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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A COMPREHENSIVE ANALYSIS OF GENOME COMPOSITION AND CODON USAGE PATTERNS OF EMERGING CORONAVIRUSES</title><author><name sortKey="Tort, Fernando L" sort="Tort, Fernando L" uniqKey="Tort F" first="Fernando L." last="Tort">Fernando L. Tort</name><affiliation><nlm:aff id="aff0005">Laboratorio de Virología Molecular, Sede Salto, Centro Universitario Regional, Litoral Norte, Universidad de la República, Gral. Rivera 1350, 50000, Salto, Uruguay</nlm:aff></affiliation></author><author><name sortKey="Castells, Matias" sort="Castells, Matias" uniqKey="Castells M" first="Matías" last="Castells">Matías Castells</name><affiliation><nlm:aff id="aff0005">Laboratorio de Virología Molecular, Sede Salto, Centro Universitario Regional, Litoral Norte, Universidad de la República, Gral. Rivera 1350, 50000, Salto, Uruguay</nlm:aff></affiliation></author><author><name sortKey="Cristina, Juan" sort="Cristina, Juan" uniqKey="Cristina J" first="Juan" last="Cristina">Juan Cristina</name><affiliation><nlm:aff id="aff0010">Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400, Uruguay</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmc">7152894</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152894</idno><idno type="RBID">PMC:7152894</idno><idno type="doi">10.1016/j.virusres.2020.197976</idno><idno type="pmid">NONE</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">001583</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001583</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A COMPREHENSIVE ANALYSIS OF GENOME COMPOSITION AND CODON USAGE PATTERNS OF EMERGING CORONAVIRUSES</title><author><name sortKey="Tort, Fernando L" sort="Tort, Fernando L" uniqKey="Tort F" first="Fernando L." last="Tort">Fernando L. Tort</name><affiliation><nlm:aff id="aff0005">Laboratorio de Virología Molecular, Sede Salto, Centro Universitario Regional, Litoral Norte, Universidad de la República, Gral. Rivera 1350, 50000, Salto, Uruguay</nlm:aff></affiliation></author><author><name sortKey="Castells, Matias" sort="Castells, Matias" uniqKey="Castells M" first="Matías" last="Castells">Matías Castells</name><affiliation><nlm:aff id="aff0005">Laboratorio de Virología Molecular, Sede Salto, Centro Universitario Regional, Litoral Norte, Universidad de la República, Gral. Rivera 1350, 50000, Salto, Uruguay</nlm:aff></affiliation></author><author><name sortKey="Cristina, Juan" sort="Cristina, Juan" uniqKey="Cristina J" first="Juan" last="Cristina">Juan Cristina</name><affiliation><nlm:aff id="aff0010">Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400, Uruguay</nlm:aff></affiliation></author></analytic><series><title level="j">Virus Research</title><idno type="ISSN">0168-1702</idno><idno type="eISSN">1872-7492</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Highlights</title><p><list list-type="simple" id="lis0005"><list-item id="lsti0005"><label>•</label><p id="par0005">A comprehensive genome and codon usage analysis of SARS-CoV-2 was performed.</p></list-item><list-item id="lsti0010"><label>•</label><p id="par0010">SARS-CoV-2 has a distinct genome composition in relation to other βCoV strains.</p></list-item><list-item id="lsti0015"><label>•</label><p id="par0015">The overall codon usage in SARS-CoV-2 is slightly biased.</p></list-item><list-item id="lsti0020"><label>•</label><p id="par0020">Most of the highly frequent codons are A- and U-ending codons.</p></list-item><list-item id="lsti0025"><label>•</label><p id="par0025">Different codon preferences in SARS-CoV-2 genes in relation to human ones were found.</p></list-item><list-item id="lsti0030"><label>•</label><p id="par0030">These differences are due to codon usage preferences.</p></list-item></list></p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Alnazawi, M" uniqKey="Alnazawi M">M. Alnazawi</name></author><author><name sortKey="Altaher, A" uniqKey="Altaher A">A. Altaher</name></author><author><name sortKey="Kandeel, M" uniqKey="Kandeel M">M. Kandeel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Afgan, E" uniqKey="Afgan E">E. Afgan</name></author><author><name sortKey="Baker, D" uniqKey="Baker D">D. Baker</name></author><author><name sortKey="Batut, B" uniqKey="Batut B">B. Batut</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Berkhout, B" uniqKey="Berkhout B">B. Berkhout</name></author><author><name sortKey="Van Hemert, F" uniqKey="Van Hemert F">F. van Hemert</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Brandao, P E" uniqKey="Brandao P">P.E. Brandao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Castells, M" uniqKey="Castells M">M. Castells</name></author><author><name sortKey="Victoria, M" uniqKey="Victoria M">M. Victoria</name></author><author><name sortKey="Colina, R" uniqKey="Colina R">R. Colina</name></author><author><name sortKey="Musto, H" uniqKey="Musto H">H. Musto</name></author><author><name sortKey="Cristina, J" uniqKey="Cristina J">J. Cristina</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cavanagh, D" uniqKey="Cavanagh D">D. Cavanagh</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chan, J F" uniqKey="Chan J">J.F. Chan</name></author><author><name sortKey="Yuan, S" uniqKey="Yuan S">S. Yuan</name></author><author><name sortKey="Kok, K H" uniqKey="Kok K">K.H. Kok</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chan, J F" uniqKey="Chan J">J.F. Chan</name></author><author><name sortKey="Kok, K H" uniqKey="Kok K">K.H. Kok</name></author><author><name sortKey="Zhu, Z" uniqKey="Zhu Z">Z. Zhu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chen, Y" uniqKey="Chen Y">Y. Chen</name></author><author><name sortKey="Liu, Q" uniqKey="Liu Q">Q. Liu</name></author><author><name sortKey="Guo, D" uniqKey="Guo D">D. Guo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chen, Y" uniqKey="Chen Y">Y. Chen</name></author><author><name sortKey="Xu, Q" uniqKey="Xu Q">Q. Xu</name></author><author><name sortKey="Yuan, X" uniqKey="Yuan X">X. Yuan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Clark, M A" uniqKey="Clark M">M.A. Clark</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cotton, M" uniqKey="Cotton M">M. Cotton</name></author><author><name sortKey="Watson, S J" uniqKey="Watson S">S.J. Watson</name></author><author><name sortKey="Zumla, A I" uniqKey="Zumla A">A.I. Zumla</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cui, J" uniqKey="Cui J">J. Cui</name></author><author><name sortKey="Li, F" uniqKey="Li F">F. Li</name></author><author><name sortKey="Shi, Z L" uniqKey="Shi Z">Z.L. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gorbalenya, A E" uniqKey="Gorbalenya A">A.E. Gorbalenya</name></author><author><name sortKey="Baker, S C" uniqKey="Baker S">S.C. Baker</name></author><author><name sortKey="Baric, R S" uniqKey="Baric R">R.S. Baric</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gu, W" uniqKey="Gu W">W. Gu</name></author><author><name sortKey="Zhou, T" uniqKey="Zhou T">T. Zhou</name></author><author><name sortKey="Ma, J" uniqKey="Ma J">J. Ma</name></author><author><name sortKey="Sun, X" uniqKey="Sun X">X. Sun</name></author><author><name sortKey="Lu, Z" uniqKey="Lu Z">Z. Lu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jenkins, G M" uniqKey="Jenkins G">G.M. Jenkins</name></author><author><name sortKey="Holmes, E C" uniqKey="Holmes E">E.C. Holmes</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kandeel, M" uniqKey="Kandeel M">M. Kandeel</name></author><author><name sortKey="Ibrahim, A" uniqKey="Ibrahim A">A. Ibrahim</name></author><author><name sortKey="Fayez, M" uniqKey="Fayez M">M. Fayez</name></author><author><name sortKey="Al Azawi, M" uniqKey="Al Azawi M">M. Al‐Nazawi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kindler, E" uniqKey="Kindler E">E. Kindler</name></author><author><name sortKey="Thiel, V" uniqKey="Thiel V">V. Thiel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kumar, S" uniqKey="Kumar S">S. Kumar</name></author><author><name sortKey="Stecher, G" uniqKey="Stecher G">G. Stecher</name></author><author><name sortKey="Li, M" uniqKey="Li M">M Li</name></author><author><name sortKey="Li, M" uniqKey="Li M">M Li</name></author><author><name sortKey="Knyaz, C" uniqKey="Knyaz C">C. Knyaz</name></author><author><name sortKey="Tamura, K" uniqKey="Tamura K">K. Tamura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lin, X D" uniqKey="Lin X">X.D. Lin</name></author><author><name sortKey="Wang, W" uniqKey="Wang W">W. Wang</name></author><author><name sortKey="Hao, Z Y" uniqKey="Hao Z">Z.Y. Hao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peiris, J S" uniqKey="Peiris J">J.S. Peiris</name></author><author><name sortKey="Guan, Y" uniqKey="Guan Y">Y. Guan</name></author><author><name sortKey="Yuen, K Y" uniqKey="Yuen K">K.Y. Yuen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Puigbo, P" uniqKey="Puigbo P">P. Puigbo</name></author><author><name sortKey="Bravo, I G" uniqKey="Bravo I">I.G. Bravo</name></author><author><name sortKey="Garcia Vallve, S" uniqKey="Garcia Vallve S">S. Garcia-Vallve</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Puigbo, P" uniqKey="Puigbo P">P. Puigbo</name></author><author><name sortKey="Bravo, I G" uniqKey="Bravo I">I.G. Bravo</name></author><author><name sortKey="Garcia Vallve, S" uniqKey="Garcia Vallve S">S. Garcia-Vallve</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pyrc, K" uniqKey="Pyrc K">K. Pyrc</name></author><author><name sortKey="Jebbink, M F" uniqKey="Jebbink M">M.F. Jebbink</name></author><author><name sortKey="Berkhout, B" uniqKey="Berkhout B">B. Berkhout</name></author><author><name sortKey="Van Der Hoek, L" uniqKey="Van Der Hoek L">L. van der Hoek</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lu, R" uniqKey="Lu R">R. Lu</name></author><author><name sortKey="Zhao, X" uniqKey="Zhao X">X. Zhao</name></author><author><name sortKey="Li, J" uniqKey="Li J">J. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Metsalu, T" uniqKey="Metsalu T">T. Metsalu</name></author><author><name sortKey="Vilo, J" uniqKey="Vilo J">J. Vilo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rambaut, A" uniqKey="Rambaut A">A. Rambaut</name></author><author><name sortKey="Drummond, A J" uniqKey="Drummond A">A.J. Drummond</name></author><author><name sortKey="Xie, D" uniqKey="Xie D">D. Xie</name></author><author><name sortKey="Baele, G" uniqKey="Baele G">G. Baele</name></author><author><name sortKey="Suchard, M A" uniqKey="Suchard M">M.A. Suchard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ranwez, V" uniqKey="Ranwez V">V. Ranwez</name></author><author><name sortKey="Harispe, S" uniqKey="Harispe S">S. Harispe</name></author><author><name sortKey="Delsuc, F" uniqKey="Delsuc F">F. Delsuc</name></author><author><name sortKey="Douzery, Ejp" uniqKey="Douzery E">EJP Douzery</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sharp, P M" uniqKey="Sharp P">P.M. Sharp</name></author><author><name sortKey="Li, W H" uniqKey="Li W">W.H. