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Cited by 35Epigenetics and DNA MethylationGenomics and Phylogenetic StudiesRNA modifications and cancer

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Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing

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Stanojević, Dominik, Li, Zhe, Bakić, Sara, Foo, Roger, Šikić, Mile·2024·Nature Communications
DOI 10.1038/s41467-024-49847-0PMC PMC11222435

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Abstract

DNA methylation plays an important role in various biological processes, including cell differentiation, ageing, and cancer development. The most important methylation in mammals is 5-methylcytosine mostly occurring in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing successfully detect 5-methylcytosine DNA modifications. However, they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish, a deep learning algorithm that significantly improves read-level 5-methylcytosine detection by using Nanopore sequencing. Rockfish is compared with other methods based on Nanopore sequencing on R9.4.1 and R10.4.1 datasets. There is an increase in the single-base accuracy and the F1 measure of up to 5 percentage points on R.9.4.1 datasets, and up to 0.82 percentage points on R10.4.1 datasets. Moreover, Rockfish shows a high correlation with whole-genome bisulfite sequencing, requires lower read depth, and achieves higher confidence in biologically important regions such as CpG-rich promoters while being computationally efficient. Its superior performance in human and mouse samples highlights its versatility for studying 5-methylcytosine methylation across varied organisms and diseases. Finally, its adaptable architecture ensures compatibility with new versions of pores and chemistry as well as modification types.

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Metadata

Authors
Stanojević, D., Li, Z., Bakić, S., Foo, R., & Šikić, M.
Affiliations
  • Agency for Science, Technology and Research (SG)
  • National University of Singapore (SG)
Corresponding author
Šikić, Mile
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-07-03
Volume
15
Issue
1
Pages
5580-5580
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-49847-0
PMC
PMC11222435
OpenAlex
W4400292335
Cited by
35
References
8
Citations by year
2026: 12 · 2025: 20 · 2024: 3
FWCI
4.71
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Hrvatska Zaklada za Znanost (IP-2018-01-5886)
  • Agency for Science, Technology and Research (C210812037)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
Keywords
DNA methylation, nanopore sequencing, transformer models, CpG methylation, promoter methylation, rockfish
MeSH terms
Whole Genome Sequencing, Deep Learning, Nanopore Sequencing, Algorithms, Animals, Humans, Sulfites, Sequence Analysis, DNA, CpG Islands, DNA Methylation, 5-Methylcytosine, Mice

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