@INBOOK{IPB-1761, author = {Hummel, J. and Strehmel, N. and Bölling, C. and Schmidt, S. and Walther D. and Kopka, J.}, title = {{Handbook of plant metabolomics}}, year = {2013}, pages = {321-343}, chapter = {{Mass spectral search and analysis using the Golm metabolome.}}, editor = {Weckwerth, W.; Kahl, G.}, doi = {10.1002/9783527669882.ch18}, url = { http://onlinelibrary.wiley.com/doi/10.1002/9783527669882.ch18/summary}, abstract = {The novel “omics” technologies of the postgenomic era generate large multiplexed phenotyping datasets, which can only inadequately be published in the traditional journal and supplemental formats. For this reason, public databases have been developed that utilize the efficient communication of knowledge through the World Wide Web. This trend also applies to the metabolomics field, which is, after genomics, transcriptomics, and proteomics, the fourth major systems-level phenotyping platform. Each different analytical technology used in metabolomics studies requires specific reference data for metabolite identification and optimal data formats for reporting the complex metabolite profiling data features. Therefore, we envision that every technology platform or even each high-throughput metabolomic laboratory will establish dedicated databases, which will communicate between each other and will be integrated by meta-databases and web services. The Golm Metabolome Database (GMD) (http://gmd.mpimp-golm.mpg.de/) is a metabolomic database, maintained by the Max Planck Institute of Molecular Plant Physiology, that was initiated around a nucleus of reference data from gas chromatography–mass spectrometry metabolite profiling data and is now developing toward a general mass spectrometry-based repository of reference metabolite profiles for essential plant tissues and typical variations of growth conditions. This chapter describes the mass spectral searches and analyses currently supported by the GMD. We specifically address the searches for the different chemical entities within GMD, namely the metabolites, reference substances, and the chemically derivatized analytes. We report the diverse options for mass spectral analyses and highlight the decision tree-supported prediction of chemical substructures, a feature of GMD that currently appears to be a unique among the many tools for the analysis of gas chromatography–electron ionization mass spectra.} }