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

gcapc_1.31.0.tar.gz
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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 186 downloads 7 exports 52 dependencies

Last updated 2 months agofrom:35dd473587. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-winOKNov 23 2024
R-4.5-linuxOKNov 23 2024
R-4.4-winOKNov 23 2024
R-4.4-macOKNov 23 2024
R-4.3-winOKNov 23 2024
R-4.3-macOKNov 23 2024

Exports:bindWidthgcapcPeaksgcEffectspeaksCATread5endCoveragerefinePeaksrefineSites

Dependencies:abindaskpassBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsBSgenomecodetoolscpp11crayoncurlDelayedArrayformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangeshttrIRangesjsonlitelambda.rlatticeMASSMatrixMatrixGenericsmatrixStatsmimeopensslR6RCurlrestfulrRhtslibrjsonRsamtoolsrtracklayerS4ArraysS4VectorssnowSparseArraySummarizedExperimentsysUCSC.utilsXMLXVectoryamlzlibbioc

The gcapc user's guide

Rendered fromgcapc.Rmdusingknitr::rmarkdownon Nov 23 2024.

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