MethyNano: supervised contrastive pretraining enables robust and generalizable methylation detection from nanopore sequencing

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ID: 315311
2026
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Abstract
Abstract Motivation 5-Methylcytosine (5mC) plays an important role in gene regulation and development. Although nanopore sequencing has enabled direct detection of 5mC, existing methods still face several limitations, including poor generalization across species and sequence contexts (CpG/CHG/CHH), as well as suboptimal integration of sequence and current signals. Results Here, we present MethyNano, a deep learning framework incorporating a contrastive learning strategy to detect 5mC from nanopore reads. By encouraging more discriminative and stable representations, the contrastive objective improves the model’s sensitivity to rare sequence contexts and reduces its prediction uncertainty in challenging regions. Across datasets from A. thaliana, O. sativa, and H. sapiens, our model achieves superior performance on key metrics compared with other existing methods. Extensive cross-species and cross-motif experiments demonstrate the robust generalization performance of MethyNano, while dimensionality-reduction visualizations of learned features provide an intuitive view of the model’s efficient representation capability. Moreover, our ablation studies show that MethyNano’s architecture enables more effective integration of critical features, leading to higher predictive accuracy. Availability The project code is available at https://github.com/baigeHUI/MethyNano and https://doi.org/10.5281/zenodo.19858400. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7162782526 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiahui Yan, Y Chen, Yucong Gong, Cheng Zhang, Jie Yang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag348
URL
https://doi.org/10.1093/bioinformatics/btag348
Keywords Keywords not found

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