Rhometa: Population recombination rate estimation from metagenomic read datasets.

Area of Science:

• Microbial evolution and genomics.
• Computational biology and bioinformatics.
• Metagenomics and population genetics.

Background:

• Prokaryotic evolution is significantly shaped by genetic recombination between species.
• Recombination rates are key indicators of a prokaryotic population's adaptive potential.
• Existing methods for estimating recombination rates are often limited with modern short-read metagenomic data.

Purpose of the Study:

• To introduce Rhometa, a novel software package for calculating population recombination rates from shotgun sequencing reads in metagenomes.
• To extend the composite likelihood approach to accommodate diverse, modern short-read metagenomic datasets.
• To provide a comprehensive tool for analyzing recombination in uncultured microbial communities.

Main Methods:

• Developed Rhometa, a software package implementing an extended composite likelihood approach.
• Evaluated Rhometa using simulated datasets across various sequencing depths and complexities.
• Validated Rhometa on real short-read data from a Streptococcus pneumoniae transformation experiment and ocean surface water metagenomes.

Main Results:

• Rhometa accurately estimates population recombination rates from metagenomic data, with accuracy increasing with genome count in simulations.
• The software successfully processed real experimental data, yielding plausible recombination rate estimates for S. pneumoniae.
• Demonstrated Rhometa's applicability to uncultured microbial communities using ocean surface water metagenomic datasets.

Conclusions:

• Rhometa provides a robust and accurate solution for determining population recombination rates from contemporary metagenomic datasets.
• The software extends the utility of established population genetics techniques to the field of metagenomics.
• Rhometa enables the study of genetic exchange and adaptation in complex, uncultured microbial populations.