Book chapter
Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics
Computational Advances in Bio and Medical Sciences, pp.149-162
Lecture Notes in Computer Science, Springer International Publishing
10/19/2022
DOI: 10.1007/978-3-031-17531-2_12
Abstract
The global COVID-19 pandemic continues to have a devastating impact on human population health. In an effort to fully characterize the virus, a significant volume of SARS-CoV-2 genomes have been collected from infected individuals and sequenced. Comprehensive application of this molecular data toward epidemiological analysis in large parts has employed methods arising from phylogenetics. While undeniably valuable, phylogenetic methods have their limitations. For instance, due to their rooted structure, outgroup samples are often needed to contextualize genetic relationships inferred by branching. In this paper we describe an alternative: global and local topological characterization of neighborhood graphs relating viral genomes collected from samples in longitudinal studies. The applicability of our approach is demonstrated by constructing and analyzing such graphs using two distinct datasets from Israel and France, respectively.
Details
- Title: Subtitle
- Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics
- Creators
- Fiona Senchyna - San Francisco State UniversityRahul Singh
- Resource Type
- Book chapter
- Publication Details
- Computational Advances in Bio and Medical Sciences, pp.149-162
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-031-17531-2_12
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 10/19/2022
- Academic Unit
- Computer Science
- Record Identifier
- 9984446545502771
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