Cross-RNA transferable sequence representation learning for lncRNA m6A site detection via novel deep domain separation networks.

Abstract

N6-methyladenosine (m6A) is a key epitranscriptomic marker enriched in long noncoding RNAs (lncRNAs) that is closely involved in complex disease mechanisms. Although accurate detection of m6A sites in lncRNAs is essential for understanding disease mechanisms, the development of effective computational predictors remains challenging due to the limited number of annotated sites. Moreover, most existing predictors are specifically designed for messenger RNAs (mRNAs) based on abundant mRNA-specific knowledge, yet they exhibit limited generalizability to lncRNAs. Given the similarities between mRNAs and lncRNAs, a transferable framework capable of leveraging their shared features is critical for advancing m6A site prediction in lncRNAs. To address this challenge, we propose DSNm6A, a deep learning framework that learns cross-RNA transferable sequence representations for effective lncRNA m6A site detection. To comprehensively capture patterns and signals of m6A sites, lncRNA and mRNA sequences are first encoded from complementary multiple facets, including One-Hot encoding, nucleotide physicochemical properties and cumulative frequency, and position-specific propensity. Based on these sequence encodings, a domain separation network integrating CNN, Bi-LSTM, and BERT modules is then employed to explicitly disentangle domain-invariant features shared between mRNAs and lncRNAs from their domain-specific counterparts. The shared features are finally fed into a fully connected layer for accurate lncRNA m6A sites prediction. Cross-validation and independent test results demonstrate that DSNm6A consistently outperforms existing methods across nearly all performance metrics, attributed to its superior capacity to learn transferable m6A-related features across RNA types. In addition, DSNm6A exhibits strong robustness and generalization across species.