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Multiple Mass Spectrometry Fragmentation Trees Revisited: Boosting Performance and Quality

  • Kerstin Scheubert
  • Franziska Hufsky
  • Sebastian Böcker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8701)

Abstract

Mass spectrometry (MS) in combination with a fragmentation technique is the method of choice for analyzing small molecules in high throughput experiments. The automated interpretation of such data is highly non-trivial. Recently, fragmentation trees have been introduced for de novo analysis of tandem fragmentation spectra (MS2), describing the fragmentation process of the molecule. Multiple-stage MS (MS n ) reveals additional information about the dependencies between fragments. Unfortunately, the computational analysis of MS n data using fragmentation trees turns out to be more challenging than for tandem mass spectra.

We present an Integer Linear Program for solving the Combined Colorful Subtree problem, which is orders of magnitude faster than the currently best algorithm which is based on dynamic programming. Using the new algorithm, we show that correlation between structural similarity and fragmentation tree similarity increases when using the additional information gained from MS n . Thus, we show for the first time that using MS n data can improve the quality of fragmentation trees.

Keywords

metabolomics computational mass spectrometry multiple-stage mass spectrometry fragmentation trees Integer Linear Programming 

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References

  1. 1.
    Allen, F., Wilson, M., Pon, A., Greiner, R., Wishart, D.: CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic. Acids Res. (2014)Google Scholar
  2. 2.
    Böcker, S., Briesemeister, S., Klau, G.W.: On optimal comparability editing with applications to molecular diagnostics. BMC Bioinformatics 10(suppl. 1), S61 (2009); Proc. of Asia-Pacific Bioinformatics Conference (APBC 2009)Google Scholar
  3. 3.
    Böcker, S., Lipták, Z.: Efficient mass decomposition. In: Proc. of ACM Symposium on Applied Computing (ACM SAC 2005), pp. 151–157. ACM Press, New York (2005)Google Scholar
  4. 4.
    Böcker, S., Lipták, Z.: A fast and simple algorithm for the Money Changing Problem. Algorithmica 48(4), 413–432 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Böcker, S., Rasche, F.: Towards de novo identification of metabolites by analyzing tandem mass spectra. Bioinformatics 24, I49–I55 (2008); Proc. of European Conference on Computational Biology (ECCB 2008)Google Scholar
  6. 6.
    Dondi, R., Fertin, G., Vialette, S.: Complexity issues in vertex-colored graph pattern matching. J. Discrete Algorithms 9(1), 82–99 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Dreyfus, S.E., Wagner, R.A.: The Steiner problem in graphs. Networks 1(3), 195–207 (1972)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Fellows, M.R., Gramm, J., Niedermeier, R.: On the parameterized intractability of motif search problems. Combinatorica 26(2), 141–167 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Gerlich, M., Neumann, S.: MetFusion: integration of compound identification strategies. J. Mass Spectrom 48(3), 291–298 (2013)CrossRefGoogle Scholar
  10. 10.
    Heinonen, M., Shen, H., Zamboni, N., Rousu, J.: Metabolite identification and molecular fingerprint prediction via machine learning. Bioinformatics 28(18), 2333–2341 (2012); Proc. of European Conference on Computational Biology (ECCB 2012)Google Scholar
  11. 11.
    Hill, D.W., Kertesz, T.M., Fontaine, D., Friedman, R., Grant, D.F.: Mass spectral metabonomics beyond elemental formula: Chemical database querying by matching experimental with computational fragmentation spectra. Anal. Chem. 80(14), 5574–5582 (2008)CrossRefGoogle Scholar
  12. 12.
    Hufsky, F., Dührkop, K., Rasche, F., Chimani, M., Böcker, S.: Fast alignment of fragmentation trees. Bioinformatics 28, i265–i273 (2012); Proc. of Intelligent Systems for Molecular Biology (ISMB 2012)Google Scholar
  13. 13.
    Leach, A.R., Gillet, V.J.: An Introduction to Chemoinformatics. Springer, Berlin (2005)Google Scholar
