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Using the NeAT Toolbox to Compare Networks to Networks, Clusters to Clusters, and Network to Clusters

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Book cover Bacterial Molecular Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 804))

Abstract

In this chapter, we present and interpret some operations on biological networks that can easily performed with NeAT, a set of Web tools aimed at studying biological networks (or graphs) and classifications. These approaches are of particular interest for biologists and scientists who need to assess the reliability of new datasets (either experimental or predicted) by comparing them to established references. Firstly, we describe the steps that will allow a nonspecialist user to compare two networks to compute their union and the statistical significance of their intersection. Next, we show how to map functional classes (e.g., GO categories, sets of regulons or complexes) onto a biological network. A third protocol explains how to compare two sets of functional classes, e.g., to assess statistically the biological relevance of some computationally returned groups of genes (clustering). The metrics as well as the results obtained by following the different protocols are extensively described and explained. NeAT is available at the following URL: http://rsat.bigre.ulb.ac.be/rsat/index_neat.html.

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References

  1. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks, Julien P, Roth A, Simonovic M, Bork P, von Mering C. (2009) STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res, 37(Database issue):D412–D416. [http://www.dx.doi.org/10.1093/nar/gkn760]].

    Google Scholar 

  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25:25–29. [http://www.dx.doi.org/10.1038/75556].

    Google Scholar 

  3. Gama-Castro S, Jiménez-Jacinto V, Peralta-Gil M, Santos-Zavaleta A, Peñaloza-Spinola MI, Contreras-Moreira B, Segura-Salazar J, Muñniz- Rascado L, Martínez-Flores I, Salgado H, Bonavides-Martínez C, Abreu- Goodger C, Rodríguez-Penagos C, Miranda-Ríos J, Morett E, Merino E, Huerta AM, Treviño-Quintanilla L, Collado-Vides J. (2008) RegulonDB (ver- sion 6.0): gene regulation model of Escherichia coli K-12 be- yond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Res, 36(Database issue):D120–D124. [http://www.dx.doi.org/10.1093/nar/gkm994]. 26.

    Google Scholar 

  4. King AD, Przulj N, Jurisica I. (2004) Protein complex prediction via cost- based clustering. Bioinformatics, 20(17):3013–3020. [http://www.dx.doi.org/10.1093/bioinformatics/bth351].

    Google Scholar 

  5. Enright AJ, Dongen SV, Ouzounis CA. (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res, 30(7):1575–1584.

    Google Scholar 

  6. Blatt M, Wiseman S, Domany E. (1996) Superparamagnetic clustering of data. Phys Rev Lett, 76(18):3251–3254.

    Google Scholar 

  7. Bader GD, Hogue CWV. (2003): An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4:2.

    Google Scholar 

  8. Naamane N, van Helden J, Eizirik DL. (2007) In silico identification of NF-kappaB-regulated genes in pancreatic beta-cells. BMC Bioinformatics, 8:55. [http://www.dx.doi.org/10.1186/1471-2105-8-55].

    Google Scholar 

  9. Dessailly BH, Lensink MF, Wodak SJ. (2007) Relating destabilizing regions to known functional sites in proteins. BMC Bioinformatics, 8:141. [http://www.dx.doi.org/10.1186/1471-2105-8-141].

    Google Scholar 

  10. Barriot R, Sherman DJ, Dutour I. (2007) How to decide which are the most pertinent overly-represented features during gene set enrichment analysis. BMC Bioinformatics, 8:332. [http://www.dx.doi.rg/10.1186/1471-2105-8-332]. 27.

    Google Scholar 

  11. Brohee S, Faust K, Lima-Mendez G, Sand O, Janky R, Vanderstocken G,Deville Y, van Helden J. (2008) NeAT: a toolbox for the analysis of bio- logical networks, clusters, classes and pathways. Nucleic Acids Res, 36(Web Server issue):W444–W451. [http://www.dx.doi.org/10.1093/nar/gkn336].

    Google Scholar 

  12. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13(11):2498–2504. [http://www.dx.doi.org/10.1101/gr.1239303].

    Google Scholar 

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Acknowledgments

KULeuven SCD-SISTA lab is funded by the Research Council KUL (GOA MaNet, GOA AMBioRICS, CoE EF/05/007 SymBioSys, PROMETA, START 1, several PhD/postdoc and fellow grants, GOA 2006/12), FWO [PhD/postdoc grants, projects G.0241.04 (Functional Genomics), G.0499.04 (Statistics), G.0232.05 (Cardiovascular), G.0318.05 (subfunctionalization), G.0553.06 (VitamineD), G.0302.07 (SVM/Kernel), research communities (ICCoS, ANMMM, MLDM)], G.0733.09 3UTR; G. 082409 (EGFR), G.0254.05 (Genetics of human heart develoment), IWT (PhD Grants, GBOU-McKnow-E (Knowledge management algorithms), GBOU-ANA (biosensors), TAD-BioScope-IT, Silicos; SBO-BioFrame, SBO-MoKa, TBM Endometriosis), the Belgian Federal Science Policy Office [IUAP P6/25 (BioMaGNet, Bioinformatics and Modeling: from Genomes to Networks, 2007–2011), IUAP P5/25 (Molecular Pathology of Genetic Diseases) and the EU-RTD (ERNSI: European Research Network on System Identification; FP6-NoE Biopattern; FP6-IP e-Tumours, FP6-MC-EST Bioptrain, FP6-STREP Strokemap). Sylvain BrohÕe is ChargÕ de Recherches at the Fonds National de la Recherche Scientifique (FNRS) de la CommunautÕ FranÓaise de Belgique and was supported by a post-doc grant of the CheartED project (in the SISTA lab).

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Correspondence to Sylvain Brohée .

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Brohée, S. (2012). Using the NeAT Toolbox to Compare Networks to Networks, Clusters to Clusters, and Network to Clusters. In: van Helden, J., Toussaint, A., Thieffry, D. (eds) Bacterial Molecular Networks. Methods in Molecular Biology, vol 804. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-361-5_18

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  • DOI: https://doi.org/10.1007/978-1-61779-361-5_18

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