Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands

  1. Bas B. Oude Munnink
  2. David F. Nieuwenhuijse
  3. Mart Stein
  4. Áine O’Toole
  5. Manon Haverkate
  6. Madelief Mollers
  7. Sandra K. Kamga
  8. Claudia Schapendonk
  9. Mark Pronk
  10. Pascal Lexmond
  11. Anne van der Linden
  12. Theo Bestebroer
  13. Irina Chestakova
  14. Ronald J. Overmars
  15. Stefan van Nieuwkoop
  16. Richard Molenkamp
  17. Annemiek van der Eijk
  18. Corine GeurtsvanKessel
  19. Harry Vennema
  20. Adam Meijer
  21. Andrew Rambaut
  22. Jaap van Dissel
  23. Reina S. Sikkema
  24. Aura Timen
  25. Marion Koopmans
  26. on behalf of the Dutch-Covid-19 response team

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Abstract

SARS-CoV-2 is a novel coronavirus that has rapidly spread across the globe. In the Netherlands, the first case of SARS-CoV-2 has been notified on the 27 th of February. Here, we describe the first three weeks of the SARS-CoV-2 outbreak in the Netherlands, which started with several different introductory events from Italy, Austria, Germany and France followed by local amplification in, and later also, outside the South of the Netherlands. The timely generation of whole genome sequences combined with epidemiological investigations facilitated early decision making in an attempt to control local transmission of SARS-CoV-2 in the Netherlands.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.21.050633: (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

    Software and Algorithms
    SentencesResources
    Sequence data analysis: The resulting raw sequence data was demultiplexed using qcat (https://github.com/nanoporetech/qcat) or Porechop (https://github.com/rrwick/Porechop).
    Porechop
    suggested: (Porechop, RRID:SCR_016967)
    The run was monitored using RAMPART (https://artic-network.github.io/rampart/) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes.
    RAMPART
    suggested: (Rampart, RRID:SCR_016742)
    The consensus genome was extracted and positions with a coverage <30 were replaced with an “N” with a custom script using biopython and pysam.
    biopython
    suggested: (Biopython, RRID:SCR_007173)
    Phylogenetic analysis: All available full-length SARS-CoV-2 genomes were retrieved from GISAID (supplementary table 2) on the 22nd of March 2020 and aligned with the Dutch SARS-CoV-2 sequences from this study using MUSCLE.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)

    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.

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.04.21.050633: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sequence data analysisThe resulting raw sequence data was demultiplexed using qcat ( https://github.com/nanoporetech/qcat ) or Porechop ( https://github.com/rrwick/Porechop).
    Porechop
    suggested: (Porechop, SCR_016967)
    The run was monitored using RAMPART ( https://artic-network.github.io/rampart/ ) and the analysis process was automated using snakemake22 which was used to perform near to real-time analysis with new data every 10 minutes .
    RAMPART
    suggested: (Rampart, SCR_016742)
    The consensus genome was extracted and positions with a coverage <30 were replaced with an “N” with a custom script using biopython and pysam .
    biopython
    suggested: (Biopython, SCR_007173)
    Phylogenetic analysisAll available full-length SARS-CoV-2 genomes were retrieved from GISAID ( supplementary table 2 ) on the 22nd of March 2020 and aligned with the Dutch SARS-CoV-2 sequences from this study using MUSCLE .
    MUSCLE
    suggested: (MUSCLE, SCR_011812)
    BEAST analysisAll available full-length SARS-CoV-2 genomes were retrieved from GISAID24,25 on the 18th of March 2020 and downsampled to include only representative sequences from epidemiologically linked cases
    BEAST
    suggested: (BEAST, SCR_010228)

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  3. Version published to 10.1101/2020.04.21.050633v1 on bioRxiv