The identification of metabolites detected in untargeted UHPLC-MS metabolomics studies can be a significant hurdle and is essential to derive biological knowledge from data. Chromatographic retention time, full-scan accurate m/z and MS
n data can all be used, in combination with chemical standards and in-silico workflows where available, to identify metabolites.
The first part of the webinar will discuss the current processes applied to identify metabolites applying UHPLC-MS platforms including the current strengths and limitations and the progress made in increasing identification success and accuracy.
The second part of the webinar will showcase advances with high resolution accurate mass (HRAM) mass spectrometry instrumentation and software so to deliver complementary information with multiple dissociation techniques and robust MS
n data in a routinely applied workflow so to maximize the number of metabolite annotations. New intelligent data acquisition strategies to collect more informative MS and MS
n data that maximizes metabolome coverage and increase confidence in the identification of metabolites will be presented.
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Key Learning Objectives
- Learn about untargeted metabolomics and its associated challenges with compound annotation and identification.
- Discover how new advanced MS workflows with intelligent data acquisition leads to more targeted and confident metabolite identifications.
- Discover how software tools and high resolution accurate mass (HRAM) databases and libraries delivers a more robust solution for identification and structural annotation of unknown compounds.
Who Should Attend
- Researchers interested in Metabolomics and Lipidomics
- Core Laboratory Scientists
- New users in mass spectrometry and metabolomics
- Researchers performing large-scale, epidemiological and disease-related metabolomics studies
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