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Evaluation of available protein mass spectra pre-processing algorithms

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Języki publikacji
EN
Abstrakty
EN
Analysis of the profiles of body fluids by MALDI – ToF mass spectrometry is a popular technique used for searching of protein biomarkers that have potential application in early detection and diagnose of a cancer. A typical strategy used in validation of new biomarkers involves two basic aspects. Predictive properties of discovered differing features must be proven by classification of patients and controls spectra. Then, these features must be connected to proteins / peptides present in the analysed samples. Therefore, most mass spectra pre-processing procedures are based on reducing dimensionality of the spectrum to only significant features (peaks), assuming that each peak corresponds to a single protein / peptide and its position in the m/z scale, and height carry direct information on the composition of the test substance. In the literature there are several different approaches of mass spectra pre-processing. But so far there are no standards for selection of techniques that are the most effective for this type of data. Only the pre-processing steps that should be done in order to extract the desired information from raw spectra are specified. This paper presents some algorithms that are compared on two levels. By the classification of real data sets differentiating potential of detected features was examined and the ability to reconstruct proteins / peptides in the test sample was checked. Since the composition of the specimen is not known, we used a virtual machine to generate artificial spectra. Despite many published studies, scientists searching for biomarkers, still encounter many serious problems. Existing methods for mass spectra pre-processing are very sensitive to changes in data collection protocols, or instrumentation. Identified biomarkers of cancer vary between different research groups. The key is to choose the appropriate settings of the methods used. Thus, there is a need to test new procedures and automate the tuning of parameters of existing algorithms. Our simulations showed, that Align and CWT algorithms eliminates false positive peaks efficiently and that Align is the most flexible for changes in signal quality from all studied mass spectra pre-processing packages.
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25--30
Opis fizyczny
Bibliogr.18 poz., rys.
Twórcy
autor
  • Institute of Automatic Control, Silesian University of Technology, 44-100 Gliwice, Poland
Bibliografia
  • 1. Petricoin III E.F., Ardekani A.M., Hitt B.A., Levine P.J., Fusaro V.A., Steinberg S.M., Mills G.B., Simone C., Fishman D.A., Kohn E.C., Liotta L.A: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002 , 359:572-577.
  • 2. Adam B.L., Qu Y., Davis J.W., Ward M.D., Clements M.A., Cazares L.H., Semmes O.J., Schellhammer P.F., Yasui Y., Feng Z., Wright G.L. Jr.: Serum protein fingerprinting coupled with a pattern-matching algorithm distinguished prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002, 62:3609-3614.
  • 3. Paweletz C.P., Trock B., Pennanen M., Tsangaris T., Magnant C., Liotta L.A., Petricoin III E.F.: Proteomic patterns of nipple aspirate fluids obtained by SELDI–TOF: potential for new biomarkers to aid in the diagnosis of breast cancer. Dis. Markers 2001, 17:301–307.
  • 4. Cruz-Marcelo A., Guerra R., Vannucci M., Li Y., Lau C.C., Man T.-K: Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics 2008, 24: 2129-2136.
  • 5. Emanuele II V.A., Gurbaxani BM: Benchmarking currently available SELDI-TOF MS preprocessing techniques. Proteomics 2009, 9:1754-62.
  • 6. Meuleman W., Engwegen J., Gast M., Beijnen J., Reinders M., Wessels L: Comparison of normalisation methods for Surface-Enhanced Laser Desorption and Ionisation Time-Of-Flight Mass Spectrometry data. BMC Bioinformatics 2008, 9(88).
  • 7. Yang C., He Z., Yu W.: Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis. BMC Bioinformatics 2009, 10(4).
  • 8. Coombes K.R., Koomen J., Baggerly K.A., Morris J.S., Kobayashi R: Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Informatics 2005, 1:41-52.
  • 9. Nowicka E., Pietrowska M., Behrendt K., Walaszczyk A., Polanska J., Polanski A., Marczak L., Stobiecki M., Widlak P., Tarnawski R: Potential clinical application of serum proteome mass spectrometry analyses in breast cancer patients diagnosis and management. Eur. J. Cancer Suppl. 2008, 6(7):82
  • 10. Pietrowska M., Marczak L., Polańska J., Behrendt K., Nowicka E., Walaszczyk A., Chmura A., Deja R., Stobiecki M., Polański A., Tarnawski R., Widłak P: Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer. -J. Transl. Med. 2009, 7:60
  • 11. 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, 21:1764–75.
  • 12. Marczyk M: Align – a software tool for mass spectra preprocessing. Proceeding of the XI International PhD Workshop OWD 2009, 88-91.
  • 13. Friedman J.H: A Variable Span Smoother, LCS Tech. Rep. 1984, 5
  • 14. Wong J.W.H., Durante C., Cartwright H.M: Application of Fast Fourier Transform Cross-Correlation for the Alignment of Large Chromatographic and Spectral Datasets. Anal. Chem. 2005, 77:5655-61.
  • 15. Mantini D., Petrucci F., Pieragostino D., Del Boccio P. ,Di Nicola M., Di Ilio C., Federici G., Sacchetta P., Comani S., Urbani A: LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinformatics 2007, 8:101.
  • 16. Du P., Kibbe W.A., Lin S.M.: Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22:2059-65.
  • 17. Coombes K.R., Baggerly K.A., Morris J.S.: Pre-processing mass spectrometry data. In Fundamentals of data mining in genomics and proteomics. Edited by Dubitzky W., Granzow M., Berrar, D.P.: New York: Springer 2007:79-99.
  • 18. Vapnik V.N.: The nature of statistical learning theory. New York: Springer-Verlag 1995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bbdf416a-b443-44e1-8aef-06cafad1911d
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