Package: epigraHMM 1.21.2

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_1.21.2.tar.gz
epigraHMM_1.21.2.zip(r-4.7)epigraHMM_1.21.2.zip(r-4.6)epigraHMM_1.21.2.zip(r-4.5)
epigraHMM_1.21.2.tgz(r-4.6-x86_64)epigraHMM_1.21.2.tgz(r-4.6-arm64)epigraHMM_1.21.2.tgz(r-4.5-x86_64)epigraHMM_1.21.2.tgz(r-4.5-arm64)
epigraHMM_1.21.2.tar.gz(r-4.7-arm64)epigraHMM_1.21.2.tar.gz(r-4.7-x86_64)epigraHMM_1.21.2.tar.gz(r-4.6-arm64)epigraHMM_1.21.2.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
epigraHMM/json (API)

# Install 'epigraHMM' in R:

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

Uses libs:

curl– Easy-to-use client-side URL transfer library
openssl– Secure Sockets Layer toolkit
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-1.21.2(bioc 3.24)epigraHMM-1.20.2(bioc 3.23)

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

chipseq atacseq dnaseseq hiddenmarkovmodel epigenetics curl openssl openblas cpp openmp

5.55 score 89 scripts 292 downloads 18 exports 136 dependencies

Last updated from:17ec9bb8e2. Checks:1 WARNING, 11 OK, 1 ERROR, 1 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	347
linux-devel-arm64	OK	500
linux-devel-x86_64	OK	638
source / vignettes	OK	865
linux-release-arm64	OK	569
linux-release-x86_64	OK	610
macos-release-arm64	OK	390
macos-release-x86_64	OK	783
macos-oldrel-arm64	OK	324
macos-oldrel-x86_64	OK	1033
windows-devel	OK	668
windows-release	ERROR	566
windows-oldrel	OK	684
wasm-release	FAIL	240

Exports:addOffsets callPatterns callPeaks cleanCounts controlEM epigraHMM epigraHMMDataSetFromBam epigraHMMDataSetFromMatrix estimateTransitionProb expStep info initializer maxStepProb normalizeCounts plotCounts plotPatterns segmentGenome simulateMarkovChain

Dependencies:abind askpass backports BH Biobase BiocBaseUtils BiocGenerics BiocIO biocmake BiocParallel Biostrings bitops boot broom BSgenome car carData cigarillo cli codetools colorspace corrplot cowplot cpp11 crayon csaw curl data.table DelayedArray Deriv dir.expiry doBy dplyr edgeR farver filelock forecast formatR Formula fracdiff futile.logger futile.options generics GenomicAlignments GenomicRanges ggplot2 ggpubr ggrepel ggsci ggsignif glue GreyListChIP gridExtra gtable httr IRanges isoband jsonlite labeling lambda.r lattice lifecycle limma lme4 lmtest locfit magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats metapod mgcv microbenchmark mime minqa modelr nlme nloptr nnet numDeriv openssl pbkrtest pheatmap pillar pkgconfig polynom purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RCurl Rdpack reformulas restfulr rhdf5 rhdf5filters Rhdf5lib Rhtslib rjson rlang Rsamtools rstatix rtracklayer S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray SparseM statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr XML XVector yaml zoo

Consensus and differential peak calling with epigraHMM

Rendered fromepigraHMM.Rmdusingknitr::rmarkdown

Last update: 2026-05-12
Started: 2021-03-01

Help page	Topics
Add offsets to epigraHMMDataSet	addOffsets
Extract posterior probabilities (or combinatorial patterns) associated with differential regions	callPatterns
Summarize peak calls and optionally create a BED 6+3 file in broadPeak format for visualization	callPeaks
Remove effects from covariates of interest	cleanCounts
Control parameters for the EM algorithm from epigraHMM	controlEM
Perform peak calling of epigenomic data sets	epigraHMM
Create a epigraHMMDataSet from a set of BAM files	epigraHMMDataSetFromBam
Create a epigraHMMDataSet from matrices of counts	epigraHMMDataSetFromMatrix
Estimate transition probability from a sequence of integers	estimateTransitionProb
E-step of HMM (forward-backward probability + posterior probability calculation)	expStep
ENCODE ChIP-seq broad data from Helas3 cell line	helas3
Get information about peak calling results	info
Initializer of epigraHMM	initializer
M-step (maximization w.r.t. initial and transition probabilities)	maxStepProb
Normalize counts	normalizeCounts
Create a plot with the results from epigraHMM	plotCounts
Create a plot of differerential patterns posterior probabilities from epigraHMM	plotPatterns
Segmentation of a genome in non-overlapping windows	segmentGenome
Simulates a Markov Chain of length 'n' given a matrix of transition probabilities P	simulateMarkovChain

Package: epigraHMM 1.21.2

epigraHMM: Epigenomic R-based analysis with hidden Markov models

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Help Manual

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