Genomics-informed outbreak investigations of SARS-CoV-2 using civet

  1. Áine O’Toole
  2. Verity Hill
  3. Ben Jackson
  4. Rebecca Dewar
  5. Nikita Sahadeo
  6. Rachel Colquhoun
  7. Stefan Rooke
  8. JT McCrone
  9. Martin P McHugh
  10. Sam Nicholls
  11. Radoslaw Poplawski
  12. The COVID-19 Genomics UK (COG-UK) Consortium
  13. COVID-19 Impact Project (Trinidad & Tobago Group)
  14. David Aanensen
  15. Matt Holden
  16. Tom Connor
  17. Nick Loman
  18. Ian Goodfellow
  19. Christine V. F. Carrington
  20. Kate Templeton
  21. Andrew Rambaut

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Abstract

The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 5 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different ’catchments’ and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health.

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    SciScore for 10.1101/2021.12.13.21267267: (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
    t Operating system(s): Unix based platforms, tested on Ubuntu and MacOSX Programming language: Python, mako All code is open-source and available on GitHub at github.com/artic-network/civet under a GNU General Public License v3.0.
    Python
    suggested: (IPython, RRID:SCR_001658)

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

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.12.13.21267267v1 on medRxiv