Abstract
As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Several graph clustering approaches have been proposed to detect functional modules from such networks. However, all methods of predicting protein interactions are known to yield a nonnegligible amount of false positives. Most of the graph clustering algorithms are challenging to be used in the network with high false positives. We extend the protein interaction network from unweighted graph to weighted graph and propose an algorithm for hierarchically clustering in the weighted graph. The proposed algorithm HC-Wpin is applied to the protein interaction network of S.cerevisiae and the identified modules are validated by GO annotations. Many significant functional modules are detected, most of which are corresponding to the known complexes. Moreover, our algorithm HC-Wpin is faster and more accurate compared to other previous algorithms. The program is available at http://bioinfo.csu.edu.cn/limin/HC-Wpin.
This research was supported in part by the National Basic Research 973 Program of China No. 2008CB317107, the National Natural Science Foundation of China under Grant No. 60773111, the Program for New Century Excellent Talents in University No. NCET-05-0683, the Program for Changjiang Scholars and Innovative Research Team in University No. IRT0661.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Uetz, P., et al.: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)
Gavin, A.C., et al.: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415(6868), 141–147 (2002)
Gavin, A.C., et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440(7084), 631–636 (2006)
Ho, Y., et al.: Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature 415(6868), 180–183 (2002)
Krogan, N.J., et al.: High-definition macromolecular. composition of yeast RNA-processing complexes. Molecular Cell 13, 225–239 (2004)
Krogan, N.J., et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)
Harwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402, c47–c52 (1999)
Cho, Y.R., Hwang, W., Ramanmathan, M., Zhang, A.D.: Semantic integration to identify overlapping functional modules in protein interaction networks. BMC Bioinformatics 8, 265 (2007)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)
Rives, A.W., Galitski, T.: Modular organization of cellular networks. Proc. Natl. Acad. Sci 100, 1128–1133 (2003)
Ravasz, E., et al.: Hierarchical organization of modularity in metaboli networks. Science 297, 1551–1555 (2002)
Luo, F., Yang, Y.F., Chen, C.F., Chang, R., Zhou, J.Z.: Modular organization of protein interaction networks. Bioinformatics 23(2), 207–214 (2007)
Brohee, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics, 7–488 (2006)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci., 12123–12128 (2003)
Bu, D., et al.: Topological structure analysis of the protein-protein interaction networks in budding yeast. Nucleic Acid Research 31(9), 2443–2450 (2003)
Brun, C., et al.: Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics 7, 488 (2004)
Bader, G.D., Hogue, C.W.: An Automated Method for Finding Molecular Complexes in Large Protein Interaction Networks. BMC Bioinformatics 4, 2 (2003)
Altaf-Ul-Amin, M., Shinbo, Y., Mihara, K., Kurokawa, K., Kanaya, S.: Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics, 7–207 (2006)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Barabási, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nature Reviews: Genetics 5, 101–114 (2004)
Friedel, C., Zimmer, R.: Inferring topology from clustering coefficients in protein-protein interaction networks. BMC Bioinformatics, 7–519 (2006)
Radicchi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. 101, 2658–2663 (2004)
Li, M., Wang, J.X., Chen, J.E.: A fast agglomerate algorithm for mining functional modules in protein interaction networks. In: Peng, Y., Zhang, Y. (eds.) Proceedings of the First International Conference on BioMedical Engineering and Informatics: Hainan, China, May 27-30, pp. 3–7 (2008)
Xenarios, I., et al.: DIP: the Database of Interaction Proteins: a research tool for studying cellular networks of protien interactions. Nucleic Acids Res. 30, 303–305 (2002)
Sharan, R., et al.: Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. 102(6), 1974–1979 (2005)
Shlomi, T., Segal, D., Ruppin, E., Sharan, R.: Qpath: a method for querying pathways in a protein-protein interaction network. BMC Bioinformatics, 7–199 (2006)
Hwang, W., Cho, Y.R., Zhang, A., Ramanathan, M.: A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology 12, 1–24 (2006)
von Mering, C., et al.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417(6887), 399–403 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, M., Wang, J., Chen, J., Pan, Y. (2009). Hierarchical Organization of Functional Modules in Weighted Protein Interaction Networks Using Clustering Coefficient . In: Măndoiu, I., Narasimhan, G., Zhang, Y. (eds) Bioinformatics Research and Applications. ISBRA 2009. Lecture Notes in Computer Science(), vol 5542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01551-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-01551-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01550-2
Online ISBN: 978-3-642-01551-9
eBook Packages: Computer ScienceComputer Science (R0)