ArticleNucleic acids research2024
MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- cyberDMR: accurate and robust identification of differentially methylated regions from WGS-derived methylomes.BMC biology · 2026Article
- MeDReaders 2.0: an updated database for modified DNA readers.BMC genomics · 2026Article
- Mechanisms and therapeutic prospects of DNA methylation-mucosal innate immunity crosstalk in inflammatory bowel disease.Frontiers in immunology · 2026Review
- Genome-wide analysis of strand-specific transcription and DNA methylation inFrontiers in microbiology · 2026Article
- Changes in nuclear and mitochondrial DNA methylation in cow blood associated with age and disease.Epigenetics · 2025Article
- OpTiles: an R package for adaptive tiling and methylation variability profiling.Bioinformatics (Oxford, England) · 2025Article
- Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing.Nature communications · 2025Article
- Research progress of DNA methylation on the regulation of substance use disorders and the mechanisms.Frontiers in cellular neuroscience · 2025Review
- Dissecting the Kaiso binding profile in clear renal cancer cells.Epigenetics & chromatin · 2024Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
DNA methylation is an epigenetic mark involved in the regulation of gene expression, and patterns of DNA methylation anticorrelate with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Toward this goal, we developed MethyLasso, a new approach to segment DNA methylation data. We use it as an all-in-one tool to perform the identification of low-methylated regions, unmethylated regions, DNA methylation valleys and partially methylated domains in a single condition as well as differentially methylated regions between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the agreement of the regions across tools, their number, size, level of DNA methylation, boundaries, cytosine-guanine content and coverage using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/bardetlab/methylasso.
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