MentaLiST - A fast MLST caller for large MLST schemes

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Multi-locus sequence typing (MLST) is a standard bacterial genotyping method crucial for pathogen surveillance.
• Advancements in whole-genome sequencing have led to larger MLST schemes (cgMLST, wgMLST) for finer resolution.
• Existing MLST callers face computational challenges with these large-scale typing schemes.

Purpose of the Study:

• To introduce MentaLiST, a novel MLST caller designed for large typing schemes.
• To address the computational limitations of current MLST analysis tools.
• To provide a faster and more accurate solution for bacterial genotyping.

Main Methods:

• Developed MentaLiST, an MLST caller utilizing a k-mer voting algorithm.
• Implemented MentaLiST in the Julia programming language for efficiency.
• Tested MentaLiST on both real and simulated genomic datasets.

Main Results:

• MentaLiST demonstrates superior speed compared to existing MLST callers.
• The tool achieves comparable or improved accuracy in bacterial genotyping.
• MentaLiST effectively handles MLST schemes comprising thousands of genes with minimal computational resources.

Conclusions:

• MentaLiST offers a computationally efficient and accurate solution for large-scale MLST.
• The tool is suitable for advanced bacterial genotyping and outbreak surveillance.
• MentaLiST is readily available with installation instructions via Conda.