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Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic.
Guo, Qian; Li, Mo; Wang, Chunhui; Guo, Jinyuan; Jiang, Xiaoqing; Tan, Jie; Wu, Shufang; Wang, Peihong; Xiao, Tingting; Zhou, Man; Fang, Zhencheng; Xiao, Yonghong; Zhu, Huaiqiu.
  • Guo Q; State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
  • Li M; Center for Quantitative Biology, Peking University, Beijing, 100871, China.
  • Wang C; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
  • Guo J; Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China.
  • Jiang X; Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China.
  • Tan J; State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
  • Wu S; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
  • Wang P; State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
  • Xiao T; Center for Quantitative Biology, Peking University, Beijing, 100871, China.
  • Zhou M; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
  • Fang Z; State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
  • Xiao Y; State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
  • Zhu H; Center for Quantitative Biology, Peking University, Beijing, 100871, China.
Sci Rep ; 11(1): 17422, 2021 08 31.
Article in English | MEDLINE | ID: covidwho-1380912
ABSTRACT
The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cats / Chiroptera / Dogs / SARS-CoV-2 / COVID-19 / Mink Type of study: Prognostic study Limits: Animals / Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-96903-6

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cats / Chiroptera / Dogs / SARS-CoV-2 / COVID-19 / Mink Type of study: Prognostic study Limits: Animals / Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-96903-6