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.

  • Protocol
  • Published:

Topological analysis and interactive visualization of biological networks and protein structures

Nature Protocols volume 7, pages 670–685 (2012)Cite this article

Subjects

Abstract

Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Outline of the protocol.
Figure 2: Screenshot of network statistics computed by NetworkAnalyzer.
Figure 3: Simultaneous view of RIN and 3D protein structure by RINalyzer.
Figure 4: Comparison network generated by RINalyzer.

Similar content being viewed by others

References

  1. Barabasi, A.L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    Article  CAS  Google Scholar 

  2. Beyer, A., Bandyopadhyay, S. & Ideker, T. Integrating physical and genetic maps: from genomes to interaction networks. Nat. Rev. Genet. 8, 699–710 (2007).

    Article  CAS  Google Scholar 

  3. Frishman, D. et al. Protein-protein interactions: analysis and prediction. in Modern Genome Annotation: the Biosapiens Network 353–410 (Springer-Verlag, 2008).

  4. Przytycka, T.M., Singh, M. & Slonim, D.K. Toward the dynamic interactome: it's about time. Brief Bioinform. 11, 15–29 (2010).

    Article  CAS  Google Scholar 

  5. Yamada, T. & Bork, P. Evolution of biomolecular networks: lessons from metabolic and protein interactions. Nat. Rev. Mol. Cell Biol. 10, 791–803 (2009).

    Article  CAS  Google Scholar 

  6. Zhang, S., Jin, G., Zhang, X.S. & Chen, L. Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 7, 2856–2869 (2007).

    Article  CAS  Google Scholar 

  7. Bode, C. et al. Network analysis of protein dynamics. FEBS Lett. 581, 2776–2782 (2007).

    Article  Google Scholar 

  8. Csermely, P. Creative elements: network-based predictions of active centres in proteins and cellular and social networks. Trends Biochem. Sci. 33, 569–576 (2008).

    Article  CAS  Google Scholar 

  9. Krishnan, A., Zbilut, J.P., Tomita, M. & Giuliani, A. Proteins as networks: usefulness of graph theory in protein science. Curr. Protein Pept. Sci. 9, 28–38 (2008).

    Article  CAS  Google Scholar 

  10. Vishveshwara, S., Ghosh, A. & Hansia, P. Intra and inter-molecular communications through protein structure network. Curr. Protein Pept. Sci. 10, 146–160 (2009).

    Article  CAS  Google Scholar 

  11. Welsch, C. et al. Molecular basis of telaprevir resistance due to V36 and T54 mutations in the NS3-4A protease of the hepatitis C virus. Genome Biol. 9, R16 (2008).

    Article  Google Scholar 

  12. Albert, R. Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005).

    Article  CAS  Google Scholar 

  13. Almaas, E. Biological impacts and context of network theory. J. Exp. Biol. 210, 1548–1558 (2007).

    Article  Google Scholar 

  14. Brohee, S., Faust, K., Lima-Mendez, G., Vanderstocken, G. & van Helden, J. Network analysis tools: from biological networks to clusters and pathways. Nat. Protoc. 3, 1616–1629 (2008).

    Article  CAS  Google Scholar 

  15. Junker, B.H. & Schreiber, F. Analysis of Biological Networks (John Wiley & Sons, 2008).

  16. Pavlopoulos, G.A. et al. Using graph theory to analyze biological networks. BioData Min. 4, 10 (2011).

    Article  Google Scholar 

  17. Przulj, N. Protein-protein interactions: making sense of networks via graph-theoretic modeling. BioEssays 33, 115–123 (2011).

    Article  CAS  Google Scholar 

  18. Zhu, X., Gerstein, M. & Snyder, M. Getting connected: analysis and principles of biological networks. Genes Dev. 21, 1010–1024 (2007).

    Article  CAS  Google Scholar 

  19. Chuang, H.Y., Hofree, M. & Ideker, T. A decade of systems biology. Annu. Rev. Cell Dev. Biol. 26, 721–744 (2011).

    Article  Google Scholar 

  20. 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 

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

    Article  CAS  Google Scholar 

  22. O'Madadhain, J., Fisher, D., White, S. & Boey, Y.B. The JUNG (Java Universal Network/Graph) Framework. Techn. Rep. UCI-ICS 03–17 (2003).

  23. Mehlhorn, K. & Näher, S. LEDA: A Platform for Combinatorial and Geometric Computing (Cambridge University Press, 1999).

