This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Visual Analysis of Gel-Free Proteome Data
July/August 2006 (vol. 12 no. 4)
pp. 497-508

Abstract—We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.

[1] F.O. Andersson, R. Kaiser, and S.P. Jakobsson, “Data Preprocessing by Wavelets and Genetic Algorithms for Enhanced Multivariate Analysis of LC Peptide Mapping,” J. Pharmaceutical and Biomedical Analysis, vol. 34, pp. 531-541, 2004.
[2] J. Bernhardt, K. Buttner, C. Scharf, and M. Hecker, “Dual Channel Imaging of Two-Dimensional Electropherograms in Bacillus Subtilis,” Electrophoresis, vol. 20, no. 11, pp. 2225-2240, 1999.
[3] J. Bernhardt, J. Weibezahn, C. Scharf, and M. Hecker, “Bacillus Subtilis during Feast and Famine: Visualization of the Overall Regulation of Protein Synthesis during Glucose Starvation by Proteome Analysis,” Genome Research, vol. 13, no. 2, pp. 224-237, 2003.
[4] P. Cignoni, C. Montani, E. Puppo, and R. Scopigno, “Multiresolution Modeling and Visualization of Volume Data,” IEEE Trans. Visualization and Computer Graphics, vol. 3, no. 4, pp. 352-369, 1997.
[5] P. Cignoni, E. Puppo, and R. Scopigno, “Representation and Visualization of Terrain Surfaces at Variable Resolution,” The Visual Computer, vol. 13, no. 5, pp. 199-217, 1997.
[6] D. Cohen-Or and Y. Levanoni, “Temporal Continuity of Levels of Detail in Delaunay Triangulated Terrain,” Proc. IEEE Conf. Visualization, pp. 37-42, 1996.
[7] J. de Corral and H. Pfister, “Hardware-Accelerated 3D Visualization of Mass Spectrometry Data,” Proc. IEEE Conf. Visualization, pp. 439-446, 2005.
[8] M. Duchaineau, M. Wolinski, D.E. Sigeti, M. Miller, C. Aldrich, and M.B. Mineev-Weinstein, “Roaming Terrain: Real-Time Optimally Adapting Meshes,” Proc. IEEE Conf. Visualization, pp. 81-88, 1997.
[9] J.B. Fenn, M. Mann, C.K. Meng, S.F. Wong, and C.M. Whitehouse, “Electrospray Ionization for Mass Spectrometry of Large Biomolecules,” Science, vol. 246, no. 64, 1989.
[10] D.R. Gilbert, M. Schroeder, and J. van Helden, “Interactive Visualization and Exploration of Relationships between Biological Objects,” Trends in Biotechnology, vol. 18, no. 12, pp. 487-494, 2000.
[11] R. Grosso, C. Lürig, and T. Ertl, “The Multilevel Finite Element Method for Adaptive Mesh Optimization and Visualization of Volume Data,” Proc. IEEE Conf. Visualization, pp. 135-142, 1997.
[12] H. Hoppe, “Smooth View-Dependent Level-of-Detail Control and Its Application to Terrain Rendering,” Proc. IEEE Conf. Visualization, pp. 35-42, 1998.
[13] M. Karas and F. Hillenkamp, “Laser Desorption Ionization of Proteins with Molecular Masses Exceeding 10 000 Daltons,” Analytical Chemistry, vol. 60, pp. 2299-2301, 1988.
[14] X.-J. Li, P.G.A. Pedrioli, J.E. J, D. Martin, E.C. Yi, H. Lee, and R. Aebersold, “A Tool to Visualize and Evaluate Data Obtained by Liquid Chromatography/Electrospray Ionization/Mass Spectrometry,” Analytical Chemistry, vol. 76, pp. 3856-3860, 2004.
[15] X.-J. Li, H. Zhang, J.R. Ranish, and R. Aebersold, “Automated Statistical Analysis of Protein Abundance Ratios from Data Generated by Stable Isotope Dilution and Tandem Mass Spectrometry,” Analytical Chemistry, vol. 75, pp. 6648-6657, 2003.
[16] P. Lindstrom, D. Koller, W. Ribarsky, L. Hodges, N. Faust, and G. Turner, “Real-Time Continuous Level of Detail Rendering of Height Fields,” Proc. SIGGRAPH, pp. 109-118, 1996.
