KPop: Accurate and scalable comparative analysis of microbial genomes by sequence embeddings

Abstract The recent explosion in the amount of available sequencing data challenges existing analysis techniques. Here we introduce KPop, a novel versatile method based on full k -mer spectra and dataset-specific transformations, through which thousands of assembled or unassembled microbial genomes can be quickly compared. Unlike minimizer-based methods that produce distances and have lower resolution, KPop is able to accurately map sequences onto a low-dimensional space. Extensive validation on simulated and real-life viral and bacterial datasets shows that KPop can correctly separate sequences at both species and sub-species levels even when the overall genomic diversity is low. KPop also rapidly identifies related sequences and systematically outperforms minimizer-based methods. KPop’s code is open-source and available on GitHub at https://github.com/PaoloRibeca/KPop .