title: Local versus global biological network alignment.
abstract: Network alignment (NA) aims to find regions of similarities between species' molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories.
  
  
  
  We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation.
  
  
  
  Software: http://www.nd.edu/~cone/LNA_GNA CONTACT: : tmilenko@nd.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering ECK Institute of Global Health and Interdisciplinary Center for Network Science and Applications.
authors.forename: Lei
authors.initials: L
authors.lastname: Meng
authors.affiliation: Department of Computer Science and Engineering Wireless Institute, University of Notre Dame, Notre Dame, IN 46556, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Striegel
authors.affiliation: Department of Computer Science and Engineering ECK Institute of Global Health and Interdisciplinary Center for Network Science and Applications.
authors.forename: Tijana
authors.initials: T
authors.lastname: Milenković
mesh_terms: D000465:Algorithms; D011506:Proteins; D016415:Sequence Alignment / Q000379:methods; D012984:Software; D013045:Species Specificity
chemical_list: D011506:Proteins