On Classification and Taxonomy of Coronaviruses (Riboviria, Nidovirales, Coronaviridae) with special focus on severe acute respiratory syndrome-related coronavirus 2 (SARS-Cov-2)

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Abstract

Coronaviruses are highly pathogenic and therefore important human and veterinary pathogens viruses worldwide ( 1 ). Members of family Coronaviridae have previously been analysed phylogenetically, resulting in proposals of virus interrelationships ( 2–5 ). However, available Coronavirus phylogenies remain unrooted, based on limited sampling, and normally depend on a single method ( 2–11 ). The main subjects of this study are the taxonomy and systematics of coronaviruses and our goal is to build the first natural classification of Coronaviridae using several methods of cladistic analyses ( 12 ), Maximum Likelihood method, as well as rigorous taxonomic sampling, making the most accurate representation of Coronaviridae’s relationships to date. Nomenclature recommendations to help effectively incorporate principles of binary nomenclature into Coronaviridae taxonomy are provided. We have stressed that no member of Sarbecovirus clade is an ancestor of SARS-Cov-2, and humans are the only known host.

One Sentence Summary

Multiple comprehensive phylogenetic analyses of all coronavirus species enabled testing of critical proposals on virus interrelationships.

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  1. SciScore for 10.1101/2020.10.17.343749: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Matrices and trees: All genomic alignments have been performed using MAFFT (13, 14, 15) following FFT-NS-I strategy with the command: mafft --inputorder --adjustdirection -- anysymbol --kimura 1 --maxiterate 1000 --6merpair input.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Manipulations with either the molecular or binary matrices and the tree-files have been performed with Mesquite v.
    Mesquite
    suggested: (Mesquite, RRID:SCR_017994)
    3.51 (22), PAUP* v. 4.0a (23) and FigTree v.
    FigTree
    suggested: (FigTree, RRID:SCR_008515)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.