Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage

TLDR

MeDIP‑seq is a major tool for epigenetic studies, yet computational analysis of its data remains limited in accuracy, sensitivity, and speed. The study proposes a time‑efficient statistical method to analyze MeDIP‑seq data with performance comparable to existing approaches. The method was applied to assess DNA methylation changes as hESCs differentiate into definitive endoderm. The method yielded higher correlation with whole‑genome bisulfite sequencing, revealed that differential methylation influences gene expression, and showed that demethylation mainly occurs in low‑CpG density regions, contrasting with de novo methylation.

Abstract

The generation of genome-wide data derived from methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) has become a major tool for epigenetic studies in health and disease. The computational analysis of such data, however, still falls short on accuracy, sensitivity, and speed. We propose a time-efficient statistical method that is able to cope with the inherent complexity of MeDIP-seq data with similar performance compared with existing methods. In order to demonstrate the computational approach, we have analyzed alterations in DNA methylation during the differentiation of human embryonic stem cells (hESCs) to definitive endoderm. We show improved correlation of normalized MeDIP-seq data in comparison to available whole-genome bisulfite sequencing data, and investigated the effect of differential methylation on gene expression. Furthermore, we analyzed the interplay between DNA methylation, histone modifications, and transcription factor binding and show that in contrast to de novo methylation, demethylation is mainly associated with regions of low CpG densities.