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Nonsynonymous Polymorphism Counts in Bacterial Genomes: a Comparative Examination.

Sara L Loo1, Anna Ong2, Wunna Kyaw2

  • 1School of Biotechology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia sara.loo@unsw.edu.au.

Applied and Environmental Microbiology
|October 24, 2020
PubMed
Summary
This summary is machine-generated.

Identifying bacterial selection from genomic single-nucleotide polymorphisms (SNPs) is challenging. This study explores SNP patterns to assess selection, finding differences between within-host and between-host bacterial evolution.

Keywords:
dN/dSnonsynonymouspathogenselectionwhole-genome sequencing

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Area of Science:

  • Microbial genomics
  • Evolutionary biology
  • Bioinformatics

Background:

  • Genomic single-nucleotide polymorphisms (SNPs) offer insights into bacterial evolution.
  • Current methods for detecting selection often rely on fixed differences, which are unsuitable for non-fixed SNPs common in bacterial pathogen data.

Purpose of the Study:

  • To investigate inferences about selection from bacterial genomic SNP data.
  • To assess if selection can be identified by analyzing SNP counts alone across multiple whole-genome sequencing (WGS) studies.
  • To compare SNP patterns between within-host and between-host bacterial populations.

Main Methods:

  • Analysis of nonsynonymous variation patterns in SNP data from bacterial pathogen WGS studies.
  • Simulation of SNP accumulation under neutral, positive, and negative selection.
  • Development of visualization techniques and a model to interpret SNP data in relation to neutral expectations.
  • Quantification of the expected proportion of nonsynonymous SNPs under neutrality.

Main Results:

  • Detecting positive selection solely from SNP data is generally difficult.
  • SNP counts observed at within-host and between-host population levels show different patterns, suggesting distinct underlying evolutionary processes.
  • A novel tool and visualization methods were developed for analyzing SNP data and identifying potential evidence of selection.

Conclusions:

  • Interpreting bacterial evolution from SNP data requires careful consideration of mutation accumulation and variant fixation.
  • Existing methods using fixed differences are inadequate for non-fixed SNP data.
  • New approaches are needed to accurately infer selection from contemporary genomic datasets, especially considering different population scales.