Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling [electronic resource].

Published:: Washington, D.C. : United States. Dept. of Energy. Office of Science, 2014.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description:: pages 884-891 : digital, PDF file
Additional Creators:: United States. Department of Energy. Office of Science and United States. Department of Energy. Office of Scientific and Technical Information

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Summary:

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.

Report Numbers:

E 1.99:1344895

Subject(s):

Note:

Published through SciTech Connect.
12/12/2014.
Analytical Chemistry 87 2 ISSN 0003-2700 AM
H. Paul Benton; Julijana Ivanisevic; Nathaniel G. Mahieu; Michael E. Kurczy; Caroline H. Johnson; Lauren Franco; Duane Rinehart; Elizabeth Valentine; Harsha Gowda; Baljit K. Ubhi; Ralf Tautenhahn; Andrew Gieschen; Matthew W. Fields; Gary J. Patti; Gary Siuzdak.
Scripps Research Inst., La Jolla, CA (United States)

Funding Information:

AC02-05CH11231

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