title: Fuse: multiple network alignment via data fusion.
abstract: Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. However, the complexity of the multiple network alignment problem grows exponentially with the number of networks being aligned and designing a multiple network aligner that is both scalable and that produces biologically relevant alignments is a challenging task that has not been fully addressed. The objective of multiple network alignment is to create clusters of nodes that are evolutionarily and functionally conserved across all networks. Unfortunately, the alignment methods proposed thus far do not meet this objective as they are guided by pairwise scores that do not utilize the entire functional and evolutionary information across all networks.
  
  
  
  To overcome this weakness, we propose Fuse, a new multiple network alignment algorithm that works in two steps. First, it computes our novel protein functional similarity scores by fusing information from wiring patterns of all aligned PPI networks and sequence similarities between their proteins. This is in contrast with the previous tools that are all based on protein similarities in pairs of networks being aligned. Our comprehensive new protein similarity scores are computed by Non-negative Matrix Tri-Factorization (NMTF) method that predicts associations between proteins whose homology (from sequences) and functioning similarity (from wiring patterns) are supported by all networks. Using the five largest and most complete PPI networks from BioGRID, we show that NMTF predicts a large number protein pairs that are biologically consistent. Second, to identify clusters of aligned proteins over all networks, Fuse uses our novel maximum weight k-partite matching approximation algorithm. We compare Fuse with the state of the art multiple network aligners and show that (i) by using only sequence alignment scores, Fuse already outperforms other aligners and produces a larger number of biologically consistent clusters that cover all aligned PPI networks and (ii) using both sequence alignments and topological NMTF-predicted scores leads to the best multiple network alignments thus far.
  
  
  
  Our dataset and software are freely available from the web site: http://bio-nets.doc.ic.ac.uk/Fuse/
  
  
  
  natasha@imperial.ac.uk
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computing, Imperial College London, London SW7 2AZ, UK.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Gligorijević
authors.affiliation: Department of Computing, Imperial College London, London SW7 2AZ, UK.
authors.forename: Noël
authors.initials: N
authors.lastname: Malod-Dognin
authors.affiliation: Department of Computing, Imperial College London, London SW7 2AZ, UK.
authors.forename: Nataša
authors.initials: N
authors.lastname: Pržulj
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping; D011506:Proteins; D016415:Sequence Alignment; D012984:Software
chemical_list: D011506:Proteins