Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery

被引：200

作者：

Styczynski, Mark P. ^{[1
]}

Moxley, Joel F. ^{[1
]}

Tong, Lily V. ^{[1
]}

Walther, Jason L. ^{[1
]}

Jensen, Kyle L. ^{[1
]}

Stephanopoulos, Gregory N. ^{[1
]}

机构：

[1] MIT, Dept Chem Engn, Cambridge, MA 02139 USA

来源：

ANALYTICAL CHEMISTRY | 2007年 / 79卷 / 03期

关键词：

D O I：

10.1021/ac0614846

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.

引用

页码：966 / 973

页数：8

共 53 条

[21] A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS [J].