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Bioinformatics 2009 25(12):i253-i258; doi:10.1093/bioinformatics/btp203
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

IsoRankN: spectral methods for global alignment of multiple protein networks

Chung-Shou Liao 1,2,3, Kanghao Lu 3,4, Michael Baym 3,4, Rohit Singh 3 and Bonnie Berger 3,4,*

1Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, 3Computer Science and Artificial Intelligence Laboratory and 4Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: With the increasing availability of large protein–protein interaction networks, the question of protein network alignment is becoming central to systems biology. Network alignment is further delineated into two sub-problems: local alignment, to find small conserved motifs across networks, and global alignment, which attempts to find a best mapping between all nodes of the two networks. In this article, our aim is to improve upon existing global alignment results. Better network alignment will enable, among other things, more accurate identification of functional orthologs across species.

Results: We introduce IsoRankN (IsoRank-Nibble) a global multiple-network alignment tool based on spectral clustering on the induced graph of pairwise alignment scores. IsoRankN outperforms existing algorithms for global network alignment in coverage and consistency on multiple alignments of the five available eukaryotic networks. Being based on spectral methods, IsoRankN is both error tolerant and computationally efficient.

Availability: Our software is available freely for non-commercial purposes on request from: http://isorank.csail.mit.edu/

Contact: bab{at}mit.edu


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W. Ali and C. M. Deane
Functionally guided alignment of protein interaction networks for module detection
Bioinformatics, December 1, 2009; 25(23): 3166 - 3173.
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