NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data
Year: 2026
Type: article
Abstract: 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. NanoTS facilitates nanopore long-read transcriptome sequencing based SNP detection and genotype calling using deep learning.
Source: Nature Methods
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Awards: Targeting alternative isoform variation for TCR discovery in platinum-resistant ovarian cancer, Computational tools and resources to study alternative splicing and mRNA isoform variation, G9000055049, Comprehensive identification and functional study of Esrp-regulated isoforms during epithelial-mesenchymal transition., Long-read strategies for elucidating transcriptome complexity and advancing genomic medicine, Mitochondrial respiratory chain disease mechanistic and therapeutic modeling
OpenAlex ID: W7213975417