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, owing to its ability to detect multiple modifications within the same full-length native RNA molecules. Although RNA modifications can be identified in the form of systematic basecalling "errors" in DRS data sets, <i>N6</i>-methyladenosine (m⁶A) modifications produce relatively low "errors" compared with 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 <i>N1</i>-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 data sets.