Announcement

Collapse
No announcement yet.

Transbound Emerg Dis. Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Transbound Emerg Dis. Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak


    Transbound Emerg Dis. 2020 Apr 19. doi: 10.1111/tbed.13588. [Epub ahead of print]
    Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak.


    Bauer DC1,2, Tay AP1, Wilson LOW1, Reti D1, Hosking C1, McAuley AJ3, Pharo E3, Todd S3, Stevens V4, Neave MJ4, Tachedjian M3, Drew TW4, Vasan SS3,5.

    Author information




    Abstract

    Pre-clinical responses to fast moving infectious disease outbreaks heavily depend on choosing the best isolates for animal models that inform diagnostics, vaccines and treatments. Current approaches are driven by practical considerations (e.g. first available virus isolate) rather than a detailed analysis of the characteristics of the virus strain chosen, which can lead to animal models that are not representative of the circulating or emerging clusters. Here, we suggest a combination of epidemiological, experimental and bioinformatics considerations when choosing virus strains for animal model generation. We discuss the currently chosen SARS-CoV-2 strains for international coronavirus disease (COVID-19) models in the context of their phylogeny as well as in a novel alignment-free bioinformatics approach. Unlike phylogenetic trees, which focus on individual shared mutations, this new approach assesses genome-wide co-developing functionalities and hence offers a more fluid view of the "cloud of variances" that RNA viruses are prone to accumulate. This joint approach concludes that while the current animal models cover the existing viral strains adequately, there is substantial evolutionary activity that is likely not considered by the current models. Based on insights from the non-discrete alignment-free approach and experimental observations, we suggest isolates for future animal models.
    This article is protected by copyright. All rights reserved.



    KEYWORDS:

    Alignment-free Phylogeny; Bioinformatics; COVID-19; Genomics; PHEIC; Viral evolution


    PMID:32306500DOI:10.1111/tbed.13588

Working...
X