Package: annotation Version: 1.2.0 Depends: R (>= 3.3.0), VariantAnnotation, AnnotationHub, Organism.dplyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.ensGene, org.Hs.eg.db, org.Mm.eg.db, Homo.sapiens, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome, TxDb.Athaliana.BioMart.plantsmart22 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 96b395199c6db0a57de2f2d24ecdd4fb NeedsCompilation: no Title: Genomic Annotation Resources Description: Annotation resources make up a significant proportion of the Bioconductor project. And there are also a diverse set of online resources available which are accessed using specific packages. This walkthrough will describe the most popular of these resources and give some high level examples on how to use them. biocViews: AnnotationWorkflow, Workflow Author: Marc RJ Carlson [aut], Herve Pages [aut], Sonali Arora [aut], Valerie Obenchain [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/help/workflows/annotation/Annotation_Resources/ VignetteBuilder: knitr source.ver: src/contrib/annotation_1.2.0.tar.gz vignettes: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.html, vignettes/annotation/inst/doc/Annotation_Resources.html vignetteTitles: Annotating Genomic Ranges, Genomic Annotation Resources hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.R, vignettes/annotation/inst/doc/Annotation_Resources.R Package: arrays Version: 1.4.0 Depends: R (>= 3.0.0) Suggests: affy, limma, hgfocuscdf, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 3bf7a9e324dc1884c34b3edcc258e2ee NeedsCompilation: no Title: Using Bioconductor for Microarray Analysis Description: Using Bioconductor for Microarray Analysis workflow biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/arrays_1.4.0.tar.gz vignettes: vignettes/arrays/inst/doc/arrays.html vignetteTitles: Using Bioconductor for Microarray Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrays/inst/doc/arrays.R Package: BiocMetaWorkflow Version: 1.0.0 Suggests: BiocStyle, knitr, rmarkdown, BiocWorkflowTools License: Artistic-2.0 MD5sum: e37e2d1d57c3228e30651d795f04ede2 NeedsCompilation: no Title: BioC Workflow about publishing a Bioc Workflow Description: Bioconductor Workflow describing how to use BiocWorkflowTools to work with a single R Markdown document to submit to both Bioconductor and F1000Research. biocViews: BasicWorkflow Author: Mike Smith [aut, cre], Andrzej Oleś [aut], Wolfgang Huber [ctb] Maintainer: Mike Smith VignetteBuilder: knitr source.ver: src/contrib/BiocMetaWorkflow_1.0.0.tar.gz vignettes: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.html vignetteTitles: Bioc Meta Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.R Package: chipseqDB Version: 1.2.0 Depends: R (>= 3.3.0), BiocStyle, ChIPpeakAnno, Gviz, Rsamtools, Rsubread, TxDb.Mmusculus.UCSC.mm10.knownGene, csaw, edgeR, knitr, locfit, org.Mm.eg.db, rtracklayer, statmod Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 154ca8d3081964c9a3d672e34cc00440 NeedsCompilation: no Title: From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data Description: This article describes a computational workflow for performing a DB analysis with sliding windows. The aim is to facilitate the practical implementation of window-based DB analyses by providing detailed code and expected output. The workflow described here applies to any ChIP-seq experiment with multiple experimental conditions and with multiple biological samples within one or more of the conditions. It detects and summarizes DB regions between conditions in a *de novo* manner, i.e., without making any prior assumptions about the location or width of bound regions.Detected regions are then annotated according to their proximity to annotated genes. In addition, the code can be easily adapted to accommodate batch effects, covariates and multiple experimental factors. biocViews: Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/chipseqDB/ VignetteBuilder: knitr source.ver: src/contrib/chipseqDB_1.2.0.tar.gz vignettes: