Maast: genotyping thousands of microbial strains efficiently

Area of Science:

• Microbial genomics
• Bioinformatics
• Population genetics

Background:

• Existing single nucleotide polymorphism (SNP) genotyping algorithms struggle to scale for large numbers of microbial strains and do not effectively handle redundant genomic data within species.
• Analyzing microbial populations at scale is crucial for understanding disease dynamics and evolution.

Approach:

• Developed Maast, a novel bioinformatics tool designed for large-scale population genetic meta-analysis of microbes.
• Maast employs a unique algorithm to select a minimal, diverse set of conspecific genomes.
• It constructs reliable SNP panels and utilizes a hybrid approach of whole-genome alignment and k-mer exact matching for rapid, accurate genotyping.

Key Points:

• Maast enables population genetic meta-analysis of microbes at an unprecedented scale.
• The tool effectively identifies a minimal set of diverse genomes and constructs species-specific SNP panels.
• Demonstrated utility in genotyping thousands of *Helicobacter pylori* strains and tracking SARS-CoV-2 diversification.

Conclusions:

• Maast overcomes scalability limitations of existing SNP genotyping methods for microbial populations.
• The tool provides a robust platform for high-throughput microbial population genomics.
• Maast facilitates advanced population genetic studies, including pathogen surveillance and evolutionary tracking.