by visual mapping of observed metabolite level changes onto metabolic pathway diagrams).Ī general lack of software for carrying out the above data analysis steps quickly and easily has been one of the greatest challenges hampering the establishment of metabolomics as a mainstream technique. Interpretation of observed metabolite-level changes in the context of prior knowledge about the metabolic system under investigation (eg. Use of statistical and exploratory data analysis tools to determine the effect(s) of experimental factors on metabolite levels.Ħ. Construction of a data matrix, usually with some form of data normalization against internal standard(s) and/or biological sample mass/volume/amount.ĥ. Assignment of some quantitative signal measurement to each identified biological analyte.Ĥ. Identification of biologically relevant signal features by matching against a reference library of known signal characteristics for biological analytes.ģ. Detection of analytically useful signal features (ie. This data extraction process may be broken down into a number of general steps:ġ. Sophisticated algorithms are therefore required to identify and quantify signals corresponding to biologically relevant analytes and obtain a quantitative description of the metabolic effect(s) associated with the experimental factor(s) of interest. Raw GC/MS metabolomic datasets are typically large and complex in nature, frequently comprised of tens to hundreds of data files - each containing convoluted signals for hundreds to thousands of analytes. GC/MS is therefore a particularly attractive target platform for development of web-based tools to support community-scale comparisons and mining of raw and processed metabolomics data.īefore biological insights derived from metabolomics-based studies can be communicated, they must first be extracted from raw instrumental datasets. Reasons for this include its relative affordability, sensitivity and reproducibility. However, GC/MS is by far the most commonly employed technology. A variety of analytical technologies have been applied to metabolomics studies. The rapidly growing field of metabolomics aims to monitor the levels of as many metabolites as possible in living systems as they respond to genetic and/or environmental perturbations.
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