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.