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Link to original content: https://pubmed.ncbi.nlm.nih.gov/16448051
XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification - PubMed Skip to main page content
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. 2006 Feb 1;78(3):779-87.
doi: 10.1021/ac051437y.

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification

Colin A Smith  1 , Elizabeth J Want, Grace O'Maille, Ruben Abagyan, Gary Siuzdak
Affiliations

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification

Colin A Smith et al. Anal Chem. .

Abstract

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/.

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