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      Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch

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          Abstract

          Background

          Klebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting.

          Methods

          We developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16 537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1636 genomes from 4 low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR.

          Results

          Using Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (eg, ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that 3 O-types (O1–O3) represented 88.9% of all genomes, whereas capsule types were much more diverse.

          Conclusions

          Pathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national, and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance.

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          Most cited references57

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          BLAST+: architecture and applications

          Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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            Roary: rapid large-scale prokaryote pan genome analysis

            Summary: A typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors. Availability and implementation: Roary is implemented in Perl and is freely available under an open source GPLv3 license from http://sanger-pathogens.github.io/Roary Contact: roary@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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              In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing.

              In the work presented here, we designed and developed two easy-to-use Web tools for in silico detection and characterization of whole-genome sequence (WGS) and whole-plasmid sequence data from members of the family Enterobacteriaceae. These tools will facilitate bacterial typing based on draft genomes of multidrug-resistant Enterobacteriaceae species by the rapid detection of known plasmid types. Replicon sequences from 559 fully sequenced plasmids associated with the family Enterobacteriaceae in the NCBI nucleotide database were collected to build a consensus database for integration into a Web tool called PlasmidFinder that can be used for replicon sequence analysis of raw, contig group, or completely assembled and closed plasmid sequencing data. The PlasmidFinder database currently consists of 116 replicon sequences that match with at least at 80% nucleotide identity all replicon sequences identified in the 559 fully sequenced plasmids. For plasmid multilocus sequence typing (pMLST) analysis, a database that is updated weekly was generated from www.pubmlst.org and integrated into a Web tool called pMLST. Both databases were evaluated using draft genomes from a collection of Salmonella enterica serovar Typhimurium isolates. PlasmidFinder identified a total of 103 replicons and between zero and five different plasmid replicons within each of 49 S. Typhimurium draft genomes tested. The pMLST Web tool was able to subtype genomic sequencing data of plasmids, revealing both known plasmid sequence types (STs) and new alleles and ST variants. In conclusion, testing of the two Web tools using both fully assembled plasmid sequences and WGS-generated draft genomes showed them to be able to detect a broad variety of plasmids that are often associated with antimicrobial resistance in clinically relevant bacterial pathogens. Copyright © 2014, American Society for Microbiology. All Rights Reserved.
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                Author and article information

                Journal
                Clin Infect Dis
                Clin Infect Dis
                cid
                Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America
                Oxford University Press (US )
                1058-4838
                1537-6591
                01 December 2021
                01 December 2021
                01 December 2021
                : 73
                : Suppl 4 , Implementing Whole Genome Sequencing for Surveillance of Antimicrobial Resistance: Local and International Insights Into Klebsiella pneumoniae
                : S325-S335
                Affiliations
                [1 ] Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus , Hinxton, Cambridge, United Kingdom
                [2 ] Centre for Genomic Pathogen Surveillance, Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Old Road Campus, Oxford, United Kingdom
                [3 ] Milner Centre for Evolution, University of Bath , Bath, United Kingdom
                [4 ] Institut Pasteur, Biodiversity and Epidemiology of Bacterial Pathogens , Paris, France
                [5 ] Department of Infectious Diseases, Central Clinical School, Monash University , Melbourne, Victoria, Australia
                [6 ] London School of Hygiene and Tropical Medicine , London, United Kingdom
                [7 ] Colombian Integrated Program for Antimicrobial Resistance Surveillance (Coipars), CI Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) , Tibaitatá–Mosquera, Cundinamarca, Colombia
                [8 ] Central Research Laboratory, Kempegowda Institute of Medical Sciences , Bengaluru, India
                [9 ] Department of Pharmaceutical Microbiology, Faculty of Pharmacy, University of Ibadan , Oyo State, Nigeria
                [10 ] Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine , Muntinlupa, The Philippines
                Author notes

                S. A. and S. D. contributed equally.

                Members of the NIHR Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance are listed in the Acknowledgments.

                Correspondence: D. M. Aanensen, Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Old Road Campus, Oxford, United Kingdom ( david.aanensen@ 123456bdi.ox.ac.uk ).
                Article
                ciab784
                10.1093/cid/ciab784
                8634497
                34850838
                4d2dbd8b-3977-428b-9e1e-dd72c27d0e9a
                © The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 December 2021
                Page count
                Pages: 11
                Funding
                Funded by: National Institute for Health Research, DOI 10.13039/501100000272;
                Award ID: 16_136_111
                Categories
                Supplement Articles
                AcademicSubjects/MED00290

                Infectious disease & Microbiology
                antimicrobial resistance,epidemiology,genomic surveillance,klebsiella,pathogenwatch

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