DMCHMM

DOI: 10.18129/B9.bioc.DMCHMM    

Differentially Methylated CpG using Hidden Markov Model

Bioconductor version: Release (3.14)

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 "4.1") and enter:

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

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")

 

HTML R Script DMCHMM: Differentially Methylated CpG using Hidden Markov Model
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Details

biocViews Coverage, DifferentialMethylation, HiddenMarkovModel, Sequencing, Software
Version 1.16.0
In Bioconductor since BioC 3.6 (R-3.4) (4 years)
License GPL-3
Depends R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool
Imports utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics
LinkingTo
Suggests testthat, knitr, rmarkdown
SystemRequirements
Enhances
URL
BugReports https://github.com/shokoohi/DMCHMM/issues
Depends On Me
Imports Me
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Build Report  

Package Archives

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

Source Package DMCHMM_1.16.0.tar.gz
Windows Binary DMCHMM_1.16.0.zip
macOS 10.13 (High Sierra) DMCHMM_1.16.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/DMCHMM
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/DMCHMM
Package Short Url https://bioconductor.org/packages/DMCHMM/
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Old Source Packages for BioC 3.14 Source Archive

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