High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.

ABSTRACT: Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstructing transmission events, and identifying antimicrobial resistance mutations. While many tools have been developed to identify single nucleotide variants (SNVs) from whole-genome sequencing, they often suffer from high false positive rates due to the complexity of bacterial genomes and the need for different filtering cutoffs across sample types and sequencing depths. As datasets increase in size, the manual filtering required for high accuracy presents a significant obstacle. Here, we present AccuSNV, a novel deep learning-based tool for high-precision and automated bacterial SNV calling. Unlike traditional methods that process one sample at a time, AccuSNV levera

SUBMITTER: Liao H 

PROVIDER: S-EPMC12622049 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature