m6Aexpress-Reader: Prediction of m6A regulated expression genes by integrating m6A sites and reader binding information in specific- context

Abstract

N⁶-methyladenosine (m⁶A) is the most abundant form of mRNA modification and plays an important role in regulating gene expression. However, the mechanisms of m⁶A regulated gene expression in cell or condition specific, are still poorly understood. Even though, some methods are able to predict m⁶A regulated expression (m⁶A-reg-exp) genes in specific context, they don't introduce the m⁶A reader binding information, while this information can help to predict m⁶A-reg-exp genes and more clearly to explain the mechanisms of m⁶A-mediated gene expression process. Thus, by integrating m⁶A sites and reader binding information, we proposed a novel method (called m⁶Aexpress-Reader) to predict m⁶A-reg-exp genes from limited MeRIP-seq data in specific context. m⁶Aexpress-Reader adopts the reader binding signal strength to weight the posterior distribution of the estimated regulatory coefficients for enhancing the prediction power. By using m⁶Aexpress-Reader, we found the complex characteristic of m⁶A on gene expression regulation and the distinct regulated pattern of m⁶A-reg-exp genes with different reader binding. m⁶A readers, YTHDF2 or IGF2BP1/3 all play an important role in various cancers and the key cancer pathways. In addition, m⁶Aexpress-Reader reveals the distinct m⁶A regulated mode of reader targeted genes in cancer. m⁶Aexpress-Reader could be a useful tool for studying the m⁶A regulation on reader target genes in specific context and it can be freely accessible at: https://github.com/NWPU-903PR/m6AexpressReader.

Keywords: Bayesian hierarchical model; M(6)A reader; M(6)A regulated gene expression; Methylation level.