Deep analysis of RNA N6-adenosine methylation (m6A) patterns in human cells.

Abstract

N6-adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotes, and affects RNA metabolism and non-coding RNA function. Previous studies suggest that m6A modifications in mammals occur on the consensus sequence DRACH (D = A/G/U, R = A/G, H = A/C/U). However, only about 10% of such adenosines can be m6A-methylated, and the underlying sequence determinants are still unclear. Notably, the regulation of m6A modifications can be cell-type-specific. In this study, we have developed a deep learning model, called TDm6A, to predict RNA m6A modifications in human cells. For cell types with limited availability of m6A data, transfer learning may be used to enhance TDm6A model performance. We show that TDm6A can learn common and cell-type-specific motifs, some of which are associated with RNA-binding proteins previously reported to be m6A readers or anti-readers. In addition, we have used TDm6A to predict m6A sites on human long non-coding RNAs (lncRNAs) for selection of candidates with high levels of m6A modifications. The results provide new insights into m6A modifications on human protein-coding and non-coding transcripts.