Dataset Information

Prior Signal Acquisition Software Versions For Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

ABSTRACT: Tandem mass tags (TMT) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap™ quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved with updated instrument acquisition software, however, 53 % of Orbitrap submissions to PRIDE were generated using the prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generate a mixed species benchmark and acquired quantitative data using the outdated software version. Sub-notch intensities are systemically underestimated, leading to over-estimation of the true differences in intensities between samples. However, when summarising reporter ion intensities to higher level features, such as peptides and proteins, few features are significantly affected. The analysis of benchmark dataset indicates that the targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find the systematic quantification bias associated with the notch is not detrimental for typical proteomics experiments

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

SUBMITTER: Rayner Queiroz

LAB HEAD: Professor Kathryn Lilley

PROVIDER: PXD027248 | Pride | 2021-07-20

REPOSITORIES: Pride

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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>.

Dataset Information

Prior Signal Acquisition Software Versions For Orbitrap Underestimate Low Isobaric Mass Tag Intensities, Without Detriment to Differential Abundance Experiments

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