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:

Network Analysis Tools: from biological networks to clusters and pathways

Nature Protocols volume 3, pages 1616–1629 (2008)Cite this article

This article has been updated

Abstract

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein–protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in ∼1 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: Flow chart of the data, tools and results described in this protocol.
Figure 2: Mapping of the clusters obtained with the MCL algorithm on the STRING database data set.
Figure 3: Fuzzy-clusters obtained by combining MCL and the graph-cluster-membership tools.
Figure 4: Most significant associations between MCL clusters versus MIPS complexes.
Figure 5: Result of the fusion between the BioGRID synthetic lethality data set (reference graph) and the yeast–protein interaction data set annotated in the STRING database (query graph).
Figure 6: Result obtained with Pathfinder upon execution of protocol with the study case.

Similar content being viewed by others

Change history

References

  1. Thomas-Chollier, M. et al. RSAT: regulatory sequence analysis tools. Nucleic Acids Res. 36, W119–W127 (2008).

    Article  CAS  Google Scholar 

  2. Brohée, S. et al. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic Acids Res. 36, W444–W451 (2008).

    Article  Google Scholar 

  3. Turatsinze, J.-V., Thomas-Chollier, M., Defrance, M. & van Helden, J. Using RSAT to scan genome sequences for transcription factor binding sites and cis-regulatory modules. Nat. Protoc. doi:10.1038/nprot.2008.97 (2008).

  4. Defrance, M., Janky, R., Sand, O. & van Helden, J. Using RSAT oligo-analysis and dyad-analysis tools to discover regulatory signals in nucleic sequences. Nat. Protoc. doi:10.1038/nprot.2008.98 (2008).

  5. Sand, O., Thomas-Chollier, M., Vervisch, E. & van Helden, J. Analyzing multiple data sets by interconnecting RSAT programs via SOAP Web services–an example with ChIP-chip data. Nat. Protoc. doi:10.1038/nprot.2008.99 (2008).

  6. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. & Barabási, A.L The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Fell, D.A. & Wagner, A. The small world of metabolism. Nat. Biotechnol. 18, 1121–1122 (2000).

    Article  CAS  Google Scholar 

  9. Blatt, M., Wiseman, S. & Domany, E. Superparamagnetic clustering of data. Phys. Rev. Lett. 76, 3251–3254 (1996).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  11. Gagneur, J., Jackson, D.B. & Casari, G. Hierarchical analysis of dependency in metabolic networks. Bioinformatics 19, 1027–1034 (2003).

    Article  CAS  Google Scholar 

  12. Spirin, V. & Mirny, L.A. Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA 100, 12123–12128 (2003).

    Article  CAS  Google Scholar 

  13. King, A.D., Przulj, N. & Jurisica, I. Protein complex prediction via cost-based clustering. Bioinformatics 20, 3013–3020 (2004).

    Article  CAS  Google Scholar 

  14. Van Dongen, S . Graph Clustering by Flow Simulation. PhD Thesis (Centers for Mathematics and Computer Science (CWI), University of Utrecht, 2000).

    Google Scholar 

  15. Pereira-Leal, J.B., Enright, A.J. & Ouzounis, C.A. Detection of functional modules from protein interaction networks. Proteins 54, 49–57 (2004).

    Article  CAS  Google Scholar 

  16. 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 

  17. Brohée, S. & van Helden, J. Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006).

    Article  Google Scholar 

  18. Scott, J., Ideker, T., Karp, R.M. & Sharan, R. Efficient algorithms for detecting signaling pathways in protein interaction networks. J. Comput. Biol. 13, 133–144 (2005).

    Article  Google Scholar 

  19. Bebek, G. & Yang, J. PathFinder: mining signal transduction pathway segments from protein-protein interaction networks. BMC Bioinformatics 8, 335 (2007).

    Article  Google Scholar 

  20. Rahman, S.A., Advani, P., Schunk, R., Schrader, R. & Schomburg, D Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC). Bioinformatics 21, 1189–1193 (2004).

    Article  Google Scholar 

  21. Croes, D., Couche, F., Wodak, S. & van Helden, J. Metabolic PathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res. 33, W326–W330 (2005).

