Abstract
Comparative analysis of protein networks has proven to be a powerful approach for elucidating network structure and predicting protein function and interaction. A fundamental challenge for the successful application of this approach is to devise an efficient multiple network alignment algorithm. Here we present a novel framework for the problem. At the heart of the framework is a novel representation of multiple networks that is only linear in their size as opposed to current exponential representations. Our alignment algorithm is very efficient, being capable of aligning 10 networks with tens of thousands of proteins each in minutes. We show that our algorithm outperforms a previous strategy for the problem that is based on progressive alignment, and produces results that are more in line with current biological knowledge.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ito, T., Chiba, T., Yoshida, M.: Exploring the yeast protein interactome using comprehensive two-hybrid projects. Trends Biotechnology 19, 23–27 (2001)
Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)
Uetz, P., et al.: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)
Ito, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA 98, 4569–4574 (2001)
Ho, Y., et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)
Gavin, A., et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)
Stelzl, U., et al.: A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 830–832 (2005)
Kelley, B., et al.: Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl. Acad. Sci. 100, 11394–11399 (2003)
Sharan, R., Ideker, T., Kelley, B., Shamir, R., Karp, R.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Journal of Computational Biology 12, 835–846 (2005)
Koyuturk, M., et al.: Pairwise local alignment of protein interaction networks guided by models of evolution. Journal of Computational Biology 13, 182–199 (2006)
Sharan, R., et al.: Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. 102, 1974–1979 (2005)
Flannick, J., Novak, A., Srinivasan, B., McAdams, H., Batzoglou, S.: Graemlin:general and robust alignment of multiple large interaction networks. Genome Research 16, 1169–1181 (2006)
Dutkowsky, J., Tiuryn, J.: Identification of functional modules from conserved ancestral protein-protein interactions. Bioinformatics 23, 149–158 (2007)
Shamir, R., Sharan, R., Tsur, D.: Cluster graph modification problems. Discrete Applied Mathematics 144, 173–182 (2004)
Pellegrini, M., Marcotte, E.M., Thompson, M.J., Eisenberg, D., Yeates, T.O.: Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. PNAS 96, 4285–4288 (1999)
Ashburner, M., et al.: The gene ontology consortium. gene ontology: Tool for the unification of biology 25, 25–29 (2000)
Boyle, E., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J., Sherlock, G.: Go:termfinder–open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 20, 3710–3715 (2004)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57 (1), 289–300 (1995)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kalaev, M., Bafna, V., Sharan, R. (2008). Fast and Accurate Alignment of Multiple Protein Networks. In: Vingron, M., Wong, L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science(), vol 4955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78839-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-78839-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78838-6
Online ISBN: 978-3-540-78839-3
eBook Packages: Computer ScienceComputer Science (R0)