Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic

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...

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Autores principales:	Qian Guo, Mo Li, Chunhui Wang, Jinyuan Guo, Xiaoqing Jiang, Jie Tan, Shufang Wu, Peihong Wang, Tingting Xiao, Man Zhou, Zhencheng Fang, Yonghong Xiao, Huaiqiu Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0f9e0c0db64499c92721fc4f375dd3b
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spelling	oai:doaj.org-article:f0f9e0c0db64499c92721fc4f375dd3b2021-12-02T16:38:25ZPredicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic10.1038/s41598-021-96903-62045-2322https://doaj.org/article/f0f9e0c0db64499c92721fc4f375dd3b2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96903-6https://doaj.org/toc/2045-2322Abstract 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.Qian GuoMo LiChunhui WangJinyuan GuoXiaoqing JiangJie TanShufang WuPeihong WangTingting XiaoMan ZhouZhencheng FangYonghong XiaoHuaiqiu ZhuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qian Guo Mo Li Chunhui Wang Jinyuan Guo Xiaoqing Jiang Jie Tan Shufang Wu Peihong Wang Tingting Xiao Man Zhou Zhencheng Fang Yonghong Xiao Huaiqiu Zhu Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
description	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.
format	article
author	Qian Guo Mo Li Chunhui Wang Jinyuan Guo Xiaoqing Jiang Jie Tan Shufang Wu Peihong Wang Tingting Xiao Man Zhou Zhencheng Fang Yonghong Xiao Huaiqiu Zhu
author_facet	Qian Guo Mo Li Chunhui Wang Jinyuan Guo Xiaoqing Jiang Jie Tan Shufang Wu Peihong Wang Tingting Xiao Man Zhou Zhencheng Fang Yonghong Xiao Huaiqiu Zhu
author_sort	Qian Guo
title	Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
title_short	Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
title_full	Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
title_fullStr	Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
title_full_unstemmed	Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic
title_sort	predicting hosts based on early sars-cov-2 samples and analyzing the 2020 pandemic
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f0f9e0c0db64499c92721fc4f375dd3b
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Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic

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