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

• Published in: Analytica chimica acta 2018-10, Vol.1029, p.50-57
• Main Authors: Li, Zhucui, Lu, Yan, Guo, Yufeng, Cao, Haijie, Wang, Qinhong, Shui, Wenqing
• Format: Article
• Language: English
• Subjects: Benchmarking; Benchmarks; Bioinformatics; Biomarkers; Biomarkers - metabolism; Computer programs; Data analysis; Data processing; Data processing software; Datasets; Discriminating marker selection; Evaluation; Feature detection; Feature quantification; Metabolites; Metabolomics; Metabolomics - methods; Piper nigrum - metabolism; Software; Software packages; Untargeted metabolomics
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2018.05.001

Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. 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. [Display omitted] •Five widely used data analysis software packages for untargeted metabolomics were compared.•Open source and commercial programs gave similar performance in detecting true features from benchmark samples.•Significant difference of software in quantification accuracy for true features.•Increased confidence of biomarker identification by combined use of XCMS and MZmine 2.