MethylAction: detecting differentially methylated regions that distinguish biological subtypes

Abstract

DNA methylation differences capture substantial information about the molecular and gene-regulatory states among biological subtypes. Enrichment-based next generation sequencing methods such as MBD-isolated genome sequencing (MiGS) and MeDIP-seq are appealing for studying DNA methylation genome-wide in order to distinguish between biological subtypes. However, current analytic tools do not provide optimal features for analyzing three-group or larger study designs. MethylAction addresses this need by detecting all possible patterns of statistically significant hyper- and hypo- methylation in comparisons involving any number of groups. Crucially, significance is established at the level of differentially methylated regions (DMRs), and bootstrapping determines false discovery rates (FDRs) associated with each pattern. We demonstrate this functionality in a four-group comparison among benign prostate and three clinical subtypes of prostate cancer and show that the bootstrap FDRs are highly useful in selecting the most robust patterns of DMRs. Compared to existing tools that are limited to two-group comparisons, MethylAction detects more DMRs with strong differential methylation measurements confirmed by whole genome bisulfite sequencing and offers a better balance between precision and recall in cross-cohort comparisons. MethylAction is available as an R package at http://jeffbhasin.github.io/methylaction.

Interplay between maternal Slc6a4 mutation and prenatal stress: a possible mechanism for autistic behavior development

“…DNA methylation was assessed using R-based pipelines MEDIPS 41 and MethylAction 42 with parameters described in the Supplementary Information. Differentially methylated regions (DMRs) called by both pipelines were used in the downstream analysis.…”

A three‐gene DNA methylation biomarker accurately classifies early stage prostate cancer

“…In prostate cancer, CpG island hypermethylation takes place in large blocks (>1 kb). [56][57][58][59][60] For example, methylation levels of seven Illumina 450K probes covering a GSTP1 CpG island were found to be consistently higher in cancer compared with benign tissue ( Figure S1). 56 Table S1).…”

Goldmine integrates information placing genomic ranges into meaningful biological contexts

“…To further demonstrate how Goldmine's annotation can facilitate biological and functional interpretation of a genomic range set, we derived DMRs between CD4+ and CD8+ T cells from Roadmap Epigenomic Project MeDIP-seq data using MethylAction ( 17 ) and annotated the results using Goldmine (Supplementary Table S3). As the CD4+ versus CD8+ lineage decision is a model for bivalent differentiation patterns ( 28 ), an analysis of this methylome-wide data can reveal how epigenetics interacts with both known and novel drivers of this developmental process.…”

“…DMRs were detected using MethylAction ( 17 ). A window size of 50 bp and a fragment size of 266 bp were selected based on the protocol referenced in the GEO records.…”