A bioinformatics workflow to detect genes with DNA methylation alterations: a case study of analyzing MeDIP-seq data in cardiac microtissue exposed to epirubicin

Abstract

Epigenetic modification such as DNA methylation can be influenced by different exposure conditions. Alterations in DNA methylation status can then lead to changes in gene and protein expressions. Therefore, studying DNA methylation is needed to provide a comprehensive view of cellular mechanisms. One of the costeffective technologies to detect DNA methylation is methylated DNA immunoprecipitation-sequencing (MeDIP-seq). While some tools have been developed to analyze the MeDIP-seq data, they mainly focus on methylated genome regions. Thus, there is still a demand for an analysis process that can detect genes with significant differential methylation. In this study, we developed a workflow built on the QSEA package, a recent launched MeDIP-seq analysis tool in R, to analyze DNA methylation at gene levels. This workflow offers an approach to identify candidate genes with strong methylation alterations. We applied this workflow to analyze MeDIP-seq data from cardiac tissues exposed to epirubicin (EPI), which is an antitumor agent with cardiotoxic adverse effects. Several genes that had strong methylation alteration such as HDAC4, EHMT1, SNHG14, SDHA, IGF1R, ADAP1, and NCOR2, were determined and can be candidates for further EPI-induced cardiotoxicity investigation.