Golm Metabolome Database: Difference between revisions

Revision as of 07:34, 1 December 2012

Revision as of 00:11, 3 December 2012

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Gas chromatography (GC) coupled to mass spectrometry (MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel biomarkers in [[metabolomics]]. However, the majority of MSTs currently measured in plant [[metabolomic]] [[profiling]] experiments remains unidentified due to the lack of authenticated pure reference substances and the expensive and time-consuming effort to maintain mass spectral RI libraries required for compound identification by GC-MS.

As the communication of analytical results and other approach-related details such as mass spectral and RI reference information within the scientific community is becoming increasingly popular, open access platforms for information exchange, such as the GMD, are obligatory.

Due to the lack of mandatory standards it remains difficult to compare individual mass spectrums.

While for the different mass spectral matching differences in the used 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 varying between different laboratories 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 assessedassesses the accuracy of RI transfer between chromatography variants and implementedimplementes means to transfer empiricalempirically determined RI properties.

Aiming at the classification and identification of un-identified MSTs, the GMD accessedaccesses the information on available reference compounds. asThese acompounds sourceserve ofas training set of data to apply [[decision trees]] (DT) as a supervised [[machine learning]] approach. Single structureStructural feature extraction was applied to classify the metabolite space of the GMD prior to DT training. DT-based predictionpredictions 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.

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.