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<p>Here we introduce <Emphasis FontCategory="NonProportional">KPop</Emphasis>, a novel versatile method based on full <i>k</i>-mer spectra and dataset-specific transformations, through which thousands of assembled or unassembled microbial genomes can be quickly compared. Unlike MinHash-based methods that produce distances and have lower resolution, <Emphasis FontCategory="NonProportional">KPop</Emphasis> is able to accurately map sequences onto a low-dimensional space. Extensive validation on simulated and real-life viral and bacterial datasets shows that <Emphasis FontCategory="NonProportional">KPop</Emphasis> can correctly separate sequences at both species and sub-species levels even when the overall genomic diversity is low. <Emphasis FontCategory="NonProportional">KPop</Emphasis> also rapidly identifies related sequences and systematically outperforms MinHash-based methods.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月18日

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  • Xavier Didelot,
  • Paolo Ribeca

摘要

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 MinHash-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 MinHash-based methods.