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NETAL: A new graph-based method for global alignment of protein-protein interaction networks

Neyshabur, B ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1093/bioinformatics/btt202
  3. Publisher: 2013
  4. Abstract:
  5. Motivation: The interactions among proteins and the resulting networks of such interactions have a central role in cell biology. Aligning these networks gives us important information, such as conserved complexes and evolutionary relationships. Although there have been several publications on the global alignment of protein networks; however, none of proposed methods are able to produce a highly conserved and meaningful alignment. Moreover, time complexity of current algorithms makes them impossible to use for multiple alignment of several large networks together.Results: We present a novel algorithm for the global alignment of protein-protein interaction networks. It uses a greedy method, based on the alignment scoring matrix, which is derived from both biological and topological information of input networks to find the best global network alignment. NETAL outperforms other global alignment methods in terms of several measurements, such as Edge Correctness, Largest Common Connected Subgraphs and the number of common Gene Ontology terms between aligned proteins. As the running time of NETAL is much less than other available methods, NETAL can be easily expanded to multiple alignment algorithm. Furthermore, NETAL overpowers all other existing algorithms in term of performance so that the short running time of NETAL allowed us to implement it as the first server for global alignment of protein-protein interaction networks
  6. Keywords:
  7. Saccharomyces cerevisiae protein ; Algorithm ; Computer program ; Human ; Metabolism ; Procedures ; Protein analysis ; Sequence alignment ; Methodology ; Software ; Algorithms ; Computer Graphics ; Humans ; Protein Interaction Mapping ; Saccharomyces cerevisiae Proteins
  8. Source: Bioinformatics ; Volume 29, Issue 13 , 2013 , Pages 1654-1662 ; 13674803 (ISSN)
  9. URL: http://bioinformatics.oxfordjournals.org/content/29/13/1654