vignettes/chipseqDB/inst/doc/work-1-intro.html, vignettes/chipseqDB/inst/doc/work-3-h3k9ac.html, vignettes/chipseqDB/inst/doc/work-4-cbp.html vignetteTitles: From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data, Detecting differential enrichment of H3K9ac in murine B cells, Detecting differential binding of CBP in mouse fibroblasts hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseqDB/inst/doc/work-1-intro.R, vignettes/chipseqDB/inst/doc/work-3-h3k9ac.R, vignettes/chipseqDB/inst/doc/work-4-cbp.R Package: cytofWorkflow Version: 1.2.0 Depends: R (>= 3.4.0) Imports: BiocStyle, knitr, readxl, matrixStats, flowCore, ggplot2, ggridges, reshape2, dplyr, limma, ggrepel, RColorBrewer, pheatmap, ComplexHeatmap, FlowSOM, ConsensusClusterPlus, Rtsne, cowplot, lme4, multcomp Suggests: knitcitations License: Artistic-2.0 MD5sum: e16bc2a105922e46feb8a0d8891a787a NeedsCompilation: no Title: CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets Description: High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations. Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). biocViews: Workflow, SingleCellWorkflow Author: Malgorzata Nowicka [aut, cre], Mark D. Robinson [aut] Maintainer: Malgorzata Nowicka URL: https://github.com/gosianow/cytofWorkflow VignetteBuilder: knitr BugReports: https://github.com/gosianow/cytofWorkflow/issues source.ver: src/contrib/cytofWorkflow_1.2.0.tar.gz vignettes: vignettes/cytofWorkflow/inst/doc/cytofWorkflow.html vignetteTitles: A workflow for differential discovery in high-throughput high-dimensional cytometry datasets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: EGSEA123 Version: 1.2.0 Depends: R (>= 3.4.0), EGSEA (>= 1.5.2), limma, edgeR, illuminaio Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 06d32de77e995ddc4a0507d28ceb2f4a NeedsCompilation: no Title: Easy and efficient ensemble gene set testing with EGSEA Description: R package that supports the F1000Research workflow article `Easy and efficient ensemble gene set testing with EGSEA', Alhamdoosh et al. (2017). biocViews: Workflow, GeneExpressionWorkflow Author: Monther Alhamdoosh, Charity Law, Luyi Tian, Julie Sheridan, Milica Ng and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://www.bioconductor.org/help/workflows/ VignetteBuilder: knitr source.ver: src/contrib/EGSEA123_1.2.0.tar.gz vignettes: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.html vignetteTitles: Easy and efficient ensemble gene set testing with EGSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.R Package: eQTL Version: 1.2.0 Depends: R (>= 3.3.0), GGdata, GGtools, GenomeInfoDb, S4Vectors, SNPlocs.Hsapiens.dbSNP144.GRCh37, bibtex, biglm, data.table, doParallel, foreach, knitcitations, lumi, lumiHumanAll.db, parallel, rmeta, scatterplot3d, snpStats, grid, yri1kgv Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: bed06d87b91085d5370d956c6ca61619 NeedsCompilation: no Title: Cloud-enabled cis-eQTL searches with Bioconductor GGtools 5.x Description: This workflow focuses on searches for eQTL in cis, so that DNA variants local to the gene assayed for expression are tested for association. biocViews: Workflow, GenomicVariantsWorkflow Author: Vincent Carey [aut, cre] Maintainer: Vincent Carey URL: https://www.bioconductor.org/help/workflows/eQTL/ VignetteBuilder: knitr source.ver: src/contrib/eQTL_1.2.0.tar.gz vignettes: vignettes/eQTL/inst/doc/cloudeqtl.html vignetteTitles: Cloud-enabled cis-eQTL searches with Bioconductor GGtools 5.x hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eQTL/inst/doc/cloudeqtl.R Package: ExpressionNormalizationWorkflow Version: 1.4.0 Imports: Biobase (>= 2.24.0), limma (>= 3.20.9), lme4 (>= 1.1.7), matrixStats (>= 0.10.3), pvca (>= 1.4.0), snm (>= 1.12.0), sva (>= 3.10.0), vsn (>= 3.32.0) Suggests: knitr, BiocStyle License: GPL (>=3) MD5sum: a5f0138c11bd9f74c800eb5ffa8259e6 NeedsCompilation: no Title: Gene Expression Normalization Workflow Description: An extensive, customized expression normalization workflow incorporating Supervised