Benchmarking currently available SELDI-TOF MS preprocessing techniques

Proteomics

Volumn 9, Issue 7, 2009, Pages 1754-1762

  Emanuele II, Vincent A ^a,b   Gurbaxani, Brian M ^a,b  

^a CENTERS FOR DISEASE CONTROL AND PREVENTION   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

MALDI; Peak detection; Protein profiling; SELDI; Signal processing

Indexed keywords

PROTEIN;

ALGORITHM; ARTICLE; CONTROLLED STUDY; MASS; PRIORITY JOURNAL; QUALITY CONTROL; SIGNAL PROCESSING; SIMULATION; SURFACE ENHANCED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY;

ALGORITHMS; DATABASES, PROTEIN; MODELS, BIOLOGICAL; PROTEINS; PROTEOMICS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION;

EID: 65349138192     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.200701171     Document Type: Article

References (44)

1. Domon, B., Aebersold, R., Mass spectrometry and protein analysis. Science 2006, 312, 212-217.
2. Petricoin, E. R, Ardekani, A. M., Hitt, B. A., Levine, P. J. et al., Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002, 359, 572-577.
3. Adam, B.-L, Qu, Y, Davis, J. W., Ward, M. D. et al., Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002, 62, 3609-3614.
4. Qu, Y, Adam, B.-L., Yasui, Y, Ward, M. D. et al., Boosted decision tree analysis of surface-enhanced laser desorption/ ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem. 2002, 48, 1835-1843.
5. Issaq, H. J., Veenstra, T. D., Conrads, T. P., Felschow, D., The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification. Biochem. Biophys. Res. Commun. 2002, 292, 587-592.
6. Issaq, H. J., Conrads, T. P., Prieto, D. A., Tirumalai, R., Veenstra, T. D., SELDI-TOF MS for diagnostic proteomics. Anal. Chem. 2003, 75, 148A-155A.
7. Wulfkuhle, J. D., Liotta, L. A., Petricoin, E. F., Proteomic applications for the early detection of cancer. Nat. Rev. Cancer 2002, 3, 267-275.
8. Wiesner, A., Detection of Tumor markers with ProteinChip® technology. Curr. Pharm. Biotechnol. 2004, 5, 45-67.
9. Sorace, J. M., Zhan, M., A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinform. 2003, 4, 24.
10. Baggerly, K. A., Morris, J. S., Coombes, K. R., Reproducibility of SELDI-TOF protein patterns in serum: Comparing datasets from different experiments. Bioinformatics 2004, 20, 777-785.
11. Diamandis, E. P., Point: Proteomic patterns in biological fluids: Do they represent the future of cancer diagnostics? Clin. Chem. 2003, 49, 1272-1275.
12. Diamandis, E. P., Analysis of serum proteomic patterns for early cancer diagnosis: Drawing attention to potential problems. J. Natl. Cancer Inst. 2004, 96, 353-356.
13. Check, E., Proteomics and cancer: Running before we can walk? Nature 2004, 429, 496-497.
14. Diamandis, E. P., van der Merwe, D.-E., Plasma protein profiling by mass spectrometry for cancer diagnosis: Opportunities and limitations. Clin. Cancer Res. 2005, 11, 963-965.
15. Diamandis, E. P., Serum proteomic profiling by matrixassisted laser desorption-ionization time-of-flight mass spectrometry for cancer diagnosis: Next steps. Cancer Res. 2006, 66, 5540-5541.
16. Pang, R. T. K., Poon, T. C. W., Chan, K. C. A., Lee, N. L. S. et al., Serum amyloid A is not useful in the diagnosis of severe acute respiratory syndrome. Clin. Chem. 2006, 52, 1202-1204.
17. White, C. N., Zhang, Z., Chan, D. W., Quality control for SELDI analysis. Clin. Chem. Lab. Med. 2005, 43, 125-126.
18. Semmes, O. J., Feng, Z., Adam, B.-L, Banez, L. L. 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. Clin. Chem. 2005, 51, 102-112.
19. Ekblad, L, Baldetorp, B., Fern, M., Olsson, H., Bratt, C., Insource decay causes artifacts in SELDI-TOF MS spectra. J. Proteome Res. 2007, 6, 1609-1614.
20. Poon, T. C. W., Opportunities and limitations of SELDI-TOF- MS in biomedical research: Practical advices. Expert Rev. Proteomics 2007, 4, 51-65.
21. Meuleman, W., Engwegen, J. Y, Gast, M.-C. W., Beijnen, J. H. et al., Comparison of normalisation methods for surfaceenhanced laser desorption and ionisation (SELDI) time-offlight (TOF) mass spectrometry data. BMC Bioinform. 2008, 9, 88.
