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Identifying currents in the gene pool for bacterial populations using an integrative approach.

Tang J, Hanage WP, Fraser C, Corander J - PLoS Comput. Biol. (2009)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi

ABSTRACT
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.

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Tentative gene flow graph in six populations.The graph topology can be succinctly termed as , where the node set  and the arrow set . The actual rates of admixture associated with the arrows were randomly generated from a uniform distribution. Note the two ways of gene flow between population 2 and 3.
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pcbi-1000455-g002: Tentative gene flow graph in six populations.The graph topology can be succinctly termed as , where the node set and the arrow set . The actual rates of admixture associated with the arrows were randomly generated from a uniform distribution. Note the two ways of gene flow between population 2 and 3.

Mentions: We specified a putative gene flow graph that consists of populations and the arrow set is specified in Figure 2. The rates of admixture between populations are characterized in the matrix , which is by definition a product of and . Therefore by simulating and we can generate a parameter set in that conforms to the graph structure in Figure 2. We chose a consistent sampling scheme for such that the diagonal elements for , and the non-diagonal elements are uniformly distributed. is also sampled from the Uniform distribution , but with the row constraints , since refers to the fraction of DNA sequence acquired from a particular source population.


Identifying currents in the gene pool for bacterial populations using an integrative approach.

Tang J, Hanage WP, Fraser C, Corander J - PLoS Comput. Biol. (2009)

Tentative gene flow graph in six populations.The graph topology can be succinctly termed as , where the node set  and the arrow set . The actual rates of admixture associated with the arrows were randomly generated from a uniform distribution. Note the two ways of gene flow between population 2 and 3.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2713424&req=5

pcbi-1000455-g002: Tentative gene flow graph in six populations.The graph topology can be succinctly termed as , where the node set and the arrow set . The actual rates of admixture associated with the arrows were randomly generated from a uniform distribution. Note the two ways of gene flow between population 2 and 3.
Mentions: We specified a putative gene flow graph that consists of populations and the arrow set is specified in Figure 2. The rates of admixture between populations are characterized in the matrix , which is by definition a product of and . Therefore by simulating and we can generate a parameter set in that conforms to the graph structure in Figure 2. We chose a consistent sampling scheme for such that the diagonal elements for , and the non-diagonal elements are uniformly distributed. is also sampled from the Uniform distribution , but with the row constraints , since refers to the fraction of DNA sequence acquired from a particular source population.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. jing.tang@helsinki.fi

ABSTRACT
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.

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