Genomics-informed outbreak investigations of SARS-CoV-2 using civet Article Swipe
Áine O’Toole
,
Verity Hill
,
Ben Jackson
,
Rebecca Dewar
,
Nikita Sahadeo
,
Rachel Colquhoun
,
Stefan Rooke
,
JT McCrone
,
M. McHugh
,
Sam Nicholls
,
Radoslaw Poplawski
,
David M. Aanensen
,
Matthew T. G. Holden
,
Thomas R. Connor
,
Nicholas J. Loman
,
Ian Goodfellow
,
Christine V. F. Carrington
,
Kate Templeton
,
Andrew Rambaut
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.13.21267267
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.13.21267267
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.
Related Topics
Concepts
Outbreak
Context (archaeology)
Metadata
Data science
Genomics
Public health
Pandemic
Cluster (spacecraft)
Geography
Biology
Coronavirus disease 2019 (COVID-19)
Computational biology
Evolutionary biology
Genome
Computer science
Virology
Genetics
World Wide Web
Medicine
Pathology
Gene
Archaeology
Programming language
Disease
Infectious disease (medical specialty)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.12.13.21267267
- https://www.medrxiv.org/content/medrxiv/early/2021/12/14/2021.12.13.21267267.full.pdf
- OA Status
- green
- Cited By
- 8
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200121277
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200121277Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.12.13.21267267Digital Object Identifier
- Title
-
Genomics-informed outbreak investigations of SARS-CoV-2 using civetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-14Full publication date if available
- Authors
-
Áine O’Toole, Verity Hill, Ben Jackson, Rebecca Dewar, Nikita Sahadeo, Rachel Colquhoun, Stefan Rooke, JT McCrone, M. McHugh, Sam Nicholls, Radoslaw Poplawski, David M. Aanensen, Matthew T. G. Holden, Thomas R. Connor, Nicholas J. Loman, Ian Goodfellow, Christine V. F. Carrington, Kate Templeton, Andrew RambautList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.12.13.21267267Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2021/12/14/2021.12.13.21267267.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2021/12/14/2021.12.13.21267267.full.pdfDirect OA link when available
- Concepts
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Outbreak, Context (archaeology), Metadata, Data science, Genomics, Public health, Pandemic, Cluster (spacecraft), Geography, Biology, Coronavirus disease 2019 (COVID-19), Computational biology, Evolutionary biology, Genome, Computer science, Virology, Genetics, World Wide Web, Medicine, Pathology, Gene, Archaeology, Programming language, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
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2024: 1, 2023: 3, 2022: 4Per-year citation counts (last 5 years)
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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