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Issue No.04 - July/August (2011 vol.8)
pp: 876-889
Nicola Ferraro , University of Calabria, Rende (Cosenza)
Luigi Palopoli , University of Calabria, Rende (Cosenza)
Simona Panni , University of Calabria, Rende (Cosenza)
Simona E. Rombo , University of Calabria, Rende (Cosenza)
ABSTRACT
Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying, and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.
INDEX TERMS
Biological networks, asymmetric alignment, master-slave analysis, network querying, evolutive conservations.
CITATION
Nicola Ferraro, Luigi Palopoli, Simona Panni, Simona E. Rombo, "Asymmetric Comparison and Querying of Biological Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 4, pp. 876-889, July/August 2011, doi:10.1109/TCBB.2011.29
REFERENCES
[1] S.F. Altschul et al., “Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs,” Nucleic Acids Research, vol. 25, no. 17, pp. 3389-3402, 1997.
[2] M. Ashburner et al., “Gene Ontology Tool for the Unification of Biology,” Nature Genetics, vol. 25, pp. 25-29, 2000.
[3] G. Blin, F. Sikora, and S. Vialette, “Querying Graphs in Protein-Protein Interactions Networks Using Feedback Vertex Set,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 7, no. 4, pp. 628-635, Oct.-Dec. 2010.
[4] B. Dost et al., “QNet: A Tool for Querying Protein Interaction Networks,” Proc. Conf Research in Computational Molecular Biology (RECOMB '07), pp. 1-15, 2007.
[5] A. Ferro et al., “NetMatch: A Cytoscape Plugin for Searching Biological Networks,” Bioinformatics, vol. 23, no. 7, pp. 910-912, 2007.
[6] J. Flannick et al., “Automatic Parameter Learning for Multiple Network Alignment,” Proc. Conf Research in Computational Molecular Biology (RECOMB '08), pp. 214-231, 2008.
[7] G.D. Forney, “The Viterbi Algorithm,” Proc. IEEE, vol. 61, no. 3, pp. 268-278, Mar. 1973.
[8] M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.
[9] T. Ito et al., “A Comprehensive Two-Hybrid Analysis to Explore the Yeast Protein Interactome,” Proc. Nat'l Academy of Sciences USA, vol. 98, no. 8, pp. 4569-4574, 2001.
[10] B.P. Kelley et al., “Conserved Pathways within Bacteria and Yeast as Revealed by Global Protein Network Alignment,” Proc. Nat'l Academy of Sciences USA, vol. 100, no. 20, pp. 11394-11399, 2003.
[11] A. Kempe, “Viterbi Algorithm Generalized for N-Tape Best-Path Search,” CoRR, vol. abs/cs/0612041, 2006.
[12] L. Kiemer et al., “WI-PHI: A Weighted Yeast Interactome Enriched for Direct Physical Interactions,” Proteomics, vol. 7, pp. 932-943, 2007.
[13] G.W. Klau, “A New Graph-Based Method for Pairwise Global Network Alignment,” BMC Bioinformatics, vol. 10, no. Suppl. 1, p. S59, 2009.
[14] M. Koyuturk et al., “Pairwise Alignment of Protein Interaction Networks,” J. Computational Biology, vol. 13, no. 2, pp. 182-199, 2006.
[15] N.J. Krogan et al., “Global Landscape of Protein Complexes in the Yeast Saccharomyces Cerevisiae,” Nature, vol. 440, no. 7084, pp. 637-643, 2006.
[16] C.-S. Liao et al., “IsoRankN: Spectral Methods for Global Alignment of Multiple Protein Networks,” Bioinformatics, vol. 25, pp. i253-i258, 2009.
[17] M. Narayanan and R.M. Karp, “Comparing Protein Interaction Networks via a Graph Match-and-Split Algorithm,” J. Computational Biology, vol. 14, no. 7, pp. 892-907, 2007.
[18] R.D. Natale et al., “SING: Subgraph Search in Non-Homogeneous Graphs,” BMC Bioinformatics, vol. 11, p. 96, 2010.
[19] R. Pinter et al., “Alignment of Metabolic Pathways,” Bioinformatics, vol. 21, no. 16, pp. 3401-3408, 2005.
[20] X. Qian, S.-H. Sze, and B.-J. Yoon, “Querying Pathways in Protein Interaction Networks Based on Hidden Markov Models,” J. Computational Biology, vol. 16, no. 2, pp. 145-157, 2009.
[21] L. Salwinski et al., “The Database of Interacting Proteins: 2004 Update,” Nucleic Acids Research, vol. 32, pp. D449-D451, 2004.
[22] T. Shlomi et al., “QPath: A Method for Querying Pathways in a Protein-Protein Interaction Network,” BMC Bioinformatics, vol. 7, article no. 199, 2006.
[23] R. Singh, J. Xu, and B. Berger, “Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology,” Proc. Conf Research in Computational Molecular Biology (RECOMB '07), pp. 16-31, 2007.
[24] R. Singh, J. Xu, and B. Berger, “Global Alignment of Multiple Protein Interaction Networks,” Proc. Pacific Symp. Biocomputing (PSB '08), 2008.
[25] C. Stark et al., “BioGRID: A General Repository for Interaction Datasets,” Nucleic Acids Research, vol. 34, pp. D535-D539, 2006.
[26] Y. Tian et al., “SAGA: A Subgraph Matching Tool for Biological Graphs,” Bioinformatics, vol. 23, no. 2, pp. 232-239, 2007.
[27] A.J. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm,” IEEE Trans. Information Theory, vol. 13, no. 2, pp. 260-269, Apr. 1967.
[28] D. von Mering et al., “Comparative Assessment of a Large-Scale Data Sets of Protein-Protein Interactions,” Nature, vol. 417, no. 6887, pp. 399-403, 2002.
[29] Q. Yang and S.-H. Sze, “Path Matching and Graph Matching in Biological Networks,” J. Computational Biology, vol. 14, no. 1, pp. 56-67, 2007.
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