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      Systematic longitudinal survey of invasive Escherichia coli in England demonstrates a stable population structure only transiently disturbed by the emergence of ST131

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          Abstract

          Escherichia coli associated with urinary tract infections and bacteremia has been intensively investigated, including recent work focusing on the virulent, globally disseminated, multidrug-resistant lineage ST131. To contextualize ST131 within the broader E. coli population associated with disease, we used genomics to analyze a systematic 11-yr hospital-based survey of E. coli associated with bacteremia using isolates collected from across England by the British Society for Antimicrobial Chemotherapy and from the Cambridge University Hospitals NHS Foundation Trust. Population dynamics analysis of the most successful lineages identified the emergence of ST131 and ST69 and their establishment as two of the five most common lineages along with ST73, ST95, and ST12. The most frequently identified lineage was ST73. Compared to ST131, ST73 was susceptible to most antibiotics, indicating that multidrug resistance was not the dominant reason for prevalence of E. coli lineages in this population . Temporal phylogenetic analysis of the emergence of ST69 and ST131 identified differences in the dynamics of emergence and showed that expansion of ST131 in this population was not driven by sequential emergence of increasingly resistant subclades. We showed that over time, the E. coli population was only transiently disturbed by the introduction of new lineages before a new equilibrium was rapidly achieved. Together, these findings suggest that the frequency of E. coli lineages in invasive disease is driven by negative frequency-dependent selection occurring outside of the hospital, most probably in the commensal niche, and that drug resistance is not a primary determinant of success in this niche.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Prokka: rapid prokaryotic genome annotation.

            T Seemann (2014)
            The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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              Search and clustering orders of magnitude faster than BLAST.

              Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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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
                August 2017
                August 2017
                : 27
                : 8
                : 1437-1449
                Affiliations
                [1 ]Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom;
                [2 ]Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, United Kingdom;
                [3 ]Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland;
                [4 ]Department of Biostatistics, University of Oslo, 0372 Oslo, Norway;
                [5 ]Public Health England, Clinical Microbiology and Public Health Laboratory, Addenbrooke's Hospital, Cambridge CB2 0QQ, United Kingdom;
                [6 ]Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, United Kingdom;
                [7 ]British Society of Antimicrobial Chemotherapy, Birmingham B1 3NJ, United Kingdom;
                [8 ]London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom
                Author notes
                Corresponding author: tk9@ 123456sanger.ac.uk
                Author information
                http://orcid.org/0000-0003-1741-6486
                http://orcid.org/0000-0002-9914-2601
                http://orcid.org/0000-0002-7069-5958
                Article
                9509184
                10.1101/gr.216606.116
                5538559
                28720578
                4f1ec638-6403-4725-94b4-64a9ac86de3f
                © 2017 Kallonen 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
                : 11 October 2016
                : 7 June 2017
                Page count
                Pages: 13
                Funding
                Funded by: Health Innovation Challenge Fund
                Award ID: HICF-T5-342
                Award ID: WT098600
                Funded by: UK Department of Health and Wellcome Trust , open-funder-registry 10.13039/100004440;
                Funded by: Department of Health, Public Health England or the Wellcome Trust , open-funder-registry 10.13039/100004440;
                Categories
                Research

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