Abstract
BackgroundWith genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure.ResultsHere we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/.ConclusionsPSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.
Highlights
With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is possible to investigate trends in prokaryotic microevolution
The PSP tool is applicable to a wide range of prokaryotic species
The sites under positive selection with posterior probabilities (PP > 99%) in one model are more likely to detect under different models
Summary
With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is possible to investigate trends in prokaryotic microevolution. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. With the next-generation sequencing data “tsunami” in our midst, sets of closely related prokaryotic genomes suitable for comparative evolutionary studies have been available [1]. Big data mining of bacterial genomes has shown that positive selection is more widespread at the molecular level than expected under a restrictive interpretation of the neutral theory [3]. Genome-wide molecular selection analyses, designed to assess selection pressure across the entire genomes of different strains, have. Estimating the ratio ω gives a measure of selective pressure, indicating neutral evolution (ω = 1), purifying selection (ω < 1) and positive selection (ω > 1). Positive selection pressure serves to maintain a given set of adaptive traits that aids in survival
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