Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection.

In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results.
PMID: 29907290 [PubMed - in process]

Source: Analytica Chimica Acta - June 18, 2018 Category: Chemistry Authors: Li Z, Lu Y, Guo Y, Cao H, Wang Q, Shui W Tags: Anal Chim Acta Source Type: research