The expanding field of epitranscriptomics might rival the epigenome in the diversity of the biological processes impacted. However, the identification of modifications in individual RNA molecules remains challenging. We present CHEUI, a new method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) at single-nucleotide and single-molecule resolution from Nanopore signals. CHEUI predicts methylation in Nanopore reads and transcriptomic sites in a single condition, and differential m6A and m5C methylation between any two conditions. Using extensive benchmarking with Nanopore data derived from synthetic and natural RNA, CHEUI showed higher accuracy than other existing methods in detecting m6A and m5C sites and quantifying the site stoichiometry levels, while maintaining a lower proportion of false positives. CHEUI provides a new capability to detect RNA modifications with high accuracy and resolution that can be cost-effectively expanded to other modifications to unveil the full span of the epitranscriptome in normal and disease conditions.

Long-read sequencing enables isoform-resolved detection of functionally important RNA elements, such as RNA chemical modifications, RNA secondary structure, or RNA-protein interaction sites. Importantly, the functional impact of these elements can relate to their positions relative to isoform-specific transcript features, such as start and stop codons, open reading frames, exon-exon junctions and transcript termini. Relating transcriptomic and genomic features during sequencing data analysis is challenged by the flexibility of RNA biogenesis and the diversity of alternative mRNA transcripts. To address these challenges and streamline the mapping between transcriptome-mapped data and genome-mapped data, we developed R2Dtool. We illustrate R2Dtool capability to process long-read transcriptomic information and potential for interpretation in the context of transcript and genomic annotation features using epitranscriptomics data. R2Dtool embraces and expedites analysis of RNA complexity, and we anticipate it will empower multiple transcriptomic studies for interpretation and discovery. R2Dtool is freely available under the MIT license from https://github.com/comprna/R2Dtool.