Package: wavClusteR 2.41.0

Federico Comoglio

wavClusteR: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

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).

Authors:Federico Comoglio and Cem Sievers

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wavClusteR/json (API)
NEWS

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

Peer review:

Datasets:
  • model - Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP data

On BioConductor:wavClusteR-2.41.0(bioc 3.21)wavClusteR-2.40.0(bioc 3.20)

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

immunooncologysequencingtechnologyripseqrnaseqbayesian

4.60 score 3 scripts 309 downloads 4 mentions 18 exports 129 dependencies

Last updated 23 days agofrom:bd37a40aff. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winWARNINGNov 18 2024
R-4.5-linuxWARNINGNov 18 2024
R-4.4-winWARNINGNov 18 2024
R-4.4-macWARNINGNov 18 2024
R-4.3-winWARNINGNov 18 2024
R-4.3-macWARNINGNov 18 2024

Exports:annotateClustersestimateFDRexportClustersexportCoverageexportHighConfSubexportSequencesfilterClustersfitMixtureModelgetAllSubgetClustersgetExpIntervalgetHighConfSubgetMetaGenegetMetaTSSplotSizeDistributionplotStatisticsplotSubstitutionsreadSortedBam

Dependencies:abindade4AnnotationDbiaskpassbackportsbase64encBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitbit64bitopsblobbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayoncurldata.tableDBIDelayedArraydigestevaluatefansifarverfastmapfontawesomeforeachforeignformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetshttrIRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimemunsellnlmennetopensslpillarpixmappkgconfigplogrpngR6rappdirsRColorBrewerRcppRcppArmadilloRCurlrestfulrRhtslibrjsonrlangrmarkdownrpartRsamtoolsRSQLiterstudioapirtracklayerS4ArraysS4VectorssassscalessegmentedseqinrsnowspSparseArraystringistringrSummarizedExperimentsystibbletinytexUCSC.utilsutf8vctrsviridisviridisLitewithrxfunXMLXVectoryamlzlibbioc

wavClusteR: a workflow for PAR-CLIP data analysis

Federico Comoglio and Cem Sievers

Rendered fromwavCluster_vignette.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2020-10-17
Started: 2015-12-14

Readme and manuals

Help Manual

Help pageTopics
A comprehensive 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).wavClusteR-package wavClusteR
Annotate clusters with respect to transcript featuresannotateClusters
Estimate False Discovery Rate within the relative substitution frequency support by integrating PAR-CLIP data and RNA-Seq dataestimateFDR
Export clusters as BED trackexportClusters
Export coverage as BigWig trackexportCoverage
Export high-confidence substitutions as BED trackexportHighConfSub
Export cluster sequences for motif search analysisexportSequences
Merge clusters and compute all relevant cluster statisticsfilterClusters
Fit a non-parametric mixture model from all identified substitutionsfitMixtureModel
Identify all substitutions observed across genomic positions exhibiting a specified minimum coveragegetAllSub
Identify clusters containing high-confidence substitutions and resolve boundaries at high resolutiongetClusters
Identify the interval of relative substitution frequencies dominated by experimental induction.getExpInterval
Classify substitutions based on identified RSF interval and return high confidence transitionsgetHighConfSub
Compute and plot distribution of average coverage or relative log-odds as metagene profile using identified clustersgetMetaCoverage
Compute and plot metagene profile using identified clustersgetMetaGene
Compute and plot read densities in genomic regions around transcription start sitesgetMetaTSS
Components of the non-parametric mixture moodel fitted on Ago2 PAR-CLIP datamodel
Plot the distribution of cluster sizesplotSizeDistribution
Pairs plot visualization of clusters statisticsplotStatistics
Barplot visualization of the number of genomic positions exhibiting a given substitution and, if model provided, additional diagnostic plots.plotSubstitutions
Load a sorted BAM filereadSortedBam