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      Comprehensive global genome dynamics of Chlamydia trachomatis show ancient diversification followed by contemporary mixing and recent lineage expansion

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      1 , 1 , 1 , 2 , 3 , 3 , 4 , 5 , 5 , 1 , 6 , 6 , 7 , 8 , 9 , 9 , 10 , 11 , 12 , 12 , 2 , 13 , 2 , 14 , 14 , 15 , 15 , 16 , 16 , 17 , 17 , 18 , 19 , 19 , 19 , 7 , 20 , 21 , 17 , 22 , 1 , 1 , 23
      Genome Research
      Cold Spring Harbor Laboratory Press

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          Abstract

          Chlamydia trachomatis is the world's most prevalent bacterial sexually transmitted infection and leading infectious cause of blindness, yet it is one of the least understood human pathogens, in part due to the difficulties of in vitro culturing and the lack of available tools for genetic manipulation. Genome sequencing has reinvigorated this field, shedding light on the contemporary history of this pathogen. Here, we analyze 563 full genomes, 455 of which are novel, to show that the history of the species comprises two phases, and conclude that the currently circulating lineages are the result of evolution in different genomic ecotypes. Temporal analysis indicates these lineages have recently expanded in the space of thousands of years, rather than the millions of years as previously thought, a finding that dramatically changes our understanding of this pathogen's history. Finally, at a time when almost every pathogen is becoming increasingly resistant to antimicrobials, we show that there is no evidence of circulating genomic resistance in C. trachomatis.

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          Most cited references53

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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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            MUSCLE: multiple sequence alignment with high accuracy and high throughput.

            We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                July 2017
                July 2017
                : 27
                : 7
                : 1220-1229
                Affiliations
                [1 ]Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom;
                [2 ]Public Health England, Public Health Laboratory Cambridge, Addenbrooke's Hospital, Cambridge CB2 0QW, United Kingdom;
                [3 ]Menzies School of Health Research, Darwin, Northern Territory 0810, Australia;
                [4 ]School of Psychological and Clinical Sciences, Charles Darwin University, Darwin 0909, Australia;
                [5 ]Department of Laboratory Medicine, University of California at San Francisco, San Francisco, California 94110, USA;
                [6 ]Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Departamento de Bioquímica Clínica, Microbiología Clínica, Buenos Aires C1113AAD, Argentina;
                [7 ]Centre for HIV and Sexually Transmitted Infections, National Institute for Communicable Diseases, National Health Laboratory Service, 2192 Johannesburg, South Africa;
                [8 ]Jefe Laboratorio de ITS, Laboratorio Nacional de Vigilancia, FM1100, Honduras;
                [9 ]Department of Medical Microbiology and Infection Control, Laboratory of Immunogenetics, VU University Medical Center, 1081 HZ Amsterdam, The Netherlands;
                [10 ]Department of Genetics and Cell Biology, Institute of Public Health Genomics, School for Oncology & Developmental Biology (GROW), Faculty of Health, Medicine and Life Sciences, University of Maastricht, 6229 ER Maastricht, The Netherlands;
                [11 ]Institute of Molecular Pathogenesis, Friedrich-Loeffler-Institut (Federal Research Institute for Animal Health), 07743 Jena, Germany;
                [12 ]Department of Virology, University of Helsinki and Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland;
                [13 ]Department of Biology, University of York, York CB2 2QQ, United Kingdom;
                [14 ]Clinical Chemistry and Microbiology Laboratory, Santo Stefano Hospital, ASL4, 59100 Prato, Italy;
                [15 ]Sexually Transmitted Bacteria Reference Unit, Microbiological Services, Public Health England, London NW9 5HT, United Kingdom;
                [16 ]Laboratory of Microbiology, D.O. Ott Research Institute of Obstetrics and Gynecology, St. Petersburg, Russia 199034;
                [17 ]WHO Collaborating Centre for Gonorrhoea and other STIs, Faculty of Medicine and Health, Örebro University Hospital, SE-701 85 Örebro, Sweden;
                [18 ]Laboratory of Intracellular Parasites, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana 59840, USA;
                [19 ]Clinical Research Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, United Kingdom;
                [20 ]Centre for Infectious Diseases and Microbiology and Marie Bashir Institute for Infectious Diseases and Biosecurity, Westmead Clinical School, University of Sydney, Sydney 2192, Australia;
                [21 ]Public Health England, Public Health Laboratory Southampton, Southampton General Hospital, Southampton SO16 6YD, United Kingdom;
                [22 ]Molecular Microbiology Group, University Medical School, Southampton General Hospital, Southampton SO16 6YD, United Kingdom;
                [23 ]Department of Pathogen Molecular Biology, The London School of Hygiene and Tropical Medicine, London WC1 7HT, United Kingdom
                Author notes

                Present addresses: 24Applied Microbiology Research, Department of Biomedicine, University of Basel, 4056 Basel, Switzerland; 25Clinical Microbiology, University Hospital Basel, 4031 Basel, Switzerland

                Corresponding author: nrt@ 123456sanger.ac.uk
                Article
                9509184
                10.1101/gr.212647.116
                5495073
                28588068
                771b035f-0ba0-4352-8ee2-04a3a1d69589
                © 2017 Hadfield 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 July 2016
                : 27 April 2017
                Page count
                Pages: 10
                Funding
                Funded by: Sanger Institute
                Funded by: Wellcome Trust , open-funder-registry 10.13039/100004440;
                Award ID: 098051
                Categories
                Research

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