tetano
Editor, Senior Moderator
Bioinformatics
. 2020 Sep 1;btaa767.
doi: 10.1093/bioinformatics/btaa767. Online ahead of print.
SARS2020: An integrated platform for identification of novel coronavirus by a consensus sequence-function model
Dachuan Zhang[SUP] 1 [/SUP], Tong Zhang[SUP] 1 [/SUP], Sheng Liu[SUP] 1 [/SUP], Dandan Sun[SUP] 1 [/SUP], Shaozhen Ding[SUP] 1 [/SUP], Xingxiang Cheng[SUP] 1 [/SUP], Pengli Cai[SUP] 1 2 [/SUP], Ailin Ren[SUP] 2 [/SUP], Mengying Han[SUP] 1 [/SUP], Dongliang Liu[SUP] 1 [/SUP], Cancan Jia[SUP] 1 [/SUP], Linlin Gong[SUP] 1 [/SUP], Rui Zhang[SUP] 1 [/SUP], Huadong Xing[SUP] 1 [/SUP], Weizhong Tu[SUP] 3 [/SUP], Junni Chen[SUP] 3 [/SUP], Qian-Nan Hu[SUP] 1 [/SUP]
Affiliations
Abstract
Motivation: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been performed to identify viruses by the enzymes that they encode, and which are key to pathogen propagation.
Results: We built a comprehensive scientific resource, SARS2020, that integrates coronavirus-related research, genomic sequences, and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the Severe Acute Respiratory Syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics.
Availability: SARS2020 is available at http://design.rxnfinder.org/sars2020/.
Supplementary information: Supplementary data are available at Bioinformatics online.
. 2020 Sep 1;btaa767.
doi: 10.1093/bioinformatics/btaa767. Online ahead of print.
SARS2020: An integrated platform for identification of novel coronavirus by a consensus sequence-function model
Dachuan Zhang[SUP] 1 [/SUP], Tong Zhang[SUP] 1 [/SUP], Sheng Liu[SUP] 1 [/SUP], Dandan Sun[SUP] 1 [/SUP], Shaozhen Ding[SUP] 1 [/SUP], Xingxiang Cheng[SUP] 1 [/SUP], Pengli Cai[SUP] 1 2 [/SUP], Ailin Ren[SUP] 2 [/SUP], Mengying Han[SUP] 1 [/SUP], Dongliang Liu[SUP] 1 [/SUP], Cancan Jia[SUP] 1 [/SUP], Linlin Gong[SUP] 1 [/SUP], Rui Zhang[SUP] 1 [/SUP], Huadong Xing[SUP] 1 [/SUP], Weizhong Tu[SUP] 3 [/SUP], Junni Chen[SUP] 3 [/SUP], Qian-Nan Hu[SUP] 1 [/SUP]
Affiliations
- PMID: 32871007
- DOI: 10.1093/bioinformatics/btaa767
Abstract
Motivation: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been performed to identify viruses by the enzymes that they encode, and which are key to pathogen propagation.
Results: We built a comprehensive scientific resource, SARS2020, that integrates coronavirus-related research, genomic sequences, and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the Severe Acute Respiratory Syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics.
Availability: SARS2020 is available at http://design.rxnfinder.org/sars2020/.
Supplementary information: Supplementary data are available at Bioinformatics online.