A software platform for the Analysis and Integration of Data from Metabolomics Experiments

Metabolomics is a rapidly maturing field and expands the scope of functional genomics beyond microarray analysis and proteomics to the level of the metabolism. Compared to other 'omics', metabolomics is closest to the actual phenotype. The metabