Cytoscape: a software environment for integrated models of biomolecular interaction networks

• Published in: Genome research Vol. 13; no. 11; pp. 2498 - 2504
• Main Authors: Shannon, Paul, Markiel, Andrew, Ozier, Owen, Baliga, Nitin S, Wang, Jonathan T, Ramage, Daniel, Amin, Nada, Schwikowski, Benno, Ideker, Trey
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.11.2003
• Subjects: Algorithms; Archaeal Proteins - chemistry; Archaeal Proteins - metabolism; Bacteriophage lambda - physiology; Computational Biology - methods; Halobacterium - chemistry; Halobacterium - cytology; Halobacterium - physiology; Internet; Models, Biological; Neural Networks (Computer); Phenotype; Resources; Software - trends; Software Design; Stochastic Processes
• ISSN: 1088-9051; 1549-5469
• DOI: 10.1101/gr.1239303

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.