    Article  CAS  Google Scholar 

  22. Croes, D., Couche, F., Wodak, S. & van Helden, J. Inferring meaningful pathways in weighted metabolic networks. J. Mol. Biol. 356, 222–236 (2006).

    Article  CAS  Google Scholar 

  23. 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 

  24. de Nooy, W., Mrvar, A. & Batagelj, V. Exploratory Social Network Analysis with Pajek Series: Structural Analysis in the Social Sciences (No. 27) (Cambridge University Press, Cambridge, 2005).

    Book  Google Scholar 

  25. Baitaluk, M., Sedova, M., Ray, A. & Gupta, A. BiologicalNetworks: visualization and analysis tool for systems biology. Nucleic Acids Res. 34, W466–W471 (2006).

    Article  CAS  Google Scholar 

  26. Hu, Z. et al. VisANT 3.0: new modules for pathway visualization, editing, prediction and construction. Nucleic Acids Res. 35, W625–W632 (2007).

    Article  Google Scholar 

  27. Hull, D. et al. Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 34, W729–W732 (2006).

    Article  CAS  Google Scholar 

  28. Lima-Mendez, G., van Helden, J., Toussaint, A. & Leplae, R. Reticulate representation of evolutionary and functional relationships between phage genomes. Mol. Biol. Evol. 25, 762–777 (2008).

    Article  CAS  Google Scholar 

  29. Croes, D., Couche, F., Wodak, S.J. & van Helden, J. Inferring meaningful pathways in weighted metabolic networks. J. Mol. Biol. 356, 222–236 (2006).

    Article  CAS  Google Scholar 

  30. Croes, D., Couche, F., Wodak, S.J. & van Helden, J. Metabolic PathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res. 33, W326–W330 (2005).

    Article  CAS  Google Scholar 

  31. von Mering, C. et al. STRING 7-recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 35, D358–D362 (2007).

    Article  CAS  Google Scholar 

  32. Breitkreutz, B.J. et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res. 36, D637–D640 (2008).

    Article  CAS  Google Scholar 

  33. Keseler, I.M. et al. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 33, D334–D337 (2005).

    Article  CAS  Google Scholar 

  34. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 42–46 (2002).

    Article  CAS  Google Scholar 

  35. Mewes, H.W. et al. MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res. 32, D41–D44 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  37. Jimenez, V.M. & Marzal, A. Computing the K shortest paths: a new algorithm and an experimental comparison. In Proceeding of the 3rd International Workshop on Algorithm Engineering (WAE 1999) Vol. 1668, 15–29 (Springer-Verlag, London, 1999).

    Google Scholar 

  38. Eppstein, D. Finding the k shortest paths. SIAM J. Comput. 28, 652–673 (1998).

    Article  Google Scholar 

Download references

Acknowledgements

S.B. is the recipient of a PhD grant from the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA). K.F. is supported by the Actions de Recherches Concertées de la Communauté Française de Belgique (ARC grant number 04/09-307). G.L.-M. was funded by a PhD grant from the Fonds Xenophilia [Université Libre de Bruxelles (ULB)] and by a postdoctoral fellowship from the Région Wallonne de Belgique (TransMaze project 415925). The BiGRe laboratory is supported by the BioSapiens Network of Excellence funded under the sixth Framework program of the European Communities (LSHG-CT-2003-503265) and by the Belgian Program on Interuniversity Attraction Poles, initiated by the Belgian Federal Science Policy Office, project P6/25 (BioMaGNet). We acknowledge the students of the Master in Bioinformatics and Modeling (ULB, Belgium) for their useful corrections and suggestions. S.B. and K.F. equally contributed to this article.

Author information

Authors and Affiliations

  1. Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Campus Plaine, CP 263, Boulevard du Triomphe, Bruxelles, B-1050, Belgium

    Sylvain Brohée, Karoline Faust, Gipsi Lima-Mendez, Gilles Vanderstocken & Jacques van Helden

Authors
  1. Sylvain Brohée
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karoline Faust
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gipsi Lima-Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gilles Vanderstocken
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jacques van Helden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jacques van Helden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brohée, S., Faust, K., Lima-Mendez, G. et al. Network Analysis Tools: from biological networks to clusters and pathways. Nat Protoc 3, 1616–1629 (2008). https://doi.org/10.1038/nprot.2008.100

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2008.100

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

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