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bioRxiv · 10.1101/2021.03.21.436312

Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020

Guo, Q.·Li, M.·Wang, C.·Guo, J.·Jiang, X.·Tan, J.·Wu, S.·Wang, P.·Xiao, T.·Zhou, M.·Fang, Z.·Xiao, Y.·Zhu, H.
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Abstract

The SARS-CoV-2 pandemic has raised the concern for identifying hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features automatically, to predict 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 applicable to any novel virus and overcame the limitation of the sequence similarity-based methods, reaching a satisfactory AUC of 0.987 on the five-classification. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existed tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of COVID-19, we inferred 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, the large-scale genome analysis, based on DeepHoFs computation for the later world-wide 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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Guo, Q., Li, M., Wang, C., Guo, J., Jiang, X., Tan, J., Wu, S., Wang, P., Xiao, T., Zhou, M., Fang, Z., Xiao, Y., Zhu, H.. 2021-03-22. Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020. https://doi.org/10.1101/2021.03.21.436312

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