Journal article icon

Journal article

Bayesian inference of ancestral dates on bacterial phylogenetic trees

Abstract:
The sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here, we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-step approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating.
Publication status:
Published
Peer review status:
Peer reviewed

Actions

Access Document

Publisher copy:
10.1093/nar/gky783

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
ORCID:
0000-0002-0940-3311


More from this funder
Funding agency for:
Wilson, DJ
Grant:
101237/Z/13/Z
More from this funder
Funding agency for:
Wilson, DJ
Grant:
101237/Z/13/Z


Publisher:
Oxford University Press
Journal:
Nucleic Acids Research More from this journal
Volume:
46
Issue:
22
Article number:
e134
Publication date:
2018-09-03
Acceptance date:
2018-08-21
DOI:
EISSN:
1362-4962
ISSN:
0305-1048
Pmid:
30184106


Language:
English
Pubs id:
pubs:914924
UUID:
uuid:d1d9ec5f-5ad2-4fc5-9b4d-c5ac02c6c833
Local pid:
pubs:914924
Source identifiers:
914924
Deposit date:
2019-02-13
ARK identifier:

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP