ArticleBMC bioinformatics2015
QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- QC4Metabolomics: Real-Time and Retrospective Quality Control of Metabolomics Data.Analytical chemistry · 2025Article
- Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.Analytical chemistry · 2024Article
- QCAnalytical chemistry · 2024Article
- DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies.Scientific reports · 2021Article
- MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.Journal of proteome research · 2019Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
- BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics.Journal of proteome research · 2016Article
- Stable Isotope-Assisted Evaluation of Different Extraction Solvents for Untargeted Metabolomics of Plants.International journal of molecular sciences · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process.
resultsThe software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection.
conclusionsQCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.
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Registered trials
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