RT Journal
A1 Mattei, Gianluca
A1 Baragli, Marta
A1 Gega, Barbara
A1 Mingrino, Alessandra
A1 Chieca, Martina
A1 Ducci, Tommaso
A1 Frigè, Gianmaria
A1 Mazzarella, Luca
A1 D'Aurizio, Romina
A1 De Logu, Francesco
A1 Nassini, Romina
A1 Pelicci, Pier Giuseppe
A1 Magi, Alberto
T1 PoreMeth2 for decoding the evolution of methylome alterations with nanopore sequencing
JF Genome Research 
JO Genome Research 
YR 2025 
FD November 01 
VO 35 
IS 11 
SP 2501 
OP 2512 
DO 10.1101/gr.280259.124 
UL http://genome.cshlp.org/content/35/11/2501.abstract 
AB 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.