Generation and transmission of interlineage recombinants in the SARS-CoV-2 pandemic

  1. Ben Jackson
  2. Maciej F. Boni
  3. Matthew J. Bull
  4. Amy Colleran
  5. Rachel M. Colquhoun
  6. Alistair C. Darby
  7. Sam Haldenby
  8. Verity Hill
  9. Anita Lucaci
  10. John T. McCrone
  11. Samuel M. Nicholls
  12. Áine O’Toole
  13. Nicole Pacchiarini
  14. Radoslaw Poplawski
  15. Emily Scher
  16. Flora Todd
  17. Hermione J. Webster
  18. Mark Whitehead
  19. Claudia Wierzbicki
  20. Nicholas J. Loman
  21. Thomas R. Connor
  22. David L. Robertson
  23. Oliver G. Pybus
  24. Andrew Rambaut

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Abstract

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  1. Rapid Reviews Infectious Diseases

    Graziano Pesole, Matteo Chiara

    Review 1: "Generation and transmission of inter-lineage recombinants in the SARS-CoV-2 pandemic"

    This preprint analyzes all complete UK SARS-CoV-2 genomes that were from the B.1.1.7 lineage to identify putative SARS-CoV-2 recombinant viruses. The reviewer deems the data as potentially informative but empathize limitations in methodological protocol.

  3. Version published to 10.1016/j.cell.2021.08.014
  4. ScreenIT

    SciScore for 10.1101/2021.06.18.21258689: (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
    1.1.7 regions at the junction of genetic regions according to the mosaic structure detected by the custom Python script described above (https://github.com/cov-ert/type_variants; Supplementary Table S1).
    Python
    suggested: (IPython, RRID:SCR_001658)
    We used samtools (Li et al. 2009), with default filters for mapping and base quality, to extract allele calls from the read data using its mpileup subroutine, and to calculate mean read depth per genome using its depth subroutine.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    Results from OddPub: Thank you for sharing your code and data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  5. Version published to 10.1101/2021.06.18.21258689v1 on medRxiv