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mCSEA: detecting subtle differentially methylated regions.

Jordi Martorell-Marugán1,2, Víctor González-Rumayor2, Pedro Carmona-Sáez1

  • 1Bioinformatics Unit. GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, Granada, Spain.

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Summary
This summary is machine-generated.

This study introduces mCSEA, an R package for identifying subtle DNA methylation differences. mCSEA enhances the detection of differentially methylated regions (DMRs) crucial for understanding complex disorders.

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Area of Science:

  • Epigenetics and bioinformatics
  • Genomics and computational biology

Background:

  • Identifying differentially methylated regions (DMRs) is key in epigenetics.
  • Existing methods often miss subtle methylation changes linked to complex disorders.

Purpose of the Study:

  • To introduce mCSEA, an R package for detecting DMRs using Gene Set Enrichment Analysis.
  • To enable the identification of subtle, consistent methylation differences in complex phenotypes.
  • To integrate gene expression data and analyze methylation-gene expression correlations.

Main Methods:

  • Utilizing Gene Set Enrichment Analysis for DMR detection from Illumina450K and EPIC array data.
  • Implementing functions for gene expression data integration.
  • Validating performance with simulated datasets and a real-world case study.

Main Results:

  • mCSEA outperforms existing tools in detecting DMRs, especially subtle changes.
  • Application to a discordant sibling pair dataset revealed DMRs in genes associated with obesity and diabetes.
  • Identified DMRs not detectable by other methods, highlighting mCSEA's potential.

Conclusions:

  • mCSEA is a powerful tool for identifying subtle DMRs in complex phenotypes.
  • The package facilitates deeper insights into epigenetic contributions to metabolic disorders.
  • mCSEA is freely available, promoting wider accessibility in epigenetic research.