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Updated: May 22, 2025

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An alignment- and reference-free strategy using k-mer present pattern for population genomic analyses.

Guohui Shi1, Yi Dai1,2, Da Zhou3

  • 1State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.

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|March 14, 2025
PubMed
Summary

This study introduces a novel k-mer based method for population genetics, offering an alignment- and reference-free approach. This technique effectively analyzes genetic diversity and population structure, even in species lacking complete genomes.

Keywords:
K-mersalignment-freegenetic diversitypopulation genomicsreference-free

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Pangenome analysis traditionally relies on graph-based methods, requiring multiple high-quality genomes and intensive computational alignment.
  • Existing methods for pangenome analysis are computationally demanding and necessitate complete genome sequences.
  • The direct application of k-mer decomposition for population genetic analyses remained unexplored.

Purpose of the Study:

  • To develop and validate a novel strategy for population genetic analysis using k-mer count variants.
  • To establish an alignment- and reference-free (ARF) framework for population genetics.
  • To explore the utility of k-mers in detecting various genetic variations beyond single nucleotide polymorphisms (SNPs).

Main Methods:

  • Developed a novel strategy utilizing k-mer count variants for population genetic analyses.
  • Compared the k-mer based method against SNP-based methods using simulated and real datasets.
  • Analyzed population structure, genetic diversity, and identified genetic variants in Saccharomyces cerevisiae.

Main Results:

  • The k-mer based approach successfully recapitulated population structure identified by SNPs.
  • The k-mer method revealed higher genetic diversity in the real dataset, indicating a greater capture of genetic variants.
  • Identified SNPs, insertion/deletion, and horizontal gene transfer (HGT) fragments associated with adaptive evolution using k-mer frequencies.

Conclusions:

  • The developed k-mer based strategy is effective for population genetic analyses, providing an alignment- and reference-free alternative.
  • This ARF method is particularly advantageous for species with incomplete or highly divergent genomes.
  • The approach broadens the scope of pangenome analysis by incorporating k-mer based insights into population genetics.