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VENLO: Interactive Visual Exploration of Aligned Biological Networks and Their Evolution

  • Conference paper
Visualization in Medicine and Life Sciences II

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

To understand life, it is fundamental to elucidate the evolution and function of biological networks in multiple species. Recently it has become possible to reconstruct the evolution of specific biological networks for several species. The data resulting from these reconstructions consists of ancestral networks and gene trees. To analyze such data, interactive visual methods are needed. We present a system that is able to visualize the evolution of biological networks in many species. We start with providing a comprehensible overview of the entire data set and provide details of the data upon demand via interaction mechanisms to select interesting subsets of the data. The selected subsets can be visualized using two main visualization types: (a) as network alignments in 2.5D (or other known) layouts or (b) as an animation of evolving networks. We developed a graph layout algorithm supporting the comparison of networks across both species and time steps without changing the graph layoutwhile switching between the overview, the animated view, and the alignment view.We evaluate our system by applying it to real-world data.

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References

  1. Sourav Bandyopadhyay, Roded Sharan, and Trey Ideker. Systematic identification of functional orthologs based on protein network comparison. Genome Res., 16(3):428-435, March2006.

    Google Scholar 

  2. Jim Blythe, Cathleen McGrath, and David Krackhardt. The effect of graph layout on inferencefrom social network data. In Franz J. Brandenburg, editor, Graph Drawing, Passau, Germany,September 20-22, 1995, 40-51. Springer, 1996.

    Google Scholar 

  3. P. Bork, L.J. Jensen, C. von Mering, A.K. Ramani, I. Lee, and E.M. Marcotte. Proteininteraction networks from yeast to human. Curr Opin Struct Biol, 14(3):292-299, 2004.

    Article  Google Scholar 

  4. Ulrik Brandes and Steven R. Corman. Visual unrolling of network evolution and the analysisof dynamic discourse. Information Visualization, 2(1):40-50, 2003.

    Article  Google Scholar 

  5. Ulrik Brandes, Tim Dwyer, and Falk Schreiber. Visual understanding of metabolic pathwaysacross organisms using layout in two and a half dimensions. Journal of IntegrativeBioinformatics, 1(1):119-132, 2004.

    Google Scholar 

  6. Ulrik Brandes and Dorothea Wagner. A bayesian paradigm for dynamic graph layout. In GD’97: Proceedings of the 5th International Symposium on Graph Drawing, 236-247, London,UK, 1997. Springer-Verlag.

    Google Scholar 

  7. Jrgen Branke. Dynamic graph drawing. In M Kaufmann and D Wagner, editors, GraphDrawing - Models and Algorithms, 228-246. Springer, Berlin, 2001.

    Google Scholar 

  8. Steffen Brasch, Lars Linsen, and Georg Fuellen. Visualization of aligned biological networks:A survey. In Franz-Erich Wolter and Alexei Sourin, editors, Proc. 2007 InternationalConference on Cyberworlds, 49-53. IEEE Computer Society, 10 2007.

    Google Scholar 

  9. Steffen Brasch, Lars Linsen, and Georg Fuellen. Vanlo interactive visual exploration of alignedbiological networks. BMC Bioinformatics, 10(327), 2009.

    Google Scholar 

  10. R.M. Cripps and E.N. Olson. Control of cardiac development by an evolutionarily conservedtranscriptional network. Dev Biol, 246(1):14-28, 2002.

    Article  Google Scholar 

  11. Ron Davidson and David Harel. Drawing graphs nicely using simulated annealing. ACM Trans-actions on Graphics, 15(4):301-331, 1996.

    Article  Google Scholar 

  12. G. Di Battista, P. Eades, R. Tamassia, and I. G. Tollis. Algorithms for drawing graphs: Anannotated bibliography. Comput. Geometry: Theory Appl., 4:235-282, 1994.

    Article  MATH  Google Scholar 

  13. Stephan Diehl and Carsten G örg. Graphs, they are changing. In GD ’02: Revised Papers fromthe 10th International Symposium on Graph Drawing, 23-30, London, UK, 2002. Springer-Verlag.

    Google Scholar 

  14. Eli Eisenberg and Erez Y. Levanon. Preferential attachment in the protein network evolution.Phys. Rev. Lett., 91(13):138701, Sep 2003.

    Google Scholar 

  15. Cesim Erten, Stephen G. Kobourov, Vu Le, and Armand Navabi. Simultaneous graph drawing:Layout algorithms and visualization schemes. J. Graph Algorithms Appl., 9(1):165-182, 2005.

    Google Scholar 

  16. Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing by force-directedplacement. Software - Practice and Experience, 21(11):1129-1164, 1991.

    Google Scholar 

  17. Todd A. Gibson and Debra S. Goldberg. Reverse Engineering the Evolution of ProteinInteraction Networks, 2009.

    Google Scholar 

  18. Carsten Görg, Peter Birke, Mathias Pohl, and Stephan Diehl. Dynamic graph drawing ofsequences of orthogonal and hierarchical graphs. In Graph Drawing, 228-238. SpringerBerlin, Heidelberg, 2004.

    Google Scholar 

  19. Ivan Herman, Guy Melançon, and M. Scott Marshall. Graph visualization and navigationin information visualization: A survey. IEEE Transactions on Visualization and ComputerGraphics, 6(1):24-43, /2000.

    Google Scholar 

  20. E. Hirsh and R. Sharan. Identification of conserved protein complexes based on a model ofprotein network evolution. Bioinformatics, 23(2), January 2007.

