Conference proceeding
Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs
2020 54th Asilomar Conference on Signals, Systems, and Computers, pp 516-519
01 Nov 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Viral subtyping can facilitate visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Understanding the virus's evolution spatiotemporally can help forensic strategies. We have identified mutation variation within SARS-CoV-2 sequences via an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Using these ISMs, we show that we can use them for a variety of downstream analyses, such as comparing countries' subtype compositions. We present association graphs as a visualization tool to connect different ISMs based on their co-occurrence across different individuals. In particular, we investigate dominant ISMs for different locations, across different factors such as gender and age.
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Details
- Title
- Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs
- Creators
- Ananya Sen Gupta - University of Iowa,Department of Electrical and Computer Engineering,Iowa City,IA,USAZhengqiao Zhao - Drexel UniversityGail Rosen - Drexel University
- Publication Details
- 2020 54th Asilomar Conference on Signals, Systems, and Computers, pp 516-519
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000681731800101
- Scopus ID
- 2-s2.0-85107786526
- Other Identifier
- 991019169578604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Telecommunications