A rapid, cost efficient and simple method to identify current SARS-CoV-2 variants of concern by Sanger sequencing part of the spike protein gene

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Abstract

In 2020, the novel coronavirus, SARS-CoV-2, caused a pandemic, which is still raging at the time of writing this. Many countries have set up high throughput RT-qPCR based diagnostics for people with COVID-19 symptoms and for the wider population. In addition, with the use of whole genome sequencing (WGS) new lineages of SARS-CoV-2 have been identified that have been associated with increased transmissibility or altered vaccine efficacy, so-called Variants of Concern (VoC). WGS is generally too labor intensive and expensive to be applied to all positive samples from the diagnostic tests, and often has a turnaround time too long to enable VoC focused contact tracing. Here, we propose to use Sanger sequencing for the detection of common variants of concern and key mutations in early 2021, using a single set of the recognized ARTIC Network primers. The proposed setup relies entirely on materials and methods already in use in diagnostic RT-qPCR labs and on existing infrastructure from companies that have specialized in cheap and rapid turnaround Sanger sequencing. In addition, we provide an automated mutation calling software ( https://github.com/kblin/covid-spike-classification ). We have validated the setup on 195 SARS-CoV-2 positive samples, and we were able to profile >85% of RT-qPCR positive samples, where the last 15% largely stem from samples with low viral count. At approximately 4€ per sample in material cost, with minimal hands-on time, little data handling, and a turnaround time of less than 30 hours, the setup is simple enough to be implemented in any SARS-CoV-2 RT-qPCR diagnostic lab. Our protocol provides results that can be used to focus contact-tracing efforts and it is cheap enough for the tracking and surveillance of all positive samples for emerging variants such as B.1.1.7, B.1.351 and P.1 as of January 2021.

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  1. SciScore for 10.1101/2021.03.27.21252266: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationTo evaluate potential false-positive calls, a reference dataset of 5000 non-VoC samples was randomly selected from this dataset.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Purified RNA from the diagnostic RT-qPCR SARS-CoV-2 test facility at the Technical University of Denmark (DTU) was set up with two primers from the ARTIC Network SARS-CoV-2 v3 amplicon set for WGS v3 (https://github.com/artic-network/artic-ncov2019/blob/master/primer_schemes/nCoV-2019/V3/nCoV-2019.tsv) (Table 1).
    WGS
    suggested: None

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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

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