes/tracktables/inst/doc/markdownexample.html htmlTitles: EBFGI.html, IGV_Example.html, IGVEx3.html, index.html, Supplementary Materials Package: trackViewer Version: 1.10.2 Depends: R (>= 3.1.0), grDevices, methods, GenomicRanges, grid Imports: GenomicAlignments, GenomicFeatures, Gviz, pbapply, Rsamtools, rtracklayer, S4Vectors, scales, tools, IRanges, AnnotationDbi, grImport Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocGenerics, BiocStyle, knitr, VariantAnnotation License: GPL (>= 2) MD5sum: 6821d2ab9a433a6cd419b9f02c0af780 NeedsCompilation: no Title: A bioconductor package with minimalist design for drawing elegant tracks or lollipop plot 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 source.ver: src/contrib/trackViewer_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/trackViewer_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/trackViewer_1.10.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trackViewer_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R htmlDocs: vignettes/trackViewer/inst/doc/trackViewer.html htmlTitles: trackViewer Vignette dependsOnMe: coMET suggestsMe: ChIPpeakAnno Package: transcriptR Version: 1.2.0 Depends: methods, R (>= 3.3) Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges, pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: 89c295ef60def07411a77d9afb22b87b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/transcriptR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/transcriptR_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/transcriptR_1.2.0.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: 1.12.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 884e8e2ba5d827048b1f87588bcc1767 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, SAM, 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 source.ver: src/contrib/tRanslatome_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tRanslatome_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tRanslatome_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tRanslatome_1.12.0.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: 1.18.0 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: ae4b3494b24f807cfc54022da4e9f919 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TransView_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TransView_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TransView_1.18.0.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: 1.4.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: 5e1eb5dd843b78b16908b8e8b80fc06f 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/traseR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/traseR_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/traseR_1.4.0.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: triform Version: 1.16.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: BiocGenerics, IRanges (>= 2.5.27), yaml Suggests: RUnit License: GPL-2 MD5sum: f2f95f438028c074d048f095cb09e402 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/triform_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/triform_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/triform_1.16.0.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: 1.20.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: 218e1fbee2e421fb65e2b652039e0403 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/trigger_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/trigger_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trigger_1.20.0.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: 3.12.0 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: cf5c1400f4071400fe95ffb39129ac59 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_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/trio_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/trio_3.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trio_3.12.0.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: 1.14.0 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: e45e4a372661b45cdebd65ec7240b579 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/triplex_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/triplex_1.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/triplex_1.14.0.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: TRONCO Version: 2.6.1 Depends: R (>= 3.3), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, gRapHD, grDevices, graphics, stats, utils, Suggests: BiocGenerics, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 5e442d1a7700b49c8feadee1681d67b0 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. biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport 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_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/TRONCO_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/TRONCO_2.6.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TRONCO_2.6.1.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRONCO/inst/doc/vignette.R Package: TSCAN Version: 1.12.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: db4b187af3d7bfb1821ff14c5cef1aab 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TSCAN_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TSCAN_1.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TSCAN_1.12.0.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: 1.32.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: ba932fdbaac798a4eaef434cd499fc9a 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tspair_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tspair_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tspair_1.32.0.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 dependsOnMe: stepwiseCM Package: TSSi Version: 1.20.0 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: f865aa1b2c88714ae171914cc043f158 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TSSi_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TSSi_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TSSi_1.20.0.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: TurboNorm Version: 1.22.0 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: 529d5d9f70cc8a90fb1a28d60e2c5f5e 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TurboNorm_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TurboNorm_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TurboNorm_1.22.0.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: 1.0.2 Depends: R (>= 3.3), methods, utils, stats Imports: BiocGenerics (>= 0.19.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, ggplot2, 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 (>= 2.1.23), knitr (>= 1.12), rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: 8f0e2e96a499c0a2b06ee44ba2f1380a 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_1.0.2.