PT  - JOURNAL ARTICLE
AU  - Youzhong Liu
AU  - Aida Mrzic
AU  - Pieter Meysman
AU  - Thomas De Vijlder
AU  - Edwin P. Romijn
AU  - Dirk Valkenborg
AU  - Wout Bittremieux
AU  - Kris Laukens
TI  - MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra
AID  - 10.1101/134189
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 134189
4099  - http://biorxiv.org/content/early/2018/12/11/134189.short
4100  - http://biorxiv.org/content/early/2018/12/11/134189.full
AB  - Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability to identify unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined structural motifs or features, and therefore it can be used for the partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.