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      Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins

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          Abstract

          The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates’ recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins, implemented in Python and C and supported on Linux and Mac OS X.

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          Most cited references40

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          Analyzing the mosaic structure of genes.

          Some genes in prokaryotes consist of a mosaic of regions derived from different ancestors by horizontal gene transfer. A method is described for demonstrating the statistical significance of such mosaic structure and for locating the crossover points separating different regions.
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            A comparison of homologous recombination rates in bacteria and archaea.

            It is a standard practice to test for the signature of homologous recombination in studies examining the genetic diversity of bacterial populations. Although it has emerged that homologous recombination rates can vary widely between species, comparing the results from different studies is made difficult by the diversity of estimation methods used. Here, Multi Locus Sequence Typing (MLST) datasets from a wide variety of bacteria and archaea are analyzed using the ClonalFrame method. This enables a direct comparison between species and allows for a first exploration of the question whether phylogeny or ecology is the primary determinant of homologous recombination rate.
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              How clonal are bacteria?

              Data from multilocus enzyme electrophoresis of bacterial populations were analyzed using a statistical test designed to detect associations between genes at different loci. Some species (e.g., Salmonella) were found to be clonal at all levels of analysis. At the other extreme, Neisseria gonorrhoeae is panmictic, with random association between loci. Two intermediate types of population structure were also found. Neisseria meningitidis displays what we have called an "epidemic" structure. There is significant association between loci, but this arises only because of the recent, explosive, increase in particular electrophoretic types; when this effect is eliminated the population is found to be effectively panmictic. In contrast, linkage disequilibrium in a population of Rhizobium meliloti exists because the sample consisted of two genetically isolated divisions, often fixed for different alleles: within each division association between loci was almost random. The method of analysis is appropriate whenever there is doubt about the extent of genetic recombination between members of a population. To illustrate this we analyzed data on protozoan parasites and again found panmictic, epidemic, and clonal population structures.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                18 February 2015
                20 November 2014
                20 November 2014
                : 43
                : 3
                : e15
                Affiliations
                [1 ]Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
                [2 ]Center for Communicable Disease Dynamics, Harvard School of Public Health, 677 Longwood Avenue, Boston, MA 02115, USA
                [3 ]Department of Infectious Disease Epidemiology, Imperial College London, St. Mary's Campus, Norfolk Place, London W2 1PG, UK
                [4 ]Cardiff School of Biosciences, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK
                [5 ]School of Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK
                [6 ]Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0SP, UK
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +44 (0)1223 494761; Fax: +44 (0)1223 494919; Email:  simon.harris@ 123456sanger.ac.uk
                Article
                10.1093/nar/gku1196
                4330336
                25414349
                4b08e9b7-2fb9-4ca4-b2b7-ce45e3a04e6e
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 November 2014
                : 20 October 2014
                : 07 July 2014
                Page count
                Pages: 13
                Categories
                7
                Methods Online
                Custom metadata
                18 February 2015

                Genetics
                Genetics

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