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TVTB_1.0.2.tgz 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 htmlDocs: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html htmlTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules Package: tweeDEseq Version: 1.20.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: dea8c9478eb7241962a438913be10b5c 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tweeDEseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tweeDEseq_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tweeDEseq_1.20.0.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: 1.50.0 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: 2d2439e3b473289434eff5ffc8ee8301 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/twilight_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/twilight_1.50.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/twilight_1.50.0.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: tximport Version: 1.2.0 Imports: utils Suggests: knitr, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6) License: GPL (>=2) MD5sum: 5bb24b273adce6467083b4078337531d NeedsCompilation: no Title: Import and summarize transcript-level estimates for 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, Charlotte Soneson, Mark Robinson Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/tximport_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tximport_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tximport_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tximport_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R htmlDocs: vignettes/tximport/inst/doc/tximport.html htmlTitles: tximport importsMe: scater suggestsMe: DESeq2, variancePartition Package: TypeInfo Version: 1.40.0 Depends: methods Suggests: Biobase License: BSD MD5sum: b2f4ca9b00d635dd2521d07756e1ffd0 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TypeInfo_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TypeInfo_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TypeInfo_1.40.0.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: 1.16.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 0f5423ed0b68fa6df1b7f03b3dbd9de5 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/UNDO_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/UNDO_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/UNDO_1.16.0.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: 1.10.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 7cb36ef6deecc85cbe66d2de3c2d5fee 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/unifiedWMWqPCR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/unifiedWMWqPCR_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/unifiedWMWqPCR_1.10.0.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: 2.14.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi Suggests: RUnit License: Artistic License 2.0 MD5sum: 01d63e23eff86d35cdd507136aa5ff44 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 Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/UniProt.ws_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/UniProt.ws_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/UniProt.ws_2.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/UniProt.ws_2.14.0.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: 1.2.0 Depends: R (>= 3.3) Imports: DBI, stringr, RSQLite, R.utils, WriteXLS, stats Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 512308e87022aec9bbde6a8ec9f8fa3c 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Uniquorn_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Uniquorn_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Uniquorn_1.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R htmlDocs: vignettes/Uniquorn/inst/doc/Uniquorn.html htmlTitles: Vignette Title Package: uSORT Version: 1.0.0 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: 22f609851d44d4429a4b22c31bb9bb59 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/uSORT_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/uSORT_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/uSORT_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R htmlDocs: vignettes/uSORT/inst/doc/uSORT_quick_start.html htmlTitles: Quick Start Package: VanillaICE Version: 1.36.0 Depends: R (>= 3.0.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.2.0) Imports: Biobase, S4Vectors (>= 0.9.25), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb, 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: 963384f405316bf4d66f5ef8504e71bc 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/VanillaICE_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/VanillaICE_1.36.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VanillaICE_1.36.0.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: CNPBayes, oligoClasses Package: variancePartition Version: 1.4.2 Depends: ggplot2, foreach, Biobase, methods Imports: MASS, pbkrtest (>= 0.4-4), iterators, splines, colorRamps, gplots, reshape2, lme4 (>= 1.1-10), doParallel, limma, grDevices, graphics, utils, stats Suggests: edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, readr, knitr, BiocStyle License: GPL (>= 2) MD5sum: 80da83c388260720cb4fd43064802669 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 linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. biocViews: RNASeq, GeneExpression, Regression, Software Author: Gabriel E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr source.ver: src/contrib/variancePartition_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/variancePartition_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/variancePartition_1.4.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/variancePartition_1.4.2.tgz vignettes: vignettes/variancePartition/inst/doc/additional_visualization.pdf, vignettes/variancePartition/inst/doc/variancePartition.pdf vignetteTitles: 2) Additional visualizations, 1) Tutorial on using variancePartition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/variancePartition.R Package: VariantAnnotation Version: 1.20.3 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), GenomeInfoDb (>= 1.7.1), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.3.1), Rsamtools (>= 1.23.10) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.9.47), IRanges (>= 2.3.25), XVector (>= 0.5.6), Biostrings (>= 2.33.5), AnnotationDbi (>= 1.27.9), BSgenome (>= 1.37.6), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.19.17) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rsamtools Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20110815, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: a16f2e9d24eb4c19607c2e5fcd1d5ea3 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 source.ver: src/contrib/VariantAnnotation_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/VariantAnnotation_1.20.3.zip win64.binary.ver: bin/windows64/contrib/3.3/VariantAnnotation_1.20.