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      TETyper: a bioinformatic pipeline for classifying variation and genetic contexts of transposable elements from short-read whole-genome sequencing data

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          Abstract

          Much of the worldwide dissemination of antibiotic resistance has been driven by resistance gene associations with mobile genetic elements (MGEs), such as plasmids and transposons. Although increasing, our understanding of resistance spread remains relatively limited, as methods for tracking mobile resistance genes through multiple species, strains and plasmids are lacking. We have developed a bioinformatic pipeline for tracking variation within, and mobility of, specific transposable elements (TEs), such as transposons carrying antibiotic-resistance genes. TETyper takes short-read whole-genome sequencing data as input and identifies single-nucleotide mutations and deletions within the TE of interest, to enable tracking of specific sequence variants, as well as the surrounding genetic context(s), to enable identification of transposition events. A major advantage of TETyper over previous methods is that it does not require a genome reference. To investigate global dissemination of Klebsiella pneumoniae carbapenemase (KPC) and its associated transposon Tn4401, we applied TETyper to a collection of over 3000 publicly available Illumina datasets containing bla KPC. This revealed surprising diversity, with over 200 distinct flanking genetic contexts for Tn4401, indicating high levels of transposition. Integration of sample metadata revealed insights into associations between geographic locations, host species, Tn4401 sequence variants and flanking genetic contexts. To demonstrate the ability of TETyper to cope with high-copy-number TEs and to track specific short-term evolutionary changes, we also applied it to the insertion sequence IS26 within a defined K. pneumoniae outbreak. TETyper is implemented in python and is freely available at https://github.com/aesheppard/TETyper.

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          Most cited references26

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

            (2013)
            Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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              Plasmids and the spread of resistance.

              Plasmids represent one of the most difficult challenge for counteracting the dissemination of antimicrobial resistance. They contribute to the spread of relevant resistance determinants, promoting horizontal gene transfer among unrelated bacteria. Undistinguishable plasmids were identified in unrelated bacterial strains isolated at huge geographically distant area, with no apparent epidemiological links. These plasmids belong to families that are largely prevalent in naturally occurring bacteria, usually carry multiple physically linked genetic determinants, conferring resistance to different classes of antibiotics simultaneously. Plasmids also harbour virulence factors and addiction systems, promoting their stability and maintenance in the bacterial host, in different environmental conditions. The characteristics of the most successful plasmids that were at the origin of the spread of carbapenemase, expanded-spectrum β-lactamase, and plasmid-mediated quinolone resistance genes are discussed in this review. Copyright © 2013 Elsevier GmbH. All rights reserved.
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                Author and article information

                Journal
                Microbial Genomics
                Microbiology Society
                2057-5858
                December 01 2018
                December 01 2018
                : 4
                : 12
                Affiliations
                [1 ] 2​National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
                [2 ] 1​Nuffield Department of Medicine, University of Oxford, Oxford, UK
                [3 ] 3​Health Information & Technology, University of Virginia Health System, Charlottesville, Virginia, USA
                [4 ] 4​Division of Infectious Disease and International Health, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, USA
                [5 ] 5​Clinical Microbiology Laboratory, Department of Pathology, University of Virginia Health System, Charlottesville, Virginia, USA
                Article
                10.1099/mgen.0.000232
                49ca9259-8305-4c5d-a195-9101c75a1d72
                © 2018
                History

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