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Distinct evolutionary patterns of Neisseria meningitidis serogroup B disease outbreaks at two universities in the USA

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      Abstract

      Neisseria meningitidis serogroup B (MnB) was responsible for two independent meningococcal disease outbreaks at universities in the USA during 2013. The first at University A in New Jersey included nine confirmed cases reported between March 2013 and March 2014. The second outbreak occurred at University B in California, with four confirmed cases during November 2013. The public health response to these outbreaks included the approval and deployment of a serogroup B meningococcal vaccine that was not yet licensed in the USA. This study investigated the use of whole-genome sequencing(WGS) to examine the genetic profile of the disease-causing outbreak isolates at each university. Comparative WGS revealed differences in evolutionary patterns between the two disease outbreaks. The University A outbreak isolates were very closely related, with differences primarily attributed to single nucleotide polymorphisms/insertion-deletion (SNP/indel) events. In contrast, the University B outbreak isolates segregated into two phylogenetic clades, differing in large part due to recombination events covering extensive regions (>30 kb) of the genome including virulence factors. This high-resolution comparison of two meningococcal disease outbreaks further demonstrates the genetic complexity of meningococcal bacteria as related to evolution and disease virulence.

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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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        RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

        RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
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          ACT: the Artemis Comparison Tool.

          The Artemis Comparison Tool (ACT) allows an interactive visualisation of comparisons between complete genome sequences and associated annotations. The comparison data can be generated with several different programs; BLASTN, TBLASTX or Mummer comparisons between genomic DNA sequences, or orthologue tables generated by reciprocal FASTA comparison between protein sets. It is possible to identify regions of similarity, insertions and rearrangements at any level from the whole genome to base-pair differences. ACT uses Artemis components to display the sequences and so inherits powerful searching and analysis tools. ACT is part of the Artemis distribution and is similarly open source, written in Java and can run on any Java enabled platform, including UNIX, Macintosh and Windows.
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            Author and article information

            Affiliations
            [ 1]Vaccine Research & Development, Pfizer Inc , 401 N. Middletown Rd, Pearl River, NY 10965, USA
            [ 2]School of Medicine, University of St. Andrews , St. Andrews, UK
            [ 3]Division of Bacterial Diseases, Centers for Diseases Control and Prevention , Atlanta, Georgia, USA
            Author notes
            *Correspondence: Li Hao, Li.Hao@ 123456pfizer.com
            Journal
            Microb Genom
            Microb Genom
            MGen
            Microbial Genomics
            Microbiology Society
            2057-5858
            April 2018
            4 April 2018
            4 April 2018
            : 4
            : 4
            29616896
            5989579
            mgen000155
            10.1099/mgen.0.000155
            © 2018

            This is an open access article under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

            Product
            Funding
            Funded by: Pfizer
            Categories
            Outbreak Report
            Microbial Evolution and Epidemiology: Population Genomics
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