Package: RCAS 1.33.0

Bora Uyar

RCAS: RNA Centric Annotation System

RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery.

Authors:Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut]

RCAS_1.33.0.tar.gz
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RCAS.pdf |RCAS.html
RCAS/json (API)
NEWS

# Install 'RCAS' in R:
install.packages('RCAS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • gff - Sample GFF file imported as a GRanges object
  • queryRegions - Sample BED file imported as a GRanges object

On BioConductor:RCAS-1.33.0(bioc 3.21)RCAS-1.32.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

softwaregenetargetmotifannotationmotifdiscoverygotranscriptomicsgenomeannotationgenesetenrichmentcoverage

6.54 score 1 packages 29 scripts 380 downloads 8 mentions 40 exports 152 dependencies

Last updated 23 days agofrom:44fd9776ff. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:calculateCoverageProfilecalculateCoverageProfileFromTxdbcalculateCoverageProfileListcalculateCoverageProfileListFromTxdbcheckSeqDbcreateControlRegionscreateDBcreateOrthologousGeneSetListdeleteSampleDataFromDBdiscoverFeatureSpecificMotifsextractSequencesfindDifferentialMotifsfindEnrichedFunctionsgenerateKmersgetFeatureBoundaryCoveragegetFeatureBoundaryCoverageBingetFeatureBoundaryCoverageMultigetIntervalOverlapMatrixgetMotifSummaryTablegetPWMgetTargetedGenesTablegetTxdbFeaturesgetTxdbFeaturesFromGRangesimportBedimportBedFilesimportGtfparseMsigdbplotFeatureBoundaryCoverageprintMsigdbDatasetqueryGffretrieveOrthologsrunGSEArunMotifDiscoveryrunMotifRGrunReportrunReportMetaAnalysisrunTopGOsummarizeDatabaseContentsummarizeQueryRegionssummarizeQueryRegionsMulti

Dependencies:abindAnnotationDbiaskpassbase64encBHBiobaseBiocFileCacheBiocGenericsBiocIOBiocParallelbiomaRtBiostringsbitbit64bitopsblobBSgenomeBSgenome.Hsapiens.UCSC.hg19bslibcachemclicliprcodetoolscolorspacecowplotcpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArraydigestdplyrDTevaluatefansifarverfastmapfilelockfontawesomeformatRfsfutile.loggerfutile.optionsgenericsgenomationGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2gluegprofiler2gridBasegridExtragtablehighrhmshtmltoolshtmlwidgetshttpuvhttrhttr2imputeIRangesisobandjquerylibjsonliteKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevallifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemunsellnlmeopensslpbapplypheatmappillarpkgconfigplogrplotlyplotrixplyrpngprettyunitsprogresspromisesproxypurrrR6rangerrappdirsRColorBrewerRcppRcppEigenRCurlreadrreshape2restfulrRhtslibrjsonrlangrmarkdownRsamtoolsRSQLitertracklayerS4ArraysS4VectorssassscalesseqLogoseqPatternsnowSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselecttinytextxdbmakertzdbUCSC.utilsutf8vctrsviridisLitevroomwithrxfunXMLxml2XVectoryamlzlibbioc

How to do meta-analysis of CLIP-seq peaks from multiple samples with RCAS

Rendered fromRCAS.metaAnalysis.vignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-02-04
Started: 2018-02-26

The RNA Centric Analysis System Report

Rendered fromRCAS.vignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-03-20
Started: 2016-04-19

Readme and manuals

Help Manual

Help pageTopics
calculateCoverageProfilecalculateCoverageProfile
calculateCoverageProfileFromTxdbcalculateCoverageProfileFromTxdb
calculateCoverageProfileListcalculateCoverageProfileList
calculateCoverageProfileListFromTxdbcalculateCoverageProfileListFromTxdb
checkSeqDbcheckSeqDb
createControlRegionscreateControlRegions
createDBcreateDB
createOrthologousMsigdbDataset This function is deprecated. For functional enrichment analysis, use findEnrichedFunctions.createOrthologousGeneSetList
deleteSampleDataFromDBdeleteSampleDataFromDB
discoverFeatureSpecificMotifsdiscoverFeatureSpecificMotifs
extractSequencesextractSequences
Find Differential MotifsfindDifferentialMotifs
findEnrichedFunctionsfindEnrichedFunctions
Generate K-mersgenerateKmers
getFeatureBoundaryCoveragegetFeatureBoundaryCoverage
getFeatureBoundaryCoverageBingetFeatureBoundaryCoverageBin
getFeatureBoundaryCoverageMultigetFeatureBoundaryCoverageMulti
getIntervalOverlapMatrixgetIntervalOverlapMatrix
getMotifSummaryTablegetMotifSummaryTable
getPWMgetPWM
getTargetedGenesTablegetTargetedGenesTable
getTxdbFeaturesgetTxdbFeatures
getTxdbFeaturesFromGRangesgetTxdbFeaturesFromGRanges
Sample GFF file imported as a GRanges objectgff
importBedimportBed
importBedFilesimportBedFiles
importGtfimportGtf
parseMsigdbparseMsigdb
plotFeatureBoundaryCoverageplotFeatureBoundaryCoverage
Print MSIGDB Dataset to a fileprintMsigdbDataset
queryGffqueryGff
Sample BED file imported as a GRanges objectqueryRegions
retrieveOrthologsretrieveOrthologs
runGSEArunGSEA
runMotifDiscoveryrunMotifDiscovery
run motifRGrunMotifRG
Generate a RCAS Report for a list of transcriptome-level segmentsrunReport
runReportMetaAnalysisrunReportMetaAnalysis
runTopGOrunTopGO
summarizeDatabaseContentsummarizeDatabaseContent
summarizeQueryRegionssummarizeQueryRegions
summarizeQueryRegionsMultisummarizeQueryRegionsMulti