The aim of untargeted metabolomics is to comprehensively profile the full range of metabolites present within a biological sample. Several experimental factors strongly influence success: extraction method, separation strategy, ionization mechanism, mass spectrometer, and informatic software. Which combination of technologies is best? Although counting signals (i.e., metabolomic features) has commonly been used as a standard metric of comparison, this approach can be misleading and provide inaccurate results due to the significant number of artifacts in metabolomic data sets.
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Here we present an alternative strategy for benchmarking metabolomic technologies called credentialing. The credentialing approach facilitates removal of artifactual features without the resource-intensive burden of structural identification. Some surprising insights gained from the application of credentialing will be presented.
Key Learning Objectives
- Understand experimental biases that are introduced during MS-based metabolomics
- Learn how to compare the performance of different metabolomic technologies with credentialing
- Identify sources of artifacts in MS-based metabolomics
Who Should Attend
- Researchers interested in performing untargeted metabolomics in the future
- Analytical chemists interested in optimizing their existing untargeted or targeted metabolomic platform
- Researchers performing large, disease-related profiling studies
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