Rapid Detection of SARS-CoV-2 Variants of Concern, Including B.1.1.28/P.1, British Columbia, Canada

  1. Nancy Matic
  2. Christopher F. Lowe
  3. Gordon Ritchie
  4. Aleksandra Stefanovic
  5. Tanya Lawson
  6. Willson Jang
  7. Matthew Young
  8. Winnie Dong
  9. Zabrina L. Brumme
  10. Chanson J. Brumme
  11. Victor Leung
  12. Marc G. Romney

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.3201/eid2706.210532
  2. ScreenIT

    SciScore for 10.1101/2021.03.04.21252928: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Providence Health Care/University of British Columbia and Simon Fraser University Research Ethics Boards (H20-01055).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    WGS was performed on the MinION (Oxford Nanopore Technologies) using the ARTIC nCOV-2019 sequencing protocol V.1 (J.
    WGS
    suggested: None
    Accurate basecalling of MinION was performed using guppy 3.1.5, and FASTQ files analyzed on the bugseq.com platform using pangoLEARN.
    MinION
    suggested: (MinION, RRID:SCR_017985)

    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: We detected the following sentences addressing limitations in the study:
    A PCR-based algorithm for identifying VOC that uses N501Y as the initial screening target, such as in this study, must acknowledge this limitation. Given the rapid emergence of new variants, ongoing surveillance is key, and any laboratory considering a PCR-based algorithm would need to adapt the algorithm as VOC prevalence changes. Nevertheless, the ability to rapidly leverage existing molecular infrastructure established during the COVID-19 pandemic to presumptively identify the most prevalent VOC within 24 hours would be an important tool to complement WGS performed at reference laboratories. In summary, our implementation of a real-time RT-PCR-based algorithm enabled identification of the most common VOC to date (B.1.1.7, B.1.351, and B.1.1.28/P.1 ) within 24 hours. This methodology would allow laboratories to perform VOC testing on all positive SARS-CoV-2 samples, and enhance VOC surveillance capacity to identify cases and support decision making for interrupting transmission.

    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 10.1101/2021.03.04.21252928v1 on medRxiv