Zelin Liu, Feng Wang, Robert Wang, David W Wu, Nicole DeBruyne, Kelsey Keith, Elizabeth M McCormick, Joseph Jee-Hwan Park, Matthew T Sullenberger, Andrew C Edmondson, Marni J Falk, Lan Lin, Yi Xing
Accurate variant detection using nanopore long-read transcriptome data remains challenging. Here we present NanoTS-a deep learning-based tool for single nucleotide polymorphism detection from diverse types of nanopore transcriptome sequencing data. NanoTS outperforms existing methods, achieving F1 scores above 0.980 and 0.966 on nanopore direct RNA and cDNA sequencing data, respectively, for single nucleotide polymorphisms with at least five supporting reads. Notably, NanoTS shows strong improvements over existing methods for allelically imbalanced variants. We also demonstrate that NanoTS enables accurate detection and genotype calling of pathogenic variants underlying Mendelian disorders, highlighting its potential clinical utility.