Topology-Free Querying of Protein Interaction Networks

  • Sharon Bruckner
  • Falk Hüffner
  • Richard M. Karp
  • Ron Shamir
  • Roded Sharan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5541)

Abstract

In the network querying problem, one is given a protein complex or pathway of species A and a protein–protein interaction network of species B; the goal is to identify subnetworks of B that are similar to the query. Existing approaches mostly depend on knowledge of the interaction topology of the query in the network of species A; however, in practice, this topology is often not known. To combat this problem, we develop a topology-free querying algorithm, which we call Torque. Given a query, represented as a set of proteins, Torque seeks a matching set of proteins that are sequence-similar to the query proteins and span a connected region of the network, while allowing both insertions and deletions. The algorithm uses alternatively dynamic programming and integer linear programming for the search task. We test Torque with queries from yeast, fly, and human, where we compare it to the QNet topology-based approach, and with queries from less studied species, where only topology-free algorithms apply. Torque detects many more matches than QNet, while in both cases giving results that are highly functionally coherent.

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References

  1. 1.
    Alon, N., Yuster, R., Zwick, U.: Color coding. Journal of the ACM 42, 844–856 (1995)CrossRefGoogle Scholar
  2. 2.
    Bader, G.D., Hogue, C.W.: Analyzing yeast protein-protein interaction data obtained from different sources. Nature Biotechnology 20(10), 991–997 (2002)CrossRefPubMedGoogle Scholar
  3. 3.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological) 57(1), 289–300 (1995)Google Scholar
  4. 4.
    Betzler, N., Fellows, M.R., Komusiewicz, C., Niedermeier, R.: Parameterized algorithms and hardness results for some graph motif problems. In: Ferragina, P., Landau, G.M. (eds.) CPM 2008. LNCS, vol. 5029, pp. 31–43. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Björklund, A., Husfeldt, T., Kaski, P., Koivisto, M.: Fourier meets Möbius: fast subset convolution. In: Proc. 39th STOC, New York, pp. 67–74 (2007)Google Scholar
  6. 6.
    Boyle, E.I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J.M., Sherlock, G.: GO:TermFinder—open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 20(18), 3710–3715 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    Fellows, M.R., Fertin, G., Hermelin, D., Vialette, S.: Borderlines for finding connected motifs in vertex-colored graphs. In: Arge, L., Cachin, C., Jurdziński, T., Tarlecki, A. (eds.) ICALP 2007. LNCS, vol. 4596, pp. 340–351. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Ferro, A., Giugno, R., Mongiovì, M., Pulvirenti, A., Skripin, D., Shasha, D.: Graphfind: enhancing graph searching by low support data mining techniques. BMC Bioinformatics 9(suppl. 4), 1471–2105 (2008)Google Scholar
  10. 10.
    FlyBase-Consortium. The FlyBase database of the drosophila genome projects and community literature. Nucleic Acids Research, 31(1):172–175 (2003)Google Scholar
  11. 11.
    Gavin, A.C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L.J., Bastuck, S., Dumpelfeld, B., et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440(7084), 631–636 (2006)CrossRefPubMedGoogle Scholar
  12. 12.
    GO Consortium. Amigo (September 2008), http://amigo.geneontology.org/
  13. 13.
    Kalaev, M., Bafna, V., Sharan, R.: Fast and accurate alignment of multiple protein networks. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 246–256. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Kelley, B.P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B.R., Ideker, T.: PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Research 32(Web Server issue) (July 2004)Google Scholar
  15. 15.
    Krogan, N.J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A.P., et al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)CrossRefPubMedGoogle Scholar
  16. 16.
    Lacroix, V., Fernandes, C., Sagot, M.: Motif search in graphs: Application to metabolic networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 3(4), 360–368 (2006)CrossRefPubMedGoogle Scholar
  17. 17.
    Lovász, L., Plummer, M.D.: Matching Theory. Annals of Discrete Mathematics, vol. 29. North-Holland, Amsterdam (1986)Google Scholar
  18. 18.
