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.
journal_name
Methods Enzymoljournal_title
Methods in enzymologyauthors
Koo I,Wei X,Zhang Xdoi
10.1016/B978-0-12-801329-8.00016-7subject
Has Abstractpub_date
2014-01-01 00:00:00pages
315-24eissn
0076-6879issn
1557-7988pii
B978-0-12-801329-8.00016-7journal_volume
543pub_type
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