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Fragmentation Trees Reloaded

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Research in Computational Molecular Biology (RECOMB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9029))

Abstract

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually relies on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are found by combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. To obtain biochemically meaningful results requires an elaborate optimization function.

We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: Both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our methods performs significantly better than the previous scoring, as well as other methods for this task. Our method can expedite the workflow for untargeted metabolomics, allowing researchers to investigate unknowns using automated computational methods.

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Authors and Affiliations

  1. Chair for Bioinformatics, Friedrich-Schiller-University, Jena, Germany

    Kai Dührkop & Sebastian Böcker

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  1. Kai Dührkop
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  2. Sebastian Böcker
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Correspondence to Kai Dührkop .

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Editors and Affiliations

  1. National Center of Biotechnology Information, Bethesda, Maryland, USA

    Teresa M. Przytycka

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Dührkop, K., Böcker, S. (2015). Fragmentation Trees Reloaded. In: Przytycka, T. (eds) Research in Computational Molecular Biology. RECOMB 2015. Lecture Notes in Computer Science(), vol 9029. Springer, Cham. https://doi.org/10.1007/978-3-319-16706-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-16706-0_10

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