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      Building a genomic framework for prospective MRSA surveillance in the United Kingdom and the Republic of Ireland

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          Abstract

          The correct interpretation of microbial sequencing data applied to surveillance and outbreak investigation depends on accessible genomic databases to provide vital genetic context. Our aim was to construct and describe a United Kingdom MRSA database containing over 1000 methicillin-resistant Staphylococcus aureus (MRSA) genomes drawn from England, Northern Ireland, Wales, Scotland, and the Republic of Ireland over a decade. We sequenced 1013 MRSA submitted to the British Society for Antimicrobial Chemotherapy by 46 laboratories between 2001 and 2010. Each isolate was assigned to a regional healthcare referral network in England and was otherwise grouped based on country of origin. Phylogenetic reconstructions were used to contextualize MRSA outbreak investigations and to detect the spread of resistance. The majority of isolates ( n = 783, 77%) belonged to CC22, which contains the dominant United Kingdom epidemic clone (EMRSA-15). There was marked geographic structuring of EMRSA-15, consistent with widespread dissemination prior to the sampling decade followed by local diversification. The addition of MRSA genomes from two outbreaks and one pseudo-outbreak demonstrated the certainty with which outbreaks could be confirmed or refuted. We identified local and regional differences in antibiotic resistance profiles, with examples of local expansion, as well as widespread circulation of mobile genetic elements across the bacterial population. We have generated a resource for the future surveillance and outbreak investigation of MRSA in the United Kingdom and Ireland and have shown the value of this during outbreak investigation and tracking of antimicrobial resistance.

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          Velvet: algorithms for de novo short read assembly using de Bruijn graphs.

          We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.
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            Toward almost closed genomes with GapFiller

            De novo assembly is a commonly used application of next-generation sequencing experiments. The ultimate goal is to puzzle millions of reads into one complete genome, although draft assemblies usually result in a number of gapped scaffold sequences. In this paper we propose an automated strategy, called GapFiller, to reliably close gaps within scaffolds using paired reads. The method shows good results on both bacterial and eukaryotic datasets, allowing only few errors. As a consequence, the amount of additional wetlab work needed to close a genome is drastically reduced. The software is available at http://www.baseclear.com/bioinformatics-tools/.
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              Scaffolding pre-assembled contigs using SSPACE.

              De novo assembly tools play a main role in reconstructing genomes from next-generation sequencing (NGS) data and usually yield a number of contigs. Using paired-read sequencing data it is possible to assess the order, distance and orientation of contigs and combine them into so-called scaffolds. Although the latter process is a crucial step in finishing genomes, scaffolding algorithms are often built-in functions in de novo assembly tools and cannot be independently controlled. We here present a new tool, called SSPACE, which is a stand-alone scaffolder of pre-assembled contigs using paired-read data. Main features are: a short runtime, multiple library input of paired-end and/or mate pair datasets and possible contig extension with unmapped sequence reads. SSPACE shows promising results on both prokaryote and eukaryote genomic testsets where the amount of initial contigs was reduced by at least 75%.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                February 2016
                February 2016
                : 26
                : 2
                : 263-270
                Affiliations
                [1 ]Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, United Kingdom;
                [2 ]Pathogen Genomics, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, United Kingdom;
                [3 ]Public Health England, Microbiology Services Division, Addenbrooke’s Hospital, Cambridge CB2 0QW, United Kingdom;
                [4 ]Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom;
                [5 ]School of Medicine, University of St. Andrews, St. Andrews KY16 9TF, United Kingdom;
                [6 ]British Society for Antimicrobial Chemotherapy, B1 3NJ, United Kingdom;
                [7 ]North Bristol NHS Trust, Bristol BS10 5NB, United Kingdom;
                [8 ]Department of Medical Microbiology, University Medical Centre Groningen, Rijksuniversiteit Groningen, 9713 GZ Groningen, The Netherlands;
                [9 ]Faculty of Medicine, School of Public Health, Imperial College, London W2 1PG, United Kingdom;
                [10 ]Department of Hospital Epidemiology, Institute for Environmental Medicine and Hospital Hygiene, University Hospital Freiburg, 79106 Freiburg, Germany;
                [11 ]The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, United Kingdom;
                [12 ]London School of Hygiene and Tropical Medicine, London, WC1E 7HT, United Kingdom
                Author notes
                Article
                9509184
                10.1101/gr.196709.115
                4728378
                26672018
                61193458-5710-4a61-b3ff-1d07a84bf199
                © 2016 Reuter et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 July 2015
                : 14 December 2015
                Page count
                Pages: 8
                Funding
                Funded by: UKCRC Translational Infection Research Initiative
                Funded by: Medical Research Council http://dx.doi.org/10.13039/501100000265
                Award ID: G1000803
                Funded by: Biotechnology and Biological Sciences Research Council http://dx.doi.org/10.13039/501100000268
                Funded by: National Institute for Health Research http://dx.doi.org/10.13039/501100000272
                Funded by: Chief Scientist Office of the Scottish Government Health Directorate
                Funded by: Wellcome Trust http://dx.doi.org/10.13039/100004440
                Award ID: 098051
                Funded by: Healthcare Infection Society http://dx.doi.org/10.13039/501100000632
                Funded by: Academy of Medical Sciences http://dx.doi.org/10.13039/501100000691
                Funded by: Health Foundation http://dx.doi.org/10.13039/501100000724
                Funded by: NIHR Cambridge Biomedical Research Centre http://dx.doi.org/10.13039/501100000272
                Funded by: Wellcome Trust http://dx.doi.org/10.13039/100004440
                Award ID: 089472
                Funded by: University of Cambridge Human Biology Research Ethics Committee
                Funded by: Cambridge University Hospitals NHS Foundation Trust Research and Development Department
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