Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology
We describe an algorithm, IsoRank, for global alignment of two protein-protein interaction (PPI) networks. IsoRank aims to maximize the overall match between the two networks; in contrast, much of previous work has focused on the local alignment problem— identifying many possible alignments, each corresponding to a local region of similarity. IsoRank is guided by the intuition that a protein should be matched with a protein in the other network if and only if the neighbors of the two proteins can also be well matched. We encode this intuition as an eigenvalue problem, in a manner analogous to Google’s PageRank method. We use IsoRank to compute the first known global alignment between the S. cerevisiae and D. melanogaster PPI networks. The common subgraph has 1420 edges and describes conserved functional components between the two species. Comparisons of our results with those of a well-known algorithm for local network alignment indicate that the globally optimized alignment resolves ambiguity introduced by multiple local alignments. Finally, we interpret the results of global alignment to identify functional orthologs between yeast and fly; our functional ortholog prediction method is much simpler than a recently proposed approach and yet provides results that are more comprehensive.
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- 16.Golub, G.H., Van Loan, C.: Matrix computations. Johns Hopkins University Press, Baltimore (2006)Google Scholar
- 18.Koyuturk, M., Grama, A., Szpankowski, W.: Pairwise local alignment of protein interaction networks guided by models of evolution. In: Proc. of the 9th International Conference on Research in Computational Molecular Biology (RECOMB) (2005)Google Scholar
- 22.Papadimitriou, C., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Dover (1998)Google Scholar
- 23.Qi, Y., Klein-Seetharaman, J., Bar-Joseph, Z.: Random forest similarity for protein-protein interaction prediction from multiple sources. Proc. of the Pacific Symposium on Biocomputation (2005)Google Scholar
- 24.Singh, R., Xu, J., Berger, B.: Struct2net: Integrating structure into protein-protein interaction prediction. Proceedings of the Pacific Symposium on Biocomputation (2006)Google Scholar
- 25.Sontag, D., Singh, R., Berger, B.: Probabilistic modeling of systematic errors in yeast two-hybrid experiments. Proceedings of the Pacific Symposium on Biocomputation (to appear, 2007)Google Scholar
- 26.Srinivasan, B.S., Novak, A., Flannick, J., Batzoglou, S., McAdams, H.: Integrated protein interaction networks for 11 microbes. Proc of the 10th International Conference on Research in Computational Molecular Biology(RECOMB) (2006)Google Scholar
- 28.Yao, M.Y., Lam, T.W., Ting, H.F.: An even faster and more unifying algorithm for comparing trees via unbalanced bipartite matchings. J. of Algorithms 40, 212 (2006)Google Scholar