MTTLm⁶A: A multi-task transfer learning approach for base-resolution mRNA m⁶A site prediction based on an improved...

N6-methyladenosine (m⁶A) is a crucial RNA modification involved in various biological activities. Computational methods have been developed for the detection of m⁶A sites in Saccharomyces cerevisiae at base-resolution due to their cost-effectiveness and efficiency. However, the generalization of these methods has been hindered by limited base-resolution datasets. Additionally, RMBase contains a vast number of low-resolution m⁶A sites for Saccharomyces cerevisiae, and base-resolution sites are often inferred from these low-resolution results through post-calibration. We propose MTTLm⁶A, a multi-task transfer learning approach for base-resolution mRNA m⁶A site prediction based on an improved transformer. First, the RNA sequences are encoded by using one-hot encoding. Then, we construct a multi-task model that combines a convolutional neural network with a multi-head-attention deep framework. This model not only detects low-resolution m⁶A sites, it also assigns reasonable probabilities to the predicted sites. Finally, we employ transfer learning to predict base-resolution m⁶A sites based on the low-resolution m⁶A sites. Experimental results on Saccharomyces cerevisiae m⁶A and Homo sapiens m¹A data demonstrate that MTTLm⁶A respectively achieved area under the receiver operating characteristic (AUROC) values of 77.13% and 92.9%, outperforming the state-of-the-art models. At the same time, it shows that the model has strong generalization ability. To enhance user convenience, we have made a user-friendly web server for MTTLm⁶A publicly available at http://47.242.23.141/MTTLm6A/index.php.