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Mass Spectra Alignments and Their Significance

  • Sebastian Böcker
  • Hans-Michael Kaltenbach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3537)

Abstract

Mass Spectrometry has become one of the most popular analysis techniques in Genomics and Systems Biology. We investigate a general framework that allows the alignment (or matching) of any two mass spectra. In particular, we examine the alignment of a reference mass spectrum generated in silico from a database, with a measured sample mass spectrum. In this context, we assess the significance of alignment scores for character-specific cleavage experiments, such as tryptic digestion of amino acids. We present an efficient approach to estimate this significance, with runtime linear in the number of detected peaks. In this context, we investigate the probability that a random string over a weighted alphabet contains a substring of some given weight.

Keywords

Reference Spectrum Mass Spectrometry Data Tryptic Digestion Tandem Mass Spectrum Alignment Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422, 198–207 (2003)CrossRefGoogle Scholar
  2. 2.
    Patterson, S.D., Aebersold, R.: Mass spectrometric approaches for the identification of gel-separated proteins. Electrophoresis 16, 1791–1814 (1995)CrossRefGoogle Scholar
  3. 3.
    Cooks, R.G. (ed.): Collision spectroscopy. Plenum Press, New York (1978)Google Scholar
  4. 4.
    Pappin, D.J., Hojrup, P., Bleasby, A.: Rapid identification of proteins by peptidemass fingerprinting. Curr. Biol. 3, 327–332 (1993)CrossRefGoogle Scholar
  5. 5.
    Wang, I.J., Diehl, C.P., Pineda, F.J.: A statistical model of proteolytic digestion. In: Proceedings of IEEE CSB 2003, Stanford, California, pp. 506–508 (2003)Google Scholar
  6. 6.
    Perkins, D.N., Pappin, D.J., Creasy, D.M., Cottrell, J.S.: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999)CrossRefGoogle Scholar
  7. 7.
    Huang, X., Waterman, M.S.: Dynamic programming algorithms for restriction map comparison. Comput. Appl. Biosci. 8, 511–520 (1992)Google Scholar
  8. 8.
    Hermjakob, H., Giegerich, R., Arnold, W.: RIFLE: Rapid identification of microorganisms by fragment length evaluation. In: Proceedings of ISMB 1997, Halkidiki, Greece, pp. 131–139 (1997)Google Scholar
  9. 9.
    Aittokallio, T., Ojala, P., Nevalainen, T.J., Nevalainen, O.: Automated detection of differently expressed fragments in mRNA differential display. Electrophoresis 22, 1935–1945 (2001)CrossRefGoogle Scholar
  10. 10.
    Bafna, V., Edwards, N.: SCOPE: A probabilistic model for scoring tandem mass spectra against a peptide database. Bioinformatics 17, S13– S21 (2001)CrossRefGoogle Scholar
  11. 11.
    Colinge, J., Masselot, A., Magnin, J.: A systematic statistical analysis of ion trap tandem mass spectra in view of peptide scoring. In: Benson, G., Page, R.D.M. (eds.) WABI 2003. LNCS (LNBI), vol. 2812, pp. 25–38. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Keller, A., Nesvizhskii, A.I., Kolker, E., Aebersold, R.: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002)CrossRefGoogle Scholar
  13. 13.
    Böcker, S.: Sequencing from compomers: Using mass spectrometry for DNA denovo sequencing of 200+ nt. J. Comput. Biol. 11, 1110–1134 (2004)CrossRefGoogle Scholar
  14. 14.
    Danćik, V., Addona, T.A., Clauser, K.R., Vath, J.E., Pevzner, P.A.: De novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol. 6, 327–342 (1999)CrossRefGoogle Scholar
  15. 15.
    Wilke, A., Rückert, C., Bartels, D., Dondrup, M., Goesmann, A., Hüser, A.T., Kespohl, S., Linke, B., Mahne, M., McHardy, A.C., Pühler, A., Meyer, F.: Bioinformatics support for high-throughput proteomics. J. Biotechnol. 106, 147–156 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sebastian Böcker
    • 1
  • Hans-Michael Kaltenbach
    • 1
  1. 1.AG Genominformatik, Technische FakultätUniversität BielefeldBielefeldGermany

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