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Evaluation of computational tools using serial mixtures of human plasma and vegetable juice (part - II)

ABSTRACT: Mass spectrometry-based metabolomics is developed rapidly in the past few decades. There are few major vendors for LC-MS platform instruments, for example, Thermo ScientificTM LTQ Orbitrap Velos and Agilent 6510 Q-TOF mass spectrometer were used for metabolomics research. The data acquired cross different platform are rarely compared other than the comparison of the instrument itself on resolution, mass accuracy, sensitivity, dynamic range, scan speed etc., which is largely due to the foundation and principle of the instrument design. Other than this, there are many choice for data preprocessing, i.e., the data acquired from the same platform may have been processed with different feature extraction software tools. The discrepancy for the feature detections with different software will lead to the variation of the down-stream statistics analysis and metabolomics pathway interpretation. In addition, the impact of the LC-MS platform and data preprocessing software tools on the quantitative capabilities is also an interesting topic. In this research, XCMS, mzMine 2.37 and apLCMS are three tools used for the feature extraction of data acquired with Thermo ScientificTM LTQ Orbitrap Velos and Agilent 6510 Q-TOF LC-MS platform by serial dilution experiment. The quantification capability is estimated at the same time based on the linearity, accuracy, and precision.

ORGANISM(S): Homo Sapiens

TISSUE(S): Blood

SUBMITTER: Yating Wang

PROVIDER: ST001162 | MetabolomicsWorkbench | Fri Mar 29 00:00:00 GMT 2019

REPOSITORIES: MetabolomicsWorkbench

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Project description: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 <a href="https://www.ebi.ac.uk/metabolights/MTBLS736" target="_blank">MTBLS736</a>.

Project description: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. AB SCIEX TripleTOF 6600 assay protocols and data are reported in the current study MTBLS736. Thermo Q Exactive HF assay protocols and data are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS733' target='_blank'>MTBLS733</a>.

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Evaluation of computational tools using serial mixtures of human plasma and vegetable juice (part - II)

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