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COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest

Albert Tian Chen, Kevin Altschuler, View ORCID ProfileShing Hei Zhan, View ORCID ProfileYujia Alina Chan, View ORCID ProfileBenjamin E. Deverman
doi: https://doi.org/10.1101/2020.09.23.310565
Albert Tian Chen
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
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Kevin Altschuler
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Shing Hei Zhan
2Department of Zoology & Biodiversity Research Centre, the University of British Columbia, Vancouver BC, Canada
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  • For correspondence: bdeverma@broadinstitute.org alinac@broadinstitute.org zhan@zoology.ubc.ca
Yujia Alina Chan
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
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  • For correspondence: bdeverma@broadinstitute.org alinac@broadinstitute.org zhan@zoology.ubc.ca
Benjamin E. Deverman
1Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
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  • For correspondence: bdeverma@broadinstitute.org alinac@broadinstitute.org zhan@zoology.ubc.ca
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Abstract

COVID-19 CG is an open resource for tracking SARS-CoV-2 single-nucleotide variations (SNVs) and lineages while filtering by location, date, gene, and mutation of interest. COVID-19 CG provides significant time, labor, and cost-saving utility to diverse projects on SARS-CoV-2 transmission, evolution, emergence, immune interactions, diagnostics, therapeutics, vaccines, and intervention tracking. Here, we describe case studies in which users can interrogate (1) SNVs in the SARS-CoV-2 Spike receptor binding domain (RBD) across different geographic regions to inform the design and testing of therapeutics, (2) SNVs that may impact the sensitivity of commonly used diagnostic primers, and (3) the recent emergence of a dominant lineage harboring an S477N RBD mutation in Australia. To accelerate COVID-19 research and public health efforts, COVID-19 CG will be continually upgraded with new features for users to quickly and reliably pinpoint mutations as the virus evolves throughout the pandemic and in response to therapeutic and public health interventions.

Competing Interest Statement

Shing Hei Zhan is a Co-founder and Director of Bioinformatics at Fusion Genomics Corporation, which develops molecular diagnostic assays for infectious diseases. The other authors declare no competing interests.

Footnotes

  • Lead Contact Benjamin E. Deverman, Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, 75 Ames St, Cambridge, MA 02142, Phone (626) 755-7523, bdeverma{at}broadinstitute.org

  • Supplementary file added to acknowledge all of the contributors of SARS-CoV-2 data to GISAID.

  • https://covidcg.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 4.0 International license.
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Posted September 28, 2020.
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COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest
Albert Tian Chen, Kevin Altschuler, Shing Hei Zhan, Yujia Alina Chan, Benjamin E. Deverman
bioRxiv 2020.09.23.310565; doi: https://doi.org/10.1101/2020.09.23.310565
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COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest
Albert Tian Chen, Kevin Altschuler, Shing Hei Zhan, Yujia Alina Chan, Benjamin E. Deverman
bioRxiv 2020.09.23.310565; doi: https://doi.org/10.1101/2020.09.23.310565

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