GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Identifying antimicrobial resistant (AMR) bacteria is crucial for public health and food safety.
• Next-generation sequencing (NGS) enables genetic variation analysis, but tools for complex bacterial mixtures are lacking.
• Accurate identification of genetic polymorphisms and copy number variations (CNVs) in bacterial samples is challenging.

Purpose of the Study:

• To develop a robust bioinformatic framework for genotype estimation in mixed bacterial samples.
• To accurately identify genetic polymorphisms and CNVs in antimicrobial resistance (AMR) genes.
• To provide a comprehensive solution for AMR gene identification and quantification in clinical samples.

Main Methods:

• Developed a Bayesian framework named Genetic Polymorphisms Assignments (GPA) for genotype estimation.
• Utilized simulation data to assess GPA's performance in identifying CNVs and single nucleotide variants (SNVs).
• Validated GPA using whole-genome sequencing and Pool-seq data from Klebsiella pneumoniae mixtures.

Main Results:

• GPA demonstrated reduced false discovery rate (FDR) and mean absolute error (MAE) in CNV and SNV identification.
• Achieved high accuracy in detecting allele fractions of CNVs and SNVs in AMR genes.
• Quantitative analysis of AMR gene fractions showed consistency with observed strain-level AMR patterns.

Conclusions:

• GPA provides an accurate and reliable method for identifying and quantifying AMR genes in complex bacterial samples.
• The integrated application offers a complete solution for AMR gene analysis, particularly for unculturable clinical samples.
• This framework advances the understanding and monitoring of antimicrobial resistance through improved genetic analysis.