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Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

[Image: see text] 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 th...

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Autores principales: Benton, H. Paul, Ivanisevic, Julijana, Mahieu, Nathaniel G., Kurczy, Michael E., Johnson, Caroline H., Franco, Lauren, Rinehart, Duane, Valentine, Elizabeth, Gowda, Harsha, Ubhi, Baljit K., Tautenhahn, Ralf, Gieschen, Andrew, Fields, Matthew W., Patti, Gary J., Siuzdak, Gary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2014
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303330/
https://www.ncbi.nlm.nih.gov/pubmed/25496351
http://dx.doi.org/10.1021/ac5025649
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author Benton, H. Paul
Ivanisevic, Julijana
Mahieu, Nathaniel G.
Kurczy, Michael E.
Johnson, Caroline H.
Franco, Lauren
Rinehart, Duane
Valentine, Elizabeth
Gowda, Harsha
Ubhi, Baljit K.
Tautenhahn, Ralf
Gieschen, Andrew
Fields, Matthew W.
Patti, Gary J.
Siuzdak, Gary
author_facet Benton, H. Paul
Ivanisevic, Julijana
Mahieu, Nathaniel G.
Kurczy, Michael E.
Johnson, Caroline H.
Franco, Lauren
Rinehart, Duane
Valentine, Elizabeth
Gowda, Harsha
Ubhi, Baljit K.
Tautenhahn, Ralf
Gieschen, Andrew
Fields, Matthew W.
Patti, Gary J.
Siuzdak, Gary
author_sort Benton, H. Paul
collection PubMed
description [Image: see text] 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. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. 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.
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spelling pubmed-43033302015-12-12 Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling Benton, H. Paul Ivanisevic, Julijana Mahieu, Nathaniel G. Kurczy, Michael E. Johnson, Caroline H. Franco, Lauren Rinehart, Duane Valentine, Elizabeth Gowda, Harsha Ubhi, Baljit K. Tautenhahn, Ralf Gieschen, Andrew Fields, Matthew W. Patti, Gary J. Siuzdak, Gary Anal Chem [Image: see text] 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. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. 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. American Chemical Society 2014-12-12 2015-01-20 /pmc/articles/PMC4303330/ /pubmed/25496351 http://dx.doi.org/10.1021/ac5025649 Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Benton, H. Paul
Ivanisevic, Julijana
Mahieu, Nathaniel G.
Kurczy, Michael E.
Johnson, Caroline H.
Franco, Lauren
Rinehart, Duane
Valentine, Elizabeth
Gowda, Harsha
Ubhi, Baljit K.
Tautenhahn, Ralf
Gieschen, Andrew
Fields, Matthew W.
Patti, Gary J.
Siuzdak, Gary
Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title_full Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title_fullStr Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title_full_unstemmed Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title_short Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
title_sort autonomous metabolomics for rapid metabolite identification in global profiling
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303330/
https://www.ncbi.nlm.nih.gov/pubmed/25496351
http://dx.doi.org/10.1021/ac5025649
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