Limits...
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.

Show MeSH
Gene flow network identified in the N. meningitidis and N. lactamica populations.To investigate the ancestral admixture of a certain population, one can look at all the arrows pointing at this population. A typical population contains the major sources of its own, denoted as a self-looping arrows, and small proportions of gene flow from other populations. For instance, population 29 has 73% of its own genetic makeup and 27% of the DNA introduced via gene flow from other populations. Two major sources of gene flow for population 29 are population 19 and population 11, with 3.3% and 6.9% in the contribution separately. The remaining 17.1% of genes comes from various sources while none of them contributes a proportion larger than 3% and therefore are not displayed due to the pruning.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2713424&req=5

pcbi-1000455-g007: Gene flow network identified in the N. meningitidis and N. lactamica populations.To investigate the ancestral admixture of a certain population, one can look at all the arrows pointing at this population. A typical population contains the major sources of its own, denoted as a self-looping arrows, and small proportions of gene flow from other populations. For instance, population 29 has 73% of its own genetic makeup and 27% of the DNA introduced via gene flow from other populations. Two major sources of gene flow for population 29 are population 19 and population 11, with 3.3% and 6.9% in the contribution separately. The remaining 17.1% of genes comes from various sources while none of them contributes a proportion larger than 3% and therefore are not displayed due to the pruning.

Mentions: The results of admixture analysis for the Neisseria data set are summarized in Figure 7. The graph was obtained by fixing the admixture significance threshold at 0.05 and then pruning the arrows with gene flow strength below 0.03. It can be seen from the grey box highlighted in Figure 7 that two admixture arrows that imply inter-species gene flow remain significant, where two of the N. meningitidis populations (11 and 19) are constantly influencing the genetic makeup of one population of N. lactamica (population 29). The admixture arrows are uniformly directed from N. meningitidis towards N. lactamica, implying that N. meningitidis might donate genetic materials into N. lactamica, while the gene flow in a reversed direction is not supported by the analysis.


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)

Gene flow network identified in the N. meningitidis and N. lactamica populations.To investigate the ancestral admixture of a certain population, one can look at all the arrows pointing at this population. A typical population contains the major sources of its own, denoted as a self-looping arrows, and small proportions of gene flow from other populations. For instance, population 29 has 73% of its own genetic makeup and 27% of the DNA introduced via gene flow from other populations. Two major sources of gene flow for population 29 are population 19 and population 11, with 3.3% and 6.9% in the contribution separately. The remaining 17.1% of genes comes from various sources while none of them contributes a proportion larger than 3% and therefore are not displayed due to the pruning.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000455-g007: Gene flow network identified in the N. meningitidis and N. lactamica populations.To investigate the ancestral admixture of a certain population, one can look at all the arrows pointing at this population. A typical population contains the major sources of its own, denoted as a self-looping arrows, and small proportions of gene flow from other populations. For instance, population 29 has 73% of its own genetic makeup and 27% of the DNA introduced via gene flow from other populations. Two major sources of gene flow for population 29 are population 19 and population 11, with 3.3% and 6.9% in the contribution separately. The remaining 17.1% of genes comes from various sources while none of them contributes a proportion larger than 3% and therefore are not displayed due to the pruning.
Mentions: The results of admixture analysis for the Neisseria data set are summarized in Figure 7. The graph was obtained by fixing the admixture significance threshold at 0.05 and then pruning the arrows with gene flow strength below 0.03. It can be seen from the grey box highlighted in Figure 7 that two admixture arrows that imply inter-species gene flow remain significant, where two of the N. meningitidis populations (11 and 19) are constantly influencing the genetic makeup of one population of N. lactamica (population 29). The admixture arrows are uniformly directed from N. meningitidis towards N. lactamica, implying that N. meningitidis might donate genetic materials into N. lactamica, while the gene flow in a reversed direction is not supported by the analysis.

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