bioRxiv2024
Sequence variation observed in populations of pathogens can be used for important public health and evolution genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. Additionally, while the volume of bacterial genomes continues to grow, tools which can accurately and quickly call genetic variation between sequences have not kept pace. There is a need for tools which can process this large volume of data, providing rapid results, but remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long term storage and processing of large files. Here we describe Split K-mer Analysis (SKA2), a method which supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations we show superior variant recall compared to reference-based methods, with no false positives. We also show that within bacterial strains, where it is possible to construct a clonal frame, SKA2 can also accurately map variants to a reference, and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyse entire collections. Given its robust implementation, inherent absence of reference bias and high accuracy, SKA2 has the potential to become the tool of choice for genotyping bacteria and can help expand the uses of genome data in evolutionary and epidemiological analyses. SKA2 is implemented in Rust and is freely available at https://github.com/bacpop/ska.rust.