Sequencing SARS-CoV-2 genomes from saliva

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Genomic sequencing is crucial to understanding the epidemiology and evolution of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Often, genomic studies rely on remnant diagnostic material, typically nasopharyngeal (NP) swabs, as input into whole-genome SARS-CoV-2 next-generation sequencing pipelines. Saliva has proven to be a safe and stable specimen for the detection of SARS-CoV-2 RNA via traditional diagnostic assays; however, saliva is not commonly used for SARS-CoV-2 sequencing. Using the ARTIC Network amplicon-generation approach with sequencing on the Oxford Nanopore MinION, we demonstrate that sequencing SARS-CoV-2 from saliva produces genomes comparable to those from NP swabs, and that RNA extraction is necessary to generate complete genomes from saliva. In this study, we show that saliva is a useful specimen type for genomic studies of SARS-CoV-2.

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  1. SciScore for 10.1101/2021.06.21.21259289: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Samples were collected after the study participant had acknowledged that they had understood the study protocol and signed the informed consent.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    25 ng of the final library was loaded on a MinION R9.4.1 flow cell and sequenced for approximately 8-10 hours.
    suggested: (MinION, RRID:SCR_017985)
    Bioinformatics processing: The RAMPART application from the ARTIC Network was used to monitor approximate genome coverage for each sample and control in real time during the sequencing run (
    suggested: (Rampart, RRID:SCR_016742)
    Fast5 files were basecalled using the Guppy basecaller 4.4.0 fast model and consensus genomes were generated according to the ARTIC bioinformatic pipeline ( which uses Nanopolish to call variants18.
    suggested: (Nanopolish, RRID:SCR_016157)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In addition to these considerations, there are some limitations to our results. We did not assess how saliva performs as a specimen for sequencing SARS-CoV-2 genomes outside of amplicon-based strategies and additional research is needed to assess how saliva would perform in metagenomic or hybrid-/capture-based sequencing approaches. Additionally, we did not quantify the effect RNA extraction has on generating complete genomes from NP swabs. Therefore, we cannot determine if the reduction in genome completeness seen from samples without RNA is a specimen type specific phenomenon. Saliva is a stable and sensitive specimen type for SARS-CoV-2 diagnostic assays. In addition to the safety afforded through self-collection opposed to NP swabs, saliva is increasingly becoming an appealing specimen type, in particular for large-scale public health surveillance. Taken together, our data indicate that saliva is a satisfactory sample type for not only diagnostic assays but also for sequencing high-quality SARS-CoV-2 genomes. This is an important finding for the scalability and streamlining of genomic epidemiological studies.

    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

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