Document Detail


Global network alignment using multiscale spectral signatures.
MedLine Citation:
PMID:  23047556     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: Protein interaction networks provide an important system-level view of biological processes. One of the fundamental problems in biological network analysis is the global alignment of a pair of networks, which puts the proteins of one network into correspondence with the proteins of another network in a manner that conserves their interactions while respecting other evidence of their homology. By providing a mapping between the networks of different species, alignments can be used to inform hypotheses about the functions of unannotated proteins, the existence of unobserved interactions, the evolutionary divergence between the two species and the evolution of complexes and pathways.
RESULTS: We introduce GHOST, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks. It combines a seed-and-extend global alignment phase with a local search procedure and exceeds state-of-the-art performance on several network alignment tasks. We show that the spectral signature used by GHOST is highly discriminative, whereas the alignments it produces are also robust to experimental noise. When compared with other recent approaches, we find that GHOST is able to recover larger and more biologically significant, shared subnetworks between species.
AVAILABILITY: An efficient and parallelized implementation of GHOST, released under the Apache 2.0 license, is available at http://cbcb.umd.edu/kingsford_group/ghost
CONTACT: rob@cs.umd.edu.
Authors:
Rob Patro; Carl Kingsford
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-10-09
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  28     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-29     Completed Date:  2013-08-05     Revised Date:  2013-12-04    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  3105-14     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Protein Interaction Mapping / methods*
Protein Interaction Maps*
Proteins / analysis
Grant Support
ID/Acronym/Agency:
1R21AI085376/AI/NIAID NIH HHS
Chemical
Reg. No./Substance:
0/Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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