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sharp, P M" uniqKey="Sharp P">P.M. Sharp</name></author><author><name sortKey="Li, W H" uniqKey="Li W">W.H. Li</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sheikh, A" uniqKey="Sheikh A">A. Sheikh</name></author><author><name sortKey="Al Aher, A" uniqKey="Al Aher A">A. Al‐Taher</name></author><author><name sortKey="Al Azawi, M" uniqKey="Al Azawi M">M. Al‐Nazawi</name></author><author><name sortKey="Al Ubarak, A I" uniqKey="Al Ubarak A">A.I. Al‐Mubarak</name></author><author><name sortKey="Kandeel, M" uniqKey="Kandeel M">M. Kandeel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Su, S" uniqKey="Su S">S. Su</name></author><author><name sortKey="Wong, G" uniqKey="Wong G">G. Wong</name></author><author><name sortKey="Shi, W" uniqKey="Shi W">W. Shi</name></author><author><name sortKey="Liu, J" uniqKey="Liu J">J. Liu</name></author><author><name sortKey="Lai, A C" uniqKey="Lai A">A.C. Lai</name></author><author><name sortKey="Zhou, J" uniqKey="Zhou J">J. Zhou</name></author><author><name sortKey="Liu, W" uniqKey="Liu W">W. Liu</name></author><author><name sortKey="Bi, Y" uniqKey="Bi Y">Y. Bi</name></author><author><name sortKey="Gao, G F" uniqKey="Gao G">G.F. Gao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Suchard, M A" uniqKey="Suchard M">M.A. Suchard</name></author><author><name sortKey="Lemey, P" uniqKey="Lemey P">P. Lemey</name></author><author><name sortKey="Beale, G" uniqKey="Beale G">G. Beale</name></author><author><name sortKey="Ayres, D L" uniqKey="Ayres D">D.L. Ayres</name></author><author><name sortKey="Drummond, A J" uniqKey="Drummond A">A.J. Drummond</name></author><author><name sortKey="Rambaut, A" uniqKey="Rambaut A">A. Rambaut</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tsai, C T" uniqKey="Tsai C">C.T. Tsai</name></author><author><name sortKey="Lin, C H" uniqKey="Lin C">C.H. Lin</name></author><author><name sortKey="Chang, C Y" uniqKey="Chang C">C.Y. Chang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vabret, N" uniqKey="Vabret N">N. Vabret</name></author><author><name sortKey="Bailly Bechet, M" uniqKey="Bailly Bechet M">M. Bailly-Bechet</name></author><author><name sortKey="Najburg, V" uniqKey="Najburg V">V. Najburg</name></author><author><name sortKey="Muller Trutwin, M" uniqKey="Muller Trutwin M">M. Muller-Trutwin</name></author><author><name sortKey="Verrier, B" uniqKey="Verrier B">B. Verrier</name></author><author><name sortKey="Tangy, F" uniqKey="Tangy F">F. Tangy</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Hemert, F" uniqKey="Van Hemert F">F. Van Hemert</name></author><author><name sortKey="Van Der Kuyl, A C" uniqKey="Van Der Kuyl A">A.C. van der Kuyl</name></author><author><name sortKey="Berkhout, B" uniqKey="Berkhout B">B. Berkhout</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Hemert, F" uniqKey="Van Hemert F">F. Van Hemert</name></author><author><name sortKey="Van Der Kuyl, A C" uniqKey="Van Der Kuyl A">A.C. van der Kuyl</name></author><author><name sortKey="Berkhout, B" uniqKey="Berkhout B">B. Berkhout</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Hemert, F J" uniqKey="Van Hemert F">F.J. Van Hemert</name></author><author><name sortKey="Van Der Kuyl, A C" uniqKey="Van Der Kuyl A">A.C. van der Kuyl</name></author><author><name sortKey="Berkhout, B" uniqKey="Berkhout B">B. Berkhout</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wessa, P" uniqKey="Wessa P">P. Wessa</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, M" uniqKey="Wang M">M. Wang</name></author><author><name sortKey="Zhang, J" uniqKey="Zhang J">J. Zhang</name></author><author><name sortKey="Zhou, J H" uniqKey="Zhou J">J.H. Zhou</name></author><author><name sortKey="Chen, H T" uniqKey="Chen H">H.T. Chen</name></author><author><name sortKey="Ma, L N" uniqKey="Ma L">L.N. Ma</name></author><author><name sortKey="Ding, Y Z" uniqKey="Ding Y">Y.Z. Ding</name></author><author><name sortKey="Liu, W Q" uniqKey="Liu W">W.Q. Liu</name></author><author><name sortKey="Liu, Y S" uniqKey="Liu Y">Y.S. Liu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wong, A C" uniqKey="Wong A">A.C. Wong</name></author><author><name sortKey="Li, X" uniqKey="Li X">X. Li</name></author><author><name sortKey="Lau, S K" uniqKey="Lau S">S.K. Lau</name></author><author><name sortKey="Woo, P C" uniqKey="Woo P">P.C. Woo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Woo, P C" uniqKey="Woo P">P.C. Woo</name></author><author><name sortKey="Wong, B H" uniqKey="Wong B">B.H. Wong</name></author><author><name sortKey="Huang, Y" uniqKey="Huang Y">Y. Huang</name></author><author><name sortKey="Lau, S K" uniqKey="Lau S">S.K. Lau</name></author><author><name sortKey="Yuen, K Y" uniqKey="Yuen K">K.Y. Yuen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wu, J T" uniqKey="Wu J">J.T. Wu</name></author><author><name sortKey="Leung, K" uniqKey="Leung K">K. Leung</name></author><author><name sortKey="Leung, G M" uniqKey="Leung G">G.M. Leung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wrigth, F" uniqKey="Wrigth F">F. Wrigth</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhao, S" uniqKey="Zhao S">S. Zhao</name></author><author><name sortKey="Lin, Q" uniqKey="Lin Q">Q. Lin</name></author><author><name sortKey="Ran, J" uniqKey="Ran J">J. Ran</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zaki, A M" uniqKey="Zaki A">A.M. Zaki</name></author><author><name sortKey="Van Boheemen, S" uniqKey="Van Boheemen S">S. van Boheemen</name></author><author><name sortKey="Bestebroer, T M" uniqKey="Bestebroer T">T.M. Bestebroer</name></author><author><name sortKey="Osterhaus, A D" uniqKey="Osterhaus A">A.D. Osterhaus</name></author><author><name sortKey="Fouchier, R A" uniqKey="Fouchier R">R.A. Fouchier</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhou, P" uniqKey="Zhou P">P. Zhou</name></author><author><name sortKey="Yang, X L" uniqKey="Yang X">X.L. Yang</name></author><author><name sortKey="Wang, X G" uniqKey="Wang X">X.G. Wang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhou, P" uniqKey="Zhou P">P. Zhou</name></author><author><name sortKey="Fan, H" uniqKey="Fan H">H. Fan</name></author><author><name sortKey="Lan, T" uniqKey="Lan T">T. Lan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhu, N" uniqKey="Zhu N">N. Zhu</name></author><author><name sortKey="Zhang, D" uniqKey="Zhang D">D. Zhang</name></author><author><name sortKey="Wang, W" uniqKey="Wang W">W. Wang</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Virus Res</journal-id><journal-id journal-id-type="iso-abbrev">Virus Res</journal-id><journal-title-group><journal-title>Virus Research</journal-title></journal-title-group><issn pub-type="ppub">0168-1702</issn><issn pub-type="epub">1872-7492</issn><publisher><publisher-name>Elsevier B.V.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">7152894</article-id><article-id pub-id-type="publisher-id">S0168-1702(20)30168-4</article-id><article-id pub-id-type="doi">10.1016/j.virusres.2020.197976</article-id><article-id pub-id-type="publisher-id">197976</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A COMPREHENSIVE ANALYSIS OF GENOME COMPOSITION AND CODON USAGE PATTERNS OF EMERGING CORONAVIRUSES</article-title></title-group><contrib-group><contrib contrib-type="author" id="aut0005"><name><surname>Tort</surname><given-names>Fernando L.</given-names></name><xref rid="aff0005" ref-type="aff">a</xref><xref rid="fn0005" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="aut0010"><name><surname>Castells</surname><given-names>Matías</given-names></name><xref rid="aff0005" ref-type="aff">a</xref><xref rid="fn0005" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="aut0015"><name><surname>Cristina</surname><given-names>Juan</given-names></name><email>cristina@cin.edu.uy</email><xref rid="aff0010" ref-type="aff">b</xref><xref rid="cor0005" ref-type="corresp">⁎</xref></contrib></contrib-group><aff id="aff0005"><label>a</label>Laboratorio de Virología Molecular, Sede Salto, Centro Universitario Regional, Litoral Norte, Universidad de la República, Gral. Rivera 1350, 50000, Salto, Uruguay</aff><aff id="aff0010"><label>b</label>Laboratorio de Virología Molecular, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, 11400, Uruguay</aff><author-notes><corresp id="cor0005"><label>⁎</label>Corresponding author. <email>cristina@cin.edu.uy</email></corresp><fn id="fn0005"><label>1</label><p id="npar0015">These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type="pmc-release"><day>12</day><month>4</month><year>2020</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="epub"><day>12</day><month>4</month><year>2020</year></pub-date><elocation-id>197976</elocation-id><history><date date-type="received"><day>29</day><month>2</month><year>2020</year></date><date date-type="rev-recd"><day>8</day><month>4</month><year>2020</year></date><date date-type="accepted"><day>8</day><month>4</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 Elsevier B.V. All rights reserved.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder></copyright-holder><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract abstract-type="author-highlights" id="abs0005"><title>Highlights</title><p><list list-type="simple" id="lis0005"><list-item id="lsti0005"><label>•</label><p id="par0005">A comprehensive genome and codon usage analysis of SARS-CoV-2 was performed.</p></list-item><list-item id="lsti0010"><label>•</label><p id="par0010">SARS-CoV-2 has a distinct genome composition in relation to other βCoV strains.</p></list-item><list-item id="lsti0015"><label>•</label><p id="par0015">The overall codon usage in SARS-CoV-2 is slightly biased.</p></list-item><list-item id="lsti0020"><label>•</label><p id="par0020">Most of the highly frequent codons are A- and U-ending codons.</p></list-item><list-item id="lsti0025"><label>•</label><p id="par0025">Different codon preferences in SARS-CoV-2 genes in relation to human ones were found.</p></list-item><list-item id="lsti0030"><label>•</label><p id="par0030">These differences are due to codon usage preferences.</p></list-item></list></p></abstract><abstract id="abs0010"><p>An outbreak of atypical pneumonia caused by a novel <italic>Betacoronavirus</italic> (βCoV), named SARS-CoV-2 has been declared a public health emergency of international concern by the World Health Organization. In order to gain insight into the emergence, evolution and adaptation of SARS-CoV-2 viruses, a comprehensive analysis of genome composition and codon usage of βCoV circulating in China was performed. A biased nucleotide composition was found for SARS-CoV-2 genome. This bias in genomic composition is reflected in its codon and amino acid usage patterns. The overall codon usage in SARS-CoV-2 is similar among themselves and slightly biased. Most of the highly frequent codons are A- and U-ending, which strongly suggests that mutational bias is the main force shaping codon usage in this virus. Significant differences in relative synonymous codon usage frequencies among SARS-CoV-2 and human cells were found. These differences are due to codon usage preferences.</p></abstract><kwd-group id="kwd0005"><title>Keywords</title><kwd>Coronavirus</kwd><kwd>evolution</kwd><kwd>codon usage</kwd><kwd>Wuhan</kwd><kwd>2019-nCoV</kwd><kwd>SARS-CoV-2</kwd></kwd-group></article-meta></front><body><sec id="sec0005"><label>1</label><title>Introduction</title><p id="par0035">The family <italic>Coronaviridae</italic> consists of four genera, namely, <italic>Alphacoronavirus</italic> (αCoV), <italic>Betacoronavirus</italic> (βCoV), <italic>Gammacoronavirus</italic> (γCoV) and <italic>Deltacoronavirus</italic> (δCoV) (<xref rid="bib0045" ref-type="bibr">Chen et al., 2020</xref>). Viruses from this family possess a single stranded, positive-sense RNA genome ranging from 26 to 32 kilobases in length (<xref rid="bib0160" ref-type="bibr">Su et al., 2016</xref>). Coronaviruses (CoV) have been identified in several avian hosts, as well as in mammals, including humans, bats, civets, mice, dogs, cats, cows and camels (<xref rid="bib0055" ref-type="bibr">Clark, 1993</xref>; <xref rid="bib0030" ref-type="bibr">Cavanagh, 2007</xref>; <xref rid="bib0250" ref-type="bibr">Zhou et al., 2018</xref>). Although several CoV are pathogenic to humans, most of them are associated with mild clinical symptoms (<xref rid="bib0160" ref-type="bibr">Su et al., 2016</xref>). Nevertheless, two notable exceptions have been described: severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV), a novel βCoV that emerged in southern China in 2002 (<xref rid="bib0105" ref-type="bibr">Peiris et al., 2004</xref>) and Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV), which was first detected in Saudi Arabia in 2012 (<xref rid="bib0240" ref-type="bibr">Zaki et al., 2012</xref>). Before the SARS epidemic, bats were not known to be hosts for CoVs. In the last 15 years, bats have been found to be hosts of more than 30 CoVs. Interactions among various bat species themselves, bat-animal and bat-human interactions, such as the presence of live bats in wildlife wet markets in China, have been proved to be important for interspecies transmission of CoVs (<xref rid="bib0205" ref-type="bibr">Wong et al., 2019</xref>). In fact, both SARS-CoV and MERS-CoV likely originated in bats, and genetically diverse CoVs that are related to these viruses were discovered in bats worldwide (<xref rid="bib0065" ref-type="bibr">Cui et al., 2019</xref>). Previous studies in different bat species from China permitted the identification of at least 41 new βCoV, all of them were <italic>Rhinolophus spp.</italic> bat-CoV (<xref rid="bib0100" ref-type="bibr">Lin et al., 2017</xref>).