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Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
Qian Guo1,2,3, Mo Li4, Chunhui Wang4
1State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Scientific Reports
|September 1, 2021
Summary
A new deep learning method, DeepHoF, accurately predicts virus hosts by analyzing genomic features. This tool identified minks, bats, dogs, and cats as potential SARS-CoV-2 hosts, highlighting minks as significant.
Area of Science:
- Virology
- Genomics
- Computational Biology
Background:
- The SARS-CoV-2 pandemic highlighted challenges in identifying viral hosts.
- Accurate host prediction is crucial for understanding and controlling zoonotic diseases.
Purpose of the Study:
- To develop an accurate deep learning tool, DeepHoF, for predicting viral host likelihood.
- To investigate potential hosts of SARS-CoV-2 using genomic analysis.
Main Methods:
- Developed DeepHoF, a deep learning model for automatic viral genomic feature extraction.
- Applied DeepHoF to predict host likelihood across five categories: plant, germ, invertebrate, non-human vertebrate, and human.
- Analyzed SARS-CoV-2 isolates from early pandemic stages and 2020 using DeepHoF.
Main Results:
- DeepHoF achieved a 0.975 AUC in five-classification host prediction, outperforming existing tools.
- Inferred minks, bats, dogs, and cats as potential SARS-CoV-2 hosts, with minks being particularly noteworthy.
- Identified specific SARS-CoV-2 genes influencing host range and confirmed a strong association between SARS-CoV-2, humans, and minks.
Conclusions:
- DeepHoF provides a reliable method for predicting hosts of novel viruses.
- Genomic analysis suggests a significant role for minks in the SARS-CoV-2 host spectrum.
- Understanding host-virus interactions is key to pandemic preparedness.
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