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VariantAnnotation_1.20.3.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: filterVcf Overview, 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, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, Rariant, SomaticSignatures, VariantFiltering, VariantTools importsMe: AllelicImbalance, BadRegionFinder, BBCAnalyzer, biovizBase, customProDB, FunciSNP, genbankr, GenomicFiles, ggbio, GGtools, gmapR, gQTLstats, gwascat, MADSEQ, maftools, methyAnalysis, motifbreakR, MutationalPatterns, PGA, SeqArray, SeqVarTools, signeR, SNPhood, systemPipeR, TVTB, YAPSA suggestsMe: AnnotationHub, CrispRVariants, GenomicRanges, GMRP, GWASTools, podkat, trackViewer, trio, vtpnet Package: VariantFiltering Version: 1.10.1 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.13.8), VariantAnnotation (>= 1.13.29) Imports: utils, DBI, RSQLite (>= 1.0.0), 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, Gviz, shiny 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.phase3.hs37d5, MafDb.ExAC.r0.3.1.snvs.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: cdb51d3f8446ba99742d8ba68c19b6fd 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] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues source.ver: src/contrib/VariantFiltering_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/VariantFiltering_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/VariantFiltering_1.10.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VariantFiltering_1.10.1.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: 1.16.0 Depends: S4Vectors (>= 0.9.25), IRanges (>= 1.99.2), GenomicRanges (>= 1.17.7), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.17.6), BiocGenerics, Biostrings, parallel, gmapR (>= 1.13.4), GenomicFeatures (>= 1.17.13), Matrix, rtracklayer (>= 1.25.3), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph License: Artistic-2.0 MD5sum: fa6f1cbb3d7af0a9a9fec7107b3cbf6a NeedsCompilation: no Title: Tools for Working with Genetic Variants Description: Tools for detecting, filtering, calling, comparing and plotting variants. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/VariantTools_1.16.0.tar.gz vignettes: vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie Package: vbmp Version: 1.42.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: fc00938ba24342b5e6bcbb7a05c03748 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/vbmp_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/vbmp_1.42.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vbmp_1.42.0.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: 1.22.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: cb494858a2166efc7256f3324c80df16 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Vega_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Vega_1.22.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Vega_1.22.0.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: 3.12.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods, genoset License: GPL-2 Archs: i386, x64 MD5sum: 021153f00b2207c8f1f934cb9d64c60a 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_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/VegaMC_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/VegaMC_3.12.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VegaMC_3.12.0.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: viper Version: 1.8.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 741f25830d38ad0cef57ec59a0b564d8 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/viper_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/viper_1.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/viper_1.8.0.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 importsMe: diggit Package: vsn Version: 3.42.3 Depends: R (>= 3.0.0), Biobase Imports: methods, affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: db262be78cb4020f9485790a0e70f9b1 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_3.42.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/vsn_3.42.3.zip win64.binary.ver: bin/windows64/contrib/3.3/vsn_3.42.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vsn_3.42.3.tgz vignettes: vignettes/vsn/inst/doc/A-vsn.pdf, vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf vignetteTitles: Introduction to vsn (Sweave version), Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R, vignettes/vsn/inst/doc/vsn.R htmlDocs: vignettes/vsn/inst/doc/vsn.html htmlTitles: Introduction to vsn (HTML version) dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, imageHTS, LVSmiRNA, metaseqR, MSnbase, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight Package: vtpnet Version: 0.14.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 3a6ec1473cde8baaeea2950142551641 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_0.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/vtpnet_0.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/vtpnet_0.14.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vtpnet_0.14.0.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: wateRmelon Version: 1.18.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM Enhances: minfi License: GPL-3 MD5sum: 8619ffd4d72713e28328ecb46b2a1143 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/wateRmelon_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/wateRmelon_1.18.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/wateRmelon_1.18.0.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: 2.8.0 Depends: R (>= 3.2), GenomicRanges (>= 1.23.16), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>= 2.5.27), Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, mclust, rtracklayer, seqinr, stringr, wmtsa Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: 80658f26308c401555423b67170bf2c3 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/wavClusteR_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/wavClusteR_2.8.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/wavClusteR_2.8.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R htmlDocs: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html htmlTitles: wavClusteR: a workflow for PAR-CLIP data analysis Package: waveTiling Version: 1.16.0 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: 9815ee110bd39d2abe764ac20b42012b 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/waveTiling_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/waveTiling_1.16.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/waveTiling_1.16.0.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: 1.40.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: bb4f562dadf71ff72028bcf2a581a0ad 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/weaver_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/weaver_1.40.