Integration of biological networks and gene expression data using Cytoscape
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Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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A number of recent papers have used pathway analysis workflow to highlight genes and pathways active in specific model organisms or diseased tissues.
“A number of recent papers have used this pathway analysis workflow to highlight genes and pathways that are active in specific model organisms or diseased tissues, such as breast cancer, using gene and protein expression, copy number variants (CNVs) and SNPs19,44-49.”
From The BioPAX community standard for pathway data sharing (2010) · cites this paper as [44]
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- core, including automatic loading of plugins and the VizMapper. K.H., J.M. and A.R.P. were instrumental in the usability analysis, specification and prototyping of many of these features. This protocol was written by M.S.C., M.S., A.K., S.M., C.W., G.D.B. and T.I. Published online at http://www.natureprotocols.com Reprints and permissions information is available online at http://npg.nature.com/ reprintsandpermissions
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The BioPAX community standard for pathway data sharing
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“A number of recent papers have used this pathway analysis workflow to highlight genes and pathways that are active in specific model organisms or diseased tissues, such as breast cancer, using gene and protein expression, copy number variants and single-nucleotide polymorphisms 19,44–49 .”
Self-citationWikiPathways: Pathway Editing for the People
Pico, A. R., Kelder, T., van Iersel, M. P., Hanspers, K., Conklin, B. R., & Evelo, C. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060184 · Cited by 701 · see citing works
Self-citationNetwork-based prediction of human tissue-specific metabolism
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ConsensusPathDB--a database for integrating human functional interaction networks
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Pathway and network-based analysis of genome-wide association studies in multiple sclerosis
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Regulatory networks define phenotypic classes of human stem cell lines
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Shulzhenko, N., Morgun, A., Hsiao, W., Battle, M., Yao, M., Gavrilova, O., Orandle, M., Mayer, L., Macpherson, A. J., McCoy, K. D., et al. (2011) · Nature Medicine · doi:10.1038/nm.2505 · Cited by 349 · see citing works
Co-occurrence of anaerobic bacteria in colorectal carcinomas
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Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development
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A Systems Biology Approach for the Investigation of the Heparin/Heparan Sulfate Interactome
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Identity and function of a large gene network underlying mutagenic repair of DNA breaks
Al Mamun, A. A. M., Lombardo, M.-J., Shee, C., Lisewski, A. M., Gonzalez, C., Lin, D., Nehring, R. B., Saint-Ruf, C., Gibson, J. L., Frisch, R. L., et al. (2012) · Science · doi:10.1126/science.1226683 · Cited by 224 · see citing works
van den Bergh, M. R., Biesbroek, G., Rossen, J. W. A., de Steenhuijsen Piters, W. A. A., Bosch, A. A. T. M., van Gils, E. J. M., Wang, X., Boonacker, C. W. B., Veenhoven, R. H., Bruin, J. P., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047711 · Cited by 208 · see citing works
Luo, H., Chen, J., Shi, L., Mikailov, M., Zhu, H., Wang, K., He, L., & Yang, L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr299 · Cited by 206 · see citing works
A Physical Interaction Network of Dengue Virus and Human Proteins
Khadka, S., Vangeloff, A. D., Zhang, C., Siddavatam, P., Heaton, N. S., Wang, L., Sengupta, R., Sahasrabudhe, S., Randall, G., Gribskov, M., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.012187 · Cited by 175 · see citing works
SHANK2 mutations associated with autism spectrum disorder cause hyperconnectivity of human neurons
Zaslavsky, K., Zhang, W.-B., McCready, F. P., Rodrigues, D. C., Deneault, E., Loo, C., Zhao, M., Ross, P. J., El Hajjar, J., Romm, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0365-8 · Cited by 174 · see citing works
Bianchi, G., Martella, R., Ravera, S., Marini, C., Capitanio, S., Orengo, A., Emionite, L., Lavarello, C., Amaro, A., Petretto, A., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.3688 · Cited by 173 · see citing works
Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution
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Time-Resolved and Tissue-Specific Systems Analysis of the Pathogenesis of Insulin Resistance
Kleemann, R., van Erk, M., Verschuren, L., van den Hoek, A. M., Koek, M., Wielinga, P. Y., Jie, A., Pellis, L., Bobeldijk-Pastorova, I., Kelder, T., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008817 · Cited by 146 · see citing works
Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data
Bandyopadhyay, S., Kelley, R., Krogan, N. J., & Ideker, T. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000065 · Cited by 141 · see citing works
Self-citationMetabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase
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Systems‐wide analysis of BCR signalosomes and downstream phosphorylation and ubiquitylation
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Dissecting the retinoid-induced differentiation of F9 embryonal stem cells by integrative genomics
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Mleczko-Sanecka, K., Roche, F., Rita da Silva, A., Call, D., D’Alessio, F., Ragab, A., Lapinski, P. E., Ummanni, R., Korf, U., Oakes, C., et al. (2014) · Blood · doi:10.1182/blood-2013-07-515957 · Cited by 74 · see citing works
Neuron Enriched Nuclear Proteome Isolated from Human Brain
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Transcriptomic profiles of aging in purified human immune cells
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A practical data processing workflow for multi-OMICS projects
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Widespread genotype-phenotype correlations in intellectual disability
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Structure and Non-Structure of Centrosomal Proteins
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Presence and utility of intrinsically disordered regions in kinases
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Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network
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MicroRNA Profiling in Aging Brain of PSEN1/PSEN2 Double Knockout Mice
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- Authors
- Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., Christmas, R., Avila-Campilo, I., Creech, M., Gross, B., Hanspers, K., Isserlin, R., Kelley, R., Killcoyne, S., Lotia, S., Maere, S., Morris, J., Ono, K., Pavlovic, V., Pico, A. R., Vailaya, A., Wang, P.-L., Adler, A., Conklin, B. R., Hood, L., Kuiper, M., Sander, C., Schmulevich, I., Schwikowski, B., Warner, G. J., Ideker, T., & Bader, G. D.
- Affiliations
- Institut Pasteur (FR)
- Memorial Sloan Kettering Cancer Center (US)
- Institute for Systems Biology (US)
- Gladstone Institutes (US)
- University of Toronto (CA)
- Ghent University (BE)
- University of California, San Francisco (US)
- Unilever (United Kingdom) (GB)
- Corresponding author
- Ideker, Trey
- ORCID
- Venue
- Nature Protocols
- Published
- 2007-09-27
- Volume
- 2
- Issue
- 10
- Pages
- 2366-2382
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC3685583
- OpenAlex
- W2137531873
- Cited by
- 2,507
- References
- 75
- Citations by year
- 2026: 44 · 2025: 94 · 2024: 107 · 2023: 106 · 2022: 126 · 2021: 158 · 2020: 137 · 2019: 128 · 2018: 155 · 2017: 173 · 2016: 169 · 2015: 194 · 2014: 195 · 2013: 157 · 2012: 181
- FWCI
- 27.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Funders
- Genome Canada
- National Institute of General Medical Sciences (R01GM070743, GM070743-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- Cytoscape, biological networks, gene expression profiling, mRNA expression, protein-protein interaction network, network integration, protein complexes, functional modules, gene ontology enrichment, functional genomics, proteomics
- MeSH terms
- RNA, Messenger, Software, Computational Biology, Gene Expression Profiling, Genomics, Proteomics, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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