Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery.
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.
Styczynski
Mark P
MP
Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469c, Cambridge, Massachusetts 02139, USA.
Moxley
Joel F
JF
Tong
Lily V
LV
Walther
Jason L
JL
Jensen
Kyle L
KL
Stephanopoulos
Gregory N
GN
D015415:Biomarkers; D004926:Escherichia coli / Q000737:chemistry / Q000378:metabolism; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008660:Metabolism; D008722:Methods; D012984:Software
D015415:Biomarkers