An Integrative Network Approach to Map the Transcriptome to the Phenome

  • Michael R. Mehan
  • Juan Nunez-Iglesias
  • Mrinal Kalakrishnan
  • Michael S. Waterman
  • Xianghong Jasmine Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


Although many studies have been successful in the discovery of cooperating groups of genes, mapping these groups to phenotypes has proved a much more challenging task. In this paper, we present the first genome-wide mapping of gene coexpression modules onto the phenome. We annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS). We then designed an efficient graph-based simulated annealing approach to identify coexpression modules frequently and specifically occurring in datasets related to individual phenotypes. By requiring phenotype-specific recurrence, we ensure the robustness of our findings. We discovered 9,183 modules specific to 47 phenotypes, and developed validation tests combining Gene Ontology, GeneRIF and UMLS. Our method is generally applicable to any kind of abundant network data with defined phenotype association, and thus paves the way for genome-wide, gene network-phenotype maps.


Simulated Annealing Coexpression Network Microarray Dataset Gene Coexpression Network Phenotype Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lander, E.S., Schork, N.J.: Genetic dissection of complex traits. Science 265(5181), 2037–2048 (1994)CrossRefGoogle Scholar
  2. 2.
    Risch, N.J.: Searching for genetic determinants in the new millennium. Nature 405(6788), 847–856 (2000)CrossRefGoogle Scholar
  3. 3.
    Zhou, X., Kao, M.C.J., Wong, W.H.: Transitive functional annotation by shortest-path analysis of gene expression data. Proc. Natl. Acad. Sci. USA 99(20), 12783–12788 (2002)CrossRefGoogle Scholar
  4. 4.
    Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003)CrossRefGoogle Scholar
  5. 5.
    Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA 100(21), 12123–12128 (2003)CrossRefGoogle Scholar
  6. 6.
    Kelley, B.P., Sharan, R., Karp, R.M., Sittler, T., Root, D.E., Stockwell, B.R., Ideker, T.: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl. Acad. Sci. U.S.A. 100(20), 11394–11399 (2003)CrossRefGoogle Scholar
  7. 7.
    Hu, H., Yan, X., Huang, Y., Han, J., Zhou, X.J.: Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics 21(Suppl 1), i213–i221 (2005)CrossRefGoogle Scholar
  8. 8.
    Yip, A.M., Horvath, S.: Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 8, 22 (2007)CrossRefGoogle Scholar
  9. 9.
    Butte, A.J., Kohane, I.S.: Creation and implications of a phenome-genome network. Nat. Biotechnol. 24(1), 55–62 (2006)CrossRefGoogle Scholar
  10. 10.
    Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I.F., Soboleva, A., Tomashevsky, M., Edgar, R.: Ncbi geo: mining tens of millions of expression profiles–database and tools update. Nucleic Acids Res. 35(Database issue), D760–D765 (2007)CrossRefGoogle Scholar
  11. 11.
    Lage, K., Karlberg, E.O., Størling, Z.M., Olason, P.I., Pedersen, A.G., Rigina, O., Hinsby, A.M., Tümer, Z., Pociot, F., Tommerup, N., Moreau, Y., Brunak, S.: A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25(3), 309–316 (2007)CrossRefGoogle Scholar
  12. 12.
    Bodenreider, O.: The unified medical language system (umls): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267–D270 (2004)CrossRefGoogle Scholar
  13. 13.
    Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Zhou, X.J., Kao, M.C.J., Huang, H., Wong, A., Nunez-Iglesias, J., Primig, M., Aparicio, O.M., Finch, C.E., Morgan, T.E., Wong, W.H.: Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat. Biotechnol. 23(2), 238–243 (2005)CrossRefGoogle Scholar
  15. 15.
    Yan, X., Mehan, M.R., Huang, Y., Waterman, M.S., Yu, P.S., Zhou, X.J.: A graph-based approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics 23(13), i577–586 (2007)CrossRefGoogle Scholar
  16. 16.
    Consortium, G.O.: The gene ontology (go) project in 2006. Nucleic Acids Res 34(Database issue), D322–D326 (2006)CrossRefGoogle Scholar
  17. 17.
    Mitchell, J.A., Aronson, A.R., Mork, J.G., Folk, L.C., Humphrey, S.M., Ward, J.M.: Gene indexing: characterization and analysis of nlm’s generifs. In: AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium, January 2003, pp. 460–464 (2003)Google Scholar
  18. 18.
    Freimer, N., Sabatti, C.: The human phenome project. Nat. Genet. 34(1), 15–21 (2003)CrossRefGoogle Scholar
  19. 19.
    Butte, A.J., Chen, R.: Finding disease-related genomic experiments within an international repository: first steps in translational bioinformatics. In: AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium, pp. 106–110 (2006)Google Scholar
  20. 20.
    Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single and multiobjective optimization. Journal of the Operational Research Society 57(10), 1143–1160 (2006)CrossRefzbMATHGoogle Scholar
  21. 21.
    Collette, Y., Siarry, P.: Multiobjective Optimization: Principles and Case Studies, 2nd edn., pp. 45–51. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  22. 22.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE-PAMI 6, 721–741 (1984)zbMATHGoogle Scholar
  23. 23.
    Jeffery, C.J.: Multifunctional proteins: examples of gene sharing. Ann. Med. 35(1), 28–35 (2003)CrossRefGoogle Scholar
  24. 24.
    Jeffery, C.J.: Moonlighting proteins: old proteins learning new tricks. Trends Genet. 19(8), 415–417 (2003)CrossRefGoogle Scholar
  25. 25.
    Zhang, M.: Multiple functions of maspin in tumor progression and mouse development. Front. Biosci. 9, 2218–2226 (2004)CrossRefGoogle Scholar
  26. 26.
    Han, J.D.J., Bertin, N., Hao, T., Goldberg, D.S., Berriz, G.F., Zhang, L.V., Dupuy, D., Walhout, A.J.M., Cusick, M.E., Roth, F.P., Vidal, M.: Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430(6995), 88–93 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michael R. Mehan
    • 1
  • Juan Nunez-Iglesias
    • 1
  • Mrinal Kalakrishnan
    • 1
  • Michael S. Waterman
    • 1
  • Xianghong Jasmine Zhou
    • 1
  1. 1.Program in Computational Biology, Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA

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