RNADSN: Transfer-Learning 5-Methyluridine (m(5)U) Modification on mRNAs from Common Features of tRNA

One of the most abundant non-canonical bases widely occurring on various RNA molecules is 5-methyluridine (m5U). Recent studies have revealed its influences on the development of breast cancer, systemic lupus erythematosus, and the regulation of stress responses. The accurate identification of m(5)U sites is crucial for understanding their biological functions. We propose RNADSN, the first transfer learning deep neural network that learns common features between tRNA m(5)U and mRNA m(5)U to enhance the prediction of mRNA m(5)U. Without seeing the experimentally detected mRNA m(5)U sites, RNADSN has already outperformed the state-of-the-art method, m5UPred. Using mRNA m(5)U classification as an additional layer of supervision, our model achieved another distinct improvement and presented an average area under the receiver operating characteristic curve (AUC) of 0.9422 and an average precision (AP) of 0.7855. The robust performance of RNADSN was also verified by cross-technical and cross-cellular validation. The interpretation of RNADSN also revealed the sequence motif of common features. Therefore, RNADSN should be a useful tool for studying m(5)U modification.