Golm Metabolome Database: Difference between revisions

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The '''Golm Metabolome Database (GMD)''' <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><ref>{{cite journal | title= Retention index thresholds for compound matching in GC-MS metabolite profiling|author= Strehmel N, Hummel J, Erban A, Strassburg K and Kopka J |journal= [[Journal of Chromatography B]] |volume=871 |pages=182–190 |year=2008 |doi=10.1016/j.jchromb.2008.04.042 | issue= 2}}</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}}</ref><ref>{{cite journal | title= [e-mail]: 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]] |volume=21 |pages=1635–1638 |year=2005 |doi=10.1093/bioinformatics/bti236 | issue= 8}}</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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Due to the lack of mandatory standards it remains difficult to compare individual mass spectrums.

While the different mass detector technologies, namely

[[Quadrupole mass analyzer|quadrupole]], [[ion trap]] and [[Time-of-flight mass spectrometry|time of flight]], can be deemed irrelevant, the chromatography settings such as temperature programming, type of capillary column and choice of column manufacturer heavily affect the empirically determined RI properties. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared library use. The GMD assesses the accuracy of RI transfer between chromatography variants and implements means to transfer empirically determined RI properties<ref>{{cite journal | title= Retention index thresholds for compound matching in GC-MS metabolite profiling|author= Strehmel N, Hummel J, Erban A, Strassburg K and Kopka J |journal= [[Journal of Chromatography B]] |volume=871 |pages=182–190 |year=2008 |doi=10.1016/j.jchromb.2008.04.042 | issue= 2}}</ref> between chromatography variants and implements means to transfer empirically determined RI properties.

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.