Format

Send to

Choose Destination
Bioinformatics. 2019 Feb 12. doi: 10.1093/bioinformatics/btz096. [Epub ahead of print]

mCSEA: Detecting subtle differentially methylated regions.

Author information

1
Bioinformatics Unit. GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, Granada, Spain.
2
Atrys Health, Barcelona, Spain.

Abstract

Motivation:

The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.

Results:

We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.

Availability:

mCSEA is freely available from the Bioconductor repository.

Supplementary information:

Supplementary data are available at Bioinformatics online.

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center