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

N6-methyladenosine (m⁶A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m⁶A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m⁶A levels are controlled and whether and how regulation of m⁶A 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 m⁶A-regulated genes and m⁶A-associated disease, which includes Deep-m⁶A, the first model for detecting condition-specific m⁶A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m⁶A, a new network-based pipeline that prioritizes functional significant m⁶A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m⁶A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m⁶A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m⁶A 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 m⁶A-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 m⁶A regulatory functions and its roles in diseases.