Metabolomics involves the identification and measurement of small-molecule metabolites of endogenous and exogenous origin in a biospecimen. These metabolites represent a diverse group of low-molecular-weight structures, such as lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols, carbohydrates, environmental chemicals, and dietary compounds. Different approaches and analytical platforms are used to detect, characterize, and quantify metabolites and related metabolic pathways, including untargeted and targeted liquid chromatography-mass spectrometry (LC-MS), gas chromatography-MS (GC-MS), and nuclear magnetic resonance (NMR). In CHEAR, most metabolomics studies use a LC-MS platform to perform untargeted metabolomics. Therefore, the purpose of the tutorial is to provide a basic overview for non-experts of how LC-MS-based untargeted metabolomics datasets are generated, which should aid in data analysis and interpretation.
NIEHS has established the Children’s Health Exposure Analysis Resource (CHEAR) infrastructure to provide the extramural research community access to laboratory and statistical analyses aimed at adding or expanding the inclusion of environmental exposures in their research. CHEAR is composed of a Coordinating Center, a Lab Network and a Data Center tasked to provide researchers access to comprehensive exposure assessment for NIH funded studies of children’s health. The Center for Data Science (“Data Center”) at the Icahn School of Medicine at Mount Sinai goals are to catalyze new scientific insight from the co¬location, integration and advanced statistical and data science analysis of multimodal data sets.
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