Evidence map›Paper›PMID 36940213›Full record

ArticlePLoS computational biology2023

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Dohoon Lee, Bonil Koo, Jeewon Yang, Sun Kim

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. An expanded view of cell competition.Development (Cambridge, England) · 2024
    Review
  7. Article
  8. Age Prediction Using DNA Methylation Heterogeneity Metrics.International journal of molecular sciences · 2024
    Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Dohoon LeeBioinformatics Institute, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0003-2163-5489
Bonil KooInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0003-4357-1850
Jeewon YangInterdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.
Sun KimInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0001-5385-9546

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DNA MethylationSoftwareSequence Analysis, DNASulfiteshydrogen sulfiteSulfites

Identifiers

PMID36940213
PMCPMC10062925

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.