Practical Approaches for Detecting Selection in Microbial Genomes.

Jessica Hedge,Daniel J Wilson,Francis Ouellette

doi:10.1371/journal.pcbi.1004739

Abstract

Microbial genome evolution is shaped by a variety of selective pressures. Understanding how these processes occur can help to address important problems in microbiology by explaining observed differences in phenotypes, including virulence and resistance to antibiotics. Greater access to whole-genome sequencing provides microbiologists with the opportunity to perform large-scale analyses of selection in novel settings, such as within individual hosts. This tutorial aims to guide researchers through the fundamentals underpinning popular methods for measuring selection in pathogens. These methods are transferable to a wide variety of organisms, and the exercises provided are designed for researchers with any level of programming experience.

Highlights

Whole-genome sequencing (WGS) of microbial samples is affordable and fast, which has enabled its widespread use in both research and clinical practice [1,2,3]
Analysis of the genetic variation within WGS data can help characterize the selective pressures acting on microbial populations [4,5] and provide novel insight into infectious disease transmission [6], the emergence of antibiotic resistance [7,8], and the population dynamics of bacterial epidemics [9,10]
We provide one approach to performing a basic population genetics analysis of evolution and selection in nonrecombining microbial populations and a supplementary exercise demonstrating how these methods can be applied to bacterial WGS data (S1 File)

Summary

Introduction

Whole-genome sequencing (WGS) of microbial samples is affordable and fast, which has enabled its widespread use in both research and clinical practice [1,2,3]. We provide one approach to performing a basic population genetics analysis of evolution and selection in nonrecombining microbial populations and a supplementary exercise demonstrating how these methods can be applied to bacterial WGS data (S1 File). Principled statistical inference of phylogenetic trees requires specification of a sequence substitution model, describing the base frequencies (fi) and rate of change from allele i (rows) to allele j (columns)

Results

Conclusion