Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data
AbstractMass 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
Externally hosted supplementary file 1
Description: 11 MM48 raw data files used for performance evaluation in the manuscript.
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Treutler, H.; Neumann, S. Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data. Metabolites 2016, 6, 37.
Treutler H, Neumann S. Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data. Metabolites. 2016; 6(4):37.Chicago/Turabian Style
Treutler, Hendrik; Neumann, Steffen. 2016. "Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data." Metabolites 6, no. 4: 37.
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