[17] L. Linsen, J. Löcherbach, M. Berth, J. Bernhardt, and D. Becher, “Differential Protein Expression Analysis via Liquid-Chromatography/Mass-Spectrometry Data Visualization,” Proc. IEEE Conf. Visualization, pp. 447-454, 2005.
[18] L. Linsen, V. Pascucci, M.A. Duchaineau, B. Hamann, and K.I. Joy, “Wavelet-Based Multiresolution with $\sqrt[n]{2}$ Subdivision,” J. Computing, vol. 71, nos. 1-2, 2004.
[19] F. Losasso and H. Hoppe, “Geometry Clipmaps: Terrain Rendering Using Nested Regular Grids,” ACM Trans. Graphics, vol. 24, no. 3, pp. 769-776, 2004.
[20] S. Luhn, M. Berth, M. Hecker, and J. Bernhardt, “Using Standard Positions and Image Fusion to Create Proteome Maps from Collections of Two-Dimensional Gel Electrophoresis Images,” Proteomics, vol. 3, no. 7, pp. 1117-1127, 2003.
[21] D.M. Maynard, J. Masuda, X. Yang, J.A. Kowalak, and S.P. Markey, “Characterizing Complex Peptide Mixtures Using a Multi-Dimensional Liquid Chromatography-Mass Spectrometry System: Saccharomyces Cerevisiae as a Model System,” J. Chromatography B, vol. 810, no. 1, pp. 69-76, 2004.
[22] D.N. Perkins, D.J. Pappin, D.M. Creasy, and J.S. Cottrell, “Probability-Based Protein Identification by Searching Sequence Databases Using Mass Spectrometry Data,” Electrophoresis, vol. 20, no. 18, pp. 3551-3567, 1999.
[23] D. Pinskiy, E. Brugger, H.R. Childs, and B. Hamann, “An Octree-Based Multiresolution Approach Supporting Interactive Rendering of Very Large Volume Data Sets,” Proc. 2001 Int'l Conf. Imaging Science, Systems, and Technology (CISST '01), vol. 1, pp. 16-22, 2001.
[24] J.T. Prince, M.W. Carlson, R. Wang, P. Lu, and E.M. Marcotte, “The Need for a Public Proteomics Repository (Commentary),” Nature Biotechnology, vol. 22, pp. 471-472, 2004.
[25] N. Shah, V. Filkov, B. Hamann, and K.I. Joy, “GeneBox: Interactive Visualization of Microarray Data Sets,” Proc. Int'l Conf. Math. and Eng. Techniques in Medicine and Biological Sciences (METMBS '03), pp. 10-16, 2003.
[26] Y. Shinagawa and T.L. Kunii, “Unconstrained Automatic Image Matching Using Multiresolutional Critical-Point Filters,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 9, pp. 994-1010, Sept. 1998.
[27] E.J. Stollnitz, T.D. DeRose, and D.H. Sales, Wavelets for Computer Graphics: Theory and Applications, The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling, B.A. Barsky (series ed.), San Francisco, Calif.: Morgan Kaufmann Publishers, 1996.
[28] C. Tang, L. Zhang, and A. Zhang, “Interactive Visualization and Analysis for Gene Expression Data,” Proc. Hawaii Int'l Conf. System Sciences, 2002.
[29] M. Tyers and M. Mann, “From Genomics to Proteomics,” Nature, vol. 422, pp. 193-197, 2003.
[30] M.P. Washburn, D. Wolters, and J.R. Yates III, “Large-Scale Analysis of the Yeast Proteome by Multidimensional Protein Identification Technology,” Nature Biotechnology, vol. 19, pp. 242-247, 2001.
[31] M.R. Wilkins, C. Pasquali, R.D. Appel, K. Ou, O. Golaz, J.C. Sanchez, J.X. Yan, A.A. Gooley, G. Hughes, I. Humphery-Smith, K.L. Williams, and D.F. Hochstrasser, “From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Amino Acid Analysis,” Biotechnology, vol. 14, pp. 61-65, 1996.

Index Terms:
Interactive visual exploration, hierarchical data representation, visualization in bioinformatics, proteomics.
Citation:
Lars Linsen, Julia L?cherbach, Matthias Berth, D?rte Becher, J? Bernhardt, "Visual Analysis of Gel-Free Proteome Data," IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 4, pp. 497-508, July-Aug. 2006, doi:10.1109/TVCG.2006.82
Usage of this product signifies your acceptance of the Terms of Use.