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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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Bootstrap mixture analyses of the Neisseria data.The figure shows the adjusted rand index between the partition based on the original data and the alternative based on a bootstrap data set by resampling in  clusters. Five repetitions were made for each of the  clusters.
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pcbi-1000455-g006: Bootstrap mixture analyses of the Neisseria data.The figure shows the adjusted rand index between the partition based on the original data and the alternative based on a bootstrap data set by resampling in clusters. Five repetitions were made for each of the clusters.

Mentions: In total 32 BAPS populations are identified, where three populations (numbered as 8, 29 and 32) belong to the N. lactamica species and the remaining 29 populations are labeled as N. meningitidis species. For accessing the robustness of the identified population structure, the partition determined using the whole data set was compared with the partition using bootstrap data generated according to the simulation scenarios. Figure 6 shows the adjusted Rand Index as a result of the comparison. Our partition method is able to identify the population structure with good accuracy, even though the performance may decrease as the complexity level of the data increases and when the number of available strains per population is quite low. It should be noted that the number of strains in the bootstrap samples was typically much smaller than the number of strains assigned to a particular population in the analysis of the original data. This illustrates that the population identification becomes highly stable when the sample sizes are sufficiently large.


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)

Bootstrap mixture analyses of the Neisseria data.The figure shows the adjusted rand index between the partition based on the original data and the alternative based on a bootstrap data set by resampling in  clusters. Five repetitions were made for each of the  clusters.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000455-g006: Bootstrap mixture analyses of the Neisseria data.The figure shows the adjusted rand index between the partition based on the original data and the alternative based on a bootstrap data set by resampling in clusters. Five repetitions were made for each of the clusters.
Mentions: In total 32 BAPS populations are identified, where three populations (numbered as 8, 29 and 32) belong to the N. lactamica species and the remaining 29 populations are labeled as N. meningitidis species. For accessing the robustness of the identified population structure, the partition determined using the whole data set was compared with the partition using bootstrap data generated according to the simulation scenarios. Figure 6 shows the adjusted Rand Index as a result of the comparison. Our partition method is able to identify the population structure with good accuracy, even though the performance may decrease as the complexity level of the data increases and when the number of available strains per population is quite low. It should be noted that the number of strains in the bootstrap samples was typically much smaller than the number of strains assigned to a particular population in the analysis of the original data. This illustrates that the population identification becomes highly stable when the sample sizes are sufficiently large.

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