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      A comparative recombination analysis of human coronaviruses and implications for the SARS-CoV-2 pandemic

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          Abstract

          The SARS-CoV-2 pandemic prompts evaluation of recombination in human coronavirus (hCoV) evolution. We undertook recombination analyses of 158,118 public seasonal hCoV, SARS-CoV-1, SARS-CoV-2 and MERS-CoV genome sequences using the RDP4 software. We found moderate evidence for 8 SARS-CoV-2 recombination events, two of which involved the spike gene, and low evidence for one SARS-CoV-1 recombination event. Within MERS-CoV, 229E, OC43, NL63 and HKU1 datasets, we noted 7, 1, 9, 14, and 1 high-confidence recombination events, respectively. There was propensity for recombination breakpoints in the non-ORF1 region of the genome containing structural genes, and recombination severely skewed the temporal structure of these data, especially for NL63 and OC43. Bayesian time-scaled analyses on recombinant-free data indicated the sampled diversity of seasonal CoVs emerged in the last 70 years, with 229E displaying continuous lineage replacements. These findings emphasize the importance of genomic based surveillance to detect recombination in SARS-CoV-2, particularly if recombination may lead to immune evasion.

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          MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

          We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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            trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses

            Summary: Multiple sequence alignments are central to many areas of bioinformatics. It has been shown that the removal of poorly aligned regions from an alignment increases the quality of subsequent analyses. Such an alignment trimming phase is complicated in large-scale phylogenetic analyses that deal with thousands of alignments. Here, we present trimAl, a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses. trimAl can consider several parameters, alone or in multiple combinations, for selecting the most reliable positions in the alignment. These include the proportion of sequences with a gap, the level of amino acid similarity and, if several alignments for the same set of sequences are provided, the level of consistency across different alignments. Moreover, trimAl can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized. Availability: trimAl has been written in C++, it is portable to all platforms. trimAl is freely available for download (http://trimal.cgenomics.org) and can be used online through the Phylemon web server (http://phylemon2.bioinfo.cipf.es/). Supplementary Material is available at http://trimal.cgenomics.org/publications. Contact: tgabaldon@crg.es
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              jModelTest 2: more models, new heuristics and parallel computing.

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                Author and article information

                Contributors
                irina.maljkovicberry.ctr@mail.mil
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 August 2021
                30 August 2021
                2021
                : 11
                : 17365
                Affiliations
                [1 ]GRID grid.507680.c, ISNI 0000 0001 2230 3166, Viral Diseases Branch, , Walter Reed Army Institute of Research, ; Silver Spring, MD USA
                [2 ]GRID grid.265436.0, ISNI 0000 0001 0421 5525, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, , Uniformed Services University of the Health Sciences, ; Bethesda, MD USA
                [3 ]GRID grid.201075.1, ISNI 0000 0004 0614 9826, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, ; Bethesda, MD USA
                [4 ]GRID grid.507680.c, ISNI 0000 0001 2230 3166, Emerging Infectious Diseases Branch, , Walter Reed Army Institute of Research, ; Silver Spring, MD USA
                Article
                96626
                10.1038/s41598-021-96626-8
                8405798
                34462471
                326505b4-cb6d-45aa-b6e3-01f5dd789c9f
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 March 2021
                : 9 August 2021
                Funding
                Funded by: Global Emerging Infections Surveillance (GEIS) Branch
                Award ID: ProMIS ID: P0140_20_WR
                Award ID: ProMIS ID: P0140_20_WR
                Award Recipient :
                Funded by: US Department of Defense Health Agency
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                phylogeny,computational biology and bioinformatics,evolution,phylogenetics,viral infection

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