Package: annotation Version: 1.12.0 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: 6135968b28c6924568c631043fd3c3bf 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 git_url: https://git.bioconductor.org/packages/annotation git_branch: RELEASE_3_11 git_last_commit: b5fdb04 git_last_commit_date: 2020-04-27 Date/Publication: 2020-05-01 source.ver: src/contrib/annotation_1.12.0.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 dependencyCount: 121 Package: arrays Version: 1.14.0 Depends: R (>= 3.0.0) Suggests: affy, limma, hgfocuscdf, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b4d33b484a6f1c69cc645b0b80cd64db 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 git_url: https://git.bioconductor.org/packages/arrays git_branch: RELEASE_3_11 git_last_commit: 4a914f5 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/arrays_1.14.0.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 dependencyCount: 0 Package: BiocMetaWorkflow Version: 1.10.0 Suggests: BiocStyle, knitr, rmarkdown, BiocWorkflowTools License: Artistic-2.0 MD5sum: 591f410f8464b75563c5da96d4e8cb89 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 git_url: https://git.bioconductor.org/packages/BiocMetaWorkflow git_branch: RELEASE_3_11 git_last_commit: fc91f7a git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/BiocMetaWorkflow_1.10.0.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 dependencyCount: 0 Package: CAGEWorkflow Version: 1.4.0 Depends: R (>= 3.6.0), CAGEfightR, nanotubes Suggests: knitr, kableExtra, rmarkdown, BiocStyle, BiocWorkflowTools, pheatmap, ggseqlogo, viridis, magrittr, ggforce, ggthemes, tidyverse, dplyr, GenomicRanges, SummarizedExperiment, GenomicFeatures, BiocParallel, InteractionSet, Gviz, DESeq2, limma, edgeR, statmod, BiasedUrn, sva, TFBSTools, motifmatchr, pathview, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, JASPAR2016, png License: GPL-3 MD5sum: 629a41e031d8c1817adf50ed0e2febd5 NeedsCompilation: no Title: A step-by-step guide to analyzing CAGE data using R/Bioconductor Description: Workflow for analyzing Cap Analysis of Gene Expression (CAGE) data using R/Bioconductor. biocViews: GeneExpressionWorkflow, AnnotationWorkflow Author: Malte Thodberg [aut, cre] Maintainer: Malte Thodberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEWorkflow git_branch: RELEASE_3_11 git_last_commit: b9a04dd git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/CAGEWorkflow_1.4.0.tar.gz vignettes: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.html vignetteTitles: CAGEWorkflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.R dependencyCount: 152 Package: chipseqDB Version: 1.12.0 Suggests: chipseqDBData, BiocStyle, BiocFileCache, ChIPpeakAnno, Gviz, Rsamtools, TxDb.Mmusculus.UCSC.mm10.knownGene, csaw, edgeR, knitr, org.Mm.eg.db, rtracklayer, rmarkdown License: Artistic-2.0 MD5sum: 828297904f92cb4efb843f5702c3c55d NeedsCompilation: no Title: A Bioconductor Workflow to Detect Differential Binding in ChIP-seq Data Description: 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 multiple biological samples in 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 genes. biocViews: ImmunoOncologyWorkflow, Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/chipseqDB/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseqDB git_branch: RELEASE_3_11 git_last_commit: 7a33927 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/chipseqDB_1.12.0.tar.gz vignettes: vignettes/chipseqDB/inst/doc/cbp.html, vignettes/chipseqDB/inst/doc/h3k27me3.html, vignettes/chipseqDB/inst/doc/h3k9ac.html, vignettes/chipseqDB/inst/doc/intro.html vignetteTitles: 3. Differential binding of CBP in fibroblasts, 4. Differential enrichment of H3K27me3 in lung epithelium, 2. Differential enrichment of H3K9ac in B cells, 1. Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseqDB/inst/doc/cbp.R, vignettes/chipseqDB/inst/doc/h3k27me3.R, vignettes/chipseqDB/inst/doc/h3k9ac.R, vignettes/chipseqDB/inst/doc/intro.R dependencyCount: 0 Package: csawUsersGuide Version: 1.4.0 Suggests: csaw, chipseqDBData, edgeR, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, rtracklayer, Rsamtools, Gviz, knitr, BiocStyle License: GPL-3 MD5sum: 9c6f56d361def6d9cf12c984429c1799 NeedsCompilation: no Title: csaw User's Guide Description: A user's guide for the csaw package for detecting differentially bound regions in ChIP-seq data. Describes how to read in BAM files to obtain a per-window count matrix, filtering to obtain high-abundance windows of interest, normalization of sample-specific biases, testing for differential binding, consolidation of per-window results