ArticleiScience2025
CovidTGI: A tool to investigate the temporal genetic instability of SARS-CoV-2 variants.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
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Abstract
The COVID-19 pandemic has underscored the need for fast and accurate epidemiology, particularly due to the high observed mutation frequency in SARS-CoV-2. This study aims to explore the evolution of SARS-CoV-2 through a global analysis. To facilitate a comparative analysis of temporal mutation data, we developed CovidTGI, a Shiny web application. CovidTGI provides insights into observed mutation frequencies and the temporal relationships among mutations across various clades in different geographical regions. Our tool relies on a database that includes 2 million samples obtained from the National Center for Biotechnology Information (NCBI), along with 500 in-house Sicilian samples collected between May 2021 and June 2022. From this smaller group of samples, we identified key variants that are prevalent within a specific clade. Our tool is designed to study the evolution of SARS-CoV-2, which clearly follows a complex trajectory. This complexity highlights the necessity for sophisticated tools like CovidTGI to understand and track the evolution of this virus.
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