Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology

  • Rohit Singh
  • Jinbo Xu
  • Bonnie Berger
Conference paper

DOI: 10.1007/978-3-540-71681-5_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4453)
Cite this paper as:
Singh R., Xu J., Berger B. (2007) Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology. In: Speed T., Huang H. (eds) Research in Computational Molecular Biology. RECOMB 2007. Lecture Notes in Computer Science, vol 4453. Springer, Berlin, Heidelberg

Abstract

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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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Rohit Singh
    • 1
  • Jinbo Xu
    • 2
  • Bonnie Berger
    • 1
  1. 1.Computer Science and AI Lab., Massachusetts Institute of Technology 
  2. 2.Toyota Technological Institute, ChicagoUSA

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