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      Toward unified molecular surveillance of RSV: A proposal for genotype definition

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          Abstract

          Background

          Human respiratory syncytial virus (RSV) is classified into antigenic subgroups A and B. Thirteen genotypes have been defined for RSV‐A and 20 for RSV‐B, without any consensus on genotype definition.

          Methods

          We evaluated clustering of RSV sequences published in GenBank until February 2018 to define genotypes by using maximum likelihood and Bayesian phylogenetic analyses and average p‐distances.

          Results

          We compared the patterns of sequence clustering of complete genomes; the three surface glycoproteins genes (SH, G, and F, single and concatenated); the ectodomain and the 2nd hypervariable region of G gene. Although complete genome analysis achieved the best resolution, the F, G, and G‐ectodomain phylogenies showed similar topologies with statistical support comparable to complete genome. Based on the widespread geographic representation and large number of available G‐ectodomain sequences, this region was chosen as the minimum region suitable for RSV genotyping. A genotype was defined as a monophyletic cluster of sequences with high statistical support (≥80% bootstrap and ≥0.8 posterior probability), with an intragenotype p‐distance ≤0.03 for both subgroups and an intergenotype p‐distance ≥0.09 for RSV‐A and ≥0.05 for RSV‐B. In this work, the number of genotypes was reduced from 13 to three for RSV‐A (GA1‐GA3) and from 20 to seven for RSV‐B (GB1‐GB7). Within these, two additional levels of classification were defined: subgenotypes and lineages. Signature amino acid substitutions to complement this classification were also identified.

          Conclusions

          We propose an objective protocol for RSV genotyping suitable for adoption as an international standard to support the global expansion of RSV molecular surveillance.

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          Most cited references34

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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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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                Author and article information

                Contributors
                goyastephanie@gmail.com
                viegasmariana@conicet.gov.ar
                Journal
                Influenza Other Respir Viruses
                Influenza Other Respir Viruses
                10.1111/(ISSN)1750-2659
                IRV
                Influenza and Other Respiratory Viruses
                John Wiley and Sons Inc. (Hoboken )
                1750-2640
                1750-2659
                05 February 2020
                May 2020
                : 14
                : 3 ( doiID: 10.1111/irv.v14.3 )
                : 274-285
                Affiliations
                [ 1 ] Virology Laboratory Ricardo Gutiérrez Children's Hospital Buenos Aires Argentina
                [ 2 ] Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET) Buenos Aires Argentina
                [ 3 ] Respiratory Virus Unit National Infection Services Public Health England London UK
                [ 4 ] National Heart and Lung Institute Imperial College London London UK
                [ 5 ] Instituto de Investigación Hospital 12 de Octubre Madrid Spain
                [ 6 ] Comisión de Investigaciones Científicas (CIC) Buenos Aires Argentina
                [ 7 ]Present address: WHO Collaborating Centre for Reference and Research on Influenza Francis Crick Institute London UK
                Author notes
                [*] [* ] Correspondence

                Stephanie Goya and Mariana Viegas, Virology Laboratory, Ricardo Gutiérrez Children's Hospital, Gallo 1330, C1425EFD Buenos Aires, Argentina.

                Emails: goyastephanie@ 123456gmail.com (SG); viegasmariana@ 123456conicet.gov.ar (MV)

                Author information
                https://orcid.org/0000-0001-7479-3064
                https://orcid.org/0000-0002-6506-1635
                Article
                IRV12715
                10.1111/irv.12715
                7182609
                32022426
                40be124d-849a-4a50-aed7-9608c4167899
                © 2020 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 May 2019
                : 13 December 2019
                : 15 December 2019
                Page count
                Figures: 3, Tables: 4, Pages: 12, Words: 7318
                Funding
                Funded by: Comisión de Investigaciones Científicas de la Provincia de Buenos Aires supported ASM
                Funded by: National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Respiratory Infections at Imperial College in partnership with PHE supported IN, MG, MZ and PO.
                Funded by: Consejo Nacional de Investigaciones Científicas y Técnicas supported SG and MV. , open-funder-registry 10.13039/501100002923;
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                May 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.1 mode:remove_FC converted:24.04.2020

                Infectious disease & Microbiology
                average genetic distance,genotypes,global molecular surveillance,human orthopneumovirus,human respiratory syncytial virus,lineages,phylogenetic analysis,subgenotypes

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