Bayesian modeling of recombination events in bacterial populations
<p>Abstract</p> <p>Background</p> <p>We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2008-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/421 |
| _version_ | 1818934170082082816 |
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| author | Dowson Chris Hanage William P Baldwin Adam Marttinen Pekka Mahenthiralingam Eshwar Corander Jukka |
| author_facet | Dowson Chris Hanage William P Baldwin Adam Marttinen Pekka Mahenthiralingam Eshwar Corander Jukka |
| author_sort | Dowson Chris |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.</p> <p>Results</p> <p>We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites.</p> <p>Conclusion</p> <p>A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus <it>Burkholderia </it>are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL <url>http://web.abo.fi/fak/mnf//mate/jc/software/brat.html</url>.</p> |
| first_indexed | 2024-12-20T05:00:01Z |
| format | Article |
| id | doaj.art-6dc8cfc9d00d45799e0049f4e25e4805 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T05:00:01Z |
| publishDate | 2008-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-6dc8cfc9d00d45799e0049f4e25e48052022-12-21T19:52:35ZengBMCBMC Bioinformatics1471-21052008-10-019142110.1186/1471-2105-9-421Bayesian modeling of recombination events in bacterial populationsDowson ChrisHanage William PBaldwin AdamMarttinen PekkaMahenthiralingam EshwarCorander Jukka<p>Abstract</p> <p>Background</p> <p>We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.</p> <p>Results</p> <p>We introduce a Bayesian spatial structural model representing the continuum of origins over sites within the observed sequences, including a probabilistic characterization of uncertainty related to the origin of any particular site. To enable a statistically accurate and practically feasible approach to the analysis of large-scale data sets representing a single genus, we have developed a novel software tool (BRAT, Bayesian Recombination Tracker) implementing the model and the corresponding learning algorithm, which is capable of identifying the posterior optimal structure and to estimate the marginal posterior probabilities of putative origins over the sites.</p> <p>Conclusion</p> <p>A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus <it>Burkholderia </it>are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL <url>http://web.abo.fi/fak/mnf//mate/jc/software/brat.html</url>.</p>http://www.biomedcentral.com/1471-2105/9/421 |
| spellingShingle | Dowson Chris Hanage William P Baldwin Adam Marttinen Pekka Mahenthiralingam Eshwar Corander Jukka Bayesian modeling of recombination events in bacterial populations BMC Bioinformatics |
| title | Bayesian modeling of recombination events in bacterial populations |
| title_full | Bayesian modeling of recombination events in bacterial populations |
| title_fullStr | Bayesian modeling of recombination events in bacterial populations |
| title_full_unstemmed | Bayesian modeling of recombination events in bacterial populations |
| title_short | Bayesian modeling of recombination events in bacterial populations |
| title_sort | bayesian modeling of recombination events in bacterial populations |
| url | http://www.biomedcentral.com/1471-2105/9/421 |
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