About this Journal Submit a Manuscript Table of Contents
Advances in Bioinformatics
Volume 2013 (2013), Article ID 920325, 10 pages
http://dx.doi.org/10.1155/2013/920325
Research Article

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

1Institute of Theoretical Computer Science, ETH Zurich, 8092 Zurich, Switzerland
2NEBION AG, Hohlstraße 515, 8048 Zurich, Switzerland

Received 12 April 2013; Accepted 7 June 2013

Academic Editor: Guohui Lin

Copyright © 2013 Tomas Hruz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Kamada and S. Kawai, “An algorithm for drawing general undirected graphs,” Information Processing Letters, vol. 31, no. 1, pp. 7–15, 1989. View at Scopus
  2. T. M. J. Ffuchterman and E. M. Reingold, “Graph drawing by force-directed place-ment,” Software, vol. 21, no. 11, pp. 1129–1164, 1991.
  3. A. T. Adai, S. V. Date, S. Wieland, and E. M. Marcotte, “LGL: creating a map of protein function with an algorithm for visualizing very large biological networks,” Journal of Molecular Biology, vol. 340, no. 1, pp. 179–190, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Han and B.-H. Ju, “A fast layout algorithm for protein interaction networks,” Bioinformatics, vol. 19, no. 15, pp. 1882–1888, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Abello, M. Resende, and S. Sudarsky, “Massive quasi-clique detection,” in LATIN 2002: Theoretical Informatics, S. Rajsbaum, Ed., vol. 2286 of Lecture Notes in Computer Science, pp. 598–612, Springer, Berlin, Germany, 2002.
  6. W. Li and H. Kurata, “A grid layout algorithm for automatic drawing of biochemical networks,” Bioinformatics, vol. 21, no. 9, pp. 2036–2042, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. S. E. Schaeffer, “Graph clustering,” Computer Science Review, vol. 1, pp. 27–64, 2007.
  8. N. Mishra, R. Schreiber, I. Stanton, and R. Tarjan, “Clustering social networks,” in Algorithms and Models For the Web-Graph, A. Bonato and F. Chung, Eds., vol. 4863 of Lecture Notes in Computer Science, pp. 56–67, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  9. J. Hastad, “Clique is hard to approximate within n1-ε,” Acta Mathematica, vol. 182, pp. 105–142, 1999. View at Publisher · View at Google Scholar
  10. A. Frick, A. Ludwig, and H. Mehldau, “A fast adaptive layout algorithm for undirected graphs (extended abstract and system demonstration),” in Graph Drawing, R. Tamassia and I. Tollis, Eds., vol. 894 of Lecture Notes in Computer Science, pp. 388–403, Springer, Berlin, Germany, 1995. View at Publisher · View at Google Scholar
  11. E. M. Reingold and J. S. Tilford, “Tidier drawings of trees,” IEEE Transactions on Software Engineering, vol. SE-7, no. 2, pp. 223–228, 1981. View at Scopus
  12. S. Grivet, D. Auber, J. P. Domenger, and G. Melancon, “Bubble tree drawing algorithm,” in Computer Vision and Graphics, K. Wojciechowski, B. Smolka, H. Palus, R. Kozera, W. Skarbek, and L. Noakes, Eds., vol. 32 of Computational Imaging and Vision, pp. 633–641, Springer, Dordrecht, The Netherlands, 2006. View at Publisher · View at Google Scholar
  13. T. Hruz, O. Laule, G. Szabo et al., “Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes,” Advances in Bioinformatics, vol. 2008, Article ID 420747, 5 pages, 2008. View at Publisher · View at Google Scholar
  14. T. Asami, Y. K. Min, K. Sekimata et al., “Mode of action of brassinazole: a specific inhibitor of brassinosteroid biosynthesis,” ACS Symposium Series, vol. 774, pp. 269–280, 2001. View at Scopus
  15. J.-X. He, J. M. Gendron, Y. Yang, J. Li, and Z.-Y. Wang, “The GSK3-like kinase BIN2 phosphorylates and destabilizes BZR1, a positive regulator of the brassinosteroid signaling pathway in Arabidopsis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 15, pp. 10185–10190, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. K. J. Halliday, “Plant hormones: the interplay of brassinosteroids and auxin,” Current Biology, vol. 14, no. 23, pp. R1008–R1010, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Y. Yip, R. P. Alexander, K.-K. Yan, and M. Gerstein, “Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data,” PLoS ONE, vol. 5, no. 1, Article ID e8121, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Mutwil, B. Usadel, M. Schütte, A. Loraine, O. Ebenhöh, and S. Persson, “Assembly of an interactive correlation network for the Arabidopsis genome using a novel Heuristic Clustering Algorithm,” Plant Physiology, vol. 152, no. 1, pp. 29–43, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky, “Revealing strengths and weaknesses of methods for gene network inference,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 14, pp. 6286–6291, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. S. De Bodt, S. Proost, K. Vandepoele, P. Rouzé, and Y. Van de Peer, “Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression,” BMC genomics, vol. 10, p. 288, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. D. R. Rhodes, S. A. Tomlins, S. Varambally et al., “Probabilistic model of the human protein-protein interaction network,” Nature Biotechnology, vol. 23, no. 8, pp. 951–959, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. A. de la Fuente, N. Bing, I. Hoeschele, and P. Mendes, “Discovery of meaningful associations in genomic data using partial correlation coefficients,” Bioinformatics, vol. 20, no. 18, pp. 3565–3574, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. J.-F. Rual, K. Venkatesan, T. Hao et al., “Towards a proteome-scale map of the human protein-protein interaction network,” Nature, vol. 437, no. 7062, pp. 1173–1178, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Ishii, T. Washio, T. Uechi, M. Yoshihama, N. Kenmochi, and M. Tomita, “Characteristics and clustering of human ribosomal protein genes,” BMC Genomics, vol. 7, article 37, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. O. Atias, B. Chor, and D. A. Chamovitz, “Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network,” BMC Systems Biology, vol. 3, p. 86, 2009. View at Scopus
  26. R. Bourqui, D. Auber, and P. Mary, “How to draw clustered weighted graphs using a multilevel force-directed graph drawing algorithm,” in Proceedings of the 11th International Conference Information Visualization (IV '07), pp. 757–764, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Kaba, N. Pinet, G. Lelandais, A. Sigayret, and A. Berry, “Clustering gene expression data using graph separators,” In Silico Biology, vol. 7, no. 4-5, pp. 433–452, 2007. View at Scopus