Marttinen, P., Baldwin, A., Hanage, W. P., Dowson, C., Mahenthiralingam, Eshwar ORCID: https://orcid.org/0000-0001-9014-3790 and Corander, J. 2008. Bayesian modeling of recombination events in bacterial populations. BMC bioinformatics 9 , 421. 10.1186/1471-2105-9-421 |
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Background 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. Results 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. Conclusion A multitude of challenging simulation scenarios and an analysis of real data from seven housekeeping genes of 120 strains of genus Burkholderia are used to illustrate the possibilities offered by our approach. The software is freely available for download at URL http://web.abo.fi/fak/mnf//mate/jc/software/brat.html webcite.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Biosciences |
Publisher: | Biomed Central |
ISSN: | 1471-2105 |
Date of First Compliant Deposit: | 30 March 2016 |
Last Modified: | 04 May 2023 05:40 |
URI: | https://orca.cardiff.ac.uk/id/eprint/8753 |
Citation Data
Cited 23 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |