Evidence for SARS-CoV-2 Delta and Omicron co-infections and recombination

  1. Alexandre Bolze
  2. Tracy Basler
  3. Simon White
  4. Andrew Dei Rossi
  5. Dana Wyman
  6. Hang Dai
  7. Pavitra Roychoudhury
  8. Alexander L. Greninger
  9. Kathleen Hayashibara
  10. Mark Beatty
  11. Seema Shah
  12. Sarah Stous
  13. John T. McCrone
  14. Eric Kil
  15. Tyler Cassens
  16. Kevin Tsan
  17. Jason Nguyen
  18. Jimmy Ramirez
  19. Scotty Carter
  20. Elizabeth T. Cirulli
  21. Kelly Schiabor Barrett
  22. Nicole L. Washington
  23. Pedro Belda-Ferre
  24. Sharoni Jacobs
  25. Efren Sandoval
  26. David Becker
  27. James T. Lu
  28. Magnus Isaksson
  29. William Lee
  30. Shishi Luo

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Abstract

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  1. Version published to 10.1016/j.medj.2022.10.002
  2. Rapid Reviews Infectious Diseases

    Kenneth Stedman

    Review 2: "Evidence for SARS-CoV-2 Delta and Omicron Co-infections and Recombination"

    Though it’s bibliographic citations and discussion section could be refined, reviewers agree that this paper provides strong evidence of co-infection, utilizes robust and appropriate methods, and addresses any caveats in their data.

  3. Rapid Reviews Infectious Diseases

    Fernando Spilki

    Review 1: "Evidence for SARS-CoV-2 Delta and Omicron Co-infections and Recombination"

    Though it’s bibliographic citations and discussion section could be refined, reviewers agree that this paper provides strong evidence of co-infection, utilizes robust and appropriate methods, and addresses any caveats in their data.

  5. Version published to 10.1101/2022.03.09.22272113v2 on medRxiv
  6. ScreenIT

    SciScore for 10.1101/2022.03.09.22272113: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: The Helix data analyzed and presented here were obtained through IRB protocol WIRB#20203438, which grants a waiver of consent for a limited dataset for the purposes of public health under section 164.512(b) of the Privacy Rule (45 CFR § 164.512(b)).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The Helix COVID-19 Test is based on the Thermo Fisher TaqPath COVID-19 Flu A, Flu B Combo Kit, which targets three respiratory pathogens (SARS-CoV-2, Influenza A, and Influenza B).
    Thermo Fisher TaqPath
    suggested: None
    The flow cell output is demultiplexed with bcl2fastq (Illumina) into per-sample FASTQ sequences that are then run through the Helix klados-fastagenerator pipeline to produce a sequence FASTA file.
    bcl2fastq
    suggested: (bcl2fastq , RRID:SCR_015058)
    First, reads are aligned to a reference comprising the SARS-CoV-2 genome (NCBI accession NC_045512.2) and the human transcriptome (GENCODE v37) using BWA-MEM.
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)

    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.

    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.

  7. Version published to 10.1101/2022.03.09.22272113v1 on medRxiv