CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200333, P. R. China.
Tianjin Institute of Industrial Biotechnology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Tianjin 300308, P. R. China.
Bioinformatics. 2021 May 23;37(8):1182-1183. doi: 10.1093/bioinformatics/btaa767.
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 conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation.
We built a comprehensive scientific resource, SARS2020, which 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.
SARS2020 is available at http://design.rxnfinder.org/sars2020/.
Supplementary data are available at Bioinformatics online.
2019 年新型冠状病毒的爆发极大地影响了全球健康和社会。因此，从病原体序列预测生物功能至关重要且迫在眉睫。然而，很少有工作通过它们编码的酶来识别病毒，而这些酶是病原体传播的关键。
我们构建了一个全面的科学资源 SARS2020，它集成了冠状病毒相关研究、基因组序列和抗病毒药物试验结果。此外，我们还构建了一个共识序列-催化功能模型，从中我们确定新型冠状病毒编码的蛋白酶与严重急性呼吸系统综合征病毒相同。这种基于数据驱动的序列策略将能够快速识别导致未来疫情的病原体。
SARS2020 可在 http://design.rxnfinder.org/sars2020/ 上获得。
补充数据可在《生物信息学》在线获取。
