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Microbial Genomics logo Microbial Genomics

Volume 11, Issue 3

Rapid, reference-free identification of bacterial pathogen transmission using optimized split -mer analysis Open Access

Abstract

Infections caused by multidrug-resistant organisms (MDROs) are difficult to treat and often life threatening and place a burden on the healthcare system. Minimizing the transmission of MDROs in hospitals is a global priority with genomics proving to be a powerful tool for identifying the transmission of MDROs. To optimize the utility of genomics for prospective infection control surveillance, results must be available in real time, reproducible and simple to communicate to clinicians. Traditional reference-based approaches suffer from several limitations for prospective genomic surveillance. Whilst reference-free or pairwise genome comparisons avoid some of these limitations, they can be computationally intensive and time consuming. Split -mer analysis (SKA) offers a viable alternative facilitating rapid reference-free pairwise comparisons of genomic data, but the optimum SKA parameters for the detection of transmission have not been determined. Additionally, the accuracy of SKA-based inferences has not been measured, nor whether modified quality control parameters are required. Here, we explore the performance of 60 SKA parameter combinations across 50 simulations to quantify the false negative and positive SNP proportions for , , and . Using the optimum parameter combination, we explore concordance between SKA, multilocus sequence typing (MLST), core genome MLST (cgMLST) and Snippy in a real-world dataset. Lastly, we investigate whether simulated plasmid gain or loss could impact SNP detection with SKA. This work identifies that the use of SKA with sequencing reads, a -mer length of 19 and a minor allele frequency filter of 0.01 is optimal for MDRO transmission detection. Whilst SNP detection with SKA (when used with sequencing reads) undercalls SNPs compared to Snippy, it is significantly faster, especially with larger datasets. SKA has excellent concordance with MLST and cgMLST and is not impacted by simulated plasmid movement. We propose that the use of SKA for the detection of bacterial pathogen transmission is superior to traditional methodologies, capable of providing results in a much shorter timeframe.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bacterial genomics , infection prevention and control , k-mer-based methodology and pathogen transmission
Funding
This study was supported by the:
  • National Health and Medical Research Council (Award GNT1105905)
    • Principal Award Recipient: NotApplicable
  • Melbourne Genomics Health Alliance
    • Principal Award Recipient: NotApplicable
© 2025 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2025-03-06
2026-01-15

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Rapid, reference-free identification of bacterial pathogen transmission using optimized split k-mer analysis
M Gen 11, 001347 (2025); https://doi.org/10.1099/mgen.0.001347
/content/journal/mgen/10.1099/mgen.0.001347
/content/journal/mgen/10.1099/mgen.0.001347
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