Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2010, Article ID 896718, 9 pages
http://dx.doi.org/10.1155/2010/896718
Review Article

Feature Detection Techniques for Preprocessing Proteomic Data

1Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA
2Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
3Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA

Received 2 October 2009; Revised 24 December 2009; Accepted 17 February 2010

Academic Editor: Shan Zhao

Copyright © 2010 Kimberly F. Sellers and Jeffrey C. Miecznikowski. 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. J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, NY, USA, 1982.
  2. X. Zhuang and R. M. Haralick, “Morphological structuring element decomposition,” Computer Vision, Graphics, and Image Processing, vol. 35, no. 3, pp. 370–382, 1986. View at Google Scholar
  3. P. Soille, Morphological Image Analysis: Principles and Applications, Springer, New York, NY, USA, 2003.
  4. P. Maragos, “Tutorial on advances in morphological image processing and analysis,” Optical Engineering, vol. 26, no. 7, pp. 623–632, 1987. View at Google Scholar
  5. D. M. Gavrila, J. Giebel, M. Perception, D. C. Res, and G. Ulm, “Shape-based pedestrian detection and tracking,” in Proceedings of the IEEE Intelligent Vehicles Symposium, vol. 1, 2000.
  6. L. Tarassenko, P. Hayton, N. Cerneaz, and M. Brady, “Novelty detection for the identification of masses in mammograms,” in Proceedings of the 4th International Conference on Artificial Neural Networks, pp. 442–447, Cambridge, UK, June 1995.
  7. E. Saber and A. M. Tekalp, “Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions,” Pattern Recognition Letters, vol. 19, no. 8, pp. 669–680, 1998. View at Google Scholar
  8. Y. Wang, C.-S. Chua, and Y.-K. Ho, “Facial feature detection and face recognition from 2D and 3D images,” Pattern Recognition Letters, vol. 23, no. 10, pp. 1191–1202, 2002. View at Publisher · View at Google Scholar
  9. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary DNA microarray,” Science, vol. 270, no. 5235, pp. 467–470, 1995. View at Google Scholar
  10. D. Shalon, S. J. Smith, and P. O. Brown, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research, vol. 6, no. 7, pp. 639–645, 1996. View at Google Scholar
  11. L. Monteoliva and J. P. Albar, “Differential proteomics: an overview of gel and non-gel based approaches,” Briefings in Functional Genomics and Proteomics, vol. 3, no. 3, pp. 220–239, 2004. View at Google Scholar
  12. B. Domon and R. Aebersold, “Challenges and opportunities in proteomics data analysis,” Molecular and Cellular Proteomics, vol. 5, no. 10, pp. 1921–1926, 2006. View at Publisher · View at Google Scholar · View at PubMed
  13. P. H. O'Farrell, “High resolution two dimensional electrophoresis of proteins,” The Journal of Biological Chemistry, vol. 250, no. 10, pp. 4007–4021, 1975. View at Google Scholar
  14. M. Ünlü, M. E. Morgan, and J. S. Minden, “Difference gel electrophoresis: a single gel method for detecting changes in protein extracts,” Electrophoresis, vol. 18, no. 11, pp. 2071–2077, 1997. View at Google Scholar
  15. I. Levner, “Feature selection and nearest centroid classification for protein mass spectrometry,” BMC Bioinformatics, vol. 6, 2005. View at Publisher · View at Google Scholar · View at PubMed
  16. JEOL Mass Spectrometers, Tandem Mass Spectrometry (MS/MS), JEOL, Boston, Mass, USA, 2006.
  17. E. de Hoffmann, “Tandem mass spectrometry: a primer,” Journal of Mass Spectrometry, vol. 31, no. 2, pp. 129–137, 1996. View at Google Scholar
  18. C. M. Delahunty and J. R. Yates III, “MudPIT: multidimensional protein identification technology,” BioTechniques, vol. 43, no. 5, pp. 563–569, 2007. View at Publisher · View at Google Scholar
