SteinerNet: A web server for integrating 'omic' data to discover hidden components of response pathways

Nucleic Acids Research

Volumn 40, Issue W1, 2012, Pages

  Tuncbag, Nurcan ^a   McCallum, Scott ^b   Huang, Shao Shan Carol ^a   Fraenkel, Ernest ^a  

^a Massachusetts Institute of Technology Cambridge   (United States)

^b WHITEHEAD INSTITUTE FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CAENORHABDITIS ELEGANS; CLIENT SERVER APPLICATION; COMPUTER PROGRAM; DROSOPHILA MELANOGASTER; GENE IDENTIFICATION; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN COMPUTER INTERACTION; INTERACTOME; INTERNET; MOLECULAR BIOLOGY; MOUSE; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; SEARCH ENGINE; WEB BROWSER;

ANIMALS; CAENORHABDITIS ELEGANS; DROSOPHILA MELANOGASTER; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; INTERNET; MICE; PROTEOMICS; SIGNAL TRANSDUCTION; SOFTWARE; YEASTS;

CAENORHABDITIS ELEGANS; DROSOPHILA MELANOGASTER;

EID: 84864470208     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gks445     Document Type: Article

References (21)

1. Palsson, B. and Zengler, K. (2010) The challenges of integrating multi-omic data sets. Nat. Chem. Biol., 6, 787-789.
2. Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W. and Selbach, M. (2011) Global quantification of mammalian gene expression control. Nature, 473, 337-342.
3. Akavia, U.D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H.C., Pochanard, P., Mozes, E., Garraway, L.A. and Pe'er, D. (2010) An integrated approach to uncover drivers of cancer. Cell, 143, 1005-1017.
4. Bailly-Bechet, M., Borgs, C., Braunstein, A., Chayes, J., Dagkessamanskaia, A., Francois, J.M. and Zecchina, R. (2010) Finding undetected protein associations in cell signaling by belief propagation. Proc. Natl Acad. Sci. USA, 108, 882-887.
5. Dittrich, M.T., Klau, G.W., Rosenwald, A., Dandekar, T. and Muller, T. (2008) Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics, 24, i223-i231.
6. Friedman, N. (2004) Inferring cellular networks using probabilistic graphical models. Science, 303, 799-805.
7. Huang, S.S. and Fraenkel, E. (2009) Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal., 2, ra40.
8. Kim, Y.A., Wuchty, S. and Przytycka, T.M. (2011) Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput. Biol., 7, e1001095.
9. Lan, A., Smoly, I.Y., Rapaport, G., Lindquist, S., Fraenkel, E. and Yeger-Lotem, E. (2011) ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res., 39, W424-W429.
10. Missiuro, P.V., Liu, K., Zou, L., Ross, B.C., Zhao, G., Liu, J.S. and Ge, H. (2009) Information flow analysis of interactome networks. PLoS Comput. Biol., 5, e1000350.
11. Ourfali, O., Shlomi, T., Ideker, T., Ruppin, E. and Sharan, R. (2007) SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics, 23, i359-i366.
12. Suthram, S., Beyer, A., Karp, R.M., Eldar, Y. and Ideker, T. (2008) eQED: an efficient method for interpreting eQTL associations using protein networks. Mol. Syst. Biol., 4, 162.
13. Tu, Z., Argmann, C., Wong, K.K., Mitnaul, L.J., Edwards, S., Sach, I.C., Zhu, J. and Schadt, E.E. (2009) Integrating siRNA and protein-protein interaction data to identify an expanded insulin signaling network. Genome Res., 19, 1057-1067.
14. Vanunu, O., Magger, O., Ruppin, E., Shlomi, T. and Sharan, R. (2010) Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol., 6, e1000641.
15. Yeang, C.H., Ideker, T. and Jaakkola, T. (2004) Physical network models. J. Comput. Biol., 11, 243-262.
16. Yeger-Lotem, E., Riva, L., Su, L.J., Gitler, A.D., Cashikar, A.G., King, O.D., Auluck, P.K., Geddie, M.L., Valastyan, J.S., Karger, D.R. et al. (2009) Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet., 41, 316-323.
17. Bailly-Bechet, M., Bradde, S., Braunstein, A., Flaxman, A., Foini, F. and Zecchina, R. (2009) Clustering with shallow trees. J. Stat. Mech., P12010.
18. Jensen, L.J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M. et al. (2009) STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412-D416.
19. Naegle, K.M., Welsch, R.E., Yaffe, M.B., White, F.M. and Lauffenburger, D.A. (2011) MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets. PLoS Comput. Biol., 7, e1002119.
20. Lopes, C.T., Franz, M., Kazi, F., Donaldson, S.L., Morris, Q. and Bader, G.D. (2010) Cytoscape web: an interactive web-based network browser. Bioinformatics, 26, 2347-2348.
21. Ljubic, I., Weiskircher, R., Pferschy, U., Klau, G.W., Mutzel, P. and Fischetti, M. (2006) An algorithmic framework for the exact solution of the Prize-Collecting Steiner Tree Problem. Math. Program., 105, 427-449.