Normalization of Microarryas(SNM), Surrogate Variable Analysis(SVA) and Principal Variance Component Analysis to identify batch effects and remove them from the expression data to enhance the ability to detect the underlying biological signals. biocViews: Workflow, GeneExpressionWorkflow Author: Karthikeyan Murugesan [aut, cre], Greg Gibson [sad, ths] Maintainer: Karthikeyan Murugesan VignetteBuilder: knitr BugReports: https://github.com/ source.ver: src/contrib/ExpressionNormalizationWorkflow_1.4.0.tar.gz vignettes: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.html vignetteTitles: Gene Expression Normalization Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.R Package: generegulation Version: 1.2.0 Depends: R (>= 3.3.0), BSgenome.Scerevisiae.UCSC.sacCer3, Biostrings, GenomicFeatures, MotifDb, S4Vectors, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, motifStack, org.Sc.sgd.db, seqLogo Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 9d2127bde1e93941e8ac6793d3acb5c2 NeedsCompilation: no Title: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching Description: The binding of transcription factor proteins (TFs) to DNA promoter regions upstream of gene transcription start sites (TSSs) is one of the most important mechanisms by which gene expression, and thus many cellular processes, are controlled. Though in recent years many new kinds of data have become available for identifying transcription factor binding sites (TFBSs) -- ChIP-seq and DNase I hypersensitivity regions among them -- sequence matching continues to play an important role. In this workflow we demonstrate Bioconductor techniques for finding candidate TF binding sites in DNA sequence using the model organism Saccharomyces cerevisiae. The methods demonstrated here apply equally well to other organisms. biocViews: Workflow, EpigeneticsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/generegulation/ VignetteBuilder: knitr source.ver: src/contrib/generegulation_1.2.0.tar.gz vignettes: vignettes/generegulation/inst/doc/generegulation.html vignetteTitles: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/generegulation/inst/doc/generegulation.R Package: highthroughputassays Version: 1.2.0 Depends: R (>= 3.3.0), flowCore, flowStats, flowViz Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 15b549b72ee4b42643f1e035ac13a6ca NeedsCompilation: no Title: Using Bioconductor with High Throughput Assays Description: The workflow illustrates use of the flow cytometry packages to load, transform and visualize the flow data and gate certain populations in the dataset. The workflow loads the `flowCore`, `flowStats` and `flowViz` packages and its dependencies. It loads the ITN data with 15 samples, each of which includes, in addition to FSC and SSC, 5 fluorescence channels: CD3, CD4, CD8, CD69 and HLADR. biocViews: Workflow, ProteomicsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/highthroughputassays/ VignetteBuilder: knitr source.ver: src/contrib/highthroughputassays_1.2.0.tar.gz vignettes: vignettes/highthroughputassays/inst/doc/high-throughput-assays.html vignetteTitles: Using Bioconductor with High Throughput Assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/highthroughputassays/inst/doc/high-throughput-assays.R Package: liftOver Version: 1.2.0 Depends: R (>= 3.3.0), gwascat, GenomicRanges, rtracklayer, Homo.sapiens, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 559a3765d5cee566fc083685ec291dbd NeedsCompilation: no Title: Changing genomic coordinate systems with rtracklayer::liftOver Description: The liftOver facilities developed in conjunction with the UCSC browser track infrastructure are available for transforming data in GRanges formats. This is illustrated here with an image of the EBI/NHGRI GWAS catalog that is, as of May 10 2017, distributed with coordinates defined by NCBI build hg38. biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/liftOver/ VignetteBuilder: knitr source.ver: src/contrib/liftOver_1.2.0.tar.gz vignettes: vignettes/liftOver/inst/doc/liftov.html vignetteTitles: Changing genomic coordinate systems with rtracklayer::liftOver hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/liftOver/inst/doc/liftov.R Package: methylationArrayAnalysis Version: 1.2.0 Depends: R (>= 3.3.0), knitr, rmarkdown, BiocStyle, limma, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, RColorBrewer, missMethyl, matrixStats, minfiData, Gviz, DMRcate, stringr, FlowSorted.Blood.450k License: Artistic-2.0 MD5sum: 6afe01e935b2b435388bc1e7af6df118 NeedsCompilation: no Title: A cross-package Bioconductor workflow for analysing methylation array data. Description: Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This Bioconductor workflow uses multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data. biocViews: Workflow, EpigeneticsWorkflow Author: Jovana Maksimovic [aut, cre] Maintainer: Jovana Maksimovic VignetteBuilder: knitr source.ver: src/contrib/methylationArrayAnalysis_1.2.0.tar.gz vignettes: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.html vignetteTitles: A cross-package Bioconductor workflow for analysing methylation array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.R Package: proteomics Version: 1.2.0 Depends: R (>= 3.3.0), mzR, mzID, MSnID, MSnbase, rpx, MLInterfaces, pRoloc, pRolocdata, MSGFplus, rols, hpar Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 3d05875a1112a0555b913b26daa4959a NeedsCompilation: no Title: Mass spectrometry and proteomics data analysis Description: This workflow illustrates R / Bioconductor infrastructure for proteomics. Topics covered focus on support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, data processing and analysis. biocViews: ProteomicsWorkflow, Workflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/proteomics/ VignetteBuilder: knitr source.ver: src/contrib/proteomics_1.2.0.tar.gz vignettes: vignettes/proteomics/inst/doc/proteomics.html vignetteTitles: An R/Bioc proteomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteomics/inst/doc/proteomics.R Package: recountWorkflow Version: 1.2.0 Depends: R (>= 3.4.0) Imports: recount (>= 1.2.3), GenomicRanges, limma, edgeR, DESeq2, regionReport (>= 1.11.2), clusterProfiler (>= 3.5.5), org.Hs.eg.db (>= 3.4.1), gplots, derfinder, rtracklayer (>= 1.36.4), GenomicFeatures, bumphunter (>= 1.17.2), derfinderPlot Suggests: BiocStyle (>= 2.5.19), BiocWorkflowTools, knitr, devtools, rmarkdown, knitcitations License: Artistic-2.0 MD5sum: 232c35e192a183332173bdcf3a07da78 NeedsCompilation: no Title: recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor Description: The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data. biocViews: Workflow, ResourceQueryingWorkflow Author: Leonardo Collado-Torres [aut, cre], Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recountWorkflow VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recountWorkflow/ source.ver: src/contrib/recountWorkflow_1.2.0.tar.gz vignettes: vignettes/recountWorkflow/inst/doc/recount-workflow.html vignetteTitles: recount workflow: accessing over 70,,000 human RNA-seq samples with Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountWorkflow/inst/doc/recount-workflow.R Package: RNAseq123 Version: 1.2.0 Depends: R (>= 3.3.0), Glimma (>= 1.1.9), limma, edgeR, Mus.musculus Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: dcddd04da65462060e5433615e0958f8 NeedsCompilation: no Title: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR Description: R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016). biocViews: Workflow, GeneExpressionWorkflow Author: Charity Law, Monther Alhamdoosh, Shian Su, Gordon Smyth and Matthew Ritchie Maintainer: Matthew Ritchie URL: http://f1000research.com/articles/5-1408/ VignetteBuilder: knitr source.ver: src/contrib/RNAseq123_1.2.0.tar.gz vignettes: vignettes/RNAseq123/inst/doc/limmaWorkflow.html vignetteTitles: RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseq123/inst/doc/limmaWorkflow.R Package: rnaseqGene Version: 1.2.0 Depends: R (>= 