  19. S. P. Gygi, B. Rist, S. A. Gerber, F. Turecek, M. H. Gelb, and R. Aebersold, “Quantitative analysis of complex protein mixtures using isotope-coded affinity tags,” Nature Biotechnology, vol. 17, no. 10, pp. 994–999, 1999. View at Publisher · View at Google Scholar · View at PubMed
  20. W. W. Wu, G. Wang, S. J. Baek, and R. F. Shen, “Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF,” Journal of Proteome Research, vol. 5, no. 3, pp. 651–658, 2006. View at Publisher · View at Google Scholar · View at PubMed
  21. C. S. Gan, P. K. Chong, T. K. Pham, and P. C. Wright, “Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ),” Journal of Proteome Research, vol. 6, no. 2, pp. 821–827, 2007. View at Publisher · View at Google Scholar · View at PubMed
  22. S. Meleth, J. Deshane, and H. Kim, “The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins,” BMC Biotechnology, vol. 5, no. 1, article 7, 2005. View at Publisher · View at Google Scholar · View at PubMed
  23. Y. Kang, T. Techanukul, A. Mantalaris, and J. M. Nagy, “Comparison of three commercially available DIGE analysis software packages: minimal user intervention in gel-based proteomics,” Journal of Proteome Research, vol. 8, no. 2, pp. 1077–1084, 2009. View at Publisher · View at Google Scholar · View at PubMed
  24. A. T. Rosengren, J. M. Salmi, T. Aittokallio et al., “Comparison of PDQuest and Progenesis software packages in the analysis of two-dimensional electrophoresis gels,” Proteomics, vol. 3, no. 10, pp. 1936–1946, 2003. View at Publisher · View at Google Scholar · View at PubMed
  25. B. Raman, A. Cheung, and M. R. Marten, “Quantitative comparison and evaluation of two commercially available, two-dimensional electrophoresis image analysis software packages, Z3 and Melanie,” Electrophoresis, vol. 23, no. 14, pp. 2194–2202, 2002. View at Google Scholar
  26. J. Chang, H. Van Remmen, W. F. Ward, F. E. Regnier, A. Richardson, and J. Cornell, “Processing of data generated by 2-dimensional gel electrophoresis for statistical analysis: missing data, normalization, and statistics,” Journal of Proteome Research, vol. 3, no. 6, pp. 1210–1218, 2004. View at Publisher · View at Google Scholar · View at PubMed
  27. E. Marengo, E. Robotti, F. Antonucci, D. Cecconi, N. Campostrini, and P. G. Righetti, “Numerical approaches for quantitative analysis of two-dimensional maps: a review of commercial software and home-made systems,” Proteomics, vol. 5, no. 3, pp. 654–666, 2005. View at Publisher · View at Google Scholar · View at PubMed
  28. R Development Core Team, R: A Language and Environment for Statistical Computing, 2008.
  29. R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, p. R80, 2004. View at Google Scholar
  30. P. Du, W. A. Kibbe, and S. M. Lin, “Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching,” Bioinformatics, vol. 22, no. 17, pp. 2059–2065, 2006. View at Publisher · View at Google Scholar · View at PubMed
  31. C. A. Smith and R. Tautenhahn, “xcms: LC/MS and GC/MS Data Analysis,” 2007. R package version 1.10.7.
  32. M. Robinson, Flagme: Fragment-Level Analysis of GCMS-Based Metabolomics Data.
  33. A. Cuadros-Inostroza, H. Redestig, M. A. Hannah, and G. Potsdam, “The TargetSearch Package”.
  34. W. Yu, R. Z. Qi, J. Liu, and H. Zhao, “Mass spectrometry based quantitative proteomics data analysis methods: a review,” Preprint 2008.
  35. Y. Yasui, M. Pepe, M. L. Thompson et al., “A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection,” Biostatistics, vol. 4, no. 3, pp. 449–463, 2003. View at Google Scholar
  36. T. Fushiki, H. Fujisawa, and S. Eguchi, “Identification of biomarkers from mass spectrometry data using a “common” peak approach,” BMC Bioinformatics, vol. 7, article 358, 2006. View at Publisher · View at Google Scholar · View at PubMed
