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Fragmentation trees reloaded.

Sebastian Böcker1, Kai Dührkop1

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A new scoring method for fragmentation trees improves compound identification in untargeted metabolomics. This computational approach enhances the accuracy of analyzing tandem mass spectrometry data for unknown molecules.

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Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Biochemistry

Background:

  • Untargeted metabolomics relies on liquid chromatography-mass spectrometry for metabolite profiling.
  • Identifying compounds from tandem mass spectrometry fragmentation spectra is a key challenge.
  • Fragmentation trees aid in interpreting mass spectrometry data without requiring spectral databases.

Purpose of the Study:

  • To develop an improved scoring function for computing fragmentation trees.
  • To enhance the accuracy and efficiency of compound identification in metabolomics.

Main Methods:

  • A novel scoring method was developed, framing combinatorial optimization as a Maximum A Posteriori estimator.
  • The new scoring function utilizes Bayesian statistics for fragmentation tree computation.
  • The method was evaluated for de novo molecular formula identification and database searching.

Main Results:

  • The new scoring method, implemented in SIRIUS 3, significantly outperforms previous versions and other existing methods.
  • SIRIUS 3 demonstrates superior performance in both identifying molecular formulas of unknown compounds and searching for structurally similar compounds.
  • The enhanced scoring leads to more biochemically meaningful fragmentation trees.

Conclusions:

  • SIRIUS 3 offers an automated computational solution for investigating unknowns in untargeted metabolomics workflows.
  • The method improves the interpretation of tandem mass spectrometry data through advanced fragmentation tree analysis.
  • Researchers can leverage SIRIUS 3 for more accurate and efficient compound discovery in complex biological samples.