title: Domain-oriented edge-based alignment of protein interaction networks.
abstract: BACKGROUND
  Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein-protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment.
  
  
  RESULTS
  Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast-fly and yeast-worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics.
  
  
  BACKGROUND
  Supplementary material and source code can be found at http://www.cs.duke.edu/ approximately amink/.
authors.affiliation: Department of Computer Science, Duke University, Durham, NC 27708-0129, USA. xinguo@cs.duke.edu
authors.forename: Xin
authors.initials: X
authors.lastname: Guo
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Hartemink
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment / Q000379:methods
chemical_list: D011506:Proteins