  24. Hagberg, A.A., Schult, D.A. & Swart, P.J. Exploring network structure, dynamics, and function using NetworkX. Proceedings of the 7th Python in Science Conference 11–15 (2008).

  25. Csárdi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695 (2006).

  26. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M. & Morris, M. statnet: software tools for the representation, visualization, analysis and simulation of network data. J. Stat. Softw. 24, 1548–7660 (2008).

    Article  Google Scholar 

  27. Butts, C.T. Social network analysis with sna. J. Stat. Softw. 24 (2008).

  28. Opsahl, T., Agneessens, F. & Skvoretz, J. Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Networks 32, 245–251 (2010).

    Article  Google Scholar 

  29. Mueller, L.A.J., Kugler, K.G., Dander, A., Graber, A. & Dehmer, M. QuACN: an R package for analyzing complex biological networks quantitatively. Bioinformatics 27, 140–141 (2011).

    Article  CAS  Google Scholar 

  30. Batagelj, V. & Mrvar, A. Pajek—program for large network analysis. Connections 21, 47–57 (1998).

    Google Scholar 

  31. Hu, Z. et al. VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res. W352–W357 (2005).

  32. Köhler, J. et al. Graph-based analysis and visualization of experimental results with ONDEX. Bioinformatics 22, 1383–1390 (2006).

    Article  Google Scholar 

  33. Garcia-Garcia, J., Guney, E., Aragues, R., Planas-Iglesias, J. & Oliva, B. Biana: a software framework for compiling biological interactions and analyzing networks. BMC Bioinformatics 11, 56 (2010).

    Article  Google Scholar 

  34. 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 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Doncheva, N.T., Klein, K., Domingues, F.S. & Albrecht, M. Analyzing and visualizing residue networks of protein structures. Trends Biochem. Sci. 36, 179–184 (2011).

    Article  CAS  Google Scholar 

  40. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  41. Astsaturov, I. et al. Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci. Signal 3, ra67 (2010).

    Article  Google Scholar 

  42. Ragusa, M. et al. Expression profile and specific network features of the apoptotic machinery explain relapse of acute myeloid leukemia after chemotherapy. BMC Cancer 10, 377 (2010).

    Article  Google Scholar 

  43. Lorenz, W. et al. Microarray analysis and scale-free gene networks identify candidate regulators in drought-stressed roots of loblolly pine (P. taeda L.). BMC Genomics 12, 264 (2011).

    Article  CAS  Google Scholar 

  44. Radrich, K. et al. Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst. Biol. 4, 114 (2010).

    Article  Google Scholar 

  45. Choura, M. & Rebaï, A. Application of computational approaches to study signalling networks of nuclear and Tyrosine kinase receptors. Biol. Direct 5, 58 (2010).

    Article  Google Scholar 

  46. Gu, H., Zhu, P., Jiao, Y., Meng, Y. & Chen, M. PRIN: a predicted rice interactome network. BMC Bioinformatics 12, 161 (2011).

    Article  Google Scholar 

  47. Yu, H. et al. Next-generation sequencing to generate interactome data sets. Nat. Methods 8, 478–480 (2011).

    Article  CAS  Google Scholar 

  48. Pettersen, E.F. et al. UCSF Chimera: a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  Google Scholar 

  49. Morris, J.H., Meng, E.C. & Ferrin, T.E. Computational tools for the interactive exploration of proteomic and structural data. Mol. Cell. Proteomics 9, 1703–1715 (2010).

    Article  CAS  Google Scholar 

  50. Su, G., Kuchinsky, A., Morris, J.H., States, D.J. & Meng, F. GLay: community structure analysis of biological networks. Bioinformatics 26, 3135–3137 (2010).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Emig, D. et al. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res. 38, W755–W762 (2011).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  56. Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  Google Scholar 

  57. Word, J.M., Lovell, S.C., Richardson, J.S. & Richardson, D.C. Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol. 285, 1735–1747 (1999).

    Article  CAS  Google Scholar 

  58. Word, J.M. et al. Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms. J. Mol. Biol. 285, 1711–1733 (1999).

    Article  CAS  Google Scholar 

  59. Fermi, G., Perutz, M.F., Shaanan, B. & Fourme, R. The crystal structure of human deoxyhaemoglobin at 1.74 A resolution. J. Mol. Biol. 175, 159–174 (1984).

