AbstractSingle-molecule detection and phasing of A-to-I RNA editing events remain an unresolved problem. Long-read and PCR-free nanopore native RNA sequencing offers a great opportunity for direct RNA editing detection. Here, we develop a neural network model, DeepEdit, that not only recognizes A-to-I editing events in single reads of Oxford Nanopore direct RNA sequencing, but also resolves the phasing of RNA editing events on transcripts. We illustrate the robustness of DeepEdit by applying it toSchizosaccharomyces pombeandHomo sapienstranscriptome data. We anticipate DeepEdit to be a powerful tool for the study of RNA editing from a new perspective.