tetano
Editor, Senior Moderator
Brief Bioinform
. 2022 Jan 19;bbab583.
doi: 10.1093/bib/bbab583. Online ahead of print.
Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2
Dalang Yu, Xiao Yang, Bixia Tang, Yi-Hsuan Pan, Jianing Yang, Guangya Duan, Junwei Zhu, Zi-Qian Hao, Hailong Mu, Long Dai, Wangjie Hu, Mochen Zhang, Ying Cui, Tong Jin, Cui-Ping Li, Lina Ma, Language translation team; Xiao Su, Guoqing Zhang, Wenming Zhao, Haipeng Li
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.
Keywords: SARS-CoV-2; coronavirus GenBrowser; evolution; genomic epidemiology; transmission.
. 2022 Jan 19;bbab583.
doi: 10.1093/bib/bbab583. Online ahead of print.
Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2
Dalang Yu, Xiao Yang, Bixia Tang, Yi-Hsuan Pan, Jianing Yang, Guangya Duan, Junwei Zhu, Zi-Qian Hao, Hailong Mu, Long Dai, Wangjie Hu, Mochen Zhang, Ying Cui, Tong Jin, Cui-Ping Li, Lina Ma, Language translation team; Xiao Su, Guoqing Zhang, Wenming Zhao, Haipeng Li
- PMID: 35043153
- 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.
Keywords: SARS-CoV-2; coronavirus GenBrowser; evolution; genomic epidemiology; transmission.