XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.
Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
Smith
Colin A
CA
Scripps Center for Mass Spectrometry and Department of Molecular Biology, Scripps Research Institute, La Jolla, California 92037, USA.
Want
Elizabeth J
EJ
O'Maille
Grace
G
Abagyan
Ruben
R
Siuzdak
Gary
G
D000465:Algorithms; D000581:Amidohydrolases / Q000032:analysis*; D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D051379:Mice; D018345:Mice, Knockout; D017711:Nonlinear Dynamics; D012680:Sensitivity and Specificity; D013997:Time Factors; D000099252:Fatty Acid Amide Hydrolases
D000581:Amidohydrolases; D000099252:Fatty Acid Amide Hydrolases