Reference-free variant calling with local graph construction with ska lo (SKA)

Abstract

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment-free and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify variants between pathogen whole-genome sequencing data by traversing a coloured De Bruijn graph and building variant groups (ie, sets of variant combinations). Through in-silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in SNP calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).