Identification of m6A-Associated RNA Binding Proteins Using an Integrative Computational Framework

Abstract

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.

Topics & Concepts