    Article  CAS  Google Scholar 

  60. Thanki, N. et al. Crystal structure of a complex of HIV-1 protease with a dihydroxyethylene-containing inhibitor: comparisons with molecular modeling. Protein Sci. 1, 1061–1072 (1992).

    Article  CAS  Google Scholar 

  61. Davies, D.R. The structure and function of the aspartic proteinases. Annu. Rev. Biophys. Biophys. Chem. 19, 189–215 (1990).

    Article  CAS  Google Scholar 

  62. Martin, A.J. et al. RING: networking interacting residues, evolutionary information and energetics in protein structures. Bioinformatics 27, 2003–2005 (2011).

    Article  CAS  Google Scholar 

  63. Diestel, R. Graph Theory (Springer-Verlag, 2010).

  64. Erdo′′s, P. & Rényi, A. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960).

    Google Scholar 

  65. Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  CAS  Google Scholar 

  66. Barabasi, A.L. Scale-free networks: a decade and beyond. Science 325, 412–413 (2009).

    Article  CAS  Google Scholar 

  67. Lima-Mendez, G. & van Helden, J. The powerful law of the power law and other myths in network biology. Mol. Biosyst. 5, 1482–1493 (2009).

    Article  CAS  Google Scholar 

  68. Dong, J. & Horvath, S. Understanding network concepts in modules. BMC Syst. Biol. 1, 24 (2007).

    Article  Google Scholar 

  69. Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910–913 (2002).

    Article  CAS  Google Scholar 

  70. Watts, D.J. & Strogatz, S.H. Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998).

    Article  CAS  Google Scholar 

  71. Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N. & Barabasi, A.L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002).

    Article  CAS  Google Scholar 

  72. Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005).

    Article  CAS  Google Scholar 

  73. Brandes, U. A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001).

    Article  Google Scholar 

  74. Shimbel, A. Structural parameters of communication networks. Bull. Math. Biophys. 15, 501–507 (1953).

    Article  Google Scholar 

  75. Yoon, J., Blumer, A. & Lee, K. An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality. Bioinformatics 22, 3106–3108 (2006).

    Article  CAS  Google Scholar 

  76. Freeman, L.C. Centrality in social networks: conceptual clarification. Soc. Networks 1, 215–239 (1979).

    Article  Google Scholar 

  77. Newman, M.E.J. A measure of betweenness centrality based on random walks. Soc. Networks 27, 39–54 (2005).

    Article  Google Scholar 

  78. Tetali, P. Random walks and effective resistance of networks. J. Theoret. Probab. 4, 101–109 (1991).

    Article  Google Scholar 

Download references

Acknowledgements

We thank D. Buezas, T. Kacprowski and C. Weichenberger for their useful comments on the workflows and the manuscript. This study was partially funded by the BMBF through the German National Genome Research Network (NGFN) and the Greifswald Approach to Individualized Medicine (GANI_MED). It was also conducted in the context of the DFG-funded Cluster of Excellence for Multimodal Computing and Interaction.

Author information

Authors and Affiliations

  1. Max Planck Institute for Informatics, Saarbrücken, Germany

    Nadezhda T Doncheva, Yassen Assenov & Mario Albrecht

  2. Center for Biomedicine, EURAC research, Bolzano, Italy

    Francisco S Domingues

  3. Institute of Biometrics and Medical Informatics, University Medicine Greifswald, Greifswald, Germany

    Mario Albrecht

Authors
  1. Nadezhda T Doncheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yassen Assenov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco S Domingues
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Albrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.T.D. conceived and drafted the workflows. Y.A. contributed to the workflows. N.T.D., Y.A., F.S.D. and M.A. wrote and approved the manuscript.

Corresponding author

Correspondence to Mario Albrecht.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

Supplementary Figure 1 | Results dialog of NetworkAnalyzer The first tab of this dialog shows the computed simple network parameters. The other tabs display complex network parameters as histograms or scatter plots. The complete set of topological parameters is referred to as network statistics. These statistics can be saved to and reloaded from a text file to avoid recomputation. The topological parameters that are computed for each node or edge, e.g., node degree, neighborhood connectivity, and betweenness, can be visualized in the network view. (PDF 1921 kb)

Supplementary Figure 2 | Mapping topological parameters to the network view In the PPI network from Yu et al. (Nat Methods, 8:478–480, 2011), the node degree and clustering coefficient are mapped to node size and node color, respectively. Nodes of low degree appear as small circles, whereas nodes of high degree are enlarged. Additionally, nodes with low clustering n green, and nodes with high clustering coefficient in red.

