m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m⁶A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m⁶A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m⁶A methylation, we develop here m⁶A-Driver, an algorithm for predicting m⁶A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m⁶A methylation under a specific condition. Specifically, m⁶A-Driver integrates the PPI network and the predicted differential m⁶A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m⁶A-driven network of m⁶A-driven genes. To evaluate the performance, we applied m⁶A-Driver to build the context-specific m⁶A-driven networks for 4 known m⁶A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m⁶A-Driver can robustly and efficiently identify m⁶A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m⁶A methylated genes. Pathway analysis of the constructed context-specific m⁶A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.