Identification of m(6)A-Associated RNA Binding Proteins Using an Integrative Computational Framework

N6-methyladenosine (m(6)A) is an abundant modification on mRNA that plays an important role in regulating essential RNA activities. Several wet lab studies have identified some RNA binding proteins (RBPs) that are related to m(6)A's regulation. The objective of this study was to identify potential m(6)A-associated RBPs using an integrative computational framework. The framework was composed of an enrichment analysis and a classification model. Utilizing RBPs' binding data, we analyzed reproducible m(6)A regions from independent studies using this framework. The enrichment analysis identified known m(6)A-associated RBPs including YTH domain-containing proteins; it also identified RBM3 as a potential m(6)A-associated RBP for mouse. Furthermore, a significant correlation for the identified m(6)A-associated RBPs is observed at the protein expression level rather than the gene expression level. On the other hand, a Random Forest classification model was built for the reproducible m(6)A regions using RBPs' binding data. The RBP-based predictor demonstrated not only competitive performance when compared with sequence-based predictions but also reflected m(6)A's action of repelling against RBPs, which suggested that our framework can infer interaction between m(6)A and m(6)A-associated RBPs beyond sequence level when utilizing RBPs' binding data. In conclusion, we designed an integrative computational framework for the identification of known and potential m(6)A-associated RBPs. We hope the analysis will provide more insights on the studies of m(6)A and RNA modifications.