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The '''Golm Metabolome Database''' ('''GMD''') <ref>{{cite doi|10.1002/9783527669882.ch18}}</ref><ref>{{cite book |last1=Hummel |first1=Jan|last2=Selbig |first2=Joachim|last3=Walther |first3=Dirk|last4=Kopka |first4=Joachim |editor-first=John |editor1-last=Nielsen |editor2-last=Jewett |title=Metabolomics |publisher=Springer Berlin Heidelberg |year=2007 |pages=75–96 |chapter=The Golm Metabolome Database: a database for GC-MS based metabolite profiling |isbn=978-3-540-74719-2 |doi=10.1007/4735_2007_0229}}</ref><ref>{{cite journal | title= GC-MS libraries for the rapid identification of metabolites in complex biological samples|author= Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T, Lundgren K, Roessner-Tunali U, Forbes MG, Willmitzer L, Fernie AR and Kopka J |journal= [[FEBS letters]] |volume=579 |pages=1332–1337 |year=2005 |doi=10.1016/j.febslet.2005.01.029 | issue= 6 |pmid=15733837}}</ref><ref>{{cite journal | title= GMD@CSB.DB: the Golm Metabolome Database|author= Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmuller E, Dormann P, Weckwerth W, Gibon Y, Stitt M, Willmitzer L, Fernie AR and Steinhauser D |journal= [[Bioinformatics (journal)|Bioinformatics]] |volume=21 |pages=1635–1638 |year=2005 |doi=10.1093/bioinformatics/bti236 | issue= 8 |pmid=15613389}}</ref> is a [[gas chromatography–mass spectrometry|gas chromatography (GC) – mass spectrometry (MS)]] reference library dedicated to [[metabolite profiling]] experiments and comprises mass spectral and retention index (RI) information for non-annotated mass spectral tags (MSTs, mass spectral information with [[retention time]] attached indices) together with data of a multitude of already identified metabolites and reference substances. The GMD is hosted at the [[Max Planck Institute for Molecular Plant Physiology|Max Planck Institute of Molecular Plant Physiology]] in [[Golm (Potsdam)|Golm]] district of [[Potsdam]], Germany.
== Background ==
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Aiming at the classification and identification of un-identified MSTs, the GMD accesses the information on available reference compounds. These compounds serve as training set of data to apply [[decision trees]] (DT) as a supervised [[machine learning]] approach. Structural feature extraction was applied to classify the metabolite space of the GMD prior to DT training. DT-based predictions of the most frequent substructures classify low resolution GC-MS mass spectra of the linked (potentially unknown) metabolite with respect to the presence or absence of the chemical moieties.<ref>{{cite journal | title= Decision tree supported substructure prediction of metabolites from GC-MS profiles|author= Hummel J, Strehmel N, Selbig J, Walther D and Kopka J|journal= [[Metabolomics]] |volume=6 |pages=322–333 |year=2010 |doi=10.1007/s11306-010-0198-7 | issue= 2}}</ref>
The web-based frontend supports conventional mass spectral and RI comparison by ranked hit lists as well as advanced DT supported substructure prediction. Batch processing is enabled via [[Simple Object Access Protocol]] (SOAP)-based web services while web-based data access services expose particular data base entities adapting [[Representational State Transfer]] (ReST) principles and mass spectral standards such as [[NIST]]-MSP and [[JCAMP]]-DX.
The GMD visualise quantitative metabolite pool size changes data.
==See also==
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