Both multivariate statistical analysis and data visualization play a critical role in extracting relevant information and interpreting the results of metabolomics experiments. Since metabolomics experiments typically produce large amounts of data, sophisticated bioinformatic tools are needed for efficient and high-throughput data processing to remove systematic bias and to explore biologically significant findings. Mass spectrometry coupled with gas chromatography (GC-MS) or liquid chromatography (LC-MS) are the most common analytical platforms, although capillary electrophoresis mass spectrometry (CE-MS) and nuclear magnetic resonance (NMR) are also widely used in metabolomics research. A number of methods have been developed for the extraction, detection, identification, and quantification of the metabolome. Īs the diversity in abundance and chemical properties of metabolites varies greatly in organisms, a range of analytical techniques must be utilized to survey the entire metabolome. Through the characterization of metabolite dynamics, interactions, and responses to genetic or environmental perturbations, metabolomics can provide a comprehensive picture of both baseline physiology and global biochemical responses to genetic, abiotic, and biotic factors.
Metabolomics is a rapidly growing discipline focusing on the global study of small molecule metabolites in biological systems.
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