Hypergraph models of biological networks to identify genes critical to pathogenic viral response
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Published:2021-05-29
Issue:1
Volume:22
Page:
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ISSN:1471-2105
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Container-title:BMC Bioinformatics
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language:en
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Short-container-title:BMC Bioinformatics
Author:
Feng Song, Heath Emily, Jefferson Brett, Joslyn Cliff, Kvinge Henry, Mitchell Hugh D., Praggastis Brenda, Eisfeld Amie J., Sims Amy C., Thackray Larissa B., Fan Shufang, Walters Kevin B., Halfmann Peter J., Westhoff-Smith Danielle, Tan Qing, Menachery Vineet D., Sheahan Timothy P., Cockrell Adam S., Kocher Jacob F., Stratton Kelly G., Heller Natalie C., Bramer Lisa M., Diamond Michael S., Baric Ralph S., Waters Katrina M., Kawaoka Yoshihiro, McDermott Jason E., Purvine EmilieORCID
Abstract
Abstract
Background
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.
Results
We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.
Conclusions
Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
Funder
National Institute of Allergy and Infectious Diseases Pacific Northwest National Laboratory
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
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