Systems-Based Approach for Optimization of Assembly-Free Bacterial MLST Mapping.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Real-time epidemiological surveillance of bacterial pathogens is crucial for public health.
• Accurate species identification and variant mapping are essential for tracking pathogen populations and predicting traits like antimicrobial resistance (AMR).
• Multi-locus sequence typing (MLST) is a standard method for bacterial typing based on seven conserved gene loci.

Purpose of the Study:

• To systematically assess the accuracy and scalability of assembly-dependent and assembly-free methods for MLST sequence type (ST) mapping.
• To determine the optimal k-mer length for the stringMLST tool across diverse bacterial pathogens.
• To integrate stringMLST into the ProkEvo platform for automated and reproducible bacterial population analysis.

Main Methods:

• Evaluated two methods for ST mapping (one assembly-dependent, one assembly-free) using default settings and associated ST schemes.
• Systematically assessed accuracy and scalability across a wide range of phylogenetically divergent, Public Health-relevant bacterial pathogens with available MLST databases.
• Integrated the stringMLST tool into the ProkEvo population genomics platform.

Main Results:

• The optimal k-mer length for stringMLST is species-specific and can be influenced by genome characteristics.
• While stringMLST parameters could be optimized for most tested organisms, some instances required adjustments beyond default settings.
• Integration into ProkEvo enabled automated and reproducible bacterial population analysis.

Conclusions:

• stringMLST shows promise for bacterial pathogen surveillance, but its optimal performance is organism-dependent.
• The ProkEvo platform, enhanced with stringMLST, provides a scalable solution for automated bacterial population genomics and surveillance.
• Further development may be needed for direct deployment of stringMLST in all surveillance scenarios.