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Uses maximum likelihood inference to simultaneously detect recombination in bacterial genomes and account for it in phylogenetic reconstruction. ClonalFrameML can analyse hundreds of genomes in a matter of hours. It implements the model underlying the popular Bayesian ClonalFrame approach in a computationally efficient manner, and we have demonstrated its ability to estimate recombination parameters and detect importation events in the context of understanding short-term transmission dynamics and long-term bacterial evolution.

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ClonalFrameML classification

ClonalFrameML specifications

Software type:
Package
Restrictions to use:
None
Programming languages:
C++
Computer skills:
Advanced
Requirements:
C++ compiler, R, ape, phangorn
Source code URL:
https://github.com/xavierdidelot/clonalframeml
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0
Stability:
Stable

Credits

Publications

  • (Didelot and Wilson, 2015) ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol.
    PMID: 25675341

Institution(s)

Department of Infectious Disease Epidemiology, Imperial College, London, UK; Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK; Wellcome Trust Centre for Human Genetics, Oxford, UK

Funding source(s)

This work was supported by the National Institute for Health Research through Health Protection Research Unit funding, a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (Grant 101237/Z/13/Z), the Oxford NIHR Biomedical Research Centre, the UKCRC Modernising Medical Microbiology Consortium, the UKCRC Translational Infection Research Initiative supported by the Medical Research Council, the Biotechnology and Biological Sciences Research Council and the National Institute for Health Research on behalf of the UK Department of Health (Grant G0800778) and the Wellcome Trust (Grant 087646/Z/08/Z).

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