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February 2, 2026Life Science Alliance3 citationsOpen Access

wgbstools : a computational suite for DNA methylation sequencing data analysis

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NLNetanel LoyferHebrew University of JerusalemJRJonathan RosenskiHebrew University of JerusalemTKTommy KaplanQueen Mary University of London

Key Points

  • This research aims to develop a computational suite that effectively analyzes DNA methylation sequencing data.
  • Development of wgbstools for methylation sequencing analysis
  • Provided custom epiread file format achieving high data compression
  • Incorporated algorithms for genomic segmentation and biomarker identification
  • Facilitated fragment-level analysis with visualizations across multiple samples
  • Achieved over 100x data compression from input BAM files
  • Enabled detailed analysis of tens of millions of CpG sites
  • Improved access and representation of methylation sequencing data

Abstract

Next-generation methylation-aware sequencing of DNA sheds light on the fundamental role of methylation in cellular function in health and disease, increasing the number of covered CpG sites from hundreds of thousands in previous array-based approaches to tens of millions across the whole genome. While array-based approaches are limited to single-CpG resolution, next-generation sequencing allows for a more detailed, single-molecule fragment-level analysis; however, existing tools to fully use this capability are not yet well developed. Here, we present wgbstools , an extensive computational suite tailored for methylation sequencing data. wgbstools allows fast access and ultracompact anonymized representation of high-throughput methylome data, obtained through various library preparation and sequencing methods, with a custom epiread file format achieving a compression factor of over 100x from the input BAM file. In addition, wgbstools contains state-of-the-art algorithms for genomic segmentation, biomarker identification, genetic and epigenetic data integration, and more. wgbstools offers fragment-level analysis and informative visualizations, across multiple genomic regions and samples.

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Cite This Study

Loyfer et al. (2026) studied this question.

synapsesocial.com/papers/6980fe57c1c9540dea81047dhttps://doi.org/10.26508/lsa.202503514
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