Abstract
In epigenetic analysis, the identification of differentially methylated regions (DMRs) typically involves the detection of consecutive CpGs groups that show significant changes in their average methylation levels. However, the methylation state of a genomic region can also be characterized by a mixture of patterns (epialleles) with variable frequencies, and the relative proportions of such patterns can provide insights into its mechanisms of formation. Traditional methods based on bisulfite conversion and high-throughput sequencing, such as Illumina, owing to the read size (150 bp) allow epiallele frequency analysis only in high CpG density regions, limiting differential methylation studies to just 50% of the human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high and low CpG density regions. Here, we introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from nanopore data to identify DMRs, providing insights into their possible mechanisms of formation, and annotate them to genic and regulatory elements. We apply PoreMeth2 to cancer and glial cell data sets, providing evidence of its advance over other state-of-the-art methods and demonstrating its ability to distinguish epigenomic alterations with a strong impact on gene expression from those with weaker effects on transcriptional activity.