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DMCHMM

This is the development version of DMCHMM; for the stable release version, see DMCHMM.

Differentially Methylated CpG using Hidden Markov Model


Bioconductor version: Development (3.19)

A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks.

Author: Farhad Shokoohi

Maintainer: Farhad Shokoohi <shokoohi at icloud.com>

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

Installation

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


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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("DMCHMM")

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

Documentation

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

browseVignettes("DMCHMM")
DMCHMM: Differentially Methylated CpG using Hidden Markov Model HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Coverage, DifferentialMethylation, HiddenMarkovModel, Sequencing, Software
Version 1.25.0
In Bioconductor since BioC 3.6 (R-3.4) (6.5 years)
License GPL-3
Depends R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool
Imports utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics
System Requirements
URL
Bug Reports https://github.com/shokoohi/DMCHMM/issues
See More
Suggests testthat, knitr, rmarkdown
Linking To
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package DMCHMM_1.25.0.tar.gz
Windows Binary DMCHMM_1.25.0.zip
macOS Binary (x86_64) DMCHMM_1.25.0.tgz
macOS Binary (arm64) DMCHMM_1.25.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/DMCHMM
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/DMCHMM
Bioc Package Browser https://code.bioconductor.org/browse/DMCHMM/
Package Short Url https://bioconductor.org/packages/DMCHMM/
Package Downloads Report Download Stats