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      Local Evolutionary Patterns of Human Respiratory Syncytial Virus Derived from Whole-Genome Sequencing

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          ABSTRACT

          Human respiratory syncytial virus (RSV) is associated with severe childhood respiratory infections. A clear description of local RSV molecular epidemiology, evolution, and transmission requires detailed sequence data and can inform new strategies for virus control and vaccine development. We have generated 27 complete or nearly complete genomes of RSV from hospitalized children attending a rural coastal district hospital in Kilifi, Kenya, over a 10-year period using a novel full-genome deep-sequencing process. Phylogenetic analysis of the new genomes demonstrated the existence and cocirculation of multiple genotypes in both RSV A and B groups in Kilifi. Comparison of local versus global strains demonstrated that most RSV A variants observed locally in Kilifi were also seen in other parts of the world, while the Kilifi RSV B genomes encoded a high degree of variation that was not observed in other parts of the world. The nucleotide substitution rates for the individual open reading frames (ORFs) were highest in the regions encoding the attachment (G) glycoprotein and the NS2 protein. The analysis of RSV full genomes, compared to subgenomic regions, provided more precise estimates of the RSV sequence changes and revealed important patterns of RSV genomic variation and global movement. The novel sequencing method and the new RSV genomic sequences reported here expand our knowledge base for large-scale RSV epidemiological and transmission studies.

          IMPORTANCE The new RSV genomic sequences and the novel sequencing method reported here provide important data for understanding RSV transmission and vaccine development. Given the complex interplay between RSV A and RSV B infections, the existence of local RSV B evolution is an important factor in vaccine deployment.

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          Most cited references57

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          SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

          The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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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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              Bayesian Phylogenetics with BEAUti and the BEAST 1.7

              Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Virol
                J. Virol
                jvi
                jvi
                JVI
                Journal of Virology
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0022-538X
                1098-5514
                21 January 2015
                1 April 2015
                21 January 2015
                : 89
                : 7
                : 3444-3454
                Affiliations
                [a ]KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
                [b ]Public Health England, Salisbury, United Kingdom
                [c ]University of Warwick, School of Life Sciences and WIDER, Warwick, United Kingdom
                [d ]The Wellcome Trust Sanger Institute, Cambridge, United Kingdom
                [e ]Division of Infection Immunity, University College London, London, United Kingdom
                Author notes
                Address correspondence to Matthew Cotten, mc13@ 123456sanger.ac.uk .

                Citation Agoti CN, Otieno JR, Munywoki PK, Mwihuri AG, Cane PA, Nokes DJ, Kellam P, Cotten M. 2015. Local evolutionary patterns of human respiratory syncytial virus derived from whole-genome sequencing. J Virol 89:3444–3454. doi: 10.1128/JVI.03391-14.

                Article
                03391-14
                10.1128/JVI.03391-14
                4403408
                25609811
                fe6e2f1d-1637-453d-84bb-674ae46baa1e
                Copyright © 2015, Agoti et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported license.

                History
                : 24 November 2014
                : 11 January 2015
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 51, Pages: 11, Words: 8235
                Categories
                Genetic Diversity and Evolution

                Microbiology & Virology
                Microbiology & Virology

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