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The 2019‐new coronavirus epidemic: Evidence for virus evolution

Open access
Benvenuto, Domenico, Giovanetti, Marta, Ciccozzi, Alessandra, Spoto, Silvia, Angeletti, Silvia, Ciccozzi, Massimo·2020·Journal of Medical Virology

Abstract

There is a worldwide concern about the new coronavirus 2019‐nCoV as a global public health threat. In this article, we provide a preliminary evolutionary and molecular epidemiological analysis of this new virus. A phylogenetic tree has been built using the 15 available whole genome sequences of 2019‐nCoV, 12 whole genome sequences of 2019‐nCoV, and 12 highly similar whole genome sequences available in gene bank (five from the severe acute respiratory syndrome, two from Middle East respiratory syndrome, and five from bat SARS‐like coronavirus). Fast unconstrained Bayesian approximation analysis shows that the nucleocapsid and the spike glycoprotein have some sites under positive pressure, whereas homology modeling revealed some molecular and structural differences between the viruses. The phylogenetic tree showed that 2019‐nCoV significantly clustered with bat SARS‐like coronavirus sequence isolated in 2015, whereas structural analysis revealed mutation in Spike Glycoprotein and nucleocapsid protein. From these results, the new 2019‐nCoV is distinct from SARS virus, probably trasmitted from bats after mutation conferring ability to infect humans.

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Metadata

Authors
Benvenuto, D., Giovanetti, M., Ciccozzi, A., Spoto, S., Angeletti, S., & Ciccozzi, M.
Venue
Journal of Medical Virology
Publisher
Wiley
Published
2020-02-07
Volume
92
Issue
4
Pages
455-459
ISSN
  • 0146-6615 (ISSN-L)
  • 1096-9071
OpenAlex
W3003752833
Cited by
713
References
5
FWCI
20.28
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The 2019‐new coronavirus epidemic: Evidence for virus evolution. https://app.cochat.ai/papers/10.1002/jmv.25688
The paper
Benvenuto, D., Giovanetti, M., Ciccozzi, A., Spoto, S., Angeletti, S., & Ciccozzi, M. (2020). The 2019‐new coronavirus epidemic: Evidence for virus evolution. Journal of Medical Virology. https://doi.org/10.1002/jmv.25688

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.