Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis

Area of Science:

• Genomics
• Computational Biology
• Epidemiology

Background:

• Pathogen sequence variation is crucial for public health and evolutionary studies.
• Current reference-based methods for identifying variation face challenges like reference bias and complex filtering.
• There is a need for efficient, user-friendly tools for bacterial genome data analysis.

Purpose of the Study:

• To introduce split k-mer analysis (SKA2), a novel method for bacterial genotyping.
• To provide a tool that is both rapid and accurate, supporting both reference-free and reference-based analyses.
• To enable efficient processing of large bacterial genome datasets without requiring extensive bioinformatics expertise or resources.

Main Methods:

• Split k-mer analysis (SKA2) was developed for genotyping bacterial populations.
• The method supports both reference-free and reference-based mapping using sequencing reads or genome assemblies.
• Performance was evaluated using outbreak simulations and compared against existing reference-based methods.

Main Results:

• SKA2 demonstrates high accuracy, especially for closely related samples.
• In simulations, SKA2 showed superior variant recall compared to reference-based methods, with no false positives.
• SKA2 is significantly faster than comparable methods and allows sequential addition of new genomes without reanalysis.

Conclusions:

• SKA2 provides an accurate, rapid, and reference-bias-free approach for bacterial genotyping.
• The tool facilitates outbreak analysis, transmission reconstruction, and evolutionary genomic studies.
• SKA2's efficiency, ease of use, and open-source availability position it as a valuable resource for bacterial genomics.