m(6)A-Driver: Identifying Context-Specific mRNA m(6)A Methylation-Driven Gene Interaction Networks

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m(6)A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m(6)A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m(6)A methylation, we develop here m(6)A-Driver, an algorithm for predicting m(6)A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m(6)A methylation under a specific condition. Specifically, m(6)A-Driver integrates the PPI network and the predicted differential m(6)A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m(6)A-driven network of m(6)A-driven genes. To evaluate the performance, we applied m(6)A-Driver to build the context-specific m(6)A-driven networks for 4 known m(6)A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m(6)A-Driver can robustly and efficiently identify m(6)A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m(6)A methylated genes. Pathway analysis of the constructed context-specific m(6)A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.