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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gcapc.pdf |gcapc.html
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NEWS

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

Peer review:

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 210 downloads 7 exports 51 dependencies

Last updated 3 months agofrom:35dd473587. Checks:7 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 22 2025
R-4.5-winOKJan 22 2025
R-4.5-linuxOKJan 22 2025
R-4.4-winOKJan 22 2025
R-4.4-macOKJan 22 2025
R-4.3-winOKJan 22 2025
R-4.3-macOKJan 22 2025

Exports:bindWidthgcapcPeaksgcEffectspeaksCATread5endCoveragerefinePeaksrefineSites

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsBSgenomecodetoolscpp11crayoncurlDelayedArrayformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangeshttrIRangesjsonlitelambda.rlatticeMASSMatrixMatrixGenericsmatrixStatsmimeopensslR6RCurlrestfulrRhtslibrjsonRsamtoolsrtracklayerS4ArraysS4VectorssnowSparseArraySummarizedExperimentsysUCSC.utilsXMLXVectoryaml

The gcapc user's guide

Rendered fromgcapc.Rmdusingknitr::rmarkdownon Jan 22 2025.

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