Skip to main content

Thank you for visiting 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.

Network visualization and analysis of gene expression data using BioLayout Express3D


Network analysis has an increasing role in our effort to understand the complexity of biological systems. This is because of our ability to generate large data sets, where the interaction or distance between biological components can be either measured experimentally or calculated. Here we describe the use of BioLayout Express3D, an application that has been specifically designed for the integration, visualization and analysis of large network graphs derived from biological data. We describe the basic functionality of the program and its ability to display and cluster large graphs in two- and three-dimensional space, thereby rendering graphs in a highly interactive format. Although the program supports the import and display of various data formats, we provide a detailed protocol for one of its unique capabilities, the network analysis of gene expression data and a more general guide to the manipulation of graphs generated from various other data types.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



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

Figure 1
Figure 2
Figure 3
Figure 4: The Class Viewer on top with the main BioLayout Express3D graph window below.
Figure 5: Rendering of GraphML files in BioLayout Express3D.
Figure 6: Rendering of pathways in BioLayout Express3D.


  1. Reed, J.L., Famili, I., Thiele, I. & Palsson, B.O. Towards multidimensional genome annotation. Nat. Rev. Genet. 7, 130–141 (2006).

    Article  CAS  Google Scholar 

  2. Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).

    Article  CAS  Google Scholar 

  3. Nurse, P. Systems biology: understanding cells. Nature 424, 883 (2003).

    Article  CAS  Google Scholar 

  4. Cassman, M. Barriers to progress in systems biology. Nature 438, 1079 (2005).

    Article  CAS  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. Enright, A.J., Kunin, V. & Ouzounis, C.A. Protein families and TRIBES in genome sequence space. Nucleic Acids Res. 31, 4632–4638 (2003).

    Article  CAS  Google Scholar 

  7. Freeman, T.C. et al. Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS Comput. Biol. 3, 2032–2042 (2007).

    Article  CAS  Google Scholar 

  8. Li, L., Stoeckert, C.J., Jr. & Roos, D.S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–2189 (2003).

    Article  CAS  Google Scholar 

  9. Bader, G.D. & Enright, A.J. In Bioinformatics: A Practical Analysis of Genes and Proteins (ed. Baxevanis, A.D.) 540 (John Wiley, New York, 2005).

    Google Scholar 

  10. Pavlopoulos, G.A. et al. Arena3D: visualization of biological networks in 3D. BMC Syst. Biol. 2, 104 (2008).

    Article  Google Scholar 

  11. Junker, B.H., Klukas, C. & Schreiber, F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7, 109 (2006).

    Article  Google Scholar 

  12. Funahashi, A., Jouraku, A., Matsuoka, Y. & Kitano, H. Integration of CellDesigner and SABIO-RK. In Silico Biol. 7, S81–S90 (2007).

    PubMed  Google Scholar 

  13. Demir, E. et al. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 18, 996–1003 (2002).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Suderman, M. & Hallett, M. Tools for visually exploring biological networks. Bioinformatics 23, 2651–2659 (2007).

    Article  CAS  Google Scholar 

  16. Pavlopoulos, G.A., Wegener, A-L. & Schneider, R. A survey of visualization tools for biological network analysis. BioData Min. 1, 12 (2008).

    Article  Google Scholar 

  17. Franke, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 1011–1025 (2006).

    Article  CAS  Google Scholar 

  18. Kim, S.K. et al. A gene expression map for Caenorhabditis elegans . Science 293, 2087–2092 (2001).

    Article  CAS  Google Scholar 

  19. Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).

    Article  CAS  Google Scholar 

  20. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article 17 (2005).

    Article  Google Scholar 

  21. Brohee, S. & van Helden, J. Evaluation of clustering algorithms for protein–protein interaction networks. BMC Bioinformatics 7, 488 (2006).

    Article  Google Scholar 

  22. van Dongen, S. Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht (2000).

    Google Scholar 

  23. Dennis, G.S.B., Jr et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, P3 (2003).

    Article  Google Scholar 

  24. Subramanian, A.T.P. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–50 (2005).

    Article  CAS  Google Scholar 

  25. Su, A.I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. USA 101, 6062–7 (2004).

    Article  CAS  Google Scholar 

  26. Raza, S. et al. A logic-based diagram of signalling pathways central to macrophage activation. BMC Syst. Biol. 2, 36 (2008).

    Article  Google Scholar 

  27. Fruchterman, T.M. & Rheingold, E.M. Graph drawing by force directed placement. Softw. Exp. Pract. 21, 1129–1164 (1991).

    Article  Google Scholar 

  28. Enright, A.J. Analysis of Protein Function in Complete Genomes PhD thesis, University of Cambridge (2003).

    Google Scholar 

Download references


We thank all those who have been involved with the development of BioLayout Express3D over the years including Leon Goldovsky, Markus Brosch, Ildefonso Cases and Christos Ouzounis. We also thank the BBSRC who are currently funding the development of the program (BB/F003722/1) together with the Wellcome Trust (GR077040RP) who previously provided support.

Author information

Authors and Affiliations



T.C.F., A.T., S.v.D. and A.J.E. wrote this paper. T.C.F. and A.T. conceived and designed the individual protocols.

Corresponding author

Correspondence to Tom C Freeman.

Supplementary information

Supplementary Manual

BioLayout Express3D manual providing details of all the functions within the tool. (PDF 1503 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Theocharidis, A., van Dongen, S., Enright, A. et al. Network visualization and analysis of gene expression data using BioLayout Express3D. Nat Protoc 4, 1535–1550 (2009).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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