Abstract
Performing global alignment between protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform this task operate symmetrically, that is to say, 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 well the corresponding organism is biologically well-characterized. For well-characterized organisms the associated PPI network supposedly encode in a sound manner all the information about their proteins and associated interactions, which is far from being the case for not well characterized ones. Here the new idea is developed to devise a method for global alignment of PPI networks that in fact exploit differences in the characterization of organisms at hand. We assume that the PPI network (called Master) of the best characterized is used 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 (and using the Slave) network. We tested our method showing that the results it returns are biologically relevant.
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References
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Reserch 25(17), 3389–3402 (1997)
Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: Biogrid: a general repository for interaction datasets. Nucleic Acid Research 34(Database Issue), 535–539 (2006)
Flannick, J., Novak, A., Do, C.B., Srinivasan, B.S., Batzoglou, S.: Automatic parameter learning for multiple network alignment. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 214–231. Springer, Heidelberg (2008)
Forney, G.D.: The Viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973)
Garey, M., Johnson, D.: Computers and intractability: A guide to the theory of NP-completeness. Freeman, New York (1979)
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., Sakaki, Y.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences of the USA 98(8), 4569–4574 (2001)
Kalaev, M., Bafna, V., Sharan, R.: Fast and accurate alignment of multiple protein networks. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 246–256. Springer, Heidelberg (2008)
Kempe, A.: Viterbi algorithm generalized for n-tape best-path search. CoRR, abs/cs/0612041 (2006)
Kiemer, L., Costa, S., Ueffing, M., Cesareni, G.: WI-PHI: A weighted yeast interactome enriched for direct physical interactions. Proteomics 7, 932–943 (2007)
Klau, G.W.: A new graph-based method for pairwise global network alignment. BMC Bioinformatics 10(suppl. 1), S59 (2009)
Krogan, N.J., Cagney, G., et al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)
Liao, C.-S., et al.: Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics 25, i253–i258 (2009)
Narayanan, M., Karp, R.M.: Comparing protein interaction networks via a graph match-and-split algorithm. Journal of Computational Biology 14(7), 892–907 (2007)
Salwinski, L., Miller, C.S., Smith, et al.: The database of interacting proteins: 2004 update. Nucleic Acids Reserch 32(Database issue), D449–D451 (2004)
Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 16–31. Springer, Heidelberg (2007)
Singh, R., Xu, J., Berger, B.: Isorank: Global alignment of multiple protein interaction networks with applications to functional orthology detection. Proceedings of the National Academy of Sciences 105(35), 12763–12768 (2008)
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inform. Theory IT–13, 260–269 (1967)
von Mering, D., Krause, C., et al.: Comparative assessment of a large-scale data sets of protein-protein interactions. Nature 417(6887), 399–403 (2002)
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Ferraro, N., Palopoli, L., Panni, S., Rombo, S.E. (2010). “Master-Slave” Biological Network Alignment. In: Borodovsky, M., Gogarten, J.P., Przytycka, T.M., Rajasekaran, S. (eds) Bioinformatics Research and Applications. ISBRA 2010. Lecture Notes in Computer Science(), vol 6053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13078-6_24
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DOI: https://doi.org/10.1007/978-3-642-13078-6_24
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