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Inferring bacterial recombination rates from large-scale sequencing datasets.

Mingzhi Lin¹, Edo Kussell^2,3

¹Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY, USA.

|January 22, 2019

Summary

We developed an efficient method to analyze bacterial homologous recombination using DNA sequencing. This approach reveals population genetics and evolutionary history across diverse bacterial species and time periods.

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Area of Science:

Microbiology
Population Genetics
Bioinformatics

Background:

Homologous recombination is a key driver of bacterial evolution and adaptation.
Accurate inference of recombination parameters is crucial for understanding bacterial populations.
Existing methods may lack efficiency or applicability across diverse genomic datasets.

Observation:

A novel computational method, mcorr, was developed for inferring bacterial recombination parameters.
The method utilizes correlation profiles of synonymous substitutions in genomic data.
It is applicable to various sequencing data types, including whole-genome and metagenomic data.

Findings:

The mcorr method was validated using laboratory experimental data.
Recombination parameters were determined for diverse bacterial species.
Analysis of Helicobacter pylori isolates revealed shared gene pool distributions.
Recombination in multidrug-resistant Escherichia coli ST131 within the infant gut microbiome was quantified.
Bacterial recombination rates and gene pool diversity were measured in ancient DNA from the 'Iceman' mummy and Black Death victims.

Implications:

Provides a robust and computationally efficient tool for bacterial population genetics research.
Enables deeper insights into bacterial evolution, adaptation, and the spread of traits like antibiotic resistance.
Facilitates the study of historical bacterial populations and their genomic diversity.