Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A travel guide to Cytoscape plugins

Abstract

Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5–2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Statistics for registered Cytoscape plugins.
Figure 2: Network analysis workflow.
Figure 3: Relationships between Cytoscape plugins and tags.
Figure 4: Examples of plugin outputs.

Similar content being viewed by others

References

  1. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Yeung, N., Cline, M.S., Kuchinsky, A., Smoot, M.E. & Bader, G.D. Exploring biological networks with Cytoscape software. Curr. Protoc. Bioinformatics 23, 8.13 (2008).

    Google Scholar 

  4. Hermjakob, H. et al. The HUPO PSI's molecular interaction format—a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004).

    CAS  PubMed  Google Scholar 

  5. Demir, E. et al. The BioPAX community standard for pathway data sharing. Nat. Biotechnol. 28, 935–942 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    CAS  PubMed  Google Scholar 

  7. Stark, C. et al. The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 39, D698–D704 (2011).

    CAS  PubMed  Google Scholar 

  8. Gao, J. et al. Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics 25, 137–138 (2009).

    CAS  PubMed  Google Scholar 

  9. Pentchev, K., Ono, K., Herwig, R., Ideker, T. & Kamburov, A. Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape. Bioinformatics 26, 2796–2797 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Hernandez-Toro, J., Prieto, C. & De las Rivas, J. APID2NET: unified interactome graphic analyzer. Bioinformatics 23, 2495–2497 (2007).

    CAS  PubMed  Google Scholar 

  11. Aranda, B. et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8, 528–529 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Gao, J. et al. Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 26, 971–973 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).

    PubMed  PubMed Central  Google Scholar 

  15. Pico, A.R. et al. WikiPathways: pathway editing for the people. PLoS Biol. 6, e184 (2008).

    PubMed  PubMed Central  Google Scholar 

  16. Cerami, E.G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).

    CAS  PubMed  Google Scholar 

  17. Vailaya, A. et al. An architecture for biological information extraction and representation. Bioinformatics 21, 430–438 (2005).

    CAS  PubMed  Google Scholar 

  18. Hamosh, A., Scott, A.F., Amberger, J.S., Bocchini, C.A. & McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514–D517 (2005).

    CAS  PubMed  Google Scholar 

  19. Cusick, M.E. et al. Literature-curated protein interaction datasets. Nat. Methods 6, 39–46 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Montojo, J. et al. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 26, 2927–2928 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee, P.H. & Lee, D. Modularized learning of genetic interaction networks from biological annotations and mRNA expression data. Bioinformatics 21, 2739–2747 (2005).

    CAS  PubMed  Google Scholar 

  22. Henry, C.S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982 (2010).

    CAS  PubMed  Google Scholar 

  23. Assenov, Y., Ramirez, F., Schelhorn, S.E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284 (2008).

    CAS  PubMed  Google Scholar 

  24. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    CAS  PubMed  Google Scholar 

  25. Ladha, J. et al. Glioblastoma-specific protein interaction network identifies PP1A and CSK21 as connecting molecules between cell cycle-associated genes. Cancer Res. 70, 6437–6447 (2010).

    CAS  PubMed  Google Scholar 

  26. Scardoni, G., Petterlini, M. & Laudanna, C. Analyzing biological network parameters with CentiScaPe. Bioinformatics 25, 2857–2859 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Enright, A.J., Van Dongen, S. & Ouzounis, C.A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    CAS  PubMed  Google Scholar 

  29. Bader, G.D. & Hogue, C.W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003).

    PubMed  PubMed Central  Google Scholar 

  30. Rivera, C.G., Vakil, R. & Bader, J.S. NeMo: Network Module identification in Cytoscape. BMC Bioinformatics 11 (suppl. 1), S61 (2010).

    PubMed  PubMed Central  Google Scholar 

  31. Rhrissorrakrai, K. & Gunsalus, K.C. MINE: Module Identification in Networks. BMC Bioinformatics 12, 192 (2011).

    PubMed  PubMed Central  Google Scholar 

  32. Morris, J.H. et al. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics 12, 436 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Li, X., Wu, M., Kwoh, C.K. & Ng, S.K. Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics 11 (suppl. 1), S3 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. Moschopoulos, C.N. et al. Which clustering algorithm is better for predicting protein complexes? BMC Res. Notes 4, 549 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Maere, S., Heymans, K. & Kuiper, M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005).

    CAS  PubMed  Google Scholar 

  37. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Smoot, M., Ono, K., Ideker, T. & Maere, S. PiNGO: a Cytoscape plugin to find candidate genes in biological networks. Bioinformatics 27, 1030–1031 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G.D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

    PubMed  PubMed Central  Google Scholar 

  41. Oesper, L., Merico, D., Isserlin, R. & Bader, G.D. WordCloud: a Cytoscape plugin to create a visual semantic summary of networks. Source Code Biol. Med. 6, 7 (2011).