to obtain per-region statistics, and annotation and visualization of the DB results. biocViews: Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csawUsersGuide git_branch: RELEASE_3_11 git_last_commit: 29ee42d git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/csawUsersGuide_1.4.0.tar.gz vignettes: vignettes/csawUsersGuide/inst/doc/csaw.pdf vignetteTitles: User's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csawUsersGuide/inst/doc/csaw.R dependencyCount: 0 Package: cytofWorkflow Version: 1.12.1 Depends: R (>= 3.6.0), BiocStyle, knitr, readxl, CATALYST, diffcyt, HDCytoData, uwot, cowplot Suggests: knitcitations License: Artistic-2.0 MD5sum: dfc649daaa07e028015d34135bd22e8e 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 updated 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 or 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: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow Author: Malgorzata Nowicka [aut], Helena L. Crowell [aut], Mark D. Robinson [aut, cre] Maintainer: Mark D. Robinson URL: https://github.com/markrobinsonuzh/cytofWorkflow VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/cytofWorkflow/issues git_url: https://git.bioconductor.org/packages/cytofWorkflow git_branch: RELEASE_3_11 git_last_commit: 2c0d54d git_last_commit_date: 2020-04-29 Date/Publication: 2020-08-07 source.ver: src/contrib/cytofWorkflow_1.12.1.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 dependencyCount: 263 Package: EGSEA123 Version: 1.12.0 Depends: R (>= 3.4.0), EGSEA (>= 1.5.2), limma, edgeR, illuminaio Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 667e0b0fabaf38ff1f216bc547af13a5 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: ImmunoOncologyWorkflow, 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 git_url: https://git.bioconductor.org/packages/EGSEA123 git_branch: RELEASE_3_11 git_last_commit: 83fced9 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/EGSEA123_1.12.0.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 dependencyCount: 188 Package: eQTL Version: 1.12.0 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: a889a38f943b216687634258057c5bdc 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 git_url: https://git.bioconductor.org/packages/eQTL git_branch: RELEASE_3_11 git_last_commit: fcf3bb7 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/eQTL_1.12.0.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 dependencyCount: 215 Package: ExpressionNormalizationWorkflow Version: 1.14.0 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: abb1d80ff24d08544dc046627143bdc1 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: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow Author: Karthikeyan Murugesan [aut, cre], Greg Gibson [sad, ths] Maintainer: Karthikeyan Murugesan VignetteBuilder: knitr BugReports: https://github.com/ git_url: https://git.bioconductor.org/packages/ExpressionNormalizationWorkflow git_branch: RELEASE_3_11 git_last_commit: a9fee04 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/ExpressionNormalizationWorkflow_1.14.0.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 dependencyCount: 102 Package: fluentGenomics Version: 1.0.1 Depends: R (>= 4.0) Imports: plyranges (>= 1.7.7), dplyr, SummarizedExperiment, readr, stats, utils Suggests: knitr, rmarkdown, bookdown, rappdirs, BiocFileCache, DESeq2, limma, ggplot2, tidyr, tximeta (>= 1.4.2), macrophage (>= 1.2.0), License: MIT + file LICENSE MD5sum: 1529cb31abb954351b5765c751dc3490 NeedsCompilation: no Title: A plyranges and tximeta workflow Description: An extended workflow using the plyranges and tximeta packages for fluent genomic data analysis. Use tximeta to correctly import RNA-seq transcript quantifications and summarize them to gene counts for downstream analysis. Use plyranges for clearly expressing operations over genomic coordinates and to combine results from differential expression and differential accessibility analyses. biocViews: Workflow, BasicWorkflow, GeneExpressionWorkflow Author: Stuart Lee [aut, cre] (), Michael Love [aut, ctb] Maintainer: Stuart Lee URL: https://github.com/sa-lee/fluentGenomics VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/sa-lee/fluentGenomics/issues git_url: https://git.bioconductor.org/packages/fluentGenomics git_branch: RELEASE_3_11 git_last_commit: 01ce9a3 git_last_commit_date: 2020-05-21 Date/Publication: 2020-05-22 source.ver: src/contrib/fluentGenomics_1.0.1.tar.gz vignettes: vignettes/fluentGenomics/inst/doc/fluentGenomics.html vignetteTitles: fluentGenomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fluentGenomics/inst/doc/fluentGenomics.R dependencyCount: 63 Package: generegulation Version: 1.12.0 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: a6233b6fa7ad2ada4b3292f90161800b 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 git_url: https://git.bioconductor.org/packages/generegulation git_branch: RELEASE_3_11 git_last_commit: 1e88b7c git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/generegulation_1.12.0.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 dependencyCount: 133 Package: highthroughputassays Version: 1.12.0 Depends: R (>= 3.3.0), flowCore, flowStats, flowWorkspace Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f4bef3f62571c30241fc7d05d7a1f425 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 `flowWorkspace` 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: ImmunoOncologyWorkflow, Workflow, ProteomicsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/highthroughputassays/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/highthroughputassays git_branch: RELEASE_3_11 git_last_commit: e2115e0 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/highthroughputassays_1.12.0.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 dependencyCount: 100 Package: liftOver Version: 1.12.0 Depends: R (>= 3.3.0), gwascat, GenomicRanges, rtracklayer, Homo.sapiens, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 98c39087391ccfccf0107a7f306f5412 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 git_url: https://git.bioconductor.org/packages/liftOver git_branch: RELEASE_3_11 git_last_commit: e7957a7 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/liftOver_1.12.0.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 dependencyCount: 121 Package: maEndToEnd Version: 2.8.0 Depends: R (>= 3.5.0), Biobase, oligoClasses, ArrayExpress, pd.hugene.1.0.st.v1, hugene10sttranscriptcluster.db, oligo, arrayQualityMetrics, limma, topGO, ReactomePA, clusterProfiler, gplots, ggplot2, geneplotter, pheatmap, RColorBrewer, dplyr, tidyr, stringr, matrixStats, genefilter, openxlsx, Rgraphviz Suggests: BiocStyle, knitr, devtools, rmarkdown License: MIT MD5sum: 2c42eac87fb84efca18c10e710b778b5 NeedsCompilation: no Title: An end to end workflow for differential gene expression using Affymetrix microarrays Description: In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene" type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis. biocViews: GeneExpressionWorkflow Author: Bernd Klaus [aut], Stefanie Reisenauer [aut, cre] Maintainer: Stefanie Reisenauer URL: https://www.bioconductor.org/help/workflows/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/maEndToEnd git_branch: RELEASE_3_11 git_last_commit: 7f0ab79 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/maEndToEnd_2.8.0.tar.gz vignettes: vignettes/maEndToEnd/inst/doc/MA-Workflow.html vignetteTitles: An end to end workflow for differential gene expression using Affymetrix microarrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maEndToEnd/inst/doc/MA-Workflow.R dependencyCount: 209 Package: methylationArrayAnalysis Version: 1.12.0 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: 8fce915e9b75b241b0f80c93e33b3f5b 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 git_url: https://git.bioconductor.org/packages/methylationArrayAnalysis git_branch: RELEASE_3_11 git_last_commit: 828ea71 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/methylationArrayAnalysis_1.12.0.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 dependencyCount: 221 Package: proteomics Version: 1.12.0 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: 7208611b4c5caac37bd9ff964f6fd99c 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: ImmunoOncologyWorkflow, ProteomicsWorkflow, Workflow Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: https://www.bioconductor.org/help/workflows/proteomics/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proteomics git_branch: RELEASE_3_11 git_last_commit: 133b0f9 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/proteomics_1.12.0.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 dependencyCount: 209 Package: recountWorkflow Version: 1.12.0 Depends: R (>= 3.6.0) Imports: recount, GenomicRanges, limma, edgeR, DESeq2, regionReport, clusterProfiler, org.Hs.eg.db, gplots, derfinder, GenomicState, bumphunter, derfinderPlot Suggests: BiocStyle, BiocWorkflowTools, knitr, sessioninfo, rmarkdown, knitcitations License: Artistic-2.0 MD5sum: d7844c249e298ce75b565aa2cdf8c428 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/ git_url: https://git.bioconductor.org/packages/recountWorkflow git_branch: RELEASE_3_11 git_last_commit: c50a814 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/recountWorkflow_1.12.0.