Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1093/nar/gky783

Type

Journal article

Journal

Nucleic acids research

Publication Date

12/2018

Volume

46

Addresses

Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.

Keywords

Shigella sonnei, Mycobacterium leprae, Streptococcus pneumoniae, DNA, Bacterial, Monte Carlo Method, Bayes Theorem, Markov Chains, Evolution, Molecular, Phylogeny, Recombination, Genetic, Genome, Bacterial, Models, Genetic, Time Factors, Computer Simulation, Software, Benchmarking, Datasets as Topic