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.

journal_name

Methods Enzymol

journal_title

Methods in enzymology

authors

Koo I,Wei X,Zhang X

doi

10.1016/B978-0-12-801329-8.00016-7

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

315-24

eissn

0076-6879

issn

1557-7988

pii

B978-0-12-801329-8.00016-7

journal_volume

543

pub_type

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