Comprehensive discovery of RNA modification sites in the human transcriptome
RNA modifications (RMs) are critical for diverse biological processes, but their study has been limited by the lack of accurate, quantitative detection methods. To address this issue, we developed AIR (Artificial Intelligence for RNA modification), a deep learning-based framework powered by Nanopore sequencing and a massive-scale dataset, three orders of magnitude larger than the comparable previous ones. The AIR dataset encompasses all unmodified 11-mer sequence contexts around RM sites, closely mirroring endogenous transcript environments. Accordingly, AIR outperformed previous methods by a substantial margin. Its single-molecule detection capability also allows precise quantification of modification stoichiometry. Using AIR, we constructed a comprehensive human m6A atlas that reveals numerous non-canonical m6A sites and differentially modified transcripts, highlighting the complexity and dynamic nature of the human epitranscriptome. AIR represents a generalizable, powerful framework for the accurate, quantitative detection of diverse RMs in the epitranscriptome, opening new avenues for understanding the functional roles of RMs.