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      Tracking SARS-CoV-2 Omicron diverse spike gene mutations identifies multiple inter-variant recombination events

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          Abstract

          The current pandemic of COVID-19 is fueled by more infectious emergent Omicron variants. Ongoing concerns of emergent variants include possible recombinants, as genome recombination is an important evolutionary mechanism for the emergence and re-emergence of human viral pathogens. In this study, we identified diverse recombination events between two Omicron major subvariants (BA.1 and BA.2) and other variants of concern (VOCs) and variants of interest (VOIs), suggesting that co-infection and subsequent genome recombination play important roles in the ongoing evolution of SARS-CoV-2. Through scanning high-quality completed Omicron spike gene sequences, 18 core mutations of BA.1 (frequency >99%) and 27 core mutations of BA.2 (nine more than BA.1) were identified, of which 15 are specific to Omicron. BA.1 subvariants share nine common amino acid mutations (three more than BA.2) in the spike protein with most VOCs, suggesting a possible recombination origin of Omicron from these VOCs. There are three more Alpha-related mutations in BA.1 than BA.2, and BA.1 is phylogenetically closer to Alpha than other variants. Revertant mutations are found in some dominant mutations (frequency >95%) in the BA.1. Most notably, multiple characteristic amino acid mutations in the Delta spike protein have been also identified in the “Deltacron”-like Omicron Variants isolated since November 11, 2021 in South Africa, which implies the recombination events occurring between the Omicron and Delta variants. Monitoring the evolving SARS-CoV-2 genomes especially for recombination is critically important for recognition of abrupt changes to viral attributes including its epitopes which may call for vaccine modifications.

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          IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

          Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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            MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

            K Katoh (2002)
            A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
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              MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization

              Abstract This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.
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                Author and article information

                Contributors
                jwu898@jnu.edu.cn
                zhangqw@jnu.edu.cn
                Journal
                Signal Transduct Target Ther
                Signal Transduct Target Ther
                Signal Transduction and Targeted Therapy
                Nature Publishing Group UK (London )
                2095-9907
                2059-3635
                26 April 2022
                26 April 2022
                2022
                : 7
                : 138
                Affiliations
                [1 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, Guangdong Provincial Key Laboratory of Virology, Institute of Medical Microbiology, , Jinan University, ; 510632 Guangzhou, China
                [2 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, BSL-3 Laboratory (Guangdong), Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, , Southern Medical University, ; 510515 Guangzhou, China
                [3 ]GRID grid.22448.38, ISNI 0000 0004 1936 8032, Bioinformatics and Computational Biology Program, School of Systems Biology, , George Mason University, ; Manassas, VA 20110 USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Ophthalmology, Howe Laboratory Massachusetts Eye and Ear, , Harvard Medical School, ; Boston, MA 02114 USA
                [5 ]Foshan Institute of Medical Microbiology, 528315 Foshan, China
                Author information
                http://orcid.org/0000-0002-1142-2845
                http://orcid.org/0000-0002-8326-2895
                http://orcid.org/0000-0002-2770-111X
                Article
                992
                10.1038/s41392-022-00992-2
                9039610
                35474215
                c8fa803f-d4b1-4d27-bc48-ec5b06f84977
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 March 2022
                : 27 March 2022
                : 7 April 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32170139
                Award Recipient :
                Funded by: National Key Research and Development Program of China (2018YFE0204503)
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                © The Author(s) 2022

                microbiology,infectious diseases
                microbiology, infectious diseases

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