    PubMed  PubMed Central  Google Scholar 

  42. Haider, S. et al. BioMart Central Portal—unified access to biological data. Nucleic Acids Res. 37, W23–W27 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. van Iersel, M.P. et al. The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. BMC Bioinformatics 11, 5 (2010).

    PubMed  PubMed Central  Google Scholar 

  44. Kincaid, R., Kuchinsky, A. & Creech, M. VistaClara: an expression browser plug-in for Cytoscape. Bioinformatics 24, 2112–2114 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Yang, L., Walker, J.R., Hogenesch, J.B. & Thomas, R.S. NetAtlas: a Cytoscape plugin to examine signaling networks based on tissue gene expression. In Silico Biol. 8, 47–52 (2008).

    PubMed  Google Scholar 

  46. Xia, T., Hemert, J.V. & Dickerson, J.A. OmicsAnalyzer: a Cytoscape plug-in suite for modeling omics data. Bioinformatics 26, 2995–2996 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 (suppl. 1), S233–S240 (2002).

    PubMed  Google Scholar 

  48. Alcaraz, N., Kücük, H., Weile, J., Wipat, A. & Baumbach, J. KeyPathwayMiner: detecting case-specific biological pathways using expression data. Internet Math. 7, 299–313 (2011).

    Google Scholar 

  49. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).

    PubMed  PubMed Central  Google Scholar 

  50. Guziolowski, C., Bourde, A., Moreews, F. & Siegel, A. BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks. BMC Genomics 10, 244 (2009).

    PubMed  PubMed Central  Google Scholar 

  51. Warsow, G. et al. ExprEssence—revealing the essence of differential experimental data in the context of an interaction/regulation net-work. BMC Syst. Biol. 4, 164 (2010).

    PubMed  PubMed Central  Google Scholar 

  52. Li, F. et al. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks. Bioinformatics 26, 275–277 (2010).

    PubMed  Google Scholar 

  53. Emig, D. et al. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res. 38, W755–W762 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wang, L., Khankhanian, P., Baranzini, S.E. & Mousavi, P. iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks. BMC Bioinformatics 12, 380 (2011).

    PubMed  PubMed Central  Google Scholar 

  55. Singhal, M. & Domico, K. CABIN: collective analysis of biological interaction networks. Comput. Biol. Chem. 31, 222–225 (2007).

    CAS  PubMed  Google Scholar 

  56. Petyuk, V.A. et al. Characterization of the mouse pancreatic islet proteome and comparative analysis with other mouse tissues. J. Proteome Res. 7, 3114–3126 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Woźniak, M., Tiuryn, J. & Dutkowski, J. MODEVO: exploring modularity and evolution of protein interaction networks. Bioinformatics 26, 1790–1791 (2010).

    PubMed  Google Scholar 

  58. Hao, Y. et al. OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms. Bioinformatics 27, 883–884 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Srivas, R. et al. Assembling global maps of cellular function through integrative analysis of physical and genetic networks. Nat. Protoc. 6, 1308–1323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Shannon, P.T., Reiss, D.J., Bonneau, R. & Baliga, N.S. The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 7, 176 (2006).

    PubMed  PubMed Central  Google Scholar 

  61. Wittkop, T. et al. Comprehensive cluster analysis with Transitivity Clustering. Nat. Protoc. 6, 285–295 (2011).

    CAS  PubMed  Google Scholar 

  62. Morris, J.H., Huang, C.C., Babbitt, P.C. & Ferrin, T.E. structureViz: linking Cytoscape and UCSF Chimera. Bioinformatics 23, 2345–2347 (2007).

    CAS  PubMed  Google Scholar 

  63. Doncheva, N.T., Klein, K., Domingues, F.S. & Albrecht, M. Analyzing and visualizing residue networks of protein structures. Trends Biochem. Sci. 36, 179–182 (2011).

    CAS  PubMed  Google Scholar 

  64. Erhard, F., Friedel, C.C. & Zimmer, R. FERN – a Java framework for stochastic simulation and evaluation of reaction networks. BMC Bioinformatics 9, 356 (2008).

    PubMed  PubMed Central  Google Scholar 

  65. Merico, D., Gfeller, D. & Bader, G.D. How to visually interpret biological data using networks. Nat. Biotechnol. 27, 921–924 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Work on this review was funded by the National Resource for Network Biology (P41 GM103504) and the San Diego Center for Systems Biology (P50 GM085764). We thank J. Dutkowski, D. Emig and G. Hannum for advice and critical reading of the manuscript. Finally, the greatest thanks go to all of the plugin developers who have enriched the Cytoscape user experience with their ideas. We apologize to those plugin authors whose excellent work was not covered here because of space limitations.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Trey Ideker.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Table 1 (PDF 888 kb)

Supplementary Data

Comprehensive list of Cytoscape plugins that we reviewed and tagged. (XLS 56 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saito, R., Smoot, M., Ono, K. et al. A travel guide to Cytoscape plugins. Nat Methods 9, 1069–1076 (2012). https://doi.org/10.1038/nmeth.2212

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2212

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing