tetano
Editor, Senior Moderator
Brief Bioinform
. 2020 Jul 31;bbaa154.
doi: 10.1093/bib/bbaa154. Online ahead of print.
Application of Bayesian phylogenetic inference modelling for evolutionary genetic analysis and dynamic changes in 2019-nCoV
Tong Shao[SUP] 1 [/SUP], Wenfang Wang[SUP] 1 [/SUP], Meiyu Duan[SUP] 2 [/SUP], Jiahui Pan[SUP] 3 [/SUP], Zhuoyuan Xin[SUP] 3 [/SUP], Baoyue Liu[SUP] 1 [/SUP], Fengfeng Zhou[SUP] 4 [/SUP], Guoqing Wang[SUP] 5 [/SUP]
Affiliations
Abstract
The novel coronavirus (2019-nCoV) has recently caused a large-scale outbreak of viral pneumonia both in China and worldwide. In this study, we obtained the entire genome sequence of 777 new coronavirus strains as of 29 February 2020 from a public gene bank. Bioinformatics analysis of these strains indicated that the mutation rate of these new coronaviruses is not high at present, similar to the mutation rate of the severe acute respiratory syndrome (SARS) virus. The similarities of 2019-nCoV and SARS virus suggested that the S and ORF6 proteins shared a low similarity, while the E protein shared the higher similarity. The 2019-nCoV sequence has similar potential phosphorylation sites and glycosylation sites on the surface protein and the ORF1ab polyprotein as the SARS virus; however, there are differences in potential modification sites between the Chinese strain and some American strains. At the same time, we proposed two possible recombination sites for 2019-nCoV. Based on the results of the skyline, we speculate that the activity of the gene population of 2019-nCoV may be before the end of 2019. As the scope of the 2019-nCoV infection further expands, it may produce different adaptive evolutions due to different environments. Finally, evolutionary genetic analysis can be a useful resource for studying the spread and virulence of 2019-nCoV, which are essential aspects of preventive and precise medicine.
Keywords: 2019-nCoV; Bayesian model; COVID-19; genetic evolutionary analysis; pneumonia.
. 2020 Jul 31;bbaa154.
doi: 10.1093/bib/bbaa154. Online ahead of print.
Application of Bayesian phylogenetic inference modelling for evolutionary genetic analysis and dynamic changes in 2019-nCoV
Tong Shao[SUP] 1 [/SUP], Wenfang Wang[SUP] 1 [/SUP], Meiyu Duan[SUP] 2 [/SUP], Jiahui Pan[SUP] 3 [/SUP], Zhuoyuan Xin[SUP] 3 [/SUP], Baoyue Liu[SUP] 1 [/SUP], Fengfeng Zhou[SUP] 4 [/SUP], Guoqing Wang[SUP] 5 [/SUP]
Affiliations
- PMID: 32743639
- DOI: 10.1093/bib/bbaa154
Abstract
The novel coronavirus (2019-nCoV) has recently caused a large-scale outbreak of viral pneumonia both in China and worldwide. In this study, we obtained the entire genome sequence of 777 new coronavirus strains as of 29 February 2020 from a public gene bank. Bioinformatics analysis of these strains indicated that the mutation rate of these new coronaviruses is not high at present, similar to the mutation rate of the severe acute respiratory syndrome (SARS) virus. The similarities of 2019-nCoV and SARS virus suggested that the S and ORF6 proteins shared a low similarity, while the E protein shared the higher similarity. The 2019-nCoV sequence has similar potential phosphorylation sites and glycosylation sites on the surface protein and the ORF1ab polyprotein as the SARS virus; however, there are differences in potential modification sites between the Chinese strain and some American strains. At the same time, we proposed two possible recombination sites for 2019-nCoV. Based on the results of the skyline, we speculate that the activity of the gene population of 2019-nCoV may be before the end of 2019. As the scope of the 2019-nCoV infection further expands, it may produce different adaptive evolutions due to different environments. Finally, evolutionary genetic analysis can be a useful resource for studying the spread and virulence of 2019-nCoV, which are essential aspects of preventive and precise medicine.
Keywords: 2019-nCoV; Bayesian model; COVID-19; genetic evolutionary analysis; pneumonia.