3.3.0), BiocStyle, airway, Rsamtools, GenomicFeatures, GenomicAlignments, BiocParallel, magrittr, DESeq2, apeglm, vsn, dplyr, ggplot2, pheatmap, RColorBrewer, PoiClaClu, ggbeeswarm, genefilter, AnnotationDbi, org.Hs.eg.db, ReportingTools, Gviz, sva, RUVSeq, fission Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 543e878c5430187811fe6749ad0e87f4 NeedsCompilation: no Title: RNA-seq workflow: gene-level exploratory analysis and differential expression Description: Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results. biocViews: Workflow, GeneExpressionWorkflow Author: Michael Love [aut, cre] Maintainer: Michael Love URL: https://github.com/mikelove/rnaseqGene/ VignetteBuilder: knitr source.ver: src/contrib/rnaseqGene_1.2.0.tar.gz vignettes: vignettes/rnaseqGene/inst/doc/rnaseqGene.html vignetteTitles: RNA-seq workflow at the gene level hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqGene/inst/doc/rnaseqGene.R Package: RnaSeqGeneEdgeRQL Version: 1.2.3 Depends: R (>= 3.3.0), edgeR, gplots, org.Mm.eg.db, GO.db Suggests: knitr, knitcitations License: Artistic-2.0 MD5sum: da06e2dc0847b1fa83021a1f32d7d1d0 NeedsCompilation: no Title: Gene-level RNA-seq differential expression and pathway analysis using Rsubread and the edgeR quasi-likelihood pipeline Description: This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis. The analysis includes publication quality plots, GO and KEGG analyses, and the analysis of a expression signature as generated by a prior experiment. biocViews: Workflow, GeneExpressionWorkflow Author: Yunshun Chen, Aaron Lun, Gordon Smyth Maintainer: Yunshun Chen , Gordon Smyth URL: http://f1000research.com/articles/5-1438 VignetteBuilder: knitr source.ver: src/contrib/RnaSeqGeneEdgeRQL_1.2.3.tar.gz vignettes: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html vignetteTitles: From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.R Package: sequencing Version: 1.2.0 Depends: R (>= 3.3.0), GenomicRanges, GenomicAlignments, Biostrings, Rsamtools, ShortRead, BiocParallel, rtracklayer, VariantAnnotation, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, RNAseqData.HNRNPC.bam.chr14 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: e38a2f1d6bfadca81c819b3ca56946a1 NeedsCompilation: no Title: Introduction to Bioconductor for Sequence Data Description: Bioconductor enables the analysis and comprehension of high- throughput genomic data. We have a vast number of packages that allow rigorous statistical analysis of large data while keeping technological artifacts in mind. Bioconductor helps users place their analytic results into biological context, with rich opportunities for visualization. Reproducibility is an important goal in Bioconductor analyses. Different types of analysis can be carried out using Bioconductor, for example; Sequencing : RNASeq, ChIPSeq, variants, copy number etc.; Microarrays: expression, SNP, etc.; Domain specific analysis : Flow cytometry, Proteomics etc. For these analyses, one typically imports and works with diverse sequence-related file types, including fasta, fastq, BAM, gtf, bed, and wig files, among others. Bioconductor packages support import, common and advanced sequence manipulation operations such as trimming, transformation, and alignment including quality assessment. biocViews: Workflow, BasicWorkflow Author: Sonali Arora [aut], Martin Morgan [aut, cre] Maintainer: Martin Morgan URL: https://www.bioconductor.org/help/workflows/sequencing/ VignetteBuilder: knitr source.ver: src/contrib/sequencing_1.2.0.tar.gz vignettes: vignettes/sequencing/inst/doc/sequencing.html vignetteTitles: Introduction to Bioconductor for Sequence Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sequencing/inst/doc/sequencing.R Package: simpleSingleCell Version: 1.2.1 Depends: R (>= 3.3.0), BiocStyle, knitr, BiocParallel, Rtsne, mvoutlier, destiny, readxl, gdata, SingleCellExperiment, scater, org.Mm.eg.db, scran, limma, pheatmap, dynamicTreeCut, cluster, edgeR, TxDb.Mmusculus.UCSC.mm10.ensGene, scRNAseq, DropletUtils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: ef8d828ec6254624d38878398855f56a NeedsCompilation: no Title: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Description: This workflow implements a low-level analysis pipeline for scRNA-seq data using scran, scater and other Bioconductor packages. It describes how to perform quality control on the libraries, normalization of cell-specific biases, basic data exploration and cell cycle phase identification. Procedures to detect highly variable genes, significantly correlated genes and subpopulation-specific marker genes are also shown. These analyses are demonstrated on a range of publicly available scRNA-seq data sets. biocViews: Workflow, SingleCellWorkflow Author: Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/simpleSingleCell/ VignetteBuilder: knitr source.ver: src/contrib/simpleSingleCell_1.2.1.tar.gz vignettes: vignettes/simpleSingleCell/inst/doc/work-0-intro.html, vignettes/simpleSingleCell/inst/doc/work-1-reads.html, vignettes/simpleSingleCell/inst/doc/work-2-umis.html, vignettes/simpleSingleCell/inst/doc/work-3-tenx.html, vignettes/simpleSingleCell/inst/doc/work-4-misc.html, vignettes/simpleSingleCell/inst/doc/work-5-mnn.html vignetteTitles: Workflows for analyzing single-cell RNA-seq data with R/Bioconductor, Analyzing scRNA-seq read count data, Analyzing scRNA-seq UMI count data, Analyzing droplet-based scRNA-seq data, Further strategies for analyzing scRNA-seq data, Correcting batch effects in scRNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSingleCell/inst/doc/work-0-intro.R, vignettes/simpleSingleCell/inst/doc/work-1-reads.R, vignettes/simpleSingleCell/inst/doc/work-2-umis.R, vignettes/simpleSingleCell/inst/doc/work-3-tenx.R, vignettes/simpleSingleCell/inst/doc/work-4-misc.R, vignettes/simpleSingleCell/inst/doc/work-5-mnn.R Package: TCGAWorkflow Version: 1.2.4 Depends: R (>= 3.4.0) Imports: AnnotationHub, knitr, ELMER, biomaRt, BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker, ComplexHeatmap, clusterProfiler, downloader (>= 0.4), gaia, GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, MotIV, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, SummarizedExperiment, TCGAbiolinks, TCGAWorkflowData, DT License: Artistic-2.0 MD5sum: 734f9d86beb751c24e34a31cfb21a216 NeedsCompilation: no Title: TCGA Workflow Analyze cancer genomics and epigenomics data using Bioconductor packages Description: Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). biocViews: Workflow, ResourceQueryingWorkflow Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Fulvio D Angelo , Gianluca Bontempi , Michele Ceccarelli , Houtan Noushmehr Maintainer: Tiago Chedraoui Silva URL: https://f1000research.com/articles/5-1542/v2 VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues git_url: https://git.bioconductor.org/packages/TCGAWorkflow git_branch: RELEASE_3_7 git_last_commit: 5b6a573 git_last_commit_date: 2018-09-24 Date/Publication: 2018-09-26 source.ver: src/contrib/TCGAWorkflow_1.2.4.tar.gz vignettes: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.html vignetteTitles: 'TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages' hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.R Package: variants Version: 1.2.0 Depends: R (>= 3.3.0), VariantAnnotation, cgdv17, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 57275ffafffa607ebb4ef4a981362c3e NeedsCompilation: no Title: Annotating Genomic Variants Description: Read and write VCF files. Identify structural location of variants and compute amino acid coding changes for non-synonymous variants. Use SIFT and PolyPhen database packages to predict consequence of amino acid coding changes biocViews: AnnotationWorkflow, Workflow Author: Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/variants/ VignetteBuilder: knitr source.ver: src/contrib/variants_1.2.0.tar.gz vignettes: vignettes/variants/inst/doc/Annotating_Genomic_Variants.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variants/inst/doc/Annotating_Genomic_Variants.R