COVID-19 has widely spread across the world, and much research is being conducted on the causative virus SARS-CoV-2. To help control the infection, we developed the Coronavirus GenBrowser (CGB) to monitor the pandemic. CGB allows visualization and analysis of the latest viral genomic data. Distributed genome alignments and an evolutionary tree built on the existing subtree are implemented for easy and frequent updates. The tree-based data are compressed at a ratio of 2,760:1, enabling fast access and analysis of SARS-CoV-2 variants. CGB can effectively detect adaptive evolution of specific alleles, such as D614G of the spike protein, in their early stage of spreading. By lineage tracing, the most recent common ancestor, dated in early March 2020, of nine strains collected from six different regions in three continents was found to cause the outbreak in Xinfadi, Beijing, China in June 2020. CGB also revealed that the first COVID-19 outbreak in Washington State was caused by multiple introductions of SARS-CoV-2. To encourage data sharing, CGB credits the person who first discovers any SARS-CoV-2 variant. As CGB is developed with eight different languages, it allows the general public in many regions of the world to easily access pre-analyzed results of more than 132,000 SARS-CoV-2 genomes. CGB is an efficient platform to monitor adaptive evolution and transmission of SARS-CoV-2.

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.

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