Ab initio detection of multiple epitranscriptomic modifications from ONT direct RNA sequencing data
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Charting the eukaryotic epitranscriptome by direct RNA sequencing is promising but still 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 ab initio simultaneous detection of up to nine modified bases through a transformer model, and NanoListener, implementing a simulated randomers strategy for robust training datasets and a new generation of ONT basecallers.
