A computational approach towards mass spectrometry data alignment and compound annotation

A key challenge in mass spectrometry-based metabolomics is the unambiguous identification of metabolites from complex sample mixtures. This gets even more complicated in comparative studies aiming at the non-targeted alignment of metabolic profiles across species.

Although a multitude of metabolites can be identified, there exists also a large number of structurally unresolved compounds. Besides that these ‘unknowns’ may have crucial biological functions, they are potential keys for a better understanding of the systems biochemistry and its metabolic versatility. Although initially leading the conceptual design of the Golm Metabolome Database (GMD@CSB.DB, now GMD) we are nowadays placing stronger emphasis on software-assisted customizable workflows for knowledge-based compound annotation and non-targeted alignment. Currently and in collaboration with Patrick Giavalisco’s group we are developing and continually improving such tools by the combination of computational and analytical methodologies. The goals of our research are:

  • to add biological relevance to structurally unresolved compounds,
  • to facilitate a reasoning for compositional, structural or further in-depth analyses, and
  • to provide a computational framework for linking compounds to gene candidates.
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