title: Analysis of metabolomic profiling data acquired on GC-MS.
abstract: Gas chromatography-mass spectrometry (GC-MS) is one of the three most popular analytical platforms for metabolomics and is largely employed for the study of oncometabolism. Large volumes of data are usually generated in a GC-MS experiment, and many analytical steps are required to extract biologically relevant information from GC-MS data. These steps include (1) spectrum deconvolution, to convert raw data into a peak list; (2) metabolite identification, to recognize metabolites associated to chromatographic peaks; (3) quantification, to compare the abundance of a specific metabolite in different samples; (4) association network analysis, to reveal correlations among the changes in the abundance of multiple metabolites; and (5) pathway analysis, to understand the biochemical interrelationship between several metabolites that vary in a coordinated or differential manner. Here, we describe in detail the analytical steps that are necessary to interpret a GC-MS dataset.
authors.affiliation: Department of Chemistry, University of Louisville, Louisville, Kentucky, USA.
authors.forename: Imhoi
authors.initials: I
authors.lastname: Koo
authors.affiliation: Department of Chemistry, University of Louisville, Louisville, Kentucky, USA.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Wei
authors.affiliation: Department of Chemistry, University of Louisville, Louisville, Kentucky, USA. Electronic address: xiang.zhang@louisville.edu.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
mesh_terms: D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D006801:Humans; D055432:Metabolomics
keywords: Association network; Metabolic profiling; Metabolite identification; Pathway analysis; Quantitative profiling; Spectrum deconvolution