Frontiers in bioscience (Landmark edition) · 2026
DT-m6A: A DenseNet-Transformer Hybrid Framework for Accurate Prediction of m6A Modification Sites across Diverse Cell Lines and Tissues.
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Tao Qiyu, Jia Jianhua
Аннотация
BACKGROUND: N6-methyladenosine (m6A) RNA methylation is a crucial epigenetic modification that plays an essential role in regulating diverse biological processes. Accurate identification of m6A sites is therefore fundamental to understanding its regulatory mechanisms. In this study, we proposed DT-m6A, a novel deep learning framework that integrates DenseNet and Transformer architectures for accurate m6A site identification across diverse cell lines and tissues.
METHODS: RNA sequences are first encoded using nucleotide chemical properties (NCP) for initial features extraction, after which DenseNet captures and reuses local sequence features through dense connections. The Transformer module then models long-range dependencies and extracts nonlinear representations, in which Batch Normalization replaces the conventional Layer Normalization in both sublayers to enhance training stability. Finally, a fully connected layer predicts m6A modification sites.
RESULTS: Evaluated on 11 independent test sets spanning eight cell lines and three tissue types, DT-m6A demonstrated robust performance, achieving average accuracy (ACC) of 76.97%, Matthews correlation coefficient (MCC) of 54.27%, precision (PRE) of 75.18%, recall (REC) of 79.76%, and F1 score of 77.26%.
CONCLUSIONS: DT-m6A surpassed the state-of-the-art method MST-m6A by 0.63% in average accuracy (p = 0.0023) and 1.4% in mean MCC (p = 0.0012) across 11 independent test sets. Although its performance on the CD8T and MOLM13 cell lines was comparable to MST-m6A, DT-m6A consistently achieved superior results across all other cell lines and tissues. Overall, DT-m6A effectively captures both local patterns and global dependencies in RNA sequences, improving prediction performance across diverse biological contexts.
METHODS: RNA sequences are first encoded using nucleotide chemical properties (NCP) for initial features extraction, after which DenseNet captures and reuses local sequence features through dense connections. The Transformer module then models long-range dependencies and extracts nonlinear representations, in which Batch Normalization replaces the conventional Layer Normalization in both sublayers to enhance training stability. Finally, a fully connected layer predicts m6A modification sites.
RESULTS: Evaluated on 11 independent test sets spanning eight cell lines and three tissue types, DT-m6A demonstrated robust performance, achieving average accuracy (ACC) of 76.97%, Matthews correlation coefficient (MCC) of 54.27%, precision (PRE) of 75.18%, recall (REC) of 79.76%, and F1 score of 77.26%.
CONCLUSIONS: DT-m6A surpassed the state-of-the-art method MST-m6A by 0.63% in average accuracy (p = 0.0023) and 1.4% in mean MCC (p = 0.0012) across 11 independent test sets. Although its performance on the CD8T and MOLM13 cell lines was comparable to MST-m6A, DT-m6A consistently achieved superior results across all other cell lines and tissues. Overall, DT-m6A effectively captures both local patterns and global dependencies in RNA sequences, improving prediction performance across diverse biological contexts.