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Background <p>Accurately identifying RNA 2ʹ-O-methylation (2OM) sites is a crucial step in gaining an in-depth understanding of RNA regulatory mechanisms. Although there are currently multiple prediction tools available, they still suffer from limited prediction accuracy and an inability to fully capture the associations between sequences and sites.</p> Results <p>This study constructs a novel low-redundancy dataset and innovatively proposes the KN-PairMatrix encoding scheme, effectively addressing the research gap in sequence-site association analysis. Based on this foundation, we developed the deep learning framework OMetaNet, which integrates residual and downsampling-optimized CNN modules, Mamba network, and a proprietary cross-modal interactive fusion module. The framework incorporates a contrastive learning-driven adaptive hybrid loss function. Employing a progressive feature disentanglement strategy, it enhances the learning capability for 2OM site-specific patterns. Independent evaluation results demonstrate that OMetaNet significantly outperforms existing methods in predicting 2OM sites across all four nucleotide types.</p> Conclusions <p>We proposed a novel computational model, OMetaNet. Its unique design structure may potentially reshape the paradigm of transcriptome analysis, open up new directions for extracting modification site information, and show significant potential in biomarker research and cross-species generalization studies.</p>

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OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2ʹ-O-methylation sites in human RNA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Peng Shen,
  • Yiyu Lin,
  • Sen Yang,
  • Ziding Zhang

摘要

Background

Accurately identifying RNA 2ʹ-O-methylation (2OM) sites is a crucial step in gaining an in-depth understanding of RNA regulatory mechanisms. Although there are currently multiple prediction tools available, they still suffer from limited prediction accuracy and an inability to fully capture the associations between sequences and sites.

Results

This study constructs a novel low-redundancy dataset and innovatively proposes the KN-PairMatrix encoding scheme, effectively addressing the research gap in sequence-site association analysis. Based on this foundation, we developed the deep learning framework OMetaNet, which integrates residual and downsampling-optimized CNN modules, Mamba network, and a proprietary cross-modal interactive fusion module. The framework incorporates a contrastive learning-driven adaptive hybrid loss function. Employing a progressive feature disentanglement strategy, it enhances the learning capability for 2OM site-specific patterns. Independent evaluation results demonstrate that OMetaNet significantly outperforms existing methods in predicting 2OM sites across all four nucleotide types.

Conclusions

We proposed a novel computational model, OMetaNet. Its unique design structure may potentially reshape the paradigm of transcriptome analysis, open up new directions for extracting modification site information, and show significant potential in biomarker research and cross-species generalization studies.