Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models

In recent years, nanopore direct RNA sequencing (DRS) became a valuable tool for studying the epitranscriptome, due to its ability to detect multiple modifications within the same full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling 'errors' in DRS datasets, N6-methyladenosine (m6A) modifications produce relatively low 'errors' compared to other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the 'error'signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads –especially in shorter RNA fractions– and increased basecalling error signatures at pseudouridine (Ψ) and N1-methylpseudouridine (m1Ψ) modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS datasets.