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. 2014 Sep 8:47:8.13.1-24.
doi: 10.1002/0471250953.bi0813s47.

Biological network exploration with Cytoscape 3

Gang Su  1 , John H Morris, Barry Demchak, Gary D Bader
Affiliations

Biological network exploration with Cytoscape 3

Gang Su et al. Curr Protoc Bioinformatics. .

Abstract

Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users.

Keywords: Cytoscape; interactive network visualization; network analysis.

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Figures

Figure 1
Figure 1
Prepare parameters to import data into Cytoscape.
Figure 2
Figure 2
Entering regular expressions for Cytoscape.
Figure 3
Figure 3
Initial display of loaded network.
Figure 4
Figure 4
Heatmap and dendrogram display.
Figure 5
Figure 5
Bingo parameter configurations.
Figure 6
Figure 6
Force based view of a Cytoscape network.
Figure 7
Figure 7
Network import panel parameters.
Figure 8
Figure 8
Force based view on a very large network.
Figure 9
Figure 9
Using viz-mapper to add annotation with attributes.
Figure 10
Figure 10
A sub-cluster of LSM genes.
Figure 11
Figure 11
Attribute import configurations.
Figure 12
Figure 12
Force based view of the human disease network (HDN).
Figure 13
Figure 13
Node attribute import parameters.
Figure 14
Figure 14
Using attribute table to verify data were imported correctly.
Figure 15
Figure 15
Visualize closely associated human diseases using attributes and vizmapper.
Figure 16
Figure 16
Fully annotated Human Disease Network (HDN).
See this image and copyright information in PMC

References

    1. Atkinson HJ, et al. Using sequence similarity networks for visualization of relationships across diverse protein superfamilies. PLoS One. 2009;4:e4345. - PMC - PubMed
    1. Bader GD, et al. BIND--The Biomolecular Interaction Network Database. Nucleic Acids Res. 2001;29:242–5. - PMC - PubMed
    1. Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. - PMC - PubMed
    1. Barrett T, et al. NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic acids 2007 - PMC - PubMed
    1. Bastian M, et al. Gephi: an open source software for exploring and manipulating networks. ICWSM 2009

Publication types