Computational methods for inferring location and genealogy of overlapping genes in virus genomes: approaches and applications.


Journal

Current opinion in virology
ISSN: 1879-6265
Titre abrégé: Curr Opin Virol
Pays: Netherlands
ID NLM: 101560941

Informations de publication

Date de publication:
02 2022
Historique:
received: 04 09 2021
revised: 21 10 2021
accepted: 22 10 2021
pubmed: 20 11 2021
medline: 17 2 2022
entrez: 19 11 2021
Statut: ppublish

Résumé

Viruses may evolve to increase the amount of encoded genetic information by means of overlapping genes, which utilize several reading frames. Such overlapping genes may be especially impactful for genomes of small size, often serving a source of novel accessory proteins, some of which play a crucial role in viral pathogenicity or in promoting the systemic spread of virus. Diverse genome-based metrics were proposed to facilitate recognition of overlapping genes that otherwise may be overlooked during genome annotation. They can detect the atypical codon bias associated with the overlap (e.g. a statistically significant reduction in variability at synonymous sites) or other sequence-composition features peculiar to overlapping genes. In this review, I compare nine computational methods, discuss their strengths and limitations, and survey how they were applied to detect candidate overlapping genes in the genome of SARS-CoV-2, the etiological agent of COVID-19 pandemic.

Identifiants

pubmed: 34798370
pii: S1879-6257(21)00131-0
doi: 10.1016/j.coviro.2021.10.009
pmc: PMC8594276
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-8

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

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Auteurs

Angelo Pavesi (A)

Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area delle Scienze 23/A I-43124, Parma, Italy. Electronic address: angelo.pavesi@unipr.it.

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Classifications MeSH