  37. K. R. Coombes, H. A. Fritsche Jr., C. Clarke et al., “Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization,” Clinical Chemistry, vol. 49, no. 10, pp. 1615–1623, 2003. View at Publisher · View at Google Scholar
  38. K. R. Coombes, S. Tsavachidis, J. S. Morris, K. A. Baggerly, M.-C. Hung, and H. M. Kuerer, “Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform,” Proteomics, vol. 5, no. 16, pp. 4107–4117, 2005. View at Publisher · View at Google Scholar · View at PubMed
  39. J. S. Morris, K. R. Coombes, J. Koomen, K. A. Baggerly, and R. Kobayashi, “Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum,” Bioinformatics, vol. 21, no. 9, pp. 1764–1775, 2005. View at Publisher · View at Google Scholar · View at PubMed
  40. M. R. Hoopmann, G. L. Finney, and M. J. MacCoss, “High-speed data reduction, feature detection, and MS/MS spectrum quality assessment of shotgun proteomics data sets using high-resolution mass spectrometry,” Analytical Chemistry, vol. 79, no. 15, pp. 5620–5632, 2007. View at Publisher · View at Google Scholar · View at PubMed
  41. P. D. von Haller, E. Yi, S. Donohoe et al., “The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation,” Molecular and Cellular Proteomics, vol. 2, no. 7, pp. 428–442, 2003. View at Google Scholar
  42. T. Srinark and C. Kambhamettu, “An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels,” Electrophoresis, vol. 29, no. 3, pp. 706–715, 2008. View at Publisher · View at Google Scholar · View at PubMed
  43. O. Langella and M. Zivy, “A method based on bead flows for spot detection on 2-D gel images,” Proteomics, vol. 8, no. 23-24, pp. 4914–4918, 2008. View at Publisher · View at Google Scholar · View at PubMed
  44. J. C. Miecznikowski, K. F. Sellers, and W. F. Eddy, Multidimensional Median Filters for Finding Bumps, 2009.
  45. K. F. Sellers, J. Miecznikowski, S. Viswanathan, J. S. Minden, and W. F. Eddy, “Lights, camera, action: systematic variation in 2-D difference gel electrophoresis images,” Electrophoresis, vol. 28, no. 18, pp. 3324–3332, 2007. View at Publisher · View at Google Scholar · View at PubMed
  46. J. C. Miecznikowski, Spot Detection in Two-Dimensional Electrophoresis Images, Ph.D. thesis, Carnegie Mellon University, Pennsylvania, Pa, USA, 2006.
  47. A. Soggiu, O. Marullo, P. Roncada, and E. Capobianco, “Empowering spot detection in 2DE images by wavelet denoising,” In Silico Biology, vol. 9, no. 3, pp. 125–133, 2009. View at Publisher · View at Google Scholar
  48. R. Gentleman, V. J. Carey, W. Huber, S. Dudoit, and R. A. Irizarry, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, Berlin, Germany, 2005.
  49. R. G. Miller, Simultaneous Statistical Inference, Springer, New York, NY, USA, 1981.
  50. S. Dudoit and M. J. Van Der Laan, Multiple Testing Procedures with Applications to Genomics, Springer, Berlin, Germany, 2008.
  51. A. J. Rai, C. A. Gelfand, B. C. Haywood et al., “HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples,” Proteomics, vol. 5, no. 13, pp. 3262–3277, 2005. View at Publisher · View at Google Scholar · View at PubMed
  52. K. A. Baggerly, J. S. Morris, and K. R. Coombes, “Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments,” Bioinformatics, vol. 20, no. 5, pp. 777–785, 2004. View at Publisher · View at Google Scholar · View at PubMed
  53. O. J. Semmes, Z. Feng, B.-L. Adam et al., “Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility,” Clinical Chemistry, vol. 51, no. 1, pp. 102–112, 2005. View at Publisher · View at Google Scholar · View at PubMed
  54. K. H. Yu, A. K. Rustgi, and I. A. Blair, “Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry,” Journal of Proteome Research, vol. 4, no. 5, pp. 1742–1751, 2005. View at Publisher · View at Google Scholar · View at PubMed