Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics

Linxing Yao; Amy M Sheflin; Corey D Broeckling; Jessica E Prenni

doi:10.1007/978-1-4939-9236-2_18

Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics

Methods Mol Biol. 2019:1978:287-299. doi: 10.1007/978-1-4939-9236-2_18.

Authors

Linxing Yao¹, Amy M Sheflin¹, Corey D Broeckling², Jessica E Prenni³

Affiliations

¹ Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA.
² Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA. corey.broeckling@colostate.edu.
³ Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO, USA. jessica.prenni@colostate.edu.

PMID: 31119670
DOI: 10.1007/978-1-4939-9236-2_18

Abstract

Gas chromatography and liquid chromatography coupled to mass spectrometry are used extensively in untargeted metabolomics, which involves the profiling of small metabolites in biological samples. The complex raw dataset produced from untargeted metabolomics requires proper processing before it can be statistically analyzed and interpreted. This chapter describes a high-throughput data processing workflow routinely used in our laboratory, including feature detection and alignment, data reduction, and spectral-matching-based annotation. This semiautomated workflow uses vendor neutral data file formats and freely available data processing tools and therefore can be readily implemented on datasets acquired from instruments of different vendors.

Keywords: GC-MS; LC-MS; RAMClustR; RAMSearch; Untargeted metabolomics; XCMS.

MeSH terms

Gas Chromatography-Mass Spectrometry / methods*
Metabolomics / methods*
Software
Tandem Mass Spectrometry / methods*
Workflow