ArticlePLoS computational biology2023
Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.
Article in PLoS computational biology, 2023. 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.
- Epigenetic insights into extreme longevity in the world's oldest terrestrial animal, Jonathan.Science advances · 2026Article
- nanoASM: Long-Read Allele-Specific DNA Methylation Profiling Enables Functional Annotation of Regulatory Noncoding Variants in Human Prostate Tissues.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- A Novel Framework for the Design of Minimized Epigenetic Clocks Using the Analysis of DNA Methylation Heterogeneity.International journal of molecular sciences · 2025Article
- Increased local DNA methylation disorder in AMLs with DNMT3A-destabilizing variants and its clinical implication.Nature communications · 2025Article
- An expanded view of cell competition.Development (Cambridge, England) · 2024Review
- Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging.Aging cell · 2024Article
- Age Prediction Using DNA Methylation Heterogeneity Metrics.International journal of molecular sciences · 2024Article
- Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
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4 authors.
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Abstract
Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.
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