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Multiple Mass Spectrometry Fragmentation Trees Revisited: Boosting Performance and Quality

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Algorithms in Bioinformatics (WABI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8701))

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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 (MSn) reveals additional information about the dependencies between fragments. Unfortunately, the computational analysis of MSn 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 MSn. Thus, we show for the first time that using MSn data can improve the quality of fragmentation trees.

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Authors and Affiliations

  1. Lehrstuhl für Bioinformatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, Jena, Germany

    Kerstin Scheubert, Franziska Hufsky & Sebastian Böcker

Authors
  1. Kerstin Scheubert
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  2. Franziska Hufsky
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  3. Sebastian Böcker
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Editor information

Editors and Affiliations

  1. David R. Cheriton School of Computer Science, University of Waterloo, ON, Canada

    Dan Brown

  2. Institute of Microbiology and Genetics, Department of Bioinformatics, University of Göttingen, Germany, Goldschmidtstr. 1, 37077, Göttingen, Germany

    Burkhard Morgenstern

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Scheubert, K., Hufsky, F., Böcker, S. (2014). Multiple Mass Spectrometry Fragmentation Trees Revisited: Boosting Performance and Quality. In: Brown, D., Morgenstern, B. (eds) Algorithms in Bioinformatics. WABI 2014. Lecture Notes in Computer Science(), vol 8701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44753-6_17

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  • DOI: https://doi.org/10.1007/978-3-662-44753-6_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44752-9

  • Online ISBN: 978-3-662-44753-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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