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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–based 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−/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 −3 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−/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. [ABSTRACT FROM AUTHOR]

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2. [ABSTRACT FROM AUTHOR]