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<p>Profiling RNA modifications is essential to understand their functions and interactions. By taking the advantages of nanopore direct RNA sequencing, we present DirectRM, enabling simultaneous detection of six abundant modifications (N4-acetylcytidine, 1-methyladenosine, 5-methylcytidine, N7-methlguanosine, N6-methyladenosine, and pseudouridine) in native RNAs. Its two-stage pipeline identifies candidate modified kmers using binary classifier, then determines specific modifications and positions using an attention-based neural network. Trained with molecule-level features extracted from native RNA samples and validated on human cell lines and viral RNAs, DirectRM demonstrates high sensitivity, precision and robustness, outperforming existing tools. Crucially, we reveal the associations between modifications at both transcript and molecule-level. Modifications tend to proximate to each other on the transcript level, while at the molecule level, the presence of one modification is likely to reduce the occurrence of modifications at adjacent positions. DirectRM offers a powerful approach for studying epitranscriptome complexity and is expandable for future research.</p>

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DirectRM: integrated detection of landscape and crosstalk between multiple RNA modifications using direct RNA sequencing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Yuxin Zhang,
  • Yuecheng Wu,
  • Jiongming Ma,
  • Yiyu Wu,
  • Liying Li,
  • Haozhe Wang,
  • Guifang Jia,
  • Daniel J. Rigden,
  • Jia Meng,
  • Daiyun Huang,
  • Kunqi Chen

摘要

Profiling RNA modifications is essential to understand their functions and interactions. By taking the advantages of nanopore direct RNA sequencing, we present DirectRM, enabling simultaneous detection of six abundant modifications (N4-acetylcytidine, 1-methyladenosine, 5-methylcytidine, N7-methlguanosine, N6-methyladenosine, and pseudouridine) in native RNAs. Its two-stage pipeline identifies candidate modified kmers using binary classifier, then determines specific modifications and positions using an attention-based neural network. Trained with molecule-level features extracted from native RNA samples and validated on human cell lines and viral RNAs, DirectRM demonstrates high sensitivity, precision and robustness, outperforming existing tools. Crucially, we reveal the associations between modifications at both transcript and molecule-level. Modifications tend to proximate to each other on the transcript level, while at the molecule level, the presence of one modification is likely to reduce the occurrence of modifications at adjacent positions. DirectRM offers a powerful approach for studying epitranscriptome complexity and is expandable for future research.