A combined deep learning framework for mammalian m6A site prediction
Rui Fan, Chunmei Cui, Boming Kang, Zecheng Chang, Guoqing Wang, Qinghua Cui
Abstract
-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 an average 16.1% and 18.3% increase in AUROC and a 43.9% and 46.4% increase in AUPRC. Finally, deepSRAMP can be successfully exploited on mammalian m6A epitranscriptome mapping under diverse cellular conditions and can potentially reveal differential m6A sites among transcript isoforms of individual genes.