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Epigenomic R-based analysis with hidden Markov models

Bioconductor version: Release (3.19)

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.

Author: Pedro Baldoni [aut, cre]

Maintainer: Pedro Baldoni <pedrobaldoni at>

Citation (from within R, enter citation("epigraHMM")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Consensus and Differential Peak Calling With epigraHMM HTML R Script
Reference Manual PDF


biocViews ATACSeq, ChIPSeq, DNaseSeq, Epigenetics, HiddenMarkovModel, Software
Version 1.12.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License MIT + file LICENSE
Depends R (>= 3.5.0)
Imports Rcpp, magrittr, data.table, SummarizedExperiment, methods, GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices
System Requirements GNU make
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Suggests testthat, knitr, rmarkdown, BiocStyle, BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData
Linking To Rcpp, RcppArmadillo, Rhdf5lib
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Follow Installation instructions to use this package in your R session.

Source Package epigraHMM_1.12.0.tar.gz
Windows Binary (64-bit only)
macOS Binary (x86_64) epigraHMM_1.12.0.tgz
macOS Binary (arm64) epigraHMM_1.12.0.tgz
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Source Repository (Developer Access) git clone
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