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A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

Bioconductor version: Development (3.19)

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Author: Koyel Majumdar [cre, aut] , Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Isobel Claire Gorley [aut] , Thomas Brendan Murphy [aut] , Florence Jaffrezic [aut], Andrea Rau [aut]

Maintainer: Koyel Majumdar <koyel.majumdar at ucdconnect.ie>

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


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

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

# The following initializes usage of Bioc devel


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:

betaHMM HTML R Script
Reference Manual PDF


biocViews BiomedicalInformatics, Coverage, DNAMethylation, DifferentialMethylation, GeneTarget, HiddenMarkovModel, ImmunoOncology, MethylationArray, Microarray, MultipleComparison, Sequencing, Software, Spatial
Version 0.99.5
In Bioconductor since BioC 3.19 (R-4.4)
License GPL-3
Depends R (>= 4.3.0), SummarizedExperiment, S4Vectors, GenomicRanges
Imports stats, ggplot2, scales, methods, pROC, foreach, doParallel, parallel, cowplot, dplyr, tidyr, tidyselect, stringr, utils
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Suggests rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
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Source Package betaHMM_0.99.5.tar.gz
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