Supplementary Figure 3 | Visual Properties dialog of RINalyzer RINalyzer provides a user-friendly interface for customizing the network view of RINs in Cytoscape. The visual properties are arranged in two main groups: general and node properties (left), and edge properties (right). For instance, the user can color nodes according to secondary structure, adjust the style of node labels, or select the colors of the different edge interaction types. The default values of the visual properties can be restored at any time; a new set of values can also be defined as default.

Supplementary Figure 4 | Backbone representation of the HIV-1 protease structure and the corresponding RIN The RIN of the HIV-1 protease (PDB identifier 1HIV) is displayed by RINalyzer in Cytoscape (top right), and the backbone representation of the corresponding 3D structure is shown in UCSF Chimera (bottom right). RIN nodes and protein residues are colored according to secondary structure: blue for helices and red for strands. In the network view of the RIN, all non-covalent interaction edges are hidden by using the RIN Visual Properties dialog (left) so that only the backbone edges are visible. This screenshot is taken after finishing Step 2B(v) of the protocol.

Supplementary Figure 5 | Node Sets interface of RINalyzer This user interface offers four menus to create, load, save, and modify sets of residue nodes (left). It also supports typical set operations such as union and intersection RINalyzer keeps track of all operations performed with each node set (left bottom). The nodes in the network view of the RIN for the HIV-1 protease (top right) and the residues in the corresponding 3D protein structure (bottom right) are colored according to the node set they belong to: light blue for Chain A; dark blue for Chain A Interface; orange for Chain B; red for Chain B Interface; and pink for Chain I. The node set Chain A Interface is a subset of Chain A. This screenshot is taken after finishing Step 2B(ix) of the protocol.

Supplementary Figure 6 | Centrality Analysis Settings of RINalyzer This dialog allows customizing a number of settings for the network centrality analysis (see Box 3 for a detailed description).

Supplementary Figure 7 | Centrality analysis results by RINalyzer The panel RINalyzer Centralities consists of different sections. The first section contains general analysis information and allows the user to show all computed centrality values in a table or to save them to a file. The other sections provide access to the values of each centrality measure. In particular, the user can apply a selection filter to select nodes with centrality values in a given range, view the values in a table, or save them to a file. This figure shows the results of the centrality analysis performed in Step 2B(xii) and explored in Steps 2B(xiii) and (xiv)

Supplementary Figure 8 | Overview of batch analysis using NetworkAnalyzer The user can perform automatized batch analysis of multiple networks. First, the user has to select an input and output directory for the batch analysis (a). Afterwards, NetworkAnalyzer performs an automatic analysis and informs about the computation progress (b). Finally, the batch analysis results are presented in a dialog that lists each loaded network with its edge interpretation and the resulting network statistics file (c).

Supplementary Figure 9 | Network statistics of the four subunits of human deoxyhaemoglobin The batch analysis results dialog allows users to open the network statistics of the analyzed networks. The computed simple topological parameters for the networks representing all subunits of the human deoxyhaemoglobin are displayed as follows: chain A (top left), chain B (top right), chain C (bottom left), and chain D (bottom right).

Supplementary Figure 10 | Simplified view of a comparison network generated by RINalyzer Edge line styles correspond to non-covalent residue interactions (edge type combi:all_all) that are preserved in both subunits (black solid lines), present only in the α subunit (green dashed lines) or only in the β subunit (red dotted lines). RIN nodes that represent aligned residues are pink, whereas nodes that correspond to unaligned residues are either green (α subunit only) or red (β subunit only). This screenshot is taken after finishing Step 2C(v) of the protocol.

Supplementary Data 1

This file is in the simple interaction format (SIF) and contains the protein-protein interactions from Yu et al. (Nat Methods, 8:478–480, 2011). (TXT 17 kb)

Supplementary Data 2

This data file contains four RINs that represent the four subunits of human deoxyhaemoglobin (chains A, B, C, and D in the PDB structure with identifier 4HHB). The RINs were generated using the RINerator package (http://rinalyzer.de/rinerator.php). (ZIP 29 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Doncheva, N., Assenov, Y., Domingues, F. et al. Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc 7, 670–685 (2012). https://doi.org/10.1038/nprot.2012.004

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2012.004

This article is cited by

Comments

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.

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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