ArticleMicrobial pathogenesis2017
Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Senecavirus A evolution.
Article in Microbial pathogenesis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Codon usage analysis in selected virulence genes of Staphylococcal species.Current genetics · 2025Article
- Isolation and Characterization of Seneca Valley Virus From Pig Transboundary Spread to the Mink Infection.Transboundary and emerging diseases · 2025Article
- Comparative genome wise analysis of codon usage of Staphylococcus Genus.Current genetics · 2024Article
- Article
- Effect of genome composition and codon bias on infectious bronchitis virus evolution and adaptation to target tissues.BMC genomics · 2021Article
- Buffalo-Origin Seneca Valley Virus in China: First Report, Isolation, Genome Characterization, and Evolution Analysis.Frontiers in veterinary science · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Senecavirus A (SVA) infection was recently confirmed in pigs in Brazil, United States of America and Canada. To better understand the molecular characteristics of isolated SVA genomes, we first reported genome-wide comprehensive analyses of codon usage and various factors that have contribute to the molecular evolution in SVA. The effective number of codons (ENC) ranged from 54.51 to 55.54 with an average of 54.87 ± 0.285, which reveals a relatively stable nucleotide composition. We found that codon usage bias of the SVA was low. Mutational pressure acted as an increasingly dominant factor for the evolution of the virus compared with the natural selection. Notably, codon usage bias was also affected by the geographic distribution and isolated time. The first systemic analysis on the codon usage bias of the SVA provides important information for the understanding of the evolution of the SVA and has fundamental and theoretical benefits.
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