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Kenar, E.* ; Franken, H.* ; Forcisi, S. ; Wörmann, K. ; Häring, H.-U. ; Lehmann, R. ; Schmitt-Kopplin, P. ; Zell, A.* ; Kohlbacher, O.*

Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data.

Mol. Cell. Proteomics 13, 348-359 (2014)
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Open Access Green as soon as Postprint is submitted to ZB.
Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Open-source Software; Lc-ms Data; Protein Quantification; Profile Data; Identification; Annotation; Extraction; Metabolomics; Regression; Alignment
ISSN (print) / ISBN 1535-9476
e-ISSN 1535-9484
Journal Molecular and Cellular Proteomics
Quellenangaben Volume: 13, Issue: 1, Pages: 348-359 Article Number: , Supplement: ,
Publisher American Society for Biochemistry and Molecular Biology
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical BioGeoChemistry (BGC)
Institute of Diabetes Research and Metabolic Diseases (IDM)
German Center for Diabetes Reseach (DZD)