Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2

• Published in: Briefings in bioinformatics Vol. 23; no. 2
• Main Authors: Yu, Dalang, Yang, Xiao, Tang, Bixia, Pan, Yi-Hsuan, Yang, Jianing, Duan, Guangya, Zhu, Junwei, Hao, Zi-Qian, Mu, Hailong, Dai, Long, Hu, Wangjie, Zhang, Mochen, Cui, Ying, Jin, Tong, Li, Cui-Ping, Ma, Lina, Su, Xiao, Zhang, Guoqing, Zhao, Wenming, Li, Haipeng
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 10.03.2022; Oxford Publishing Limited (England)
• Subjects: Computational Biology - methods; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - virology; Data retrieval; Databases, Genetic; Disease transmission; DNA Mutational Analysis; Epidemiology; Evolution; Genome, Viral; Genomics; Humans; Java; Molecular Epidemiology - methods; Molecular Sequence Annotation; Mutation; Nomenclature; Pandemics; Positive selection; Problem Solving Protocol; Public Health Surveillance - methods; SARS-CoV-2 - genetics; Severe acute respiratory syndrome coronavirus 2; Software; Viral diseases; Web Browser; SARS-CoV-2; transmission; evolution; coronavirus GenBrowser; genomic epidemiology
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab583

Abstract 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.