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 dependencyCount: 235 Package: RNAseq123 Version: 1.12.0 Depends: R (>= 3.3.0), Glimma (>= 1.1.9), limma, edgeR, gplots, RColorBrewer, Mus.musculus, R.utils Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b158faea19633e755652d93c3b1d9a02 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, ImmunoOncologyWorkflow Author: Charity Law, Monther Alhamdoosh, Shian Su, Xueyi Dong, Luyi Tian, Gordon Smyth and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://f1000research.com/articles/5-1408/v3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAseq123 git_branch: RELEASE_3_11 git_last_commit: 19eff82 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/RNAseq123_1.12.0.tar.gz vignettes: vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.html, vignettes/RNAseq123/inst/doc/limmaWorkflow.html vignetteTitles: RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (Chinese version), RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (English version) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.R, vignettes/RNAseq123/inst/doc/limmaWorkflow.R dependencyCount: 105 Package: rnaseqDTU Version: 1.8.0 Depends: R (>= 3.5.0), DRIMSeq, DEXSeq, stageR, DESeq2, edgeR, rafalib, devtools Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: ef6e920249bc7dd1e291629b40724210 NeedsCompilation: no Title: RNA-seq workflow for differential transcript usage following Salmon quantification Description: RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Michael Love [aut, cre], Charlotte Soneson [aut], Rob Patro [aut] Maintainer: Michael Love URL: https://github.com/mikelove/rnaseqDTU/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqDTU git_branch: RELEASE_3_11 git_last_commit: 12d9d3e git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/rnaseqDTU_1.8.0.tar.gz vignettes: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.html vignetteTitles: RNA-seq workflow for differential transcript usage following Salmon quantification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.R dependencyCount: 159 Package: rnaseqGene Version: 1.12.0 Depends: R (>= 3.3.0), BiocStyle, airway (>= 1.5.3), tximeta, magrittr, DESeq2, apeglm, vsn, dplyr, ggplot2, hexbin, pheatmap, RColorBrewer, PoiClaClu, glmpca, ggbeeswarm, genefilter, AnnotationDbi, org.Hs.eg.db, ReportingTools, Gviz, sva, RUVSeq, fission Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 126145037db198995657d08caf1d8f39 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, ImmunoOncologyWorkflow Author: Michael Love [aut, cre] Maintainer: Michael Love URL: https://github.com/mikelove/rnaseqGene/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqGene git_branch: RELEASE_3_11 git_last_commit: c426609 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/rnaseqGene_1.12.0.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 dependencyCount: 221 Package: RnaSeqGeneEdgeRQL Version: 1.12.1 Depends: R (>= 3.3.0), edgeR, gplots, org.Mm.eg.db, GO.db, BiocStyle Suggests: knitr, knitcitations License: Artistic-2.0 MD5sum: 280b88f44f3c6fd08f5fcff8a5c78e81 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, ImmunoOncologyWorkflow Author: Yunshun Chen, Aaron Lun, Gordon Smyth Maintainer: Yunshun Chen URL: http://f1000research.com/articles/5-1438 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqGeneEdgeRQL git_branch: RELEASE_3_11 git_last_commit: 9d9f450 git_last_commit_date: 2020-06-14 Date/Publication: 2020-06-16 source.ver: src/contrib/RnaSeqGeneEdgeRQL_1.12.1.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 dependencyCount: 58 Package: sequencing Version: 1.12.0 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: 6cec36cab1d64bfed73d1268671f31d9 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: ImmunoOncologyWorkflow, Workflow, BasicWorkflow Author: Sonali Arora [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/sequencing/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sequencing git_branch: RELEASE_3_11 git_last_commit: 432d523 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/sequencing_1.12.0.