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Comparative Study
. 2020 Jul 2:283:197976.
doi: 10.1016/j.virusres.2020.197976. Epub 2020 Apr 12.

A comprehensive analysis of genome composition and codon usage patterns of emerging coronaviruses

Affiliations
Comparative Study

A comprehensive analysis of genome composition and codon usage patterns of emerging coronaviruses

Fernando L Tort et al. Virus Res. .

Abstract

An outbreak of atypical pneumonia caused by a novel Betacoronavirus (βCoV), named SARS-CoV-2 has been declared a public health emergency of international concern by the World Health Organization. In order to gain insight into the emergence, evolution and adaptation of SARS-CoV-2 viruses, a comprehensive analysis of genome composition and codon usage of βCoV circulating in China was performed. A biased nucleotide composition was found for SARS-CoV-2 genome. This bias in genomic composition is reflected in its codon and amino acid usage patterns. The overall codon usage in SARS-CoV-2 is similar among themselves and slightly biased. Most of the highly frequent codons are A- and U-ending, which strongly suggests that mutational bias is the main force shaping codon usage in this virus. Significant differences in relative synonymous codon usage frequencies among SARS-CoV-2 and human cells were found. These differences are due to codon usage preferences.

Keywords: 2019-nCoV; Codon usage; Coronavirus; Evolution; SARS-CoV-2; Wuhan.

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Conflict of interest statement

Declaration of Competing Interest None.

Figures

Fig. 1
Fig. 1
Genome composition of βCoV strains. Heatmap of frequencies of G, A, T and C at the third codon position, GC total content, GC content at the third codon position and effective number of codons (ENC) in βCoV ORFs is shown. Unit variance scaling was applied. Each column corresponds to a different βCoV strain, who’s host and virus type are shown in the upper part of the figure. Both rows and columns are clustered using correlation distance and average linkage.
Fig. 2
Fig. 2
Heatmaps of codon and amino acid usage in βCoV ORFs. Unit variance scaling was applied. Each column corresponds to a different βCoV strain, who’s host and virus type are shown in the upper part of the figures. Both rows and columns are clustered using correlation distance and average linkage. In (a) and (b) codon and amino acids usage is shown, respectively.
Fig. 3
Fig. 3
Nucleotide composition of CoV genomes. Nucleotide composition for SARS-CoV-2 (accession number NC_045512), MERS (JX869059), SARS (NC004718), CoV 229E (KF514433), CoV OC43 (NC005147), CoV NL63 (JX504050), HKU-1A (DQ415914), HKU-1B (DQ415911) and HKU-1C (DQ415912) are shown.
Fig. 4
Fig. 4
Effective number of codons (ENC) used in βCoV ORFs plotted against the GC3S. The orange curve plots the relationship between GC3S and ENC in absence of selection. Red dots show the results obtained for SARS-CoV-2 strains. Blue dots show the results obtained for the rest of βCoV strains enrolled in these studies. N = 81 datapoints.

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