4.8 Article

Bayesian inference of ancestral dates on bacterial phylogenetic trees

期刊

NUCLEIC ACIDS RESEARCH
卷 46, 期 22, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gky783

关键词

-

资金

  1. Medical Research Council [MR/N010760/1]
  2. UK National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling Methodology at Imperial College London
  3. Public Health England (PHE) [HPRU-2012-10080]
  4. Wellcome Trust [101237/Z/13/Z]
  5. Royal Society [101237/Z/13/Z]
  6. MRC [MR/R015600/1, MR/N010760/1] Funding Source: UKRI

向作者/读者索取更多资源

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, whichmakes 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据