Abstract
Many biological networks contain recurring overrepresented elements, called network motifs. Finding these substructures is a computationally hard task related to graph isomorphism. G-Tries are an efficient data structure, based on multiway trees, capable of efficiently identifying common substructures in a set of subgraphs. They are highly successful in constraining the search space when finding the occurrences of those subgraphs in a larger original graph. This leads to speedups up to 100 times faster than previous methods that aim for exact and complete results. In this paper we present a new efficient sampling algorithm for subgraph frequency estimation based on g-tries. It is able to uniformly traverse a fraction of the search space, providing an accurate unbiased estimation of subgraph frequencies. Our results show that in the same amount of time our algorithm achieves better precision than previous methods, as it is able to sustain higher sampling speeds.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albert, I., Albert, R.: Conserved network motifs allow protein-protein interaction prediction. Bioinformatics 20(18), 3346–3352 (2004)
Bu, D., Zhao, Y., Cai, L., Xue, H., Zhu, X., Lu, H., Zhang, J., Sun, S., Ling, L., Zhang, N., Li, G., Chen, R.: Topological structure analysis of the protein-protein interaction network in budding yeast. Nucl. Acids Res. 31(9), 2443–2450 (2003)
Ciriello, G., Guerra, C.: A review on models and algorithms for motif discovery in protein-protein interaction networks. Brief Funct. Genomic Proteomic 7(2), 147–156 (2008)
da Costa Luciano, F., Oliveira Jr., O.N., Travieso, G., Rodrigues, F.A., Villas Boas, P.R., Antiqueira, L., Viana, M.P., da Rocha, L.E.C.: Analyzing and modeling real-world phenomena with complex networks: A survey of applications. ArXiv e-prints 0711(3199) (2007)
Dobrin, R., Beg, Q.K., Barabasi, A., Oltvai, Z.: Aggregation of topological motifs in the escherichia coli transcriptional regulatory network. BMC Bioinformatics 5, 10 (2004)
Duch, J., Arenas, A.: Community identification using extremal optimization. Phys. Rev. E. (Stat. Nonlin. Soft Matter Phys.) 72, 027104 (2005)
Grochow, J., Kellis, M.: Network motif discovery using subgraph enumeration and symmetry-breaking. Research in Computational Molecular Biology, 92–106 (2007)
Itzkovitz, S., Levitt, R., Kashtan, N., Milo, R., Itzkovitz, M., Alon, U.: Coarse-graining and self-dissimilarity of complex networks. Phys. Rev. E (Stat. Nonlin. Soft Matter Phys.) 71(1 Pt. 2) (January 2005)
Juszczyszyn, K., Kazienko, P., Musial, K.: Local topology of social network based on motif analysis. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 97–105. Springer, Heidelberg (2008)
Kashani, Z., Ahrabian, H., Elahi, E., Nowzari-Dalini, A., Ansari, E., Asadi, S., Mohammadi, S., Schreiber, F., Masoudi-Nejad, A.: Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 10(1), 318 (2009)
Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746–1758 (2004)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: IEEE International Conference on Data Mining, p. 313 (2001)
Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. can geographic isolation explain this unique trait? Behavioral Ecology and Sociobiology 54(4), 396–405 (2003)
McKay, B.: Practical graph isomorphism. Cong. Numerantium 30, 45–87 (1981)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Omidi, S., Schreiber, F., Masoudi-Nejad, A.: Moda: An efficient algorithm for network motif discovery in biological networks. Genes & genetic systems 84(5), 385–395 (2009)
Ribeiro, P., Silva, F.: G-tries: an efficient data structure for discovering network motifs. In: ACM Symposium on Applied Computing (2010)
Ribeiro, P., Silva, F., Kaiser, M.: Strategies for network motifs discovery. In: 5th IEEE International Conference on e-Science. IEEE CS Press, Oxford (2009)
Schreiber, F., Schwobbermeyer, H.: Towards motif detection in networks: Frequency concepts and flexible search. In: Proc. of the Int. Workshop on Network Tools and Applications in Biology (NETTAB 2004), pp. 91–102 (2004)
Sporns, O., Kotter, R.: Motifs in brain networks. PLoS Biology 2 (2004)
Valverde, S., Solé, R.V.: Network motifs in computational graphs: A case study in software architecture. Phys. Rev. E (Stat. Nonlin. Soft Matter Phys.) 72(2) (2005)
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)
Wernicke, S.: Efficient detection of network motifs. IEEE/ACM Trans. Comput. Biol. Bioinformatics 3(4), 347–359 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ribeiro, P., Silva, F. (2010). Efficient Subgraph Frequency Estimation with G-Tries. In: Moulton, V., Singh, M. (eds) Algorithms in Bioinformatics. WABI 2010. Lecture Notes in Computer Science(), vol 6293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15294-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-15294-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15293-1
Online ISBN: 978-3-642-15294-8
eBook Packages: Computer ScienceComputer Science (R0)