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Open AccessFeature PaperArticle

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Department of Stress and Developmental Biology, Leibniz Institute for Plant Biochemistry, Weinberg 3, Halle 06120, Germany
Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Von-Seckendorff-Platz 1, Halle 06120, Germany
Author to whom correspondence should be addressed.
Academic Editor: Peter D. Karp
Metabolites 2016, 6(4), 37;
Received: 31 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92 % of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0. View Full-Text
Keywords: isotope cluster; software; raw data isotope cluster; software; raw data
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Treutler, H.; Neumann, S. Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data. Metabolites 2016, 6, 37.

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