Package: gcapc 1.31.0

Mingxiang Teng

gcapc: GC Aware Peak Caller

Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.

Authors:Mingxiang Teng and Rafael A. Irizarry

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NEWS

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

Bug tracker:https://github.com/tengmx/gcapc/issues

On BioConductor:gcapc-1.31.0(bioc 3.21)gcapc-1.30.0(bioc 3.20)

sequencingchipseqbatcheffectpeakdetection

4.95 score 9 stars 7 scripts 252 downloads 7 exports 51 dependencies

Last updated 4 months agofrom:35dd473587. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 21 2025
R-4.5-winOKFeb 21 2025
R-4.5-macOKFeb 21 2025
R-4.5-linuxOKFeb 21 2025
R-4.4-winOKFeb 21 2025
R-4.4-macOKFeb 21 2025
R-4.3-winOKFeb 21 2025
R-4.3-macOKFeb 21 2025

Exports:bindWidthgcapcPeaksgcEffectspeaksCATread5endCoveragerefinePeaksrefineSites

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsBSgenomecodetoolscpp11crayoncurlDelayedArrayformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangeshttrIRangesjsonlitelambda.rlatticeMASSMatrixMatrixGenericsmatrixStatsmimeopensslR6RCurlrestfulrRhtslibrjsonRsamtoolsrtracklayerS4ArraysS4VectorssnowSparseArraySummarizedExperimentsysUCSC.utilsXMLXVectoryaml

The gcapc user's guide

Rendered fromgcapc.Rmdusingknitr::rmarkdownon Feb 21 2025.

Last update: 2017-06-08
Started: 2016-10-19