Decision tree supported substructure prediction of metabolites from GC-MS profiles

By Jan Hummel, Nadine Strehmel, Joachim Selbig, Dirk Walther, Joachim Kopka. Metabolomics, 2010

346 citations recorded by the source corpus.

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/28eb08848286ea24058856c5ee71df224888cac2

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In Computational Theory and Mathematics

1. Sebastian Böcker
2. Kerstin Scheubert
3. Franziska Hufsky

In Molecular Biology

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Citations in the corpus

• Computational mass spectrometry for small molecules (2013, methodology): “Hummel et al [167] used structural features to subdivide the Golm Metabolome Database into several classes.”
• Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, neutral): “Primary metabolites were identified by comparison of mass spectra and retention times to those of standards, to the online Golm database (http://gmd.mpimp-golm.mpg. de/analysisinput.aspx; Kopka et al., 2005; Hummel et al., 2010), and to Kovats indices.”