High Throughput Nanopore Sequencing of SARS-CoV-2 Viral Genomes from Patient Samples

  1. Adrian A. Pater
  2. Michael S. Bosmeny
  3. Mansi Parasrampuria
  4. Seth B. Eddington
  5. Katy N. Ovington
  6. Adam A. White
  7. Christopher L. Barkau
  8. Paige E. Metz
  9. Rourke J. Sylvain
  10. Ramadevi Chilamkurthy
  11. Abadat O. Yinusa
  12. Scott W. Benzinger
  13. Madison M. Hebert
  14. Keith T. Gagnon

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Abstract

In late 2019, a novel coronavirus began spreading in Wuhan, China, causing a potentially lethal respiratory viral infection. By early 2020, the novel coronavirus, called SARS-CoV-2, had spread globally, causing the COVID-19 pandemic. The infection and mutation rates of SARS-CoV-2 make it amenable to tracking movement and evolution by viral genome sequencing. Efforts to develop effective public health policies, therapeutics, or vaccines to treat or prevent COVID-19 are also expected to benefit from tracking mutations of the SARS-CoV-2 virus. Here we describe a set of comprehensive working protocols, from viral RNA extraction to analysis using online visualization tools, for high throughput sequencing of SARS-CoV-2 viral genomes using a MinION instrument. This set of protocols should serve as a reliable ‘how-to’ reference for generating quality SARS-CoV-2 genome sequences with ARTIC primer sets and next-generation nanopore sequencing technology. In addition, many of the preparation, quality control, and analysis steps will be generally applicable to other sequencing platforms.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.09.430478: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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 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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  2. Version published to 10.1101/2021.02.09.430478v2 on bioRxiv
  3. Version published to 10.1101/2021.02.09.430478v1 on bioRxiv