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 dependencyCount: 115 Package: simpleSingleCell Version: 1.12.0 Suggests: knitr, rmarkdown, BiocStyle, readxl, R.utils, SingleCellExperiment, scater, scran, limma, BiocFileCache, org.Mm.eg.db License: Artistic-2.0 MD5sum: 39a47b0c08b2ec20dd8a15e603f0c019 NeedsCompilation: no Title: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Description: Almost all content that was previously in these workflows have been migrated to the "Orchestrating Single-Cell Analyses with Bioconductor" book at https://osca.bioconductor.org. Most vignettes here are retained largely for back-compatibility with existing external links, and provide links to the relevant OSCA book chapters. biocViews: ImmunoOncologyWorkflow, 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 git_url: https://git.bioconductor.org/packages/simpleSingleCell git_branch: RELEASE_3_11 git_last_commit: ccc26b2 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/simpleSingleCell_1.12.0.tar.gz vignettes: vignettes/simpleSingleCell/inst/doc/batch.html, vignettes/simpleSingleCell/inst/doc/bigdata.html, vignettes/simpleSingleCell/inst/doc/de.html, vignettes/simpleSingleCell/inst/doc/doublets.html, vignettes/simpleSingleCell/inst/doc/intro.html, vignettes/simpleSingleCell/inst/doc/misc.html, vignettes/simpleSingleCell/inst/doc/multibatch.html, vignettes/simpleSingleCell/inst/doc/qc.html, vignettes/simpleSingleCell/inst/doc/reads.html, vignettes/simpleSingleCell/inst/doc/spike.html, vignettes/simpleSingleCell/inst/doc/tenx.html, vignettes/simpleSingleCell/inst/doc/umis.html, vignettes/simpleSingleCell/inst/doc/var.html vignetteTitles: 05. Correcting batch effects, 12. Scalability for big data, 10. Detecting differential expression, 08. Detecting doublets, 01. Introduction, 13. Further analysis strategies, 11. Advanced batch correction, 06. Quality control details, 02. Read count data, 07. Spike-in normalization, 04. Droplet-based data, 03. UMI count data, 09. Advanced variance modelling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSingleCell/inst/doc/intro.R, vignettes/simpleSingleCell/inst/doc/misc.R dependencyCount: 0 Package: SingscoreAMLMutations Version: 1.4.0 Depends: R (>= 3.6.0) Imports: dcanr, edgeR, ggplot2, gridExtra, GSEABase, mclust, org.Hs.eg.db, plyr, reshape2, rtracklayer, singscore, SummarizedExperiment, TCGAbiolinks Suggests: knitr, rmarkdown, BiocStyle, BiocWorkflowTools, spelling License: Artistic-2.0 MD5sum: b5f7810ba16b7882995ae9b5bec90472 NeedsCompilation: no Title: Using singscore to predict mutations in AML from transcriptomic signatures Description: This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis. biocViews: GeneExpressionWorkflow, GenomicVariantsWorkflow, ImmunoOncologyWorkflow, Workflow Author: Dharmesh D. Bhuva [aut, cre] (), Momeneh Foroutan [aut] (), Yi Xie [aut] (), Ruqian Lyu [aut], Joseph Cursons [aut] (), Melissa J. Davis [aut] () Maintainer: Dharmesh D. Bhuva URL: https://github.com/DavisLaboratory/SingscoreAMLMutations VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/SingscoreAMLMutations/issues git_url: https://git.bioconductor.org/packages/SingscoreAMLMutations git_branch: RELEASE_3_11 git_last_commit: 5192aa1 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/SingscoreAMLMutations_1.4.0.tar.gz vignettes: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.html, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.html vignetteTitles: Using singscore to predict mutations in AML from transcriptomic signatures (Chinese version), Using singscore to predict mutations in AML from transcriptomic signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.R, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.R dependencyCount: 162 Package: TCGAWorkflow Version: 1.12.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, knitr, ELMER, biomaRt, BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker, ComplexHeatmap, ggpubr, clusterProfiler, downloader (>= 0.4), gaia, GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, MotIV, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, SummarizedExperiment, TCGAbiolinks, TCGAWorkflowData (>= 1.9.0), DT License: Artistic-2.0 MD5sum: 95f0a9f28d74247e48f5e7bebf439e53 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_11 git_last_commit: 8df0bb2 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/TCGAWorkflow_1.12.0.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 dependencyCount: 285 Package: variants Version: 1.12.0 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: a160a77827c48527d2da815c0369bed3 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: ImmunoOncologyWorkflow, AnnotationWorkflow, Workflow Author: Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/variants/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/variants git_branch: RELEASE_3_11 git_last_commit: c05da7b git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-28 source.ver: src/contrib/variants_1.12.0.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 dependencyCount: 92