Untargeted metabolomics suffers from incomplete raw data processing

Untargeted metabolomics suffers from incomplete raw data processing Metabolomics (2017) 13:107 DOI 10.1007/s11306-017-1246-3 SHORT COMMUNICATION Untargeted metabolomics suffers from incomplete raw data processing Richard Baran   Received: 1 June 2017 / Accepted: 3 August 2017 © Springer Science+Business Media, LLC 2017 Abstract reliably detect relevant signals in raw data and to correctly Introduction Untargeted metabolomics is a powerful tool interpret the underlying spectral features of the compounds for biological discoveries. To analyze the complex raw data, (Kind and Fiehn 2007; Brown et al. 2011; Kuhl et al. 2012; significant advances in computational approaches have been Dunn et al. 2013; Scheubert et al. 2013; Baran and Northen made, yet it is not clear how exhaustive and reliable the data 2013; Libiseller et al. 2015; Kind et al. 2017). The chal- analysis results are. lenging complexity of the data analysis process is well Objectives Assessment of the quality of raw data process- recognized and computational tools facilitating this step, ing in untargeted metabolomics. are available (Weber et al. 2017). However, it is not clear Methods Five published untargeted metabolomics studies, how exhaustive and reliable the current raw data process- were reanalyzed. ing results are. Exhaustive detection of relevant signals in Results Omissions of at least 50 relevant compounds from the raw data, correct ion type interpretation and assignment http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Metabolomics Springer Journals

Untargeted metabolomics suffers from incomplete raw data processing

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Springer US
Copyright © 2017 by Springer Science+Business Media, LLC
Life Sciences; Biochemistry, general; Molecular Medicine; Cell Biology; Developmental Biology; Biomedicine, general
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