Ab initio detection of multiple epitranscriptomic modifications from Oxford nanopore technology direct RNA sequencing data.

ABSTRACT: Charting the eukaryotic epitranscriptome by direct RNA sequencing is promising but still very challenging, as current bioinformatics tools are based on modification-unaware software and require multiple modification-specific learning steps. Here, we introduce NanoSpeech, a modification-aware basecaller for the ab initio simultaneous detection of multiple modified bases using a transformer model, and NanoListener, which implements a simulated randomers strategy for robust training datasets and a new generation of ONT basecallers. NanoListener and NanoSpeech are independent of the specific ONT chemistry. Once a training dataset has been created, a single model with an expanded vocabulary can accurately basecall both unmodified and modified bases.

SUBMITTER: Fonzino A 

PROVIDER: S-EPMC12848937 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature