Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning

N6-methyladenosine (m(6)A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m(6)A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m(6)A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m(6)A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m(6)A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m(6)A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m(6)A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base–classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82–0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m(6)A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77–0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m(6)A site prediction.