Emergence of SARS-CoV-2 through recombination and strong purifying selection

  1. Xiaojun Li
  2. Elena E. Giorgi
  3. Manukumar Honnayakanahalli Marichannegowda
  4. Brian Foley
  5. Chuan Xiao
  6. Xiang-Peng Kong
  7. Yue Chen
  8. S. Gnanakaran
  9. Bette Korber
  10. Feng Gao

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Abstract

Frequent recombination and strong purifying selection facilitate the cross-species transmission of coronaviruses to humans.

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  1. Version published to 10.1126/sciadv.abb9153
  2. ScreenIT

    SciScore for 10.1101/2020.03.20.000885: (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
    Pan_SL-CoV_GD/P1La sequence was generated by combining Pan_SL-CoV_GD/P1L (10) with some additional sequences from the NCBI BioProject database PRJNA5732983 (11, 37) to have a maximal coverage of the complete genome sequence for analysis.
    BioProject
    suggested: (NCBI BioProject, RRID:SCR_004801)
    The alignments for all coding regions were manually optimized based on the amino acid sequence alignment using SeaView 5.0.1.
    SeaView
    suggested: (SeaView, RRID:SCR_015059)
    Selection Analyses: Cumulative plots of the average behavior of each codon for all pairwise comparisons in the input data, for insertions and deletions (indels), synonymous (syn), and nonsynonymous (nonsyn) mutations and values of the ratios of the rate of synonymous nucleotide substitutions per synonymous site and nonsynonymous substitutions per nonsynonymous site (dN/dS, or ω) were obtained using the LANL database tool SNAP (42).
    SNAP
    suggested: (SNAP, RRID:SCR_007936)
    To investigate the impact of the deletion between residue 473 to 486 to the binding interface between SARS-CoV-2 and human ACE2, a homology model with the deletion was generated using I-TASSER (45).
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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.

  3. Version published to 10.1101/2020.03.20.000885v2 on bioRxiv
  4. Version published to 10.1101/2020.03.20.000885v1 on bioRxiv