  14. 14.
    Li, J.W.-H., Vederas, J.C.: Drug discovery and natural products: End of an era or an endless frontier? Science 325(5937), 161–165 (2009)CrossRefGoogle Scholar
  15. 15.
    Ljubić, I., Weiskircher, R., Pferschy, U., Klau, G.W., Mutzel, P., Fischetti, M.: Solving the prize-collecting Steiner tree problem to optimality. In: Proc. of Algorithm Engineering and Experiments (ALENEX 2005), pp. 68–76. SIAM (2005)Google Scholar
  16. 16.
    Oberacher, H., Pavlic, M., Libiseller, K., Schubert, B., Sulyok, M., Schuhmacher, R., Csaszar, E., Köfeler, H.C.: On the inter-instrument and inter-laboratory transferability of a tandem mass spectral reference library: 1. Results of an Austrian multicenter study. J. Mass Spectrom. 44(4), 485–493 (2009)CrossRefGoogle Scholar
  17. 17.
    Patti, G.J., Yanes, O., Siuzdak, G.: Metabolomics: The apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13(4), 263–269 (2012)CrossRefGoogle Scholar
  18. 18.
    Rasche, F., Scheubert, K., Hufsky, F., Zichner, T., Kai, M., Svatoš, A., Böcker, S.: Identifying the unknowns by aligning fragmentation trees. Anal. Chem. 84(7), 3417–3426 (2012)CrossRefGoogle Scholar
  19. 19.
    Rasche, F., Svatoš, A., Maddula, R.K., Böttcher, C., Böcker, S.: Computing fragmentation trees from tandem mass spectrometry data. Anal. Chem. 83(4), 1243–1251 (2011)CrossRefGoogle Scholar
  20. 20.
    Rauf, I., Rasche, F., Nicolas, F., Böcker, S.: Finding maximum colorful subtrees in practice. In: Chor, B. (ed.) RECOMB 2012. LNCS, vol. 7262, pp. 213–223. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Rogers, D.J., Tanimoto, T.T.: A computer program for classifying plants. Science 132(3434), 1115–1118 (1960)CrossRefGoogle Scholar
  22. 22.
    Rojas-Chertó, M., Kasper, P.T., Willighagen, E.L., Vreeken, R.J., Hankemeier, T., Reijmers, T.H.: Elemental composition determination based on MSn. Bioinformatics 27, 2376–2383 (2011)CrossRefGoogle Scholar
  23. 23.
    Scheubert, K., Hufsky, F., Rasche, F., Böcker, S.: Computing fragmentation trees from metabolite multiple mass spectrometry data. In: Bafna, V., Sahinalp, S.C. (eds.) RECOMB 2011. LNCS, vol. 6577, pp. 377–391. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Scheubert, K., Hufsky, F., Rasche, F., Böcker, S.: Computing fragmentation trees from metabolite multiple mass spectrometry data. J. Comput. Biol. 18(11), 1383–1397 (2011)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Sheldon, M.T., Mistrik, R., Croley, T.R.: Determination of ion structures in structurally related compounds using precursor ion fingerprinting. J. Am. Soc. Mass. Spectrom 20(3), 370–376 (2009)CrossRefGoogle Scholar
  26. 26.
    Shen, H., Dührkop, K., Böcker, S., Rousu, J.: Metabolite identification through multiple kernel learning on fragmentation trees. Bioinformatics (2014) Accepted Proc. of Intelligent Systems for Molecular Biology (ISMB 2014)Google Scholar
  27. 27.
    Sikora, F.: Aspects algorithmiques de la comparaison d’éléments biologiques. PhD thesis, Université Paris-Est (2011)Google Scholar
  28. 28.
    Steinbeck, C., Hoppe, C., Kuhn, S., Floris, M., Guha, R., Willighagen, E.L.: Recent developments of the Chemistry Development Kit (CDK) - an open-source Java library for chemo- and bioinformatics. Curr. Pharm. Des. 12(17), 2111–2120 (2006)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., Bryant, S.H.: PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 37(Web Server issue), W623–W633 (2009)Google Scholar
  30. 30.
    White, W.T.J., Beyer, S., Dührkop, K., Chimani, M., Böcker, S.: Speedy colorful subtrees. Submitted to European Conference on Computational Biology, ECCB 2014 (2014)Google Scholar
  31. 31.
    Wolf, S., Schmidt, S., Müller-Hannemann, M., Neumann, S.: In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics 11, 148 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Kerstin Scheubert
    • 1
  • Franziska Hufsky
    • 1
  • Sebastian Böcker
    • 1
  1. 1.Lehrstuhl für BioinformatikFriedrich-Schiller-Universität JenaJenaGermany

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