Package: epigraHMM 1.15.0

Pedro Baldoni

epigraHMM: Epigenomic R-based analysis with hidden Markov models

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.

Authors:Pedro Baldoni [aut, cre]

epigraHMM_1.15.0.tar.gz
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epigraHMM_1.15.0.tgz(r-4.4-x86_64)epigraHMM_1.15.0.tgz(r-4.4-arm64)epigraHMM_1.15.0.tgz(r-4.3-x86_64)epigraHMM_1.15.0.tgz(r-4.3-arm64)
epigraHMM_1.15.0.tar.gz(r-4.5-noble)epigraHMM_1.15.0.tar.gz(r-4.4-noble)
epigraHMM.pdf |epigraHMM.html✨
epigraHMM/json (API)
NEWS

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

Peer review:

Uses libs:
  • zlib– Compression library
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • helas3 - ENCODE ChIP-seq broad data from Helas3 cell line

On BioConductor:epigraHMM-1.15.0(bioc 3.21)epigraHMM-1.14.0(bioc 3.20)

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

chipseqatacseqdnaseseqhiddenmarkovmodelepigeneticszlibopenblascppopenmp

4.94 score 88 scripts 188 downloads 18 exports 127 dependencies

Last updated 2 months agofrom:524cd3d55d. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-win-x86_64NOTENov 29 2024
R-4.5-linux-x86_64NOTENov 29 2024
R-4.4-win-x86_64NOTENov 29 2024
R-4.4-mac-x86_64NOTENov 29 2024
R-4.4-mac-aarch64NOTENov 29 2024
R-4.3-win-x86_64NOTENov 29 2024
R-4.3-mac-x86_64NOTENov 29 2024
R-4.3-mac-aarch64NOTENov 29 2024

Exports:addOffsetscallPatternscallPeakscleanCountscontrolEMepigraHMMepigraHMMDataSetFromBamepigraHMMDataSetFromMatrixestimateTransitionProbexpStepinfoinitializermaxStepProbnormalizeCountsplotCountsplotPatternssegmentGenomesimulateMarkovChain

Dependencies:abindaskpassbackportsbamsignalsBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsbootbroomBSgenomecarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayoncsawcurldata.tableDelayedArrayDerivdoBydplyredgeRfansifarverformatRFormulafutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggplot2ggpubrggrepelggsciggsignifglueGreyListChIPgridExtragtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclelimmalme4locfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmetapodmgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslpbkrtestpheatmappillarpkgconfigpolynompurrrquantregR6RColorBrewerRcppRcppArmadilloRcppEigenRCurlrestfulrrhdf5rhdf5filtersRhdf5libRhtslibrjsonrlangRsamtoolsrstatixrtracklayerS4ArraysS4VectorsscalessnowSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXMLXVectoryamlzlibbioc

Consensus and differential peak calling with epigraHMM

Pedro L. Baldoni, Naim U. Rashid, Joseph G. Ibrahim

Rendered fromepigraHMM.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2021-09-22
Started: 2021-03-01