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/weaver_1.40.0.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: 1.46.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: 1e78ac32eb42e17d9d5fdc76f1a6b206 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/webbioc_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/webbioc_1.46.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/webbioc_1.46.0.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: 1.52.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 66112f14cda80ecf57cecb4fc9014ce2 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/widgetTools_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/widgetTools_1.52.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/widgetTools_1.52.0.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: XBSeq Version: 1.4.0 Depends: DESeq2, R (>= 3.2.0) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, Delaporte, magrittr, roar Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) MD5sum: ce32ab4af47860b538f2f0221f9da0ec 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: rmarkdown source.ver: src/contrib/XBSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/XBSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/XBSeq_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XBSeq_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: xcms Version: 1.50.1 Depends: R (>= 2.14.0), methods, mzR (>= 1.1.6), BiocGenerics, ProtGenerics, Biobase Imports: lattice, RColorBrewer, plyr, RANN, multtest, MassSpecWavelet (>= 1.5.2), BiocParallel, S4Vectors Suggests: BiocStyle, knitr (>= 1.1.0), faahKO, msdata, ncdf4, rgl, RUnit Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 141ee8a11a1f26a11e37eb6dce3474f8 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_1.50.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/xcms_1.50.1.zip win64.binary.ver: bin/windows64/contrib/3.3/xcms_1.50.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xcms_1.50.1.tgz vignettes: vignettes/xcms/inst/doc/xcmsDirect.pdf, vignettes/xcms/inst/doc/xcmsInstall.pdf, vignettes/xcms/inst/doc/xcmsMSn.pdf, vignettes/xcms/inst/doc/xcmsPreprocess.pdf vignetteTitles: Grouping FTICR-MS data with xcms, Installation Instructions for xcms, Processing Tandem-MS and MS$^n$ data with xcms, LC/MS Preprocessing and Analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/xcmsDirect.R, vignettes/xcms/inst/doc/xcmsMSn.R, vignettes/xcms/inst/doc/xcmsPreprocess.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, proFIA importsMe: CAMERA, cosmiq, Risa suggestsMe: MassSpecWavelet, msPurity, RMassBank, ropls Package: XDE Version: 2.20.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), methods, graphics Imports: Biobase, BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm Suggests: siggenes, genefilter, MASS, RColorBrewer, GeneMeta, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: 2b2d6edcc4e1c022c03ed3615a454c30 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/XDE_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/XDE_2.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XDE_2.20.0.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: 1.32.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: aec80bb5dd9d3a1c096f8291f331a1b3 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/xmapbridge_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/xmapbridge_1.32.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xmapbridge_1.32.0.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: 1.34.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) MD5sum: b365346f597fafe54c6cd375b0ef1e57 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_v5.34.05 - See README file for installation instructions. source.ver: src/contrib/xps_1.34.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xps_1.34.0.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: FALSE 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: 0.14.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.19.2), S4Vectors (>= 0.9.29), IRanges (>= 2.5.27) Imports: methods, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 27445c0311b9eab3cf1cf032bc55f538 NeedsCompilation: yes Title: Representation and manpulation 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_0.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/XVector_0.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/XVector_0.14.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XVector_0.14.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, triplex importsMe: Biostrings, BSgenome, ChIPsim, CNEr, compEpiTools, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, IONiseR, kebabs, MatrixRider, R453Plus1Toolbox, Rsamtools, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges Package: yamss Version: 1.0.4 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: 7dc8eb8e5561590c551d57c0a78916f0 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_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/yamss_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/yamss_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/yamss_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R htmlDocs: vignettes/yamss/inst/doc/yamss.html htmlTitles: yamss User's Guide Package: YAPSA Version: 1.0.0 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: f34f5a5f9dd96ef12e690bd853123ed3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/YAPSA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/YAPSA_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/YAPSA_1.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/YAPSA.R htmlDocs: vignettes/YAPSA/inst/doc/YAPSA.html htmlTitles: YAPSA Package: yaqcaffy Version: 1.34.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: ed61494fa21c589c110a52aee997002e 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/yaqcaffy_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/yaqcaffy_1.34.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/yaqcaffy_1.34.0.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: 1.0.1 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: 143e46d549a57666da8c17f47c5355a6 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/yarn_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/yarn_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/yarn_1.0.1.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: zlibbioc Version: 1.20.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: f68b45fca70f39c7958fb43c0504c546 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/zlibbioc_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/zlibbioc_1.20.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/zlibbioc_1.20.0.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, rhdf5, Rhtslib, Rsamtools, rtracklayer, seqbias, ShortRead, snpStats, Starr, TransView, VariantAnnotation, XVector