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epigraHMM

Epigenomic R-based analysis with hidden Markov models

Bioconductor version: 3.23 · Package version: 1.20.2

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("epigraHMM")

Details

MaintainerPedro Baldoni <pedrobaldoni@gmail.com>
AuthorPedro Baldoni [aut, cre]
LicenseMIT + file LICENSE
System RequirementsGNU make
Downloads rank545
Source branchRELEASE_3_23
biocViewsATACSeq, ChIPSeq, DNaseSeq, Epigenetics, HiddenMarkovModel, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageepigraHMM_1.20.2.tar.gz
Windows binary (x86_64)epigraHMM_1.20.2.zip
macOS binary (arm64)epigraHMM_1.20.2.tgz
macOS binary (x86_64)epigraHMM_1.20.2.tgz
Dependencies

Depends: R (>= 3.5.0)

Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, Seqinfo, GenomicRanges, rtracklayer, IRanges, Rsamtools, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices

LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib

Suggests: GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, gcapc, genomationData