    Google Scholar 

  21. Danny Holten. Hierarchical edge bundles: Visualization of adjacency relations in hierarchicaldata. IEEE Transactions on Visualization and Computer Graphics, 12(5):741-748, 2006.

    Article  Google Scholar 

  22. Zhenjun Hu, Joe Mellor, Jie Wu, Minoru Kanehisa, Joshua M. Stuart, and Charles Delisi.Towards zoomable multidimensional maps of the cell. Nature Biotechnology, 25(5):547-554,May 2007.

    Article  Google Scholar 

  23. Zhenjun Hu, Joseph Mellor, Jie Wu, and Charles Delisi. Visant: an online visualization andanalysis tool for biological interaction data. BMC Bioinformatics, 5:17, 2004.

    Google Scholar 

  24. F. Iragne, M. Nikolski, B. Mathieu, D. Auber, and D.J. Sherman. Proviz: protein interactionvisualization and exploration. Bioinformatics, 21(2):272-274, 2005. Received on August 4,2003; revised on June 15, 2004; accepted on August 18, 2004. Advance Access Publication September 3, 2004.

    Google Scholar 

  25. Bjorn H. Junker, Christian Klukas, and Falk Schreiber. Vanted: A system for advanced dataanalysis and visualization in the context of biological networks. BMC Bioinformatics, 7:109,2006.

    Google Scholar 

  26. T. Kamada and S. Kawai. An algorithm for drawing general undirected graphs. Inf. Process.Lett., 31(1):7-15, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  27. M. Kanehisa and S. Goto. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic AcidsRes., 28:27-30, 2000.

    Article  Google Scholar 

  28. B. P. Kelley, R. Sharan, R. M. Karp, T. Sittler, D. E. Root, B. R. Stockwell, and T. Ideker.Conserved pathways within bacteria and yeast as revealed by global protein network alignment.Proc Natl Acad Sci U S A, 100(20):11394-11399, September 2003.

    Google Scholar 

  29. M. Koyut ürk, Y. Kim, S. Subramaniam, W. Szpankowski, and A. Grama. Detecting conservedinteraction patterns in biological networks. J Comput Biol, 13(7):1299-1322, September 2006.

    Google Scholar 

  30. L.R. Matthews, P. Vaglio, J. Reboul, H. Ge, B.P. Davis, J. Garrels, S. Vincent, and M. Vidal.Identification of potential interaction networks using sequence-based searches for conservedprotein-protein interactions or ”interologs”. Genome Res, 11(12):2120-2126, 2001.

    Article  Google Scholar 

  31. Ratmann O, Wiuf C, and Pinney JW. From evidence to inference: probing the evolution ofprotein interaction networks. HFSP Journal, 3(5):290-306, December 2009.

    Article  Google Scholar 

  32. Roderic D. Page and Michael A. Charleston. From gene to organismal phylogeny: Reconciledtrees and the gene tree/species tree problem. Molecular Phylogenetics and Evolution,7 (2):231-240, April 1997.

    Google Scholar 

  33. Les Piegl and Wayne Tiller. The Nurbs Book. Springer Berlin/ Heidelberg/ New York, 1997.

    Google Scholar 

  34. Helen C. Purchase, Eve Hoggan, and Carsten Goerg. How important is the mental map? -an empirical investigation of a dynamic graph layout algorithm. In Michael Kaufmann andDorothea Wagner, editors, LNCS, Graph Drawing, 184-195. Springer, Berlin / Heidelberg,2007.

    Google Scholar 

  35. A.K. Ramani and E.M. Marcotte. Exploiting the co-evolution of interacting proteins to discoverinteraction specificity. J Mol Biol, 327(1):273-284, 2003.

    Article  Google Scholar 

  36. Falk Schreiber. Visual comparison of metabolic pathways. J. Vis. Lang. Comput., 14(4):327-340,2003.

    Google Scholar 

  37. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin,B. Schwikowski, and T. Ideker. Cytoscape: a software environment for integrated modelsof biomolecular interaction networks. Genome Res, 13(11):2498-2504, November 2003.

    Article  Google Scholar 

  38. Roded Sharan and Trey Ideker. Modeling cellular machinery through biological networkcomparison. Nature Biotechnology, 24(4):427-433, April 2006.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Ernst-Moritz-Arndt Universität, Greifswald, Germany

    Steffen Brasch

  2. Institute for Biostatistics and Informatics in Medicine and Aging Research, University Rostock, Rostock, Germany

    Georg Fuellen

  3. School of Engineering and Science, Jacobs University, Bremen, Germany

    Lars Linsen

Authors
  1. Steffen Brasch
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  2. Georg Fuellen
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  3. Lars Linsen
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Corresponding author

Correspondence to Steffen Brasch .

Editor information

Editors and Affiliations

  1. Bremen School of Engineering and Science, Jacobs University, 75 05 61, 28725, Bremen, Germany

    Lars Linsen

  2. FB Informatik, Universität Kaiserslautern, 30 49, 67653, Kaiserslautern, Germany

    Hans Hagen

  3. Institute for Data Analysis and Visualization Department of Computer Science, University of California, Davis, One Shields Avenue, Davis, CA, 95616-8562, USA

    Bernd Hamann

  4. Visualization and Data Analysis, Zuse Institute Berlin (ZIB), Takustr. 7, 14195, Berlin, Germany

    Hans-Christian Hege

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Brasch, S., Fuellen, G., Linsen, L. (2012). VENLO: Interactive Visual Exploration of Aligned Biological Networks and Their Evolution. In: Linsen, L., Hagen, H., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences II. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21608-4_13

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