methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

By Altuna Akalin, Matthias Kormaksson, Sheng Li, Francine E. Garrett-Bakelman, Maria E. Figueroa, A. Melnick, Christopher E. Mason. Genome Biology, 2012

1,910 citations recorded by the source corpus.

DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation,cellular specification and cancer development. Here, we describe an R package, methylKit, thatrapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation andhydroxymethylation sequencing experiments. methylKit includes functions for clustering, samplequality visualization, differential methylation analysis and annotation features, thus automatingand simplifying many of the steps for discerning statistically significant bases or regions of DNAmethylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statisticallysignificant regions of differential methylation and stratify tumor subtypes. methylKit is availableat http://code.google.com/p/methylkit.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8f35cc66c34edb05b0a3ba5ec2cb98a3de321bc1

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Citations in the corpus

The transcriptional landscape of polyploid wheat (2018, neutral): “2 (56), genomation (57), and ggplot2 packages (58).”
metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data (2016, methodology): “After testing single CpGs for differential methylation (DMC), significant DMCs are merged to DMRs using various approaches (Akalin et al. 2012; Hebestreit et al. 2013; Park et al. 2014; Stockwell et al. 2014; Sun et al. 2014).”
RnBeads 2.0: comprehensive analysis of DNA methylation data (2019, methodology): “html), watermelon [38], and ChAMP [39], and with the methyKit [40] package for analyzing bisulfite sequencing data.”
De novo identification of differentially methylated regions in the human genome (2015, background): “Many DMR-finding methods are available to the bioinformatics community [25-41].”
Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates (2015, methodology): “A number of statistical methods and computational tools have been developed recently, including BSmooth (7), MethylKit (8), GBSA (9), BiSeq (10), DSS”
Next-Generation Sequencing Technology: Current Trends and Advancements (2023, background): “Epigenomics-Methyl Seq Sequence aligners Bwameth [135], BS-Seeker2 [136], Bismark [137] Methylation level quantification MethylDackel * Differential methylation Metilene [138], BSsmooth [139], methylKit [140]”
Differential methylation analysis for BS-seq data under general experimental design (2016, methodology): “…several years, a number of statistical methods and software tools have been developed for identifying DML/DMRs from BS-seq data (Hansen et al., 2012; Akalin et al., 2012b; Benoukraf et al., 2013; Feng et al., 2014; Sun et al., 2014; Park et al., 2014; Stockwell et al., 2014; Saito et al., 2014).”