Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites

N6-methyladenosine (m⁶A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m⁶A mapping have accelerated the accumulation of m⁶A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m⁶A site prediction. However, it is still a major challenge to precisely predict m⁶A sites using in silico approaches. To advance the computational support for m⁶A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m⁶A modification. We revisited 52 computational approaches published since 2015 for m⁶A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m⁶A identification and facilitate more rigorous comparisons of new methods in the future.