Dataset Information
Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection (Thermo Q Exactive HF assay)
Ontology highlight
ABSTRACT: 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. Further-more, 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. Thermo Q Exactive HF assay protocols and data are reported in the current study MTBLS733. AB SCIEX TripleTOF 6600 assay protocols and data are reported in MTBLS736.
INSTRUMENT(S): Liquid Chromatography MS - Positive (LC-MS (Positive))
SUBMITTER: yan lu
PROVIDER: MTBLS733 | MetaboLights | 2019-11-19
REPOSITORIES: MetaboLights
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MTBLS733 | Other | |||
| FILES | Other | |||
| a_MTBLS733_mass_spectrometry.txt | Txt | |||
| i_Investigation.txt | Txt | |||
| m_MTBLS733_mass_spectrometry_v2_maf.tsv | Tabular |
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Publications
Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection.
Analytica chimica acta 20180504
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 ...[more]
