Identifying currents in the gene pool for bacterial populations using an integrative approach.
The evolution of bacterial populations has recently become considerably better understood due to large-scale sequencing of population samples. It has become clear that DNA sequences from a multitude of genes, as well as a broad sample coverage of a target population, are needed to obtain a relatively unbiased view of its genetic structure and the patterns of ancestry connected to the strains. However, the traditional statistical methods for evolutionary inference, such as phylogenetic analysis, are associated with several difficulties under such an extensive sampling scenario, in particular when a considerable amount of recombination is anticipated to have taken place. To meet the needs of large-scale analyses of population structure for bacteria, we introduce here several statistical tools for the detection and representation of recombination between populations. Also, we introduce a model-based description of the shape of a population in sequence space, in terms of its molecular variability and affinity towards other populations. Extensive real data from the genus Neisseria are utilized to demonstrate the potential of an approach where these population genetic tools are combined with an phylogenetic analysis. The statistical tools introduced here are freely available in BAPS 5.2 software, which can be downloaded from http://web.abo.fi/fak/mnf/mate/jc/software/baps.html.
Tang
Jing
J
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi
Hanage
William P
WP
Fraser
Christophe
C
Corander
Jukka
J
D000465:Algorithms; D001419:Bacteria / Q000235:genetics*; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D051456:Gene Flow; D005788:Gene Pool; D005798:Genes, Bacterial; D008957:Models, Genetic; D042806:Neisseria lactamica / Q000235:genetics; D009345:Neisseria meningitidis / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods