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 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.Keywords: N6-methyladenosine (m 6 A), methylated RNA immunoprecipitation sequencing (MeRIP-Seq ), convolutional neural networks, m 6 A site prediction, m 6 A functional prediction, m 6 A-disease association
Author summaryThe goal of this work is to identify functional significant m 6 A-regulated genes and m 6 A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m 6 A, a CNN model for single-base m 6 A prediction. To our knowledge, this is the first condition-specific single-base m 6 A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset show that Deep-m 6 A outperformed two sequence-based models. We applied Deep-m 6 A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m 6 A-regulated genes and further predict m 6 A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m 6 A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipe...