title: Exploratory Analysis of Biological Networks through Visualization, Clustering, and Functional Annotation in Cytoscape.
abstract: Biological networks define how genes, proteins, and other cellular components interact with one another to carry out specific functions, providing a scaffold for understanding cellular organization. Although in-depth network analysis requires advanced mathematical and computational knowledge, a preliminary visual exploration of biological networks is accessible to anyone with basic computer skills. Visualization of biological networks is used primarily to examine network topology, identify functional modules, and predict gene functions based on gene connectivity within the network. Networks are excellent at providing a bird's-eye view of data sets and have the power of illustrating complex ideas in simple and intuitive terms. In addition, they enable exploratory analysis and generation of new hypotheses, which can then be tested using rigorous statistical and experimental tools. This protocol describes a simple procedure for visualizing a biological network using the genetic interaction similarity network for Saccharomyces cerevisiae as an example. The visualization procedure described here relies on the open-source network visualization software Cytoscape and includes detailed instructions on formatting and loading the data, clustering networks, and overlaying functional annotations.
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544.
authors.forename: Anastasia
authors.identifier: 0000-0002-0123-2052
authors.initials: A
authors.lastname: Baryshnikova
mesh_terms: D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D060066:Protein Interaction Maps; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000502:physiology*; D049490:Systems Biology / Q000379:methods*