    Narayanan, M., Karp, R.M.: Comparing protein interaction networks via a graph match-and-split algorithm. Journal of Computational Biology 14(7), 892–907 (2007)CrossRefPubMedGoogle Scholar
  19. 19.
    Niedermeier, R.: Invitation to Fixed-Parameter Algorithms. Oxford Lecture Series in Mathematics and Its Applications, vol. 31. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  20. 20.
    Peri, S., Navarro, J.D., Amanchy, R., Kristiansen, T.Z., Jonnalagadda, C.K., Surendranath, V., Niranjan, V., Muthusamy, B., Gandhi, T.K., Gronborg, M., et al.: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research 13(10), 2363–2371 (2003)CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Pinter, R.Y., Rokhlenko, O., Yeger-Lotem, E., Ziv-Ukelson, M.: Alignment of metabolic pathways. Bioinformatics 21(16), 3401–3408 (2005)CrossRefPubMedGoogle Scholar
  22. 22.
    Reguly, T., Breitkreutz, A., Boucher, L., Breitkreutz, B.J., Hon, G.C., Myers, C.L., Parsons, A., Friesen, H., Oughtred, R., Tong, A., et al.: Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae. Journal of Biology 5(4), 11 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Sharan, R., Dost, B., Shlomi, T., Gupta, N., Ruppin, E., Bafna, V.: Qnet: A tool for querying protein interaction networks. Journal of Computational Biology 15(7), 913–925 (2008)CrossRefPubMedGoogle Scholar
  24. 24.
    Rual, J.F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G.F., Gibbons, F.D., Dreze, M., Ayivi-Guedehoussou, N., et al.: Towards a proteome-scale map of the human protein-protein interaction network. Nature 437(7062), 1173–1178 (2005)CrossRefPubMedGoogle Scholar
  25. 25.
    Ruepp, A., Brauner, B., Dunger-Kaltenbach, I., Frishman, G., Montrone, C., Stransky, M., Waegele, B., Schmidt, T., Doudieu, O.N., Stümpflen, V., Mewes, H.W.: Corum: the comprehensive resource of mammalian protein complexes. Nucleic Acids Research 36(Database issue), 646–650 (2008)Google Scholar
  26. 26.
    Scott, J., Ideker, T., Karp, R.M., Sharan, R.: Efficient algorithms for detecting signaling pathways in protein interaction networks. Journal of Computational Biology 13(2), 133–144 (2006)CrossRefPubMedGoogle Scholar
  27. 27.
    SGD project. Saccharomyces genome database (September 2008), http://www.yeastgenome.org/
  28. 28.
    Sharan, R., Ideker, T., Kelley, B.P., Shamir, R., Karp, R.M.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Journal of Computational Biology 12(6), 835–846 (2005)CrossRefPubMedGoogle Scholar
  29. 29.
    Shlomi, T., Segal, D., Ruppin, E., Sharan, R.: QPath: a method for querying pathways in a protein-protein interaction network. BMC Bioinformatics 7, 199 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Sohler, F., Zimmer, R.: Identifying active transcription factors and kinases from expression data using pathway queries. Bioinformatics 21(suppl. 2), ii115–ii122 (2005)Google Scholar
  31. 31.
    Stanyon, C.A., Liu, G., Mangiola, B.A., Patel, N., Giot, L., Kuang, B., Zhang, H., Zhong, J., Finley Jr., R.L.: A drosophila protein-interaction map centered on cell-cycle regulators. Genome Biol. 5(12), R96 (2004)CrossRefGoogle Scholar
  32. 32.
    Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck, F.H., Goehler, H., Stroedicke, M., Zenkner, M., Schoenherr, A., Koeppen, S., et al.: A human protein-protein interaction network: a resource for annotating the proteome. Cell 122(6), 957–968 (2005)CrossRefPubMedGoogle Scholar
  33. 33.
    The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)Google Scholar
  34. 34.
    Xenarios, I., Salwínski, L., Joyce, X., Higney, P., Kim, S., Eisenberg, D.: Dip, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research 30, 303–305 (2002)CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Yang, Q., Sze, S.-H.: Path matching and graph matching in biological networks. Journal of Computational Biology 14(1), 56–67 (2007)CrossRefPubMedGoogle Scholar
  36. 36.
    Yu, et al.: High-quality binary protein interaction map of the yeast interactome network. Science 322(5898), 104–110 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Zheng, Y., Szustakowski, J.D., Fortnow, L., Roberts, R.J., Kasif, S.: Computational identification of operons in microbial genomes. Genome Research 12(8), 1221–1230 (2002)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sharon Bruckner
    • 1
  • Falk Hüffner
    • 1
  • Richard M. Karp
    • 2
  • Ron Shamir
    • 1
  • Roded Sharan
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.International Computer Science InstituteBerkeleyUSA

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