{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:28:53Z","timestamp":1787009333492,"version":"build-2736575974"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Three prevalent SARS-CoV-2 variants of concern (VOCs) emerged and caused epidemic waves. It is essential to uncover advantageous mutations that cause the high transmissibility of VOCs. However, viral mutations are tightly linked, so traditional population genetic methods, including machine learning\u2013based methods, cannot reliably detect mutations conferring a fitness advantage. In this study, we developed an approach based on the sequential occurrence order of mutations and the accelerated furcation rate in the pandemic-scale phylogenomic tree. We analyzed 3,777,753 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata using the Coronavirus GenBrowser. We found that two noncoding mutations at the same position (g.a28271\u2212\/u) may be crucial to the high transmissibility of Alpha, Delta, and Omicron VOCs although the noncoding mutations alone cannot increase viral transmissibility. Both mutations cause an A-to-U change at the core position \u22123 of the Kozak sequence of the N gene and significantly reduce the protein expression ratio of ORF9b to N. Using a convergent evolutionary analysis, we found that g.a28271\u2212\/u, S:p.P681H\/R, and N:p.R203K\/M occur independently on three VOC lineages, suggesting that coordinated changes of S, N, and ORF9b proteins are crucial to high viral transmissibility. Our results provide new insights into high viral transmissibility co-modulated by advantageous noncoding and nonsynonymous changes.<\/jats:p>","DOI":"10.1093\/molbev\/msad142","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T21:12:56Z","timestamp":1687468376000},"source":"Crossref","is-referenced-by-count":14,"title":["A Noncoding A-to-U Kozak Site Change Related to the High Transmissibility of Alpha, Delta, and Omicron VOCs"],"prefix":"10.1093","volume":"40","author":[{"given":"Jianing","family":"Yang","sequence":"first","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingmin","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalang","family":"Yu","sequence":"additional","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruifang","family":"Cao","sequence":"additional","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and 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Molecular Cell Science, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Sun","sequence":"additional","affiliation":[{"name":"Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese 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