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Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of Invasive Staphylococcus aureus in Europe

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      The implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasive Staphylococcus aureus isolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL ( Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show that in silico predictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) community-oriented database infrastructure and analysis tools.


      The spread of antibiotic-resistant bacteria is a public health emergency of global concern, threatening medical intervention at every level of health care delivery. Several recent studies have demonstrated the promise of routine whole-genome sequencing (WGS) of bacterial pathogens for epidemiological surveillance, outbreak detection, and infection control. However, as this technology becomes more widely adopted, the key challenges of generating representative national and international data sets and the development of bioinformatic tools to manage and interpret the data become increasingly pertinent. This study provides a road map for the integration of WGS data into routine pathogen surveillance. We emphasize the importance of large-scale routine surveys to provide the population context for more targeted or localized investigation and the development of open-access bioinformatic tools to provide the means to combine and compare independently generated data with publicly available data sets.

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      Most cited references 64

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      Basic local alignment search tool.

      A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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        The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

        Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at Contact: Supplementary information: Supplementary data are available at Bioinformatics online.

            Author and article information

            [a ]Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
            [b ]The Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
            [c ]The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom
            [d ]School of Medicine, University of St. Andrews, St. Andrews, United Kingdom
            [e ]Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
            [f ]Department of Biology, Drexel University, Philadelphia, Pennsylvania, USA
            [g ]Programa de Genómica Evolutiva, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de Mexico, Cuernavaca, Morelos, Mexico
            [h ]Helsinki Institute for Information Technology HIIT, Aalto, Finland
            [i ]Department of Mathematics, Imperial College London, London, United Kingdom
            [j ]Department of Medical Microbiology, University Medical Center Groningen, Rijksuniversteit Groningen, Groningen, The Netherlands
            [k ]National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
            [l ]Centre Hospitalier Universitaire de Nice, Nice, France
            [m ]EUCAST Development Laboratory, Växjö, Sweden
            [n ]Department of Infection Prevention and Hospital Hygiene, Faculty of Medicine, University of Freiburg, Freiburg, Germany
            Author notes
            Address correspondence to Hajo Grundmann, hajo.grundmann@ .

            D.M.A. and E.J.F. contributed equally to this work.


            We deeply regret the untimely loss of our dear friend and colleague Helmut Mittermayer, to whom we dedicate this paper.

            Editor Keith P. Klugman, Department of Global Health, Emory University

            This article is a direct contribution from a Fellow of the American Academy of Microbiology. External solicited reviewers: Jennifer Gardy, B.C. Centre for Disease Control; Geoffrey Coombs, Murdoch University, Australia.

            American Society for Microbiology (1752 N St., N.W., Washington, DC )
            5 May 2016
            May-Jun 2016
            : 7
            : 3
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            Copyright © 2016 Aanensen et al.

            This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

            supplementary-material: 10, Figures: 6, Tables: 2, Equations: 0, References: 66, Pages: 15, Words: 12748
            Funded by: Wellcome Trust
            Award ID: 098051
            Award Recipient : Matthew Holden Award Recipient : Janina Dordel Award Recipient : Julian Parkhill Award Recipient : Stephen Bentley
            Funded by: Wellcome Trust
            Award ID: 099202
            Award Recipient : David M. Aanensen Award Recipient : Corin Yeats Award Recipient : Artemij Fedosejev
            Funded by: Wellcome Trust
            Award ID: 089472
            Award Recipient : Brian Spratt
            Funded by: Medical Research Council (MRC)
            Award ID: G1000803
            Award Recipient : Santiago Castillo-Ramírez
            The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Research Article
            Custom metadata
            May/June 2016

            Life sciences


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