Rapid, reference-free identification of bacterial pathogen transmission using optimized split k-mer analysis.

Area of Science:

• Genomic epidemiology
• Infectious disease surveillance
• Computational biology

Background:

• Multidrug-resistant organisms (MDROs) pose a significant threat to public health and healthcare systems.
• Genomic surveillance is crucial for controlling MDRO transmission, but current methods have limitations in speed and accessibility.
• Split k-mer analysis (SKA) presents a promising alternative for rapid, reference-free genomic comparisons.

Purpose of the Study:

• To determine the optimal parameters for Split k-mer analysis (SKA) in detecting multidrug-resistant organism (MDRO) transmission.
• To evaluate the accuracy and performance of SKA compared to traditional methods.
• To assess the impact of plasmid dynamics on SKA-based SNP detection.

Main Methods:

• Simulated 60 SKA parameter combinations across 50 datasets for four key MDROs (E. coli, E. faecium, K. pneumoniae, S. aureus).
• Quantified false negative and positive single nucleotide polymorphism (SNP) proportions.
• Validated optimal SKA parameters against real-world data, comparing with MLST, cgMLST, and Snippy.

Main Results:

• The optimal SKA parameters for MDRO transmission detection involve using sequencing reads, a k-mer length of 19, and a minor allele frequency filter of 0.01.
• SKA is significantly faster than traditional methods like Snippy, especially for large datasets, though it may undercall SNPs.
• SKA demonstrated excellent concordance with MLST and cgMLST and was unaffected by simulated plasmid gain or loss.

Conclusions:

• SKA is a superior and rapid method for bacterial pathogen transmission detection compared to traditional approaches.
• The identified optimal SKA parameters enhance its utility for real-time genomic surveillance in clinical settings.
• SKA provides a computationally efficient and accurate tool for infection control and public health.