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Issue No.06 - Nov.-Dec. (2012 vol.9)

pp: 1776-1789

Jianxin Wang , Sch. of Inf. Eng. & Sci., Central South Univ., Changsha, China

Yuannan Huang , Sch. of Inf. Eng. & Sci., Central South Univ., Changsha, China

Fang-Xiang Wu , Dept. of Mech. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada

Yi Pan , Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.119

ABSTRACT

Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.

INDEX TERMS

Biological information theory, Computational biology, Bioinformatics,decompression, Network motif, biological network, symmetry, compression, subgraph enumeration, graph isomorphism

CITATION

Jianxin Wang, Yuannan Huang, Fang-Xiang Wu, Yi Pan, "Symmetry Compression Method for Discovering Network Motifs",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.9, no. 6, pp. 1776-1789, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.119REFERENCES

- [1] Z. Alamgir and S. Abbasi, “Combinatorial Algorithms For Listing Paths in Minimal Change Order,”
Proc. Fourth Conf. Combinatorial and Algorithmic Aspects of Networking, pp. 112-130, 2007.- [2] N. Alon, P. Dao, I. Hajirasouliha, F. Hormozdiari, and S. Sahinalp, “Biomolecular Network Motif Counting and Discovery by Color Coding,”
Bioinformatics, vol. 24, no. 13, pp. i241-i249, 2008.- [3] U. Alon, “Network Motifs: Theory and Experimental Approaches,”
Nature Rev. Genetics, vol. 8, no. 6, pp. 450-461, 2007.- [4] V. Batagelj and A. Mrvar, “Pajek-Analysis and Visualization of Large Networks,”
Graph Drawing Software, pp. 77-103, Springer series Mathematics and Visualization, 2004.- [5] B. Bollobás,
Modern Graph Theory, vol. 184. Springer Verlag, 1998.- [6] P. Cameron,
Permutation Groups, vol. 45. Cambridge Univ. Press, 1999.- [7] J. Chen, W. Hsu, M. Lee, and S. Ng, “Nemofinder: Dissecting Genome-Wide Protein-Protein Interactions with Meso-Scale Network Motifs,”
Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 106-115, 2006.- [8]
GAP - Groups, Algorithms, and Programming, Version 4.4.12, The GAP Group, http:/www.gap-system.org, 2008.- [9] J. Grochow and M. Kellis, “Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking,”
Research in Computational Molecular Biology, pp. 92-106, Springer, 2007.- [10] M. He and S. Petoukhov,
Mathematics of Bioinformatics: Theory, Methods and Applications, vol. 11. Wiley-Interscience, 2011.- [11] S. Itzkovitz, R. Milo, N. Kashtan, G. Ziv, and U. Alon, “Subgraphs in Random Networks,”
Physical Rev. E, vol. 68, no. 2, p. 026127, 2003.- [12] D. Johnson and M. Garey,
Computers and Intractability: A Guide to the Theory of Np-Completeness. Freeman & Co, 1979.- [13] Z. Kashani, H. Ahrabian, E. Elahi, A. Nowzari-Dalini, E. Ansari, S. Asadi, S. Mohammadi, F. Schreiber, and A. Masoudi-Nejad, “Kavosh: A New Algorithm for Finding Network Motifs,”
BMC Bioinformatics, vol. 10, no. 1,article 318, 2009.- [14] N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon, “Mfinder Tool Guide,” Department of Molecular Cell Biology and Computer Science and Applied Math., Weizmann Inst. of Science, Rehovot Israel, technical report, 2002.
- [15] N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon, “Efficient Sampling Algorithm for Estimating Subgraph Concentrations and Detecting Network Motifs,”
Bioinformatics, vol. 20, no. 11, pp. 1746-1758, 2004.- [16] T. Kim, J. Kim, P. Heslop-Harrison, and K. Cho, “Evolutionary Design Principles and Functional Characteristics Based on Kingdom-Specific Network Motifs,”
Bioinformatics, vol. 27, no. 2, p. 245, 2011.- [17] J. Lauri and R. Scapellato,
Topics in Graph Automorphisms and Reconstruction. Cambridge Univ. Press, 2003.- [18] B. MacArthur, R. Sánchez-García, and J. Anderson, “Symmetry in Complex Networks,”
Discrete Applied Math., vol. 156, no. 18, pp. 3525-3531, 2008.- [19] D. Marcus and Y. Shavitt, “Efficient Counting of Network Motifs,”
Proc. IEEE 30th Int'l Conf. Distributed Computing Systems Workshops, pp. 92-98, 2010.- [20] B. McKay, “Practical Graph Isomorphism,”
Congressus Numerantium, vol. 30, pp. 45-87, 1981.- [21] B. McKay, “Isomorph-Free Exhaustive Generation,”
J. Algorithms, vol. 26, no. 2, pp. 306-324, 1998.- [22] R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon, “Superfamilies of Evolved and Designed Networks,”
Science, vol. 303, no. 5663, p. 1538, 2004.- [23] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, “Network Motifs: Simple Building Blocks of Complex Networks,”
Science, vol. 298, no. 5594, p. 824, 2002.- [24] L. Parida, “Discovering Topological Motifs Using a Compact Notation,”
J. Computational Biology, vol. 14, no. 3, pp. 300-323, 2007.- [25] F. Schreiber and H. Schwöbbermeyer, “Mavisto: A Tool for the Exploration of Network Motifs,”
Bioinformatics, vol. 21, no. 17, pp. 3572-3574, 2005.- [26] P. Shannon, A. Markiel, O. Ozier, N. Baliga, J. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker, “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,”
Genome Research, vol. 13, no. 11, pp. 2498-2504, 2003.- [27] C. Stark, B. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and M. Tyers, “Biogrid: A General Repository for Interaction Datasets,”
Nucleic Acids Research, vol. 34, no. suppl 1, pp. D535-D539, 2006.- [28] D. Stinson,
Combinatorial Algorithms: Generation, Enumeration, and Search. CRC Press, 1999.- [29] J. Sun, E. Bollt, and D. Ben-Avraham, “Graph Compression-Save Information by Exploiting Redundancy,”
J. Statistical Mechanics: Theory and Experiment, vol. 2008, p. P06001, 2008.- [30] S. Wernicke, “A Faster Algorithm for Detecting Network Motifs,”
Proc. Fifth Int'l Conf. Algorithms in Bioinformatics, pp. 165-177, 2005.- [31] S. Wernicke, “Efficient Detection of Network Motifs,”
IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 3, no. 4, pp. 347-359, Oct.-Dec. 2006.- [32] S. Wernicke and F. Rasche, “Fanmod: A Tool for Fast Network Motif Detection,”
Bioinformatics, vol. 22, no. 9, pp. 1152-1153, 2006.- [33] E. Wong, B. Baur, S. Quader, and C. Huang, “Biological Network Motif Detection: Principles and Practice,”
Briefings in Bioinformatics, vol. 13, no. 2, pp. 202-215, 2011.- [34] I. Xenarios, L. Salwinski, X. Duan, P. Higney, S. Kim, and D. Eisenberg, “Dip, the Database of Interacting Proteins: A Research Tool for Studying Cellular Networks of Protein Interactions,”
Nucleic Acids Research, vol. 30, no. 1, pp. 303-305, 2002. |