From Delta to Omicron SARS-CoV-2 variant: Switch to saliva sampling for higher detection rate

This article has been Reviewed by the following groups

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2022.03.17.22272538: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Informed consent and questionnaire (assessing eating, drinking, chewing or smoking 30 minutes before test and test indication) were completed under the guidance of the researcher.
    Field Sample Permit: Based on the Belgian Sciensano guidelines for saliva collection [6], the participant collected the saliva sample in a CE-labeled sterile buffer-free recipient by spitting.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In brief: WGS was performed using the Research Use Only AmpliSeq for Illumina SARS-CoV-2 Research Panel on Illumina MiSeq (80 samples on V2 flow cell) according to the manufacturer’s standard protocol: 8 µl RNA extract was reverse transcribed using Lunascript RT SuperMix Kit (New England Biolabs, MA, USA), followed by amplification of 237 virus specific amplicons covering > 99% of the 30kb reference genome aiming at a median coverage above 500x, minimal coverage for mutation calling of 10x and variant allele frequency > 90% and a minimum of 30,000 reads per sample and a maximum of 1kb bases below minimal coverage.
    WGS
    suggested: None
    A consensus sequence was constructed using an in-house pipeline containing Trimomatic for trimming, alignment by BWA, mutation calling by Freebayes and inspection of sequence quality by IGV.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Freebayes
    suggested: (FreeBayes, RRID:SCR_010761)

    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.