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

Denis C. Bauer, Aidan P. Tay, Laurence O. W. Wilson, Daniel Reti, Cameron Hosking, Alexander J. McAuley, Elizabeth Pharo, Shawn Todd, Vicky Stevens, Matthew J. Neave, Mary Tachedjian, Trevor W. Drew, Seshadri S. Vasan*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    26 Citations (Scopus)
    6 Downloads (Pure)

    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 bioinformatic 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 bioinformatic 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.

    Original languageEnglish
    Pages (from-to)1453-1462
    Number of pages10
    JournalTransboundary and Emerging Diseases
    Volume67
    Issue number4
    DOIs
    Publication statusPublished - 1 Jul 2020

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • alignment-free phylogeny
    • bioinformatics
    • COVID-19
    • genomics
    • PHEIC
    • viral evolution

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