ModuleAlign: Module-based global alignment of protein-protein interaction networks

Somaye Hashemifar, Jianzhu Ma, Hammad Naveed, Stefan Canzar, Jinbo Xu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

Motivation: As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency. Results: In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen-Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis. Availability: http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html.

Original languageEnglish (US)
Pages (from-to)i658-i664
JournalBioinformatics
Volume32
Issue number17
DOIs
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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