22. Beyer, S., Walter, Y, Hellmann, J., Kramer, P.-J. et al., Comparison of software tools to improve the detection of carcinogen induced changes in the rat liver proteome by analyzing SELDI-TOF-MS spectra. J. Proteome Res. 2006, 5, 254- 261.
23. Morris, J. S., Coombes, K. R., Koomen, J., Baggerly, K. A., Kobayashi, R., Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 2005, 21, 1764-1775.
24. Du, P., Kibbe, W. A., Lin, S. M., Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22, 2059-2065.
25. Larman, M., Katz-Jaffe, M., Sheehan, C., Gardner, D., 1,2- propanediol and the type of cryopreservation procedure adversely affect mouse oocyte physiology. Hum. Reprod. 2007, 22, 250-259.
26. Chung, L., Clifford, D., Buckley, M., Baxter, R. C., Novel biomarkers of human growth hormone action from serum proteomic profiling using protein chip mass spectrometry. J. Clin. Endocrinol. Metab. 2006, 91, 671-677.
27. Borgia, J. A., Frankenberger, C., Kaiser, K., McCormack, S. E. et al., Serum biomarker discovery for ovarian serous carcinoma using novel proteomic methods. J. Clin. Oncol. (Meeting Abstracts) 2007, 25, 16058.
28. Tuszynski, J., Processing & Classification of Protein Mass Spectra (SELDI) Data: The caMassClass Package. Bioconductor Software Documentation 2006.
29. Cruz-Marcelo,A.,Guerra, R., Vannucci, M., Li, Y et al., Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics 2008, 24, 2129-2136.
30. Fung, E. T, Enderwick, C., ProteinChip clinical proteomics: Computational challenges and solutions. BioTechniques 2002, 34-38, 40-41.
31. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B. et al., Bioconductor: Open software development for computational biology and bioinformatics. Genome. Biol. 2004, 5, R80.
32. Tan, C. S., Ploner, A., Quandt, A., Lehti, J., Pawitan, Y, Finding regions of significance in SELDI measurements for identifying protein biomarkers. Bioinformatics 2006, 22, 1515-1523.
33. Coombes, K. R., Koomen, J. M., Baggerly, K. A., Morris, J. S., Kobayashi, R., Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Inform. 2005, 1, 41-52.
34. Benjamini, Y, Hochberg, Y, Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B Stat. Meth. 1995, 57, 289-300.
35. Fawcett, T, ROC graphs: Notes and practical considerations for data mining researchers. Tech Report HPL-2003-4, HP Laboratories, Palo Alto, CA, USA, 2003.
36. Choe, S. E., Boutros, M., Michelson, A. M., Church, G. M., Halfon, M. S., Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol. 2005, 6, R16.
37. Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S. et al., Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics 2004, 20, 3034- 3044.
38. Corthals, G. L., Wasinger, V. C., Hochstrasser, D. F., Sanchez, J. C., The dynamic range of protein expression: A challenge for proteomic research. Electrophoresis 2000, 21, 1104- 1115.
39. Coombes, K. R., Tsavachidis, S., Morris, J. S., Baggerly, K. A. et al., 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 2005, 5, 4107-4117.
40. White, C. N., Chan, D. W., Zhang, Z., Bioinformatics strategies for proteomic profiling. Clin. Biochem. 2004, 37, 636- 641.
41. Rollin, D., Whistler, T, Vernon, S. D., Laboratory methods to improve SELDI peak detection and quantitation. Proteome Sci. 2007, 5, 9.
42. Schwab, M., Karrenbach, N., Claerbout, J., Making scientific computations reproducible. Comput. Sci. Eng. 2000, 2, 61-67.
43. Malyarenko, D. I., Cooke, W. E., Adam, B.-L., Malik, G. et al., Enhancement of sensitivity and resolution of surfaceenhanced laser desorption/ionization time-of-flight mass spectrometric records for serum peptides using time-series analysis techniques. Clin. Chem. 2005, 51, 65-74.
44. Mani, D. R., Gillette, M., New Generation of Data Mining Applications ch. Proteomic Data Analysis: Pattern Recognition for Medical Diagnosis and Biomarker Discovery, IEEE Press, Hoboken, NJ, USA 2005.