MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data. Issue 4 (31st December 2022)

• Record Type: Journal Article
• Authors: De Riso, Giulia; Sarnataro, Antonella; Scala, Giovanni; Cuomo, Mariella; Della Monica, Rosa; Amente, Stefano; Chiariotti, Lorenzo; Miele, Gennaro; Cocozza, Sergio
• Is Part Of: NAR genomics and bioinformatics. Volume 4:Issue 4(2022)
• Publication Date: 2022-12-31
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqac096
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.