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

N6-methyladenosine (mA) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide mA mapping have accelerated the accumulation of mA site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for mA site prediction. However, it is still a major challenge to precisely predict mA sites using in silico approaches. To advance the computational support for mA site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., , , , , , , , , and ). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on mA modification. We revisited 52 computational approaches published since 2015 for mA 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 mA identification and facilitate more rigorous comparisons of new methods in the future.