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dc.contributor.authorDudek, Christian-Alexander
dc.contributor.authorSchlicker, Lisa
dc.contributor.authorHiller, Karsten
dc.date.accessioned2020-03-16T14:01:53Z
dc.date.available2020-03-16T14:01:53Z
dc.identifier.citationMethods Mol Biol. 2020;2088:17-32. doi: 10.1007/978-1-0716-0159-4_2.en_US
dc.identifier.pmid31893368
dc.identifier.doi10.1007/978-1-0716-0159-4_2
dc.identifier.urihttp://hdl.handle.net/10033/622207
dc.description.abstractGas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectData analysisen_US
dc.subjectGCMSen_US
dc.subjectGas chromatographyen_US
dc.subjectMass isotopomer distributionen_US
dc.subjectMass spectrometryen_US
dc.subjectMetabolismen_US
dc.subjectNontargeted metabolomicsen_US
dc.subjectStable isotope labelingen_US
dc.titleNon-Targeted Mass Isotopolome Analysis Using Stable Isotope Patterns to Identify Metabolic Changes.en_US
dc.typeArticleen_US
dc.identifier.eissn1940-6029
dc.contributor.departmentHZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.en_US
dc.identifier.journalMethods in molecular biology (Clifton, N.J.)en_US
dc.source.volume2088
dc.source.beginpage17
dc.source.endpage32
dc.source.journaltitleMethods in molecular biology (Clifton, N.J.)
dc.source.countryUnited States


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