PoreMeth2: decoding the evolution of methylome alterations with Nanopore sequencing.
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ABSTRACT:
PROVIDER: PRJNA1162461 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In epigenetic analysis, identifying differentially methylated regions (DMRs) typically involves detecting groups of consecutive CpGs that show a significant change in their average methylation level. However, the methylation state of a genomic region can also be defined by a mixture of patterns (epialleles) with variable frequencies and the relative proportion of such patterns can provide information on its mechanisms of formation. However, traditional methods based on bisulfite conversion and NGS, due to the read size (150 bp), allow epiallele frequency analysis only in high-CpG-density regions, limiting differential methylation studies to only 50% of the entire human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high- and low-density CpG regions. We introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from Nanopore data to identify DMRs, assess their formation mechanisms and annotate them to genic and regulatory elements. We applied PoreMeth2 to cancer and glial cell datasets, demonstrating its ability to distinguish epigenomic changes with strong effect on gene expression from those with weaker effect on transcriptional activity. PoreMeth2 is publicly available at https://github.com/Lab-CoMBINE/PoreMeth2.
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Project description:In epigenetic analysis, identifying differentially methylated regions (DMRs) typically involves detecting groups of consecutive CpGs that show a significant change in their average methylation level. However, the methylation state of a genomic region can also be defined by a mixture of patterns (epialleles) with variable frequencies and the relative proportion of such patterns can provide information on its mechanisms of formation. However, traditional methods based on bisulfite conversion and NGS, due to the read size (150 bp), allow epiallele frequency analysis only in high-CpG-density regions, limiting differential methylation studies to only 50% of the entire human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high- and low-density CpG regions. We introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from Nanopore data to identify DMRs, assess their formation mechanisms and annotate them to genic and regulatory elements. We applied PoreMeth2 to cancer and glial cell datasets, demonstrating its ability to distinguish epigenomic changes with strong effect on gene expression from those with weaker effect on transcriptional activity. PoreMeth2 is publicly available at https://github.com/Lab-CoMBINE/PoreMeth2.
2025-07-11 | GSE277453 | GEO
Project description:In epigenetic analysis, identifying differentially methylated regions (DMRs) typically involves detecting groups of consecutive CpGs that show a significant change in their average methylation level. However, the methylation state of a genomic region can also be defined by a mixture of patterns (epialleles) with variable frequencies and the relative proportion of such patterns can provide information on its mechanisms of formation. However, traditional methods based on bisulfite conversion and NGS, due to the read size (150 bp), allow epiallele frequency analysis only in high-CpG-density regions, limiting differential methylation studies to only 50% of the entire human methylome. Nanopore sequencing, with its long reads, enables the analysis of epiallele frequency across both high- and low-density CpG regions. We introduce a novel computational approach, PoreMeth2, an R library that integrates epiallelic diversity and methylation frequency changes from Nanopore data to identify DMRs, assess their formation mechanisms and annotate them to genic and regulatory elements. We applied PoreMeth2 to cancer and glial cell datasets, demonstrating its ability to distinguish epigenomic changes with strong effect on gene expression from those with weaker effect on transcriptional activity. PoreMeth2 is publicly available at https://github.com/Lab-CoMBINE/PoreMeth2.
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