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      Analysis of partial sequences of the RNA-dependent RNA polymerase gene as a tool for genus and subgenus classification of coronaviruses

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          Abstract

          The recent reclassification of the Riboviria, and the introduction of multiple new taxonomic categories including both subfamilies and subgenera for coronaviruses (family Coronaviridae, subfamily Orthocoronavirinae), represents a major shift in how official classifications are used to designate specific viral lineages. While the newly defined subgenera provide much-needed standardization for commonly cited viruses of public health importance, no method has been proposed for the assignment of subgenus based on partial sequence data, or for sequences that are divergent from the designated holotype reference genomes. Here, we describe the genetic variation of a 387 nt region of the coronavirus RNA-dependent RNA polymerase (RdRp), which is one of the most used partial sequence loci for both detection and classification of coronaviruses in molecular epidemiology. We infer Bayesian phylogenies from more than 7000 publicly available coronavirus sequences and examine clade groupings relative to all subgenus holotype sequences. Our phylogenetic analyses are largely coherent with whole-genome analyses based on designated holotype members for each subgenus. Distance measures between sequences form discrete clusters between taxa, offering logical threshold boundaries that can attribute subgenus or indicate sequences that are likely to belong to unclassified subgenera both accurately and robustly. We thus propose that partial RdRp sequence data of coronaviruses are sufficient for the attribution of subgenus-level taxonomic classifications and we supply the R package, MyCoV, which provides a method for attributing subgenus and assessing the reliability of the attribution.

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          Most cited references36

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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              Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

              Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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                Author and article information

                Journal
                J Gen Virol
                J Gen Virol
                jgv
                jgv
                The Journal of General Virology
                Microbiology Society
                0022-1317
                1465-2099
                December 2020
                9 September 2020
                9 September 2020
                : 101
                : 12
                : 1261-1269
                Affiliations
                [ 1] departmentUniversité de La Réunion , UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT) INSERM 1187, CNRS 9192, IRD 249 , Sainte-Clotilde, La Réunion, France
                [ ]Present address: Evolutionary Ecology Group, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium
                Author notes
                *Correspondence: David A. Wilkinson, david.wilkinson@ 123456univ-reunion.fr
                Author information
                https://orcid.org/0000-0002-9986-6212
                https://orcid.org/0000-0003-3718-7182
                https://orcid.org/0000-0002-0922-7573
                https://orcid.org/0000-0002-2213-5673
                Article
                001494
                10.1099/jgv.0.001494
                7819353
                32902374
                40060d31-8915-4eab-b0c9-9f46ad125643
                © 2020 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution License. The Microbiology Society waived the open access fees for this article.

                History
                : 06 March 2020
                : 14 August 2020
                Categories
                Research Article
                Animal
                Positive-strand RNA Viruses
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                Microbiology & Virology
                coronavirus,phylogenetics,taxonomy
                Microbiology & Virology
                coronavirus, phylogenetics, taxonomy

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