Displaying Chemical Information on a Biological Network Using Cytoscape

  • Iain M. Wallace
  • Gary D. Bader
  • Guri Giaever
  • Corey NislowEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 781)


Cytoscape is an open-source software package that is widely used to integrate and visualize diverse data sets in biology. This chapter explains how to use Cytoscape to integrate open-source chemical information with a biological network. By visualizing information about known compound–target interactions in the context of a biological network of interest, one can rapidly identify novel avenues to perturb the system with compounds and, for example, potentially identify therapeutically relevant targets. Herein, two different protocols are explained in detail, with no prior knowledge of Cytoscape assumed, which demonstrate how to incorporate data from the ChEMBL database with either a gene–gene or a protein–protein interaction network. ChEMBL is a very large, open-source repository of compound–target information available from the European Molecular Biology Laboratory.

Key words

Cytoscape Network visualization Druggable targets Chemical biological networks Chemical networks 



The second protocol discussed here is inspired by and adapted from a presentation by Anna Gaulton at the “Small molecule ­bioactivity resources at the EBI” course in January 2010. This work was supported by grants from the NHGRI to CN and GG, the CIHR to CN (MOPS-84305) and to GG (MOPS-81340), by NSERC support to GB and IMW, and from a Marie Curie ­fellowship to IMW. GG is a Canada Research Chair in Chemical Biology.


  1. 1.
    Shannon P, et al., Cytoscape: a software ­environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498–504.Google Scholar
  2. 2.
    Merico D, D Gfeller, and GD Bader, How to visually interpret biological data using networks. Nat Biotechnol, 2009. 27(10): p. 921–4.Google Scholar
  3. 3.
    Cline MS, et al., Integration of biological ­networks and gene expression data using Cytoscape. Nature protocols, 2007. 2(10): p. 2366–82.Google Scholar
  4. 4.
    Costanzo M, et al., The genetic landscape of a cell. Science, 2010. 327(5964): p. 425–31.Google Scholar
  5. 5.
    Audeh MW, et al., Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial. Lancet, 2010. 376(9737): p. 245–51.Google Scholar
  6. 6.
    Wishart DS, et al., DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 2006. 34(Database issue): p. D668–72.Google Scholar
  7. 7.
    Kuhn M, et al., STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res, 2008. 36(Database issue): p. D684–8.Google Scholar
  8. 8.
    Montojo J, et al., GeneMANIA Cytoscape Plugin: Fast gene function predictions on the desktop. Bioinformatics, 2010.Google Scholar
  9. 9.
    Ferro A, et al., NetMatch: a Cytoscape plugin for searching biological networks. Bioinformatics, 2007. 23(7): p. 910–2.Google Scholar
  10. 10.
    Cytoscape Home Page.
  11. 11.
    ChemViz Plugin Home page.
  12. 12.
  13. 13.
    Barbie DA, et al., Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 2009. 462(7269): p. 108–12.Google Scholar
  14. 14.
    Luo J, et al., A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell, 2009. 137(5): p. 835–48.Google Scholar
  15. 15.
    Irwin JJ and BK Shoichet, ZINC--a free database of commercially available compounds for virtual screening. Journal of chemical information and modeling, 2005. 45(1): p. 177–82.Google Scholar
  16. 16.
    Zhu F, et al., Update of TTD: Therapeutic Target Database. Nucleic Acids Research, 2010. 38(Database issue): p. D787–91.Google Scholar
  17. 17.
    Orchard S, et al., implementing data standards: a report on the HUPOPSI workshop September 2009, Toronto, Canada. Proteomics, 2010. 10(10): p. 1895–8.Google Scholar
  18. 18.
    Ceol A, et al., MINT, the molecular interaction database: 2009 update. Nucleic Acids Res, 2010. 38(Database issue): p. D532–9.Google Scholar
  19. 19.
  20. 20.
    Liang D-C, et al., K-Ras mutations and N-Ras mutations in childhood acute leukemias with or without mixed-lineage leukemia gene rearrangements. Cancer, 2006. 106(4): p. 950-6.Google Scholar
  21. 21.
    Han L, Y Wang, and SH Bryant, A survey of across-target bioactivity results of small molecules in PubChem. Bioinformatics, 2009. 25(17): p. 2251–5.Google Scholar
  22. 22.
  23. 23.
    Côté RG, et al., The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases. BMC Bioinformatics, 2007. 8: p. 401.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Iain M. Wallace
    • 1
    • 2
    • 3
  • Gary D. Bader
    • 4
    • 1
  • Guri Giaever
    • 1
    • 2
    • 3
  • Corey Nislow
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Molecular GeneticsUniversity of TorontoTorontoCanada
  2. 2.Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoCanada
  3. 3.Department of Pharmaceutical SciencesUniversity of TorontoTorontoCanada
  4. 4.Banting and Best Department of Medical ResearchCentre for Cellular and Biomolecular Research (CCBR)TorontoCanada

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