Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

N6-methyladenosine (m(6)A) is the most abundant methylation, existing in > 25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m(6)A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m(6)A levels are controlled and whether and how regulation of m(6)A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m(6)A-regulated genes and m(6)A-associated disease, which includes Deep-m(6)A, the first model for detecting condition-specific m(6)A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m(6)A, a new network-based pipeline that prioritizes functional significant m(6)A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m(6)A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m(6)A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m(6)A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m(6)A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m(6)A regulatory functions and its roles in diseases.