bioRxiv2023
N6-methyladenosine (m6A) is an essential RNA modification that regulates gene expression and influences diverse cellular processes. Yet, fully characterizing its transcriptome-wide landscape and biogenesis mechanisms remains challenging. Traditional next-generation sequencing (NGS) methods rely on short-reads aggregation, overlooking the inherent heterogeneity of RNA transcripts. Third-generation sequencing (TGS) platforms offer direct RNA sequencing (DRS) at the resolution of individual RNA molecules, enabling simultaneous detection of RNA modifications and RNA processing events. In this study, we introduce SingleMod, a deep learning model tailored for precise m6A modification mapping on individual RNA molecules from DRS data. Applying SingleMod to human cell lines, we systematically dissect the transcriptome-wide m6A landscape at single-molecule and single-base resolution, characterizing m6A heterogeneity in RNA molecules from the same transcript and revealing that multiple m6A sites on an RNA molecule can cumulatively influence its splicing and stability. Through comparative analyses across eight diverse species, we quantitatively elucidate three distinct m6A distribution patterns that suggest divergent regulatory mechanisms. This study provides a novel framework for understanding the shaping of epitranscriptome in a single-molecule perspective.