</p><p id="par0040">At this moment, in Wuhan, the capital city of Hubei province of the People’s Republic of China, an outbreak of atypical pneumonia caused by a novel coronavirus (SARS-CoV-2) is currently underway. The outbreak appears to have started from a zoonotic transmission at a market in Wuhan where animals and meat were sold (<xref rid="bib0035" ref-type="bibr">Chan et al., 2020a</xref>). As February 26th, 2020, there have been 77,780 cases of SARS-CoV-2 confirmed in China, including 2,666 deaths (<xref rid="bib0215" ref-type="bibr">WHO, 2020a</xref>). In addition, SARS-CoV-2 has been reported in 33 countries outside China, with 2,459 cases confirmed and 34 deaths (<xref rid="bib0215" ref-type="bibr">WHO, 2020a</xref>). Very recent studies revealed that SARS-CoV-2 can be considered a new human-infecting βCoV (<xref rid="bib0125" ref-type="bibr">Lu et al., 2020</xref>).</p><p id="par0045">The World Health Organization declare this 2019-nCoV outbreak as a public health emergency of international concern on January 30th, 2020 (<xref rid="bib0220" ref-type="bibr">WHO, 2020b</xref>) and the disease caused by this specific virus species have recently been designated as COVID-19 (Coronavirus Infectious Disase-19) (<xref rid="bib0215" ref-type="bibr">WHO, 2020a</xref>). In this sense, the Coronavirus Study Group of the International Committee on Taxonomy of Viruses (ICTV), formally recognizes this virus as a relative to severe acute respiratory syndrome SARS-CoVs and designates it as severe acute respiratory syndrome coronavirus 2: SARS-CoV-2 (<xref rid="bib0070" ref-type="bibr">Gorbalenya et al., 2020</xref>).</p><p id="par0050">In order to gain insight into the emergence, evolution, adaptation and spread of the SARS-CoV-2 viruses, a comprehensive analysis of genome composition and codon usage of βCoV circulating in China was performed.</p></sec><sec id="sec0010"><label>2</label><title>Material and methods</title><sec id="sec0015"><label>2.1</label><title>Sequences</title><p id="par0055">Available and comparable complete genome sequences of 81 βCoV strains isolated in China, including SARS-CoV-2 as well as βCoV isolated from different hosts, were obtained from GenBank database (available at: <ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov" id="intr0005">http://www.ncbi.nlm.nih.gov</ext-link>). For accession number, strain name, host and date of isolation, see Supplementary material Table 1. For each strain ORFs1a + ORF1b + S+E + N+M were concatenated. Sequences were aligned using the MACSE program (<xref rid="bib0140" ref-type="bibr">Ranwez et al., 2011</xref>). MACSE algorithm is a useful tool for accommodating sequencing errors and other biological deviations from the coding frame (<xref rid="bib0140" ref-type="bibr">Ranwez et al., 2011</xref>). The alignment is available upon request. The dataset comprised a total of 725,433 codons.</p></sec><sec id="sec0020"><label>2.2</label><title>Data analysis</title><p id="par0060">Codon usage, amino acid usage, dinucleotide frequencies, base composition, the relative synonymous codon usage (RSCU) (<xref rid="bib0150" ref-type="bibr">Sharp and Li, 1986</xref>), total GC content, GC content in the third position of the codon (GC3s) and effective numbers of codons (ENC) were calculated using the program CodonW (written by John Peden) as implemented in the Galaxy server version 1.4.4 (<xref rid="bib0010" ref-type="bibr">Afgan et al., 2018</xref>).</p><p id="par0065">The relationship between compositional variables and samples was obtained using multivariate statistical analyses. Principal component analysis (PCA) is a type of multivariate analysis that allows a dimensionality reduction. Singular Value Decomposition (SVD) method was used to calculate the principal components (PC). Unit variance was used as scaling method. By the same approach, Heatmaps were also constructed, which is a data matrix for visualizing values in the dataset by the use of a color gradient. This gives a good overview of the largest and smallest values in the matrix. Rows and/or columns of the matrix are clustered so that sets of rows or columns rather than individual ones can be interpreted. PCA and Heatmaps analysis were done using the ClustVis program (<xref rid="bib0130" ref-type="bibr">Metsalu and Vilo, 2015</xref>).</p><p id="par0070">The RSCU values of human cells were obtained from Kazusa database (available at: <ext-link ext-link-type="uri" xlink:href="http://www.kazusa.or.jp/codon/" id="intr0010">http://www.kazusa.or.jp/codon/</ext-link>).</p><p id="par0075">To study codon usage preferences in SARS-CoV-2 in relation to the codon usage of human cells, we employed the codon adaptation index (CAI) (<xref rid="bib0145" ref-type="bibr">Sharp & Li, 1987</xref>). CAI was calculated using the approach of <xref rid="bib0110" ref-type="bibr">Puigbo et al. (2008a)</xref> (available at: <ext-link ext-link-type="uri" xlink:href="http://genomes.urv.es/CAIcal" id="intr0015">http://genomes.urv.es/CAIcal</ext-link>) for human cells. This method allows to compare a given codon usage (in our case, SARS-CoV-2) to a predefined reference set (human). A statistically significant difference among CAI values was addressed applying a Wilcoxom & Mann-Whitney test (<xref rid="bib0195" ref-type="bibr">Wessa, 2012</xref>). In order to discern if statistically significant differences in the CAI values arise from codon preferences, we used e-CAI (<xref rid="bib0115" ref-type="bibr">Puigbo et al., 2008b</xref>) to calculate the expected value of CAI (eCAI) at the 95 % confident interval. A Kolmogorov-Smirnov test for the expected CAI was also performed (<xref rid="bib0115" ref-type="bibr">Puigbo et al., 2008b</xref>).</p></sec></sec><sec id="sec0025"><label>3</label><title>Results</title><sec id="sec0030"><label>3.1</label><title>Trends in compositional properties across βCoV strains circulating in China</title><p id="par0080">In order to study the genetic composition of SARS-CoV-2 emerging in China, 81 ORFs sequences from βCOV strains isolated in China (including SARS-CoV-2 and SARS-CoV from humans and βCoV isolated from bats, civets and ferrets) were aligned and the nucleotide frequencies at third codon position, total GC content, GC content at the third codon position, ENC and dinucleotide frequencies were established for all strains ORFs and PCA was performed. The results of these studies are shown in <xref rid="fig0005" ref-type="fig">Fig. 1</xref>.<fig id="fig0005"><label>Fig. 1</label><caption><p><bold>Genome composition of βCoV strains.</bold> Heatmap of frequencies of G, A, T and C at the third codon position, GC total content, GC content at the third codon position and effective number of codons (ENC) in βCoV ORFs is shown. Unit variance scaling was applied. Each column corresponds to a different βCoV strain, who’s host and virus type are shown in the upper part of the figure. Both rows and columns are clustered using correlation distance and average linkage.</p></caption><alt-text id="at0005">Fig. 1</alt-text><graphic xlink:href="gr1_lrg"></graphic></fig></p><p id="par0085">Heatmap analysis on genomic composition of all βCoV enrolled in these studies revealed a distinct genome composition of SARS-CoV-2 strains isolated in China, in relation to all other strains isolated from humans as well as bats, civets or ferrets (see <xref rid="fig0005" ref-type="fig">Fig. 1</xref>).</p><p id="par0090">To study if these differences in genomic composition can be observed at codon and amino acid usage, these variables were established for all βCoV strains enrolled in these studies and their relations were observed by Heatmap analysis. The results of these studies are shown in <xref rid="fig0010" ref-type="fig">Fig. 2</xref>. Significant differences in codon and amino acid usage was observed among SARS-CoV-2 strains and all other βCoV strains included in these studies (see <xref rid="fig0010" ref-type="fig">Fig. 2</xref>). Among β-CoV strains isolated from bats, a significant variation was also observed. Linkage analysis suggests a closer relation among SARS-CoV-2 and β-CoV isolated from bats, and a more distant relation with SARS-CoV and other β-CoV isolated from civet and ferret enrolled in these analyses (<xref rid="fig0010" ref-type="fig">Fig. 2</xref>).<fig id="fig0010"><label>Fig. 2</label><caption><p><bold>Heatmaps of codon and amino acid usage in βCoV ORFs.</bold> Unit variance scaling was applied. Each column corresponds to a different βCoV strain, who’s host and virus type are shown in the upper part of the figures. Both rows and columns are clustered using correlation distance and average linkage. In (a) and (b) codon and amino acids usage is shown, respectively.</p></caption><alt-text id="at0010">Fig. 2</alt-text><graphic xlink:href="gr2_lrg"></graphic></fig></p></sec><sec id="sec0035"><label>3.2</label><title>The nucleotide frequencies in SARS-CoV-2 genome</title><p id="par0095">To gain insight into the genomic composition of SARS-CoV-2 strains, the nucleotide frequencies found for this virus where compared to the nucleotide frequencies found for other human CoVs. The results of these studies are shown in <xref rid="fig0015" ref-type="fig">Fig. 3</xref>.<fig id="fig0015"><label>Fig. 3</label><caption><p><bold>Nucleotide composition of CoV genomes.</bold> Nucleotide composition for SARS-CoV-2 (accession number NC_045512), MERS (JX869059), SARS (NC004718), CoV 229E (KF514433), CoV OC43 (NC005147), CoV NL63 (JX504050), HKU-1A (DQ415914), HKU-1B (DQ415911) and HKU-1C (DQ415912) are shown.</p></caption><alt-text id="at0015">Fig. 3</alt-text><graphic xlink:href="gr3_lrg"></graphic></fig></p><p id="par0100">Some general characteristics of CoVs were observed, since the U-frequencies is significantly above average, while the C-frequencies are below the expected frequencies. In the case of purines, A is preferred over G (<xref rid="fig0015" ref-type="fig">Fig. 3</xref>). As it can be seen in <xref rid="fig0015" ref-type="fig">Fig. 3</xref>, most variation occurs in the C/U and not the A/G section. Moreover, the results of these studies also revealed the presence of species-specific trends, since the C/U ratio differs profoundly per coronavirus type (see <xref rid="fig0015" ref-type="fig">Fig. 3</xref>). Comparison of SARS-CoV-2 with the other two recent zoonotic transmission to the human population (SARS and MERS-CoVs) reveals that SARS-CoV-2 is quite extreme with a C-count of 18.4 % (<xref rid="fig0015" ref-type="fig">Fig. 3</xref>). On the other hand, SARS-CoV-2 has the highest A-count among CoVs enrolled in these studies (29.9 %, mean A-count of 27.6 ± 1.1 for all human CoVs enrolled in these studies) (see <xref rid="fig0015" ref-type="fig">Fig. 3</xref>).</p></sec><sec id="sec0040"><label>3.3</label><title>General codon usage patterns in βCoV</title><p id="par0105">To study the extent of codon usage bias in SARS-CoV-2, the ENC’s values were calculated for all SARS-CoV-2 strains enrolled in these studies. A mean value of 48.54 ± 2.34 was obtained. Due to the fact that all values obtained were >40, the results of these studies suggest that the overall codon usage among SARS-CoV-2 is similar among themselves and slightly biased. Mean ENC values of 49.11 ± 0.02,</p><p id="par0110">49.66 ± 0.52 and 49.21 ± 0.05 were found for SARS and βCoV isolated from bats and civets, respectively. ENC quantifies how far a codon usage departs from equal usage of synonymous codons and is a measure of codon usage biases in genomes that ranges from 20 (maximal bias) to 61 (unbiased) (Wright, 1990). Although βCoV ENC values are roughly similar, in the case of SARS-CoV-2 strains we observed a range of ENC values from 45.18 to 50.09.</p><p id="par0115">Since codon usage by its very nature is multivariate, it is necessary to analyze the data using different and complementary approaches. An ENC–GC3S plot (ENC plotted against GC content at the third codon position) can be used as a method that quantifies how far the codon usage of a gene departs from equal usage of synonymous codons (<xref rid="bib0230" ref-type="bibr">Wrigth, 1990</xref>). If GC3S is the only determinant factor shaping the codon usage pattern, the values of ENC would fall on a continuous curve, which represents random codon usage (Jiang et al., 2007). If G + C compositional constraint influences the codon usage, then the GC3S and ENC correlated spots would lie on or below the expected curve (<xref rid="bib0170" ref-type="bibr">Tsai et al., 2007</xref>).</p><p id="par0120">When the ENC–GC3S plot was constructed with values obtained for all 81 βCoV strains enrolled in this analysis (including SARS-CoV-2 strains), all spots lie below the expected curve, indicating that G + C compositional constraints may play a role in all βCoVs codon usage (see <xref rid="fig0020" ref-type="fig">Fig. 4</xref>).<fig id="fig0020"><label>Fig. 4</label><caption><p><bold>Effective number of codons (ENC) used in βCoV ORFs plotted against the GC3S.</bold> The orange curve plots the relationship between GC3S and ENC in absence of selection. Red dots show the results obtained for SARS-CoV-2 strains. Blue dots show the results obtained for the rest of βCoV strains enrolled in these studies. N = 81 datapoints.</p></caption><alt-text id="at0020">Fig. 4</alt-text><graphic xlink:href="gr4_lrg"></graphic></fig></p></sec><sec id="sec0045"><label>3.4</label><title>Codon usage preferences in SARS-CoV-2</title><p id="par0125">In order to compare the codon usage preferences of SARS-CoV-2 with those of human cells, the RSCU values of the codons in SARS-CoV-2 ORFs were calculated and compared with those of human. The results of these studies are shown in <xref rid="tbl0005" ref-type="table">Table 1</xref>.<table-wrap position="float" id="tbl0005"><label>Table 1</label><caption><p>Codon usage in SARS-CoV-2, displayed as RSCU<xref rid="tblfn0005" ref-type="table-fn">a</xref> values.</p></caption><alt-text id="at0025">Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th align="left">AA</th><th align="left">Cod</th><th align="left">HC</th><th align="left">CoV-2</th><th align="left">SARS</th><th align="left">MERS</th><th align="left">AA</th><th align="left">Cod</th><th align="left">HC</th><th align="left">CoV-2</th><th align="left">SARS</th><th align="left">MERS</th></tr></thead><tbody><tr><td align="left">Phe</td><td align="left"><italic><underline>UUU</underline></italic></td><td align="left">0.92</td><td align="left">1.42</td><td align="left">1.23</td><td align="left">1.28</td><td align="left">Ser</td><td align="left"><italic><underline>UCU</underline></italic></td><td align="left">1.14</td><td align="left">2.00</td><td align="left">1.96</td><td align="left">2.11</td></tr><tr><td align="left"></td><td align="left"><bold>UUC</bold></td><td align="left">1.08</td><td align="left">0.58</td><td align="left">0.77</td><td align="left">0.71</td><td align="left"></td><td align="left"><bold>UCC</bold></td><td align="left">1.32</td><td align="left">0.44</td><td align="left">0.42</td><td align="left">0.71</td></tr><tr><td align="left">Leu</td><td align="left"><italic><underline>UUA</underline></italic></td><td align="left">0.48</td><td align="left">1.66</td><td align="left">1.04</td><td align="left">1.21</td><td align="left"></td><td align="left"><italic><underline>UCA</underline></italic></td><td align="left">0.90</td><td align="left">1.63</td><td align="left">1.70</td><td align="left">1.21</td></tr><tr><td align="left"></td><td align="left">UUG</td><td align="left">0.78</td><td align="left">1.06</td><td align="left">1.10</td><td align="left">1.43</td><td align="left"></td><td align="left">UCG</td><td align="left">0.30</td><td align="left">0.11</td><td align="left">0.23</td><td align="left">0.18</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left"></td><td align="left"><italic><underline>CUU</underline></italic></td><td align="left">0.78</td><td align="left">1.75</td><td align="left">1.79</td><td align="left">1.69</td><td align="left">Pro</td><td align="left"><italic><underline>CCU</underline></italic></td><td align="left">1.16</td><td align="left">1.92</td><td align="left">1.74</td><td align="left">1.94</td></tr><tr><td align="left"></td><td align="left"><bold>CUC</bold></td><td align="left">1.20</td><td align="left">0.57</td><td align="left">0.83</td><td align="left">0.70</td><td align="left"></td><td align="left"><bold>CCC</bold></td><td align="left">1.28</td><td align="left">0.31</td><td align="left">0.40</td><td align="left">0.65</td></tr><tr><td align="left"></td><td align="left">CUA</td><td align="left">0.42</td><td align="left">0.68</td><td align="left">0.64</td><td align="left">0.70</td><td align="left"></td><td align="left"><italic><underline>CCA</underline></italic></td><td align="left">1.12</td><td align="left">1.63</td><td align="left">1.70</td><td align="left">1.21</td></tr><tr><td align="left"></td><td align="left"><bold>CUG</bold></td><td align="left">2.40</td><td align="left">0.28</td><td align="left">0.60</td><td align="left">0.48</td><td align="left"></td><td align="left">CCG</td><td align="left">0.44</td><td align="left">0.14</td><td align="left">0.16</td><td align="left">0.18</td></tr><tr><td align="left">Ile</td><td align="left"><italic><underline>AUU</underline></italic></td><td align="left">1.08</td><td align="left">1.54</td><td align="left">1.72</td><td align="left">1.71</td><td align="left">Thr</td><td align="left"><italic><underline>ACU</underline></italic></td><td align="left">1.00</td><td align="left">1.77</td><td align="left">1.66</td><td align="left">1.95</td></tr><tr><td align="left"></td><td align="left"><bold>AUC</bold></td><td align="left">1.40</td><td align="left">0.54</td><td align="left">0.67</td><td align="left">0.57</td><td align="left"></td><td align="left"><bold>ACC</bold></td><td align="left">1.44</td><td align="left">0.39</td><td align="left">0.59</td><td align="left">0.68</td></tr><tr><td align="left"></td><td align="left"><italic><underline>AUA</underline></italic></td><td align="left">0.51</td><td align="left">0.92</td><td align="left">0.62</td><td align="left">0.71</td><td align="left"></td><td align="left"><italic><underline>ACA</underline></italic></td><td align="left">1.12</td><td align="left">1.65</td><td align="left">1.57</td><td align="left">1.18</td></tr><tr><td align="left">Met</td><td align="left">AUG</td><td align="left">1.00</td><td align="left">1.00</td><td align="left">1.00</td><td align="left">1.00</td><td align="left"></td><td align="left">ACG</td><td align="left">0.44</td><td align="left">0.15</td><td align="left">0.18</td><td align="left">0.17</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left">Val</td><td align="left"><italic><underline>GUU</underline></italic></td><td align="left">0.72</td><td align="left">1.93</td><td align="left">1.71</td><td align="left">1.78</td><td align="left">Ala</td><td align="left"><italic><underline>GCU</underline></italic></td><td align="left">1.08</td><td align="left">2.19</td><td align="left">2.08</td><td align="left">2.06</td></tr><tr><td align="left"></td><td align="left">GUC</td><td align="left">0.96</td><td align="left">0.58</td><td align="left">0.67</td><td align="left">0.76</td><td align="left"></td><td align="left"><bold>GCC</bold></td><td align="left">1.60</td><td align="left">0.57</td><td align="left">0.58</td><td align="left">0.63</td></tr><tr><td align="left"></td><td align="left"><italic><underline>GUA</underline></italic></td><td align="left">0.48</td><td align="left">0.89</td><td align="left">0.83</td><td align="left">0.72</td><td align="left"></td><td align="left">GCA</td><td align="left">0.92</td><td align="left">1.09</td><td align="left">1.13</td><td align="left">0.98</td></tr><tr><td align="left"></td><td align="left"><bold>GUG</bold></td><td align="left">1.84</td><td align="left">0.59</td><td align="left">0.78</td><td align="left">0.73</td><td align="left"></td><td align="left">GCG</td><td align="left">0.44</td><td align="left">0.15</td><td align="left">0.22</td><td align="left">0.30</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left">Tyr</td><td align="left"><italic><underline>UAU</underline></italic></td><td align="left">0.88</td><td align="left">1.23</td><td align="left">1.12</td><td align="left">1.26</td><td align="left">Cys</td><td align="left"><italic><underline>UGU</underline></italic></td><td align="left">0.92</td><td align="left">1.59</td><td align="left">1.27</td><td align="left">1.19</td></tr><tr><td align="left"></td><td align="left">UAC</td><td align="left">1.12</td><td align="left">0.77</td><td align="left">0.88</td><td align="left">0.72</td><td align="left"></td><td align="left"><bold>UGC</bold></td><td align="left">1.08</td><td align="left">0.41</td><td align="left">0.73</td><td align="left">0.80</td></tr><tr><td align="left">TER</td><td align="left">UAA</td><td align="left">**</td><td align="left">**</td><td align="left">**</td><td align="left">**</td><td align="left">TER</td><td align="left">UGA</td><td align="left">**</td><td align="left">**</td><td align="left">**</td><td align="left">**</td></tr><tr><td align="left"></td><td align="left">UAG</td><td align="left">**</td><td align="left">**</td><td align="left">**</td><td align="left">**</td><td align="left">Trp</td><td align="left">UGG</td><td align="left">1.00</td><td align="left">1.00</td><td align="left">1.00</td><td align="left">1.00</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left">His</td><td align="left"><italic><underline>CAU</underline></italic></td><td align="left">0.84</td><td align="left">1.43</td><td align="left">1.29</td><td align="left">1.31</td><td align="left">Arg</td><td align="left"><italic><underline>CGU</underline></italic></td><td align="left">0.48</td><td align="left">1.45</td><td align="left">1.77</td><td align="left">1.82</td></tr><tr><td align="left"></td><td align="left"><bold>CAC</bold></td><td align="left">1.16</td><td align="left">0.57</td><td align="left">0.71</td><td align="left">0.68</td><td align="left"></td><td align="left"><bold>CGC</bold></td><td align="left">1.08</td><td align="left">0.60</td><td align="left">0.72</td><td align="left">1.10</td></tr><tr><td align="left">Gln</td><td align="left"><italic><underline>CAA</underline></italic></td><td align="left">0.54</td><td align="left">1.40</td><td align="left">1.16</td><td align="left">1.14</td><td align="left"></td><td align="left">CGA</td><td align="left">0.66</td><td align="left">0.31</td><td align="left">0.44</td><td align="left">0.45</td></tr><tr><td align="left"></td><td align="left"><bold>CAG</bold></td><td align="left">1.46</td><td align="left">0.60</td><td align="left">0.84</td><td align="left">0.86</td><td align="left"></td><td align="left"><bold>CGG</bold></td><td align="left">1.20</td><td align="left">0.20</td><td align="left">0.09</td><td align="left">0.43</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left">Asn</td><td align="left"><italic><underline>AAU</underline></italic></td><td align="left">0.94</td><td align="left">1.36</td><td align="left">1.24</td><td align="left">1.39</td><td align="left">Ser</td><td align="left"><italic><underline>AGU</underline></italic></td><td align="left">0.90</td><td align="left">1.46</td><td align="left">1.17</td><td align="left">1.33</td></tr><tr><td align="left"></td><td align="left">AAC</td><td align="left">1.06</td><td align="left">0.64</td><td align="left">0.76</td><td align="left">0.60</td><td align="left"></td><td align="left"><bold>AGC</bold></td><td align="left">1.44</td><td align="left">0.36</td><td align="left">0.52</td><td align="left">0.43</td></tr><tr><td align="left">Lys</td><td align="left">AAA</td><td align="left">0.86</td><td align="left">1.29</td><td align="left">1.04</td><td align="left">1.00</td><td align="left">Arg</td><td align="left"><italic><underline>AGA</underline></italic></td><td align="left">1.26</td><td align="left">2.64</td><td align="left">2.08</td><td align="left">1.34</td></tr><tr><td align="left"></td><td align="left">AAG</td><td align="left">1.14</td><td align="left">0.71</td><td align="left">0.96</td><td align="left">0.99</td><td align="left"></td><td align="left">AGG</td><td align="left">1.26</td><td align="left">0.82</td><td align="left">0.90</td><td align="left">0.84</td></tr><tr><td colspan="12" align="left">
</td></tr><tr><td align="left">Asp</td><td align="left">GAU</td><td align="left">0.92</td><td align="left">1.29</td><td align="left">1.24</td><td align="left">1.28</td><td align="left">Gly</td><td align="left"><italic><underline>GGU</underline></italic></td><td align="left">0.64</td><td align="left">2.36</td><td align="left">2.02</td><td align="left">2.05</td></tr><tr><td align="left"></td><td align="left">GAC</td><td align="left">1.08</td><td align="left">0.71</td><td align="left">0.76</td><td align="left">0.72</td><td align="left"></td><td align="left">GGC</td><td align="left">1.36</td><td align="left">0.70</td><td align="left">0.95</td><td align="left">1.00</td></tr><tr><td align="left">Glu</td><td align="left"><italic><underline>GAA</underline></italic></td><td align="left">0.84</td><td align="left">1.45</td><td align="left">1.04</td><td align="left">1.05</td><td align="left"></td><td align="left">GGA</td><td align="left">1.00</td><td align="left">0.81</td><td align="left">0.85</td><td align="left">0.64</td></tr><tr><td align="left"></td><td align="left"><bold>GAG</bold></td><td align="left">1.16</td><td align="left">0.55</td><td align="left">0.96</td><td align="left">0.95</td><td align="left"></td><td align="left"><bold>GGG</bold></td><td align="left">1.00</td><td align="left">0.12</td><td align="left">0.17</td><td align="left">0.29</td></tr></tbody></table><table-wrap-foot><fn id="tblfn0005"><label>a</label><p id="npar0005">RSCU, relative synonymous codon usage; AA, amino acid; Cod, codons; HC, human cells; CoV-2, SARS-CoV-2; SARS, SARS-CoV; MERS, MERS-CoV. **, termination codons. Highly increased codons in SARS-CoV-2 with respect to human (Δ≥0.30) are shown underlined and in italics. Underrepresented codons are shown in bold.</p></fn></table-wrap-foot></table-wrap></p><p id="par0130">The frequencies of RSCU in SARS-CoV-2 ORFs are significantly different in relation to human cells. Highly biased frequencies were found for UUU (Phe), UUA (Leu), CUU (Leu), AUU (Ile), AUA (Ile), GUU (Val), UAU (Tyr), CAU (His), CAA (Gln), AAU (Asp), GAA (Glu), UCU (Ser), CCU (Pro), CCA (Pro), GCU (Ala), UGU (Cys), CGU (Arg), AGA (Arg), and GGU (Gly). As it can be seen, most of the highly preferred codons are A and U-ending, while most of the highly underrepresented codons are C and G-endind codons, particularly CG containing codons. These results strongly suggest that mutational bias is a main force shaping codon usage in SARS-CoV-2 (see <xref rid="tbl0005" ref-type="table">Table 1</xref>). In fact, when the occurrences of dinucleotides are established, they are not random and no dinucleotide is present at the expected frequencies (see Supplementary material Table 2). The relative abundances of CpG and CpC and GpG showed a strong deviation from the expected frequencies (i.e. 1.0) (mean ± S.D. = 0.22 ± 0.00, 0.48 ± 0.00 and 0.66 ± 0.00, respectively) and were markedly under-represented. Cytosine deamination and selection against CpG motifs have been proposed as two independent selection forces that shape codon usage bias in CoV (<xref rid="bib0210" ref-type="bibr">Woo et al. 2007</xref>), suggesting that immune selection may play a role in SARS-CoV-2 codon usage bias. On the other hand, UpU, ApA, UpG are markedly over-used (mean ± S.D. = 1.96 ± 0.00, 1.55 ± 0.00 and 1.40 ± 0.00, respectively) (Supplementary material Table 2). These results indicate that the composition of dinucleotides also determines the variation in synonymous codon usage among SARS-CoV-2 strains. Comparison of SARS-CoV-2 RSCU values with the ones of SARS-CoV (<xref rid="bib0075" ref-type="bibr">Gu et al., 2004</xref>) and MERS-CoV (<xref rid="bib0050" ref-type="bibr">Chen et al., 2017</xref>) revealed a similar pattern for most of the codons (<xref rid="tbl0005" ref-type="table">Table 1</xref>). Highly underrepresented codons ACG (Thr), GCG (Ala) and GGG (Gly) were observed in all three viruses in relation to human cells (<xref rid="tbl0005" ref-type="table">Table 1</xref>). On the other hand, significant differences in frequencies of two glutamine codons (GAG and GAA) were observed among SARS-CoV-2 and SARS and MERS-CoVs (<xref rid="tbl0005" ref-type="table">Table 1</xref>). While GAG codon frequency is significantly different from the expected frequency (i.e. 1.0) in SARS-CoV-2 (0.55), it is not in SARS-CoV and MERS-CoV viruses (0.96 and 0.95, respectively). In the case of GAA, this codon is highly expressed in SARS-CoV-2 (1.45) and its frequency is very near the expected frequency in SARS-CoV and MERS-CoVs (1.04 and 1.05, respectively). These results suggest that although a general codon usage pattern among human βCoV can be found, each virus may evolve to a unique codon usage in its adaptation to the host’cells.</p></sec><sec id="sec0050"><label>3.5</label><title>Codon usage adaptation in SARS-CoV-2</title><p id="par0135">In order to compare the codon usage preferences of SARS-CoV-2 with those of humans, CAI values for all triplets were calculated, using human codon usage as reference set. The results of these studies are shown in <xref rid="tbl0010" ref-type="table">Table 2</xref>.<table-wrap position="float" id="tbl0010"><label>Table 2</label><caption><p>Codon adaptation of SARS-CoV-2 genes in relation to human codon usage, displayed as CAI<xref rid="tblfn0010" ref-type="table-fn">a</xref> values.</p></caption><alt-text id="at0030">Table 2</alt-text><table frame="hsides" rules="groups"><thead><tr><th align="left"></th><th align="left">CAI-Hs</th></tr></thead><tbody><tr><td align="left">SARS-CoV-2 genes</td><td align="left">0.710 ± 0.003</td></tr><tr><td align="left">Human genes</td><td align="left">0.809 ± 0.038</td></tr></tbody></table><table-wrap-foot><fn id="tblfn0010"><label>a</label><p id="npar0010">CAI, codon adaptation index. CAI-Hs; codon adaptation index in relation to <italic>H. sapiens</italic> reference codon usage set. In all cases, mean ± standard deviation values are shown. NA, not available.</p></fn></table-wrap-foot></table-wrap></p><p id="par0140">CAI index ranges from 0 to 1, being 1 if the frequency of codon usage by SARS-CoV-2 equals the frequency of usage of the reference set. A mean value of 0.710 was obtained for SARS-CoV-2 genes in relation to human (see <xref rid="tbl0010" ref-type="table">Table 2</xref>). To evaluate if the differences were statistically significant, we performed a Wilcoxon & Mann-Whitney test. The results of this tests revealed that the differences in CAI values were statistically significant (T = 256, p-value < 0.001).</p><p id="par0145">To discern if the statistically significant differences in CAI values arise from codon preferences (<xref rid="bib0110" ref-type="bibr">Puigbo et al., 2008a</xref>), the expected CAI (e-CAI) values were calculated for SARS-CoV-2 sequences in relation to human codon usage reference set. The e-CAI algorithm (<xref rid="bib0115" ref-type="bibr">Puigbo et al., 2008b</xref>) generated 500 random sequences with the same nucleotide and amino acid composition as the sequences of interest (in this case SARS-CoV-2 sequences). Then, we calculated the CAI values for all of them, and applied a Kolmogorov-Smirnov test for the e-CAI of these random sequences in order to show whether the generated sequences follow a normal distribution. The results of these studies revealed an e-CAI value of 0.719 (p < 0.05). Kolmogorov-Smirnov test revealed a normal distribution of the generated sequences (Kolmogorov-Smirnov test of e-CAI value of 0.028, which is below critical value of 0.061). Taking all these results together, our studies revealed that the CAI values for SARS-CoV-2 genes are different from the CAI values obtained from human cells. Again, these results suggest that these differences are related to codon usage preferences.</p></sec></sec><sec id="sec0055"><label>4</label><title>Discussion</title><p id="par0150">On January 30th 2020, the World Health Organization declared the current SARS-CoV-2 outbreak a public health emergency of international concern (<xref rid="bib0215" ref-type="bibr">WHO, 2020a</xref>). To gain insight into the biology and evolution of emerging SARS-CoV-2, a comprehensive analysis of genome composition, codon and amino acid usage of βCoV strains isolated in China from humans, bats, civets and ferret hosts was performed, including SARS-CoV-2 strains recently isolated from current outbreak.</p><p id="par0155">The results of these studies revealed that SARS-CoV-2 strains enrolled in these analyses have a distinct genome composition in relation to other βCoV strains isolated from human (SARS-CoV), bats, civets and ferrets (see <xref rid="fig0005" ref-type="fig">Fig. 1</xref>). This is in agreement with very recent studies revealing that SARS-CoV-2 is sufficiently divergent from SARS-CoV to be considered a new human-infecting βCoV (<xref rid="bib0125" ref-type="bibr">Lu et al., 2020</xref>; <xref rid="bib0255" ref-type="bibr">Zhu et al., 2020</xref>). This distinct genomic composition is also reflected in its codon and amino acid usage patterns (see <xref rid="fig0010" ref-type="fig">Fig. 2</xref>). Moreover, correlation distances and average linkage suggests a closer relation among SARS-CoV-2 and bats βCoV isolated in China and a more distant relation to SARS-CoV or SARS-like βCoV isolated from civets or ferrets (see <xref rid="fig0010" ref-type="fig">Fig. 2</xref>). This is in agreement with recent results suggesting that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan (<xref rid="bib0125" ref-type="bibr">Lu et al., 2020</xref>; <xref rid="bib0040" ref-type="bibr">Chan et al., 2020b</xref>). This speaks of the importance of bats as a reservoir of potential emerging CoV. Moreover, significant numbers of new βCoV have been discovered in Chinese bats species, particularly <italic>Rhinolophus affinis</italic> (<xref rid="bib0100" ref-type="bibr">Lin et al., 2017</xref>). Interestingly, a clear degree of variation in codon and amino acid usage was observed among βCoV isolated from bats included in these studies (see <xref rid="fig0010" ref-type="fig">Fig. 2</xref>). Nevertheless, bats might represent an intermediate host facilitating the emergence of the SARS-CoV-2 in humans (<xref rid="bib0205" ref-type="bibr">Wong et al., 2019</xref>). More studies will be needed to address this important issue.</p><p id="par0160">In these studies, a biased nucleotide composition was found for SARS-CoV-2 genome (<xref rid="fig0015" ref-type="fig">Fig. 3</xref>). This bias can also have a major influence on derived parameters, as previously demonstrated for other CoVs (<xref rid="bib0015" ref-type="bibr">Berkhout and Van Hemert, 2015</xref>). This is in agreement with the results of this work, since the nucleotide composition of SARS-CoV-2 has a strong influence in the codons that are used by this virus for the translation of its RNA genome. Previous studies on codon usage in RNA viruses have shown that mutational pressure is the major factor in shaping codon usage patterns in comparison with natural selection (<xref rid="bib0080" ref-type="bibr">Jenkins and Holmes, 2003</xref>; <xref rid="bib0200" ref-type="bibr">Wang et al., 2011</xref>). Although mutational pressure is still a major driving force, it is certainly not the only evolutionary force that might be considered in RNA viruses. In these studies, a significant A genomic content was found in SARS-CoV-2 in comparison with other human CoVs (<xref rid="fig0015" ref-type="fig">Fig. 3</xref>). Previous studies done in Human Immunodeficiency virus (HIV) found that this virus has an A-rich genome and this property has been proposed to help the virus to avoid recognition by the innate immune system (<xref rid="bib0175" ref-type="bibr">Vabret et al., 2012</xref>). This could provide a strong selective pressure on retroviruses as well as many others RNA viruses, including CoVs (<xref rid="bib0090" ref-type="bibr">Kindler and Thiel, 2014</xref>; <xref rid="bib0185" ref-type="bibr">van Hemert et al., 2014</xref>). Moreover, previous studies done in HIV suggest that an RNA genome with A-rich domains may provide a molecular signature that is recognized during virus replication (<xref rid="bib0190" ref-type="bibr">van Hemert et al., 2013</xref>). In the context of CoV, A-rich regions, like the Transcription Regulation Sequence (TRS), which is involved in transcription of sub-genomics RNAs have been established (<xref rid="bib0120" ref-type="bibr">Pyrc et al., 2004</xref>). This suggest that nucleotide bias may serve distinct biological function in SARS-CoV-2 as well as in other CoVs and is in direct relation to the characteristic codon usage of these viruses (<xref rid="bib0015" ref-type="bibr">Berkhout and Van Hemert, 2015</xref>).</p><p id="par0165">A mean ENC value of 48.54 ± 2.34 was obtained for SARS-CoV-2 strains enrolled in these studies, suggesting that the overall codon usage among SARS-CoV-2 is similar among themselves and slightly biased. This is in agreement with mean ENC values obtained for other CoV, like SARS-CoV (ENC = 48.99) (<xref rid="bib0075" ref-type="bibr">Gu et al., 2004</xref>); Bovine Coronavirus (BCoV) (ENC = 43.78) (<xref rid="bib0025" ref-type="bibr">Castells et al., 2017</xref>); MERS-CoV (ENC = 55.50) (<xref rid="bib0005" ref-type="bibr">Alnazawi et al., 2017</xref>) or Avian CoV (ENC = 51.33) (NSP2; <xref rid="bib0020" ref-type="bibr">Brandao, 2013</xref>). ENC-GC3s plot of the values obtained for SARS-CoV-2 revealed that all spots cluster below the expected curve, suggesting that G + C compositional constraints play a role in SARS-CoV-2 (see <xref rid="fig0015" ref-type="fig">Fig. 3</xref>).</p><p id="par0170">In these studies, significant differences in RSCU frequencies among SARS-CoV-2 and human cells were found (<xref rid="tbl0005" ref-type="table">Table 1</xref>). A strong bias toward A and U ending codons was found. This in agreement with very recent studies revealing a significant predominance of A and U at third codon positions in CoV genomes (<xref rid="bib0155" ref-type="bibr">Sheikh et al., 2020</xref>; Wang et al., 2018; <xref rid="bib0085" ref-type="bibr">Kandeel et al., 2020</xref>). These results also suggest that these differences are related to codon usage preferences. Previous studies have shown that both cytosine deamination and selection of CpG-suppressed clones are the major factors that shape codon bias in CoV genomes (<xref rid="bib0210" ref-type="bibr">Woo et al., 2007</xref>).</p><p id="par0175">MERS-CoV codon usage revealed a bias among hydrophobic amino acids, being CCG (Pro) and GUU (Val) the least and most frequently used codons (<xref rid="bib0050" ref-type="bibr">Chen et al., 2017</xref>). The same results were found in this work for SARS-CoV-2 ORFs, since CCG y GUU resulted to be the least and most frequently hydrophobic codons (11 and 352 times, respectively). Regarding hydrophilic amino acids, the least and most frequently used codons in MERS-CoV were CGG (Arg) and GAU (Asp), respectively (<xref rid="bib0050" ref-type="bibr">Chen et al., 2017</xref>). These codons were also the least and most frequently hydrophilic codons used in SARS-CoV-2 ORF’s (11 and 310 times, respectively). Similarly, CpG and UpU dinucleotide frequencies resulted to be the lowest and highest frequencies found in MERS-CoV (<xref rid="bib0050" ref-type="bibr">Chen et al., 2017</xref>). The same results were obtained in these studies on SARS-CoV-2, since CpG and UpU were the lowest and highest dinucleotide frequencies found (0.22 and 1.96, respectively) (see Supplementary material Table 2). Again, these analyses revealed that genomic composition affects the codon usage pattern in both, MERS-CoV and SARS-CoV-2 viruses.</p></sec><sec id="sec0060"><label>5</label><title>Conclusions</title><p id="par0180">The results of these studies revealed that SARS-CoV-2 strains enrolled in these analyses have a distinct genome composition in relation to other βCoV strains. This distinct genomic composition is also reflected in its codon and amino acid usage patterns. Most of the highly frequent codons are A- and U-ending, which strongly suggests that mutational bias is the main force shaping codon usage in this virus. Significant differences in RSCU frequencies among SARS-CoV-2 and human cells were found. These differences are due to codon usage preferences.</p></sec><sec id="sec0065"><title>Funding</title><p id="par0185">This research was funded by <funding-source id="gs0005">Agencia Nacional de Investigación e Innovación; Comisión Sectorial de Investigaciones Científica, Universidad de la República, Uruguay</funding-source>, through Grupos I + D grant and PEDECIBA, Uruguay.</p></sec><sec id="sec0070"><title>Uncited references</title><p id="par0190"><xref rid="bib0060" ref-type="bibr">Cotton et al. (2014)</xref>, <xref rid="bib0095" ref-type="bibr">Kumar et al. (2018)</xref>, <xref rid="bib0135" ref-type="bibr">Rambaut et al. (2018)</xref>, <xref rid="bib0165" ref-type="bibr">Suchard et al. (2018)</xref>, <xref rid="bib0180" ref-type="bibr">Van Hemert et al. (2016)</xref>, <xref rid="bib0225" ref-type="bibr">Wu et al. (2020)</xref>, <xref rid="bib0235" ref-type="bibr">Zhao et al. (2020)</xref> and <xref rid="bib0245" ref-type="bibr">Zhou et al. (2020)</xref>.</p></sec><sec id="sec0075"><title>CRediT authorship contribution statement</title><p id="par0195"><bold>Fernando L. Tort:</bold> Visualization, Investigation, Data curation. <bold>Matías Castells:</bold> Visualization, Investigation, Data curation. <bold>Juan Cristina:</bold> Conceptualization, Methodology, Data curation, Writing - review & editing.</p></sec><sec sec-type="COI-statement"><title>Declaration of Competing Interest</title><p id="par0200">None.</p></sec></body><back><ref-list id="bibl0005"><title>References</title><ref id="bib0005"><element-citation publication-type="journal" id="sbref0005"><person-group person-group-type="author"><name><surname>Alnazawi</surname><given-names>M.</given-names></name><name><surname>Altaher</surname><given-names>A.</given-names></name><name><surname>Kandeel</surname><given-names>M.</given-names></name></person-group><article-title>Comparative genomic analysis MERS CoV isolated from humans and camels with special reference to virus encoded helicase</article-title><source>Biol. Pharm. Bull.</source><volume>40</volume><year>2017</year><fpage>1289</fpage><lpage>1298</lpage><pub-id pub-id-type="pmid">28769010</pub-id></element-citation></ref><ref id="bib0010"><element-citation publication-type="journal" id="sbref0010"><person-group person-group-type="author"><name><surname>Afgan</surname><given-names>E.</given-names></name><name><surname>Baker</surname><given-names>D.</given-names></name><name><surname>Batut</surname><given-names>B.</given-names></name></person-group><article-title>The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update</article-title><source>Nucleic Acids Res.</source><volume>46</volume><year>2018</year><fpage>W537</fpage><lpage>W544</lpage><pub-id pub-id-type="pmid">29790989</pub-id></element-citation></ref><ref id="bib0015"><element-citation publication-type="journal" id="sbref0015"><person-group person-group-type="author"><name><surname>Berkhout</surname><given-names>B.</given-names></name><name><surname>van Hemert</surname><given-names>F.</given-names></name></person-group><article-title>On the biased nucleotide composition of the human coronavirus RNA genome</article-title><source>Virus Res.</source><volume>202</volume><year>2015</year><fpage>41</fpage><lpage>47</lpage><pub-id pub-id-type="pmid">25656063</pub-id></element-citation></ref><ref id="bib0020"><element-citation publication-type="journal" id="sbref0020"><person-group person-group-type="author"><name><surname>Brandao</surname><given-names>P.E.</given-names></name></person-group><article-title>The evolution of codon usage in structural and non-structural viral genes: the case of Avian coronavirus and its natural host Gallus gallus</article-title><source>Virus Res.</source><volume>178</volume><year>2013</year><fpage>264</fpage><lpage>271</lpage><pub-id pub-id-type="pmid">24091362</pub-id></element-citation></ref><ref id="bib0025"><element-citation publication-type="journal" id="sbref0025"><person-group person-group-type="author"><name><surname>Castells</surname><given-names>M.</given-names></name><name><surname>Victoria</surname><given-names>M.</given-names></name><name><surname>Colina</surname><given-names>R.</given-names></name><name><surname>Musto</surname><given-names>H.</given-names></name><name><surname>Cristina</surname><given-names>J.</given-names></name></person-group><article-title>Genome-wide analysis of codon usage bias in Bovine Coronavirus</article-title><source>Virol. J.</source><volume>14</volume><year>2017</year><fpage>115</fpage><pub-id pub-id-type="pmid">28623921</pub-id></element-citation></ref><ref id="bib0030"><element-citation publication-type="journal" id="sbref0030"><person-group person-group-type="author"><name><surname>Cavanagh</surname><given-names>D.</given-names></name></person-group><article-title>Coronavirus avian infectious bronchitis virus</article-title><source>Vet. Res.</source><volume>38</volume><year>2007</year><fpage>281</fpage><lpage>297</lpage><pub-id pub-id-type="pmid">17296157</pub-id></element-citation></ref><ref id="bib0035"><element-citation publication-type="journal" id="sbref0035"><person-group person-group-type="author"><name><surname>Chan</surname><given-names>J.F.</given-names></name><name><surname>Yuan</surname><given-names>S.</given-names></name><name><surname>Kok</surname><given-names>K.H.</given-names></name></person-group><article-title>A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster</article-title><source>Lancet</source><year>2020</year><comment>doi: S0140-6736(20)30154-9</comment></element-citation></ref><ref id="bib0040"><element-citation publication-type="journal" id="sbref0040"><person-group person-group-type="author"><name><surname>Chan</surname><given-names>J.F.</given-names></name><name><surname>Kok</surname><given-names>K.H.</given-names></name><name><surname>Zhu</surname><given-names>Z.</given-names></name></person-group><article-title>Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan</article-title><source>Emerging Microbes & Infec.</source><volume>9</volume><issue>1</issue><year>2020</year><fpage>221</fpage><lpage>236</lpage></element-citation></ref><ref id="bib0045"><element-citation publication-type="journal" id="sbref0045"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>Y.</given-names></name><name><surname>Liu</surname><given-names>Q.</given-names></name><name><surname>Guo</surname><given-names>D.</given-names></name></person-group><article-title>Emerging coronaviruses: genome structure, replication, and pathogenesis</article-title><source>J Med Virol.</source><volume>2020</volume><issue>January (22)</issue><year>2020</year></element-citation></ref><ref id="bib0050"><element-citation publication-type="journal" id="sbref0050"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>Y.</given-names></name><name><surname>Xu</surname><given-names>Q.</given-names></name><name><surname>Yuan</surname><given-names>X.</given-names></name></person-group><article-title>Analysis of the codon usage pattern in Middle East Respiratory Syndrome Coronavirus</article-title><source>Oncotarget</source><volume>8</volume><year>2017</year><fpage>110337</fpage><lpage>110349</lpage><pub-id pub-id-type="pmid">29299151</pub-id></element-citation></ref><ref id="bib0055"><element-citation publication-type="journal" id="sbref0055"><person-group person-group-type="author"><name><surname>Clark</surname><given-names>M.A.</given-names></name></person-group><article-title>Bovine coronavirus</article-title><source>Br.Vet. J.</source><volume>149</volume><year>1993</year><fpage>51</fpage><lpage>70</lpage><pub-id pub-id-type="pmid">8382546</pub-id></element-citation></ref><ref id="bib0060"><element-citation publication-type="journal" id="sbref0060"><person-group person-group-type="author"><name><surname>Cotton</surname><given-names>M.</given-names></name><name><surname>Watson</surname><given-names>S.J.</given-names></name><name><surname>Zumla</surname><given-names>A.I.</given-names></name></person-group><article-title>Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus</article-title><source>mBio</source><volume>5</volume><year>2014</year><comment>pii: e01062-13</comment></element-citation></ref><ref id="bib0065"><element-citation publication-type="journal" id="sbref0065"><person-group person-group-type="author"><name><surname>Cui</surname><given-names>J.</given-names></name><name><surname>Li</surname><given-names>F.</given-names></name><name><surname>Shi</surname><given-names>Z.L.</given-names></name></person-group><article-title>Origin and evolution of pathogenic coronaviruses</article-title><source>Nat. Rev. Microbiol.</source><volume>17</volume><year>2019</year><fpage>181</fpage><lpage>192</lpage><pub-id pub-id-type="pmid">30531947</pub-id></element-citation></ref><ref id="bib0070"><element-citation publication-type="journal" id="sbref0070"><person-group person-group-type="author"><name><surname>Gorbalenya</surname><given-names>A.E.</given-names></name><name><surname>Baker</surname><given-names>S.C.</given-names></name><name><surname>Baric</surname><given-names>R.S.</given-names></name></person-group><article-title>Severe acute respiratory syndrome-related coronavirus: The species and its viruses – a statement of the Coronavirus Study Group</article-title><source>bioRxiv</source><year>2020</year></element-citation></ref><ref id="bib0075"><element-citation publication-type="journal" id="sbref0075"><person-group person-group-type="author"><name><surname>Gu</surname><given-names>W.</given-names></name><name><surname>Zhou</surname><given-names>T.</given-names></name><name><surname>Ma</surname><given-names>J.</given-names></name><name><surname>Sun</surname><given-names>X.</given-names></name><name><surname>Lu</surname><given-names>Z.</given-names></name></person-group><article-title>Analysis of synonymous codon usage in SARS Coronavirus and other viruses in the Nidovirales</article-title><source>Virus Res.</source><volume>101</volume><year>2004</year><fpage>155</fpage><lpage>161</lpage><pub-id pub-id-type="pmid">15041183</pub-id></element-citation></ref><ref id="bib0080"><element-citation publication-type="journal" id="sbref0080"><person-group person-group-type="author"><name><surname>Jenkins</surname><given-names>G.M.</given-names></name><name><surname>Holmes</surname><given-names>E.C.</given-names></name></person-group><article-title>The extent of codon usage bias in human RNAviruses and its evolutionary origin</article-title><source>Virus Res.</source><volume>92</volume><year>2003</year><fpage>1</fpage><lpage>7</lpage><pub-id pub-id-type="pmid">12606071</pub-id></element-citation></ref><ref id="bib0085"><element-citation publication-type="journal" id="sbref0085"><person-group person-group-type="author"><name><surname>Kandeel</surname><given-names>M.</given-names></name><name><surname>Ibrahim</surname><given-names>A.</given-names></name><name><surname>Fayez</surname><given-names>M.</given-names></name><name><surname>Al‐Nazawi</surname><given-names>M.</given-names></name></person-group><article-title>From SARS and MERS CoVs to SARS‐CoV‐2, moving toward more biased codon usage in viral structural and nonstructural genes</article-title><source>J Med Virol.</source><year>2020</year><fpage>1</fpage><lpage>7</lpage><pub-id pub-id-type="pmid">31502247</pub-id></element-citation></ref><ref id="bib0090"><element-citation publication-type="journal" id="sbref0090"><person-group person-group-type="author"><name><surname>Kindler</surname><given-names>E.</given-names></name><name><surname>Thiel</surname><given-names>V.</given-names></name></person-group><article-title>To sense or not to sense viral RNA-essentials of coronavirusinnate immune evasion</article-title><source>Curr. Opin. Microbiol.</source><volume>20C</volume><year>2014</year><fpage>69</fpage><lpage>75</lpage></element-citation></ref><ref id="bib0095"><element-citation publication-type="journal" id="sbref0095"><person-group person-group-type="author"><name><surname>Kumar</surname><given-names>S.</given-names></name><name><surname>Stecher</surname><given-names>G.</given-names></name><name><surname>Li</surname><given-names>M</given-names></name><name><surname>Li</surname><given-names>M</given-names></name><name><surname>Knyaz</surname><given-names>C.</given-names></name><name><surname>Tamura</surname><given-names>K.</given-names></name></person-group><article-title>MEGA-X: Molecular evolutionary genetics analysis across computing platforms</article-title><source>Mol. Biol. Evol.</source><volume>35</volume><year>2018</year><fpage>1547</fpage><lpage>1549</lpage><pub-id pub-id-type="pmid">29722887</pub-id></element-citation></ref><ref id="bib0100"><element-citation publication-type="journal" id="sbref0100"><person-group person-group-type="author"><name><surname>Lin</surname><given-names>X.D.</given-names></name><name><surname>Wang</surname><given-names>W.</given-names></name><name><surname>Hao</surname><given-names>Z.Y.</given-names></name></person-group><article-title>Extensive diversity of coronaviruses in bats from China</article-title><source>Virology</source><volume>507</volume><year>2017</year><fpage>1</fpage><lpage>10</lpage><pub-id pub-id-type="pmid">28384506</pub-id></element-citation></ref><ref id="bib0105"><element-citation publication-type="journal" id="sbref0105"><person-group person-group-type="author"><name><surname>Peiris</surname><given-names>J.S.</given-names></name><name><surname>Guan</surname><given-names>Y.</given-names></name><name><surname>Yuen</surname><given-names>K.Y.</given-names></name></person-group><article-title>Severe acute respiratory syndrome</article-title><source>Nat. Med.</source><volume>10</volume><year>2004</year><fpage>S88</fpage><lpage>S97</lpage><pub-id pub-id-type="pmid">15577937</pub-id></element-citation></ref><ref id="bib0110"><element-citation publication-type="journal" id="sbref0110"><person-group person-group-type="author"><name><surname>Puigbo</surname><given-names>P.</given-names></name><name><surname>Bravo</surname><given-names>I.G.</given-names></name><name><surname>Garcia-Vallve</surname><given-names>S.</given-names></name></person-group><article-title>CAIcal: a combined set of tools to assess codon usage adaptation</article-title><source>BiologyDirect</source><volume>3</volume><year>2008</year><fpage>e38</fpage></element-citation></ref><ref id="bib0115"><element-citation publication-type="journal" id="sbref0115"><person-group person-group-type="author"><name><surname>Puigbo</surname><given-names>P.</given-names></name><name><surname>Bravo</surname><given-names>I.G.</given-names></name><name><surname>Garcia-Vallve</surname><given-names>S.</given-names></name></person-group><article-title>E-CAI: a novel server to estimate an expected value of Codon Adaptation Index (eCAI)</article-title><source>BMC Bioinformatics</source><volume>9</volume><year>2008</year><fpage>e65</fpage></element-citation></ref><ref id="bib0120"><element-citation publication-type="journal" id="sbref0120"><person-group person-group-type="author"><name><surname>Pyrc</surname><given-names>K.</given-names></name><name><surname>Jebbink</surname><given-names>M.F.</given-names></name><name><surname>Berkhout</surname><given-names>B.</given-names></name><name><surname>van der Hoek</surname><given-names>L.</given-names></name></person-group><article-title>Genome structure and transcriptional regulation of human coronavirus NL63</article-title><source>Virol. J.</source><volume>1</volume><year>2004</year><fpage>7</fpage><pub-id pub-id-type="pmid">15548333</pub-id></element-citation></ref><ref id="bib0125"><element-citation publication-type="journal" id="sbref0125"><person-group person-group-type="author"><name><surname>Lu</surname><given-names>R.</given-names></name><name><surname>Zhao</surname><given-names>X.</given-names></name><name><surname>Li</surname><given-names>J.</given-names></name></person-group><article-title>Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding</article-title><source>Lancet</source><year>2020</year></element-citation></ref><ref id="bib0130"><element-citation publication-type="journal" id="sbref0130"><person-group person-group-type="author"><name><surname>Metsalu</surname><given-names>T.</given-names></name><name><surname>Vilo</surname><given-names>J.</given-names></name></person-group><article-title>Clustvis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap</article-title><source>Nucleic Acids Res.</source><volume>43</volume><year>2015</year><fpage>W566</fpage><lpage>W570</lpage><pub-id pub-id-type="pmid">25969447</pub-id></element-citation></ref><ref id="bib0135"><element-citation publication-type="journal" id="sbref0135"><person-group person-group-type="author"><name><surname>Rambaut</surname><given-names>A.</given-names></name><name><surname>Drummond</surname><given-names>A.J.</given-names></name><name><surname>Xie</surname><given-names>D.</given-names></name><name><surname>Baele</surname><given-names>G.</given-names></name><name><surname>Suchard</surname><given-names>M.A.</given-names></name></person-group><article-title>Posterior summarization in Bayesian phylogenetics using Tracer</article-title><source>Syst. Biol.</source><volume>65</volume><year>2018</year><fpage>901</fpage><lpage>904</lpage></element-citation></ref><ref id="bib0140"><element-citation publication-type="journal" id="sbref0140"><person-group person-group-type="author"><name><surname>Ranwez</surname><given-names>V.</given-names></name><name><surname>Harispe</surname><given-names>S.</given-names></name><name><surname>Delsuc</surname><given-names>F.</given-names></name><name><surname>Douzery</surname><given-names>EJP</given-names></name></person-group><article-title>MACSE: Multiple alignment of coding sequences accounting for frameshifts and stop codons</article-title><source>PLoS ONE</source><volume>6</volume><issue>9</issue><year>2011</year><object-id pub-id-type="publisher-id">e22594</object-id></element-citation></ref><ref id="bib0145"><element-citation publication-type="journal" id="sbref0145"><person-group person-group-type="author"><name><surname>Sharp</surname><given-names>P.M.</given-names></name><name><surname>Li</surname><given-names>W.H.</given-names></name></person-group><article-title>The codon adaptation index – a measure of directional synonymous codon usage bias, and its potential applications</article-title><source>Nucleic Acids Res.</source><volume>15</volume><year>1987</year><fpage>1281</fpage><lpage>1295</lpage><pub-id pub-id-type="pmid">3547335</pub-id></element-citation></ref><ref id="bib0150"><element-citation publication-type="journal" id="sbref0150"><person-group person-group-type="author"><name><surname>Sharp</surname><given-names>P.M.</given-names></name><name><surname>Li</surname><given-names>W.H.</given-names></name></person-group><article-title>An evolutionary perspective on synonymous codon usage in unicellular organisms</article-title><source>J. Mol. Evol.</source><volume>24</volume><year>1986</year><fpage>28</fpage><lpage>38</lpage><pub-id pub-id-type="pmid">3104616</pub-id></element-citation></ref><ref id="bib0155"><element-citation publication-type="journal" id="sbref0155"><person-group person-group-type="author"><name><surname>Sheikh</surname><given-names>A.</given-names></name><name><surname>Al‐Taher</surname><given-names>A.</given-names></name><name><surname>Al‐Nazawi</surname><given-names>M.</given-names></name><name><surname>Al‐Mubarak</surname><given-names>A.I.</given-names></name><name><surname>Kandeel</surname><given-names>M.</given-names></name></person-group><article-title>Analysis of preferred codon usage in the coronavirus N genes and their implications for genome evolution and vaccine design</article-title><source>J Virol Methods.</source><volume>277</volume><year>2020</year><object-id pub-id-type="publisher-id">113806</object-id></element-citation></ref><ref id="bib0160"><element-citation publication-type="journal" id="sbref0160"><person-group person-group-type="author"><name><surname>Su</surname><given-names>S.</given-names></name><name><surname>Wong</surname><given-names>G.</given-names></name><name><surname>Shi</surname><given-names>W.</given-names></name><name><surname>Liu</surname><given-names>J.</given-names></name><name><surname>Lai</surname><given-names>A.C.</given-names></name><name><surname>Zhou</surname><given-names>J.</given-names></name><name><surname>Liu</surname><given-names>W.</given-names></name><name><surname>Bi</surname><given-names>Y.</given-names></name><name><surname>Gao</surname><given-names>G.F.</given-names></name></person-group><article-title>Epidemiology, genetic recombination, and pathogenesis of coronaviruses</article-title><source>Trends Microbiol.</source><volume>24</volume><year>2016</year><fpage>490</fpage><lpage>502</lpage><pub-id pub-id-type="pmid">27012512</pub-id></element-citation></ref><ref id="bib0165"><element-citation publication-type="journal" id="sbref0165"><person-group person-group-type="author"><name><surname>Suchard</surname><given-names>M.A.</given-names></name><name><surname>Lemey</surname><given-names>P.</given-names></name><name><surname>Beale</surname><given-names>G.</given-names></name><name><surname>Ayres</surname><given-names>D.L.</given-names></name><name><surname>Drummond</surname><given-names>A.J.</given-names></name><name><surname>Rambaut</surname><given-names>A.</given-names></name></person-group><article-title>Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10</article-title><source>Virus Evol.</source><volume>4</volume><year>2018</year><comment>vey016</comment></element-citation></ref><ref id="bib0170"><element-citation publication-type="journal" id="sbref0170"><person-group person-group-type="author"><name><surname>Tsai</surname><given-names>C.T.</given-names></name><name><surname>Lin</surname><given-names>C.H.</given-names></name><name><surname>Chang</surname><given-names>C.Y.</given-names></name></person-group><article-title>Analysis of codon usage bias and base compositional constraints in iridovirus genomes</article-title><source>Virus Res.</source><volume>126</volume><year>2007</year><fpage>196</fpage><lpage>206</lpage><pub-id pub-id-type="pmid">17434639</pub-id></element-citation></ref><ref id="bib0175"><element-citation publication-type="journal" id="sbref0175"><person-group person-group-type="author"><name><surname>Vabret</surname><given-names>N.</given-names></name><name><surname>Bailly-Bechet</surname><given-names>M.</given-names></name><name><surname>Najburg</surname><given-names>V.</given-names></name><name><surname>Muller-Trutwin</surname><given-names>M.</given-names></name><name><surname>Verrier</surname><given-names>B.</given-names></name><name><surname>Tangy</surname><given-names>F.</given-names></name></person-group><article-title>The biased nucleotide composition of HIV-1 triggers type I interferon response and correlates with subtype D increased pathogenicity</article-title><source>PLoS ONE</source><volume>7</volume><year>2012</year><object-id pub-id-type="publisher-id">e33502</object-id></element-citation></ref><ref id="bib0180"><element-citation publication-type="journal" id="sbref0180"><person-group person-group-type="author"><name><surname>Van Hemert</surname><given-names>F.</given-names></name><name><surname>van der Kuyl</surname><given-names>A.C.</given-names></name><name><surname>Berkhout</surname><given-names>B.</given-names></name></person-group><article-title>Impact of the biased nucleotide composition of viral RNA genomes on RNA structure and codon usage</article-title><source>J Gen Virol.</source><volume>97</volume><issue>10</issue><year>2016</year><fpage>2608</fpage><lpage>2619</lpage><pub-id pub-id-type="pmid">27519195</pub-id></element-citation></ref><ref id="bib0185"><element-citation publication-type="journal" id="sbref0185"><person-group person-group-type="author"><name><surname>Van Hemert</surname><given-names>F.</given-names></name><name><surname>van der Kuyl</surname><given-names>A.C.</given-names></name><name><surname>Berkhout</surname><given-names>B.</given-names></name></person-group><article-title>On the nucleotide compositionand structure of retroviral RNA genomes</article-title><source>Virus Res.</source><volume>193</volume><year>2014</year><fpage>16</fpage><lpage>23</lpage><pub-id pub-id-type="pmid">24675274</pub-id></element-citation></ref><ref id="bib0190"><element-citation publication-type="journal" id="sbref0190"><person-group person-group-type="author"><name><surname>Van Hemert</surname><given-names>F.J.</given-names></name><name><surname>van der Kuyl</surname><given-names>A.C.</given-names></name><name><surname>Berkhout</surname><given-names>B.</given-names></name></person-group><article-title>The A-nucleotide preference of HIV-1 in the context of its structured RNA genome</article-title><source>RNA Biol.</source><volume>10</volume><year>2013</year><fpage>211</fpage><lpage>215</lpage><pub-id pub-id-type="pmid">23235488</pub-id></element-citation></ref><ref id="bib0195"><element-citation publication-type="other" id="sbref0195"><person-group person-group-type="author"><name><surname>Wessa</surname><given-names>P.</given-names></name></person-group><article-title>Free Statistics Software</article-title><publisher-name>Office for Research Development and Education, version 1.1.23-r7</publisher-name><ext-link ext-link-type="uri" xlink:href="http://www.wessa.net" id="intr0025">http://www.wessa.net</ext-link><year>2012</year></element-citation></ref><ref id="bib0200"><element-citation publication-type="journal" id="sbref0200"><person-group person-group-type="author"><name><surname>Wang</surname><given-names>M.</given-names></name><name><surname>Zhang</surname><given-names>J.</given-names></name><name><surname>Zhou</surname><given-names>J.H.</given-names></name><name><surname>Chen</surname><given-names>H.T.</given-names></name><name><surname>Ma</surname><given-names>L.N.</given-names></name><name><surname>Ding</surname><given-names>Y.Z.</given-names></name><name><surname>Liu</surname><given-names>W.Q.</given-names></name><name><surname>Liu</surname><given-names>Y.S.</given-names></name></person-group><article-title>Analysis of codon usage in bovine viral diarrhea virus</article-title><source>Arch. Virol.</source><volume>156</volume><year>2011</year><fpage>153</fpage><lpage>160</lpage><pub-id pub-id-type="pmid">21069395</pub-id></element-citation></ref><ref id="bib0205"><element-citation publication-type="journal" id="sbref0205"><person-group person-group-type="author"><name><surname>Wong</surname><given-names>A.C.</given-names></name><name><surname>Li</surname><given-names>X.</given-names></name><name><surname>Lau</surname><given-names>S.K.</given-names></name><name><surname>Woo</surname><given-names>P.C.</given-names></name></person-group><article-title>Global Epidemiology of Bat Coronaviruses</article-title><source>Viruses</source><volume>11</volume><year>2019</year><comment>pii: E174</comment></element-citation></ref><ref id="bib0210"><element-citation publication-type="journal" id="sbref0210"><person-group person-group-type="author"><name><surname>Woo</surname><given-names>P.C.</given-names></name><name><surname>Wong</surname><given-names>B.H.</given-names></name><name><surname>Huang</surname><given-names>Y.</given-names></name><name><surname>Lau</surname><given-names>S.K.</given-names></name><name><surname>Yuen</surname><given-names>K.Y.</given-names></name></person-group><article-title>Cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape codon usage bias in coronaviruses</article-title><source>Virology</source><volume>369</volume><year>2007</year><fpage>431</fpage><lpage>442</lpage><pub-id pub-id-type="pmid">17881030</pub-id></element-citation></ref><ref id="bib0215"><element-citation publication-type="book" id="sbref0215"><person-group person-group-type="author"><name><surname>World Health Organization</surname></name></person-group><chapter-title>Coronavirus disease (COVID-19) outbreak. Situation report – 36, 25 February 2020</chapter-title><comment>Available at: <ext-link ext-link-type="uri" xlink:href="https://www.who.int" id="intr0030">https://www.who.int</ext-link> (Accessed 26 February 2020)</comment><year>2020</year></element-citation></ref><ref id="bib0220"><element-citation publication-type="book" id="sbref0220"><person-group person-group-type="author"><name><surname>World Health Organization</surname></name></person-group><chapter-title>Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV)</chapter-title><comment>Available at: <ext-link ext-link-type="uri" xlink:href="https://www.who.int" id="intr0035">https://www.who.int</ext-link> (Accessed 26 February 2020)</comment><year>2020</year></element-citation></ref><ref id="bib0225"><element-citation publication-type="journal" id="sbref0225"><person-group person-group-type="author"><name><surname>Wu</surname><given-names>J.T.</given-names></name><name><surname>Leung</surname><given-names>K.</given-names></name><name><surname>Leung</surname><given-names>G.M.</given-names></name></person-group><article-title>Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study</article-title><source>Lancet</source><year>2020</year></element-citation></ref><ref id="bib0230"><element-citation publication-type="journal" id="sbref0230"><person-group person-group-type="author"><name><surname>Wrigth</surname><given-names>F.</given-names></name></person-group><article-title>The “effective number of codons” used in a gene</article-title><source>Gene</source><volume>87</volume><year>1990</year><fpage>23</fpage><lpage>29</lpage><pub-id pub-id-type="pmid">2110097</pub-id></element-citation></ref><ref id="bib0235"><element-citation publication-type="journal" id="sbref0235"><person-group person-group-type="author"><name><surname>Zhao</surname><given-names>S.</given-names></name><name><surname>Lin</surname><given-names>Q.</given-names></name><name><surname>Ran</surname><given-names>J.</given-names></name></person-group><article-title>Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak</article-title><source>Int. J. Infect. Dis.</source><issue>January (30)</issue><year>2020</year><comment>pii: S1201-9712(20)30053-9</comment></element-citation></ref><ref id="bib0240"><element-citation publication-type="journal" id="sbref0240"><person-group person-group-type="author"><name><surname>Zaki</surname><given-names>A.M.</given-names></name><name><surname>van Boheemen</surname><given-names>S.</given-names></name><name><surname>Bestebroer</surname><given-names>T.M.</given-names></name><name><surname>Osterhaus</surname><given-names>A.D.</given-names></name><name><surname>Fouchier</surname><given-names>R.A.</given-names></name></person-group><article-title>Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia</article-title><source>N. Engl. J. Med.</source><volume>8</volume><year>2012</year><fpage>1814</fpage><lpage>1820</lpage></element-citation></ref><ref id="bib0245"><element-citation publication-type="journal" id="sbref0245"><person-group person-group-type="author"><name><surname>Zhou</surname><given-names>P.</given-names></name><name><surname>Yang</surname><given-names>X.L.</given-names></name><name><surname>Wang</surname><given-names>X.G.</given-names></name></person-group><article-title>A pneumonia outbreak associated with a new coronavirus of probable bat origin</article-title><source>Nature</source><issue>Febryary (3)</issue><year>2020</year></element-citation></ref><ref id="bib0250"><element-citation publication-type="journal" id="sbref0250"><person-group person-group-type="author"><name><surname>Zhou</surname><given-names>P.</given-names></name><name><surname>Fan</surname><given-names>H.</given-names></name><name><surname>Lan</surname><given-names>T.</given-names></name></person-group><article-title>Fatal swine acute diarrhoea syndrome caused by an HKU2-related coronavirus of bat origin</article-title><source>Nature</source><volume>556</volume><year>2018</year><fpage>255</fpage><lpage>258</lpage><pub-id pub-id-type="pmid">29618817</pub-id></element-citation></ref><ref id="bib0255"><element-citation publication-type="journal" id="sbref0255"><person-group person-group-type="author"><name><surname>Zhu</surname><given-names>N.</given-names></name><name><surname>Zhang</surname><given-names>D.</given-names></name><name><surname>Wang</surname><given-names>W.</given-names></name></person-group><article-title>A novel Coronavirus from patients with pneumonia in China, 2019</article-title><source>N. Engl. J. Med.</source><volume>2020</volume><issue>January (24)</issue><year>2020</year></element-citation></ref></ref-list><sec id="sec0085" sec-type="supplementary-material"><label>Appendix A</label><title>Supplementary data</title><p id="par0215">The following are Supplementary data to this article:<supplementary-material content-type="local-data" id="upi0005"><media xlink:href="mmc1.docx"></media></supplementary-material><supplementary-material content-type="local-data" id="upi0010"><media xlink:href="mmc2.docx"></media></supplementary-material><supplementary-material content-type="local-data" id="upi0015"><media xlink:href="mmc3.zip"></media></supplementary-material></p></sec><ack id="ack0005"><title>Acknowledgements</title><p>We acknowledge Drs. Pilar Moreno, Gonzalo Moratorio and Rodney Colina for critical reading of this work.</p></ack><fn-group><fn id="sec0080" fn-type="supplementary-material"><label>Appendix A</label><p id="par0210">Supplementary material related to this article can be found, in the online version, at doi:<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1016/j.virusres.2020.197976" id="intr0020">https://doi.org/10.1016/j.virusres.2020.197976</ext-link>.</p></fn></fn-group></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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