title: Finding alignments of conserved graphlets in protein interaction networks.
abstract: As the amount of data describing biological interactions increases, it becomes possible to analyze the complex interactions of genes and proteins across multiple networks at the genome scale. While the most popular techniques to study conservation of patterns in biological networks are through the use of network alignment techniques or the identification of network motifs, we show that it is possible to exhaustively enumerate all graphlet alignments, which consist of at least two vertex-disjoint subgraphs that share a common topology and contain homologous proteins at the same position in the topology. We compare the performance of our algorithm to network alignment algorithms and show that our algorithm is able to cover significantly more proteins in the given networks while maintaining comparable or higher sensitivity and specificity with respect to functional enrichment.
authors: 1 Department of Computer Science and Engineering, Texas A&M University , College Station, Texas.
authors: Mu-Fen
authors: MF
authors: Hsieh
authors: Sing-Hoi
authors: SH
authors: Sze
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D017124:Conserved Sequence / Q000235:genetics*; D008962:Models, Theoretical; D060066:Protein Interaction Maps / Q000235:genetics*; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment
chemical_list: D011506:Proteins