Abstract
Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.
Highlights
Liquid chromatography–mass spectrometry (LC-MS)-based untargeted metabolomics and lipidomics technologies are used for delineating precise changes in metabolism that can be correlated with a phenotype
The Metabolomics Research Group (MRG) collected fragmentation data using MSE
The MRG collected fragmentation data using energy) with the QC samples and these files were available upon request for metabolite identification
Summary
Liquid chromatography–mass spectrometry (LC-MS)-based untargeted metabolomics and lipidomics technologies are used for delineating precise changes in metabolism that can be correlated with a phenotype. Untargeted metabolomics offers researchers an unprecedented chance to make new discoveries that provide novel insights into disease onset and progression, using a wide array of biological matrices. The high throughput nature of LC-MS-based metabolomics allows for the analysis of large batches of samples. Biofluid-based biomarkers have a high clinical translation potential after suitable validation studies. Several publications recognize that data analysis outcome variability is caused by different data treatment approaches [1,2,3]. There is a lack of interlaboratory reproducibility studies that have investigated the contribution of data analysis techniques toward variability/overlap of results.
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