BERMP: a cross-species classifier for predicting m⁶A sites by integrating a deep learning algorithm and a random forest approach

Abstract

N⁶-methyladenosine (m⁶A) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of m⁶A sites identified from different species using high-throughput experiments provides a rich resource to construct <i>in-silico</i> approaches for identifying m⁶A sites. The existing m⁶A predictors are developed using conventional machine-learning (ML) algorithms and most are species-centric. In this paper, we develop a novel cross-species deep-learning classifier based on bidirectional Gated Recurrent Unit (BGRU) for the prediction of m⁶A sites. In comparison with conventional ML approaches, BGRU achieves outstanding performance for the <i>Mammalia</i> dataset that contains over fifty thousand m⁶A sites but inferior for the <i>Saccharomyces cerevisiae</i> dataset that covers around a thousand positives. The accuracy of BGRU is sensitive to the data size and the sensitivity is compensated by the integration of a random forest classifier with a novel encoding of enhanced nucleic acid content. The integrated approach dubbed as BGRU-based Ensemble RNA Methylation site Predictor (BERMP) has competitive performance in both cross-validation test and independent test. BERMP also outperforms existing m⁶A predictors for different species. Therefore, BERMP is a novel multi-species tool for identifying m⁶A sites with high confidence. This classifier is freely available at http://www.bioinfogo.org/bermp.