COVID-19 CG

COVID-19 CG tracks SARS-CoV-2 single-nucleotide variations (SNVs) and viral lineages to support analysis of viral evolution, geographic spread, and impacts on diagnostics, therapeutics, and vaccines.


Key Features:

  • Comprehensive SNV and lineage repository: Provides a repository of SARS-CoV-2 single-nucleotide variations (SNVs) and viral lineage information for monitoring genetic changes over time.
  • Customizable filters: Enables filtering of sequence data and mutation records by geographic location, collection date, gene, and mutation type.
  • Spike RBD analysis: Allows targeted investigation of SNVs within the Spike protein receptor binding domain (RBD) across regions.
  • Diagnostic primer assessment: Enables examination of mutations that may affect diagnostic primers.
  • Emerging lineage detection: Identifies and reports emerging dominant viral lineages and specific mutations (e.g., S477N observed in Australia).

Scientific Applications:

  • Therapeutics and testing development: Investigating Spike RBD SNVs across regions to inform development and testing of therapeutics.
  • Diagnostic assay evaluation: Assessing mutations that could impact diagnostic primer binding and assay accuracy.
  • Genomic surveillance: Monitoring emergence and geographic spread of dominant viral lineages to inform public health strategies.
  • Research on viral biology and interventions: Supporting studies of transmission, evolution, emergence, immune interactions, diagnostics, therapeutics, vaccine development, and tracking interventions.

Methodology:

Filters sequences and associated metadata by geographic location, collection date, gene, and mutation type to track and report SARS-CoV-2 SNVs and viral lineage distributions.

Topics

Collections

Details

License:
MIT
Tool Type:
web application
Programming Languages:
JavaScript, Python
Added:
1/18/2021
Last Updated:
2/17/2021

Operations

Publications

Chen AT, Altschuler K, Zhan SH, Chan YA, Deverman BE. COVID-19 CG: Tracking SARS-CoV-2 mutations by locations and dates of interest. Unknown Journal. 2020. doi:10.1101/2020.09.23.310565. PMID:32995794. PMCID:PMC7523124.

Links