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

Author

Listed:
  • Dominik Stanojević

    (Technology and Research (A*STAR)
    University of Zagreb)

  • Zhe Li

    (Technology and Research (A*STAR))

  • Sara Bakić

    (Technology and Research (A*STAR)
    National University of Singapore)

  • Roger Foo

    (National University of Singapore)

  • Mile Šikić

    (Technology and Research (A*STAR)
    University of Zagreb)

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.

Suggested Citation

  • Dominik Stanojević & Zhe Li & Sara Bakić & Roger Foo & Mile Šikić, 2024. "Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49847-0
    DOI: 10.1038/s41467-024-49847-0
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