Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

ABSTRACT: As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3'UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based

SUBMITTER: Huang D 

PROVIDER: S-EPMC9561283 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature