Correlated Mutations and Homologous Recombination Within Bacterial Populations

Area of Science:

• Population genetics
• Microbial evolution
• Bioinformatics

Background:

• Quantifying homologous recombination rates in bacterial populations is crucial for understanding bacterial evolution.
• Phylogenetic and coalescent methods for estimating recombination rates are often computationally expensive and difficult to apply to large datasets due to sequence similarity and bacterial DNA transfer processes.

Purpose of the Study:

• To develop an efficient and computationally inexpensive method for inferring bacterial recombination rates.
• To provide a tool applicable to large sequencing datasets and address challenges posed by sample selection bias.

Main Methods:

• Introduced a set of mutational correlation functions derived from pairwise sequence comparisons.
• Developed analytical expressions for these functions that accurately model simulation results.
• Applied the method to whole-genome data from Escherichia coli and Streptococcus pneumoniae populations, fitting correlation functions at synonymous substitutions.

Main Results:

• The mutational correlation functions effectively characterize bacterial recombination.
• The method accurately recapitulates simulation results for neutral and adapting populations.
• Recombination rates and relative sample ages were inferred by fitting analytical forms to sequence data, with corrections for sample selection bias.

Conclusions:

• The developed method offers a fast and efficient approach to inferring bacterial recombination rates and sample ages.
• This population genetic modeling-based approach bypasses computationally intensive phylogenetic inference.
• The method's efficiency makes it highly suitable for analyzing large-scale bacterial sequencing data.