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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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Graphical representation of the evolutionary model for a sample of two bacterial populations.Strain sequences are represented as vertical bars with horizontal lines indicating the mutations that have occurred since the global ancestor. Stage-1 mutations are defined as those that occurred on local ancestors which provide candidate sites for gene flow between the populations. Mutations that occurred after the local ancestors are referred to as stage-2 mutations.
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pcbi-1000455-g001: Graphical representation of the evolutionary model for a sample of two bacterial populations.Strain sequences are represented as vertical bars with horizontal lines indicating the mutations that have occurred since the global ancestor. Stage-1 mutations are defined as those that occurred on local ancestors which provide candidate sites for gene flow between the populations. Mutations that occurred after the local ancestors are referred to as stage-2 mutations.

Mentions: The assumptions for the data generation are based on a simplified, yet reasonable evolutionary model of bacterial populations. We assumed that each population has a common local ancestor, and further back in time these local ancestors originated from a common ancestor of the whole population, termed as a global ancestor. This assumption enables a tree representation of the evolutionary relationships among the populations (Figure 1). It is important to note that we do not explicitly model the time at which these ancestral events occurred and therefore the edges in Figure 1 are in arbitrary length.


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)

Graphical representation of the evolutionary model for a sample of two bacterial populations.Strain sequences are represented as vertical bars with horizontal lines indicating the mutations that have occurred since the global ancestor. Stage-1 mutations are defined as those that occurred on local ancestors which provide candidate sites for gene flow between the populations. Mutations that occurred after the local ancestors are referred to as stage-2 mutations.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000455-g001: Graphical representation of the evolutionary model for a sample of two bacterial populations.Strain sequences are represented as vertical bars with horizontal lines indicating the mutations that have occurred since the global ancestor. Stage-1 mutations are defined as those that occurred on local ancestors which provide candidate sites for gene flow between the populations. Mutations that occurred after the local ancestors are referred to as stage-2 mutations.
Mentions: The assumptions for the data generation are based on a simplified, yet reasonable evolutionary model of bacterial populations. We assumed that each population has a common local ancestor, and further back in time these local ancestors originated from a common ancestor of the whole population, termed as a global ancestor. This assumption enables a tree representation of the evolutionary relationships among the populations (Figure 1). It is important to note that we do not explicitly model the time at which these ancestral events occurred and therefore the edges in Figure 1 are in arbitrary length.

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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