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CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design

View ORCID ProfileHayden C. Metsky, View ORCID ProfileCatherine A. Freije, Tinna-Solveig F. Kosoko-Thoroddsen, View ORCID ProfilePardis C. Sabeti, View ORCID ProfileCameron Myhrvold
doi: https://doi.org/10.1101/2020.02.26.967026
Hayden C. Metsky
1Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
2Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
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  • For correspondence: hmetsky@broadinstitute.org cmyhrvol@broadinstitute.org
Catherine A. Freije
1Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
3Program in Virology, Harvard Medical School, Boston, MA, USA
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Tinna-Solveig F. Kosoko-Thoroddsen
1Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
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Pardis C. Sabeti
1Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
4Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
5Howard Hughes Medical Institute, Chevy Chase, MD, USA
6Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Cameron Myhrvold
1Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA
4Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
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  • For correspondence: hmetsky@broadinstitute.org cmyhrvol@broadinstitute.org
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Abstract

The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay. We demonstrate the sensitivity and speed of this assay using synthetic targets with fluorescent and lateral flow detection. Moreover, our provided protocol can be extended for testing the other 66 provided designs. Assay designs are available at https://adapt.sabetilab.org/.

Footnotes

  • Amended title; revised language around diagnostics to more accurately reflect current state; fixed issue with links.

  • https://adapt.sabetilab.org

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 02, 2020.
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CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
Hayden C. Metsky, Catherine A. Freije, Tinna-Solveig F. Kosoko-Thoroddsen, Pardis C. Sabeti, Cameron Myhrvold
bioRxiv 2020.02.26.967026; doi: https://doi.org/10.1101/2020.02.26.967026
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CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
Hayden C. Metsky, Catherine A. Freije, Tinna-Solveig F. Kosoko-Thoroddsen, Pardis C. Sabeti, Cameron Myhrvold
bioRxiv 2020.02.26.967026; doi: https://doi.org/10.1101/2020.02.26.967026

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