biostudies-literatureUnknown4(12)Fan RN⁶-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various conditions and dissecting the mechanisms governing its deposition. Here, we design a combined framework of Transformer architecture and recurrent neural network, deepSRAMP, to identify m6A sites using sequence-based and genome-derived features. As a result, deepSRAMP achieves a notably enhanced performance compared to its predecessor, SRAMP, the most-used predictor in this field. Moreover, based on multiple benchmark datasets, deepSRAMP greatly outperforms other state-of-the-art m6A predictors, including WHISTLE and DeepPromise, with Cell genomics100697https://www.ebi.ac.uk/biostudies/studies/S-EPMC11701256biostudies-literatureA combined deep learning framework for mammalian m6A site prediction.PMC11701256Kang BWang GCui CChang ZFan RCui QfalseA combined deep learning framework for mammalian m6A site prediction.N⁶-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various conditions and dissecting the mechanisms governing its deposition. Here, we design a combined framework of Transformer architecture and recurrent neural network, deepSRAMP, to identify m6A sites using sequence-based and genome-derived features. As a result, deepSRAMP achieves a notably enhanced performance compared to its predecessor, SRAMP, the most-used predictor in this field. Moreover, based on multiple benchmark datasets, deepSRAMP greatly outperforms other state-of-the-art m6A predictors, including WHISTLE and DeepPromise, with 2024-01-01T00:00:00Z2024 Dec2026-06-01T11:04:41.896Z2025-04-07T00:32:34.507ZS-EPMC117012563957157310.1016/j.xgen.2024.100697