Language-Informed Basecalling Architecture for Nanopore Direct RNA Sequencing

Abstract

Algorithms developed for basecalling Nanopore signals have primarily focused on DNA to date and utilise the raw signal as the only input. However, it is known that messenger RNA (mRNA), which dominates Nanopore direct RNA (dRNA) sequencing libraries, contains specific nucleotide patterns that are implicitly encoded in the Nanopore signals since RNA is always sequenced from the 3' to 5' direction. In this study we present an approach to exploit the sequence biases in mRNA as an additional input to dRNA basecalling. We developed a probabilistic model of mRNA language and propose a modified CTC beam search decoding algorithm to conditionally incorporate the language model during basecalling. Our findings demonstrate that inclusion of mRNA language is able to guide CTC beam search decoding towards the more probable nucleotide sequence. We also propose a time efficient approach to decoding variable length nanopore signals. This work provides the first demonstration of the potential for biological language to inform Nanopore basecalling. Code is available at: https://github.com/comprna/radian.