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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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Color coding scheme used in the BAPS populations and the NJ trees.For example, the first row shows the BAPS populations highlighted in the first NJ tree in Figure 9.
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pcbi-1000455-g013: Color coding scheme used in the BAPS populations and the NJ trees.For example, the first row shows the BAPS populations highlighted in the first NJ tree in Figure 9.

Mentions: To facilitate comparison of the phylogenetic analysis with the partition yielded by BAPS, we labelled strains with colors indicating population memberships. However, given the large number of strains included in the analysis and the large number of populations inferred by BAPS, it would be very challenging to visually extract information from a single NJ tree harbouring all the populations simultaneously. Therefore, four separate NJ trees are displayed in Figures 9–12, each of which shows a subset of the BAPS populations indicated with distinct colors. The strains remaining outside this particular subset are indicated by white circles. Since it is difficult to specify more than approximately 20 colors which remain clearly distinguishable from each other, independent coloring schemes were used for each tree to show the phylogenetic composition of the populations. Thus, it is not possible to compare the color codes directly with those in the gene flow network in Figure 7. The color coding scheme for the populations is shown in Figure 13 to enable comparison of the phylogenetic analysis and the gene flow network.


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)

Color coding scheme used in the BAPS populations and the NJ trees.For example, the first row shows the BAPS populations highlighted in the first NJ tree in Figure 9.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000455-g013: Color coding scheme used in the BAPS populations and the NJ trees.For example, the first row shows the BAPS populations highlighted in the first NJ tree in Figure 9.
Mentions: To facilitate comparison of the phylogenetic analysis with the partition yielded by BAPS, we labelled strains with colors indicating population memberships. However, given the large number of strains included in the analysis and the large number of populations inferred by BAPS, it would be very challenging to visually extract information from a single NJ tree harbouring all the populations simultaneously. Therefore, four separate NJ trees are displayed in Figures 9–12, each of which shows a subset of the BAPS populations indicated with distinct colors. The strains remaining outside this particular subset are indicated by white circles. Since it is difficult to specify more than approximately 20 colors which remain clearly distinguishable from each other, independent coloring schemes were used for each tree to show the phylogenetic composition of the populations. Thus, it is not possible to compare the color codes directly with those in the gene flow network in Figure 7. The color coding scheme for the populations is shown in Figure 13 to enable comparison of the phylogenetic analysis and the gene flow network.

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.

Show MeSH