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Related Experiment Video

Updated: Jul 4, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

asms: finding allele-specific methylation in human genomes without phasing.

Emanuele Raineri1,2, Miguel Ángel Esteve Marco3, Eloi Casals1,2

  • 1Centro Nacional de Análisis Genómico (CNAG), Baldiri Reixac 4, 08028 Barcelona, Spain.

NAR Genomics and Bioinformatics
|July 3, 2026
PubMed
Summary

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Allele-specific methylation (ASM) analysis is improved with asms, a new tool identifying differential DNA methylation without prior phasing. This method efficiently detects ASM across various sequencing platforms, aiding in understanding gene regulation and disease.

Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Allele-specific methylation (ASM) is differential DNA methylation between alleles, crucial for gene regulation, biological variation, and disease.
  • ASM can be tissue-specific, complicating analysis.
  • Current detection methods often require computationally intensive phasing, which can fail in low-heterozygosity regions.

Purpose of the Study:

  • To develop a novel computational tool, asms (Allele-Specific Methylation Scanner), for efficient ASM detection.
  • To overcome limitations of existing phasing-dependent methods.
  • To enable ASM analysis from methylation data without prior genetic variant phasing.

Main Methods:

  • asms identifies potential ASM loci by analyzing methylation heterogeneity across sequencing reads, without prior phasing.

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Last Updated: Jul 4, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

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  • The tool segregates reads based on methylation patterns to examine thousands of loci.
  • It can utilize Oxford Nanopore Technologies (ONT) data and other platforms storing methylation information in BAM files (MM/ML tags).

    • Main Results:

    • asms efficiently identifies potential ASM loci by detecting methylation differences across reads.
    • The tool does not require prior phasing of genetic variants.
    • It can verify allele-specific methylation profiles when variant information is available.

    Conclusions:

    • asms provides an efficient and versatile method for detecting allele-specific methylation.
    • The tool overcomes key limitations of previous ASM detection approaches.
    • asms facilitates deeper understanding of gene regulation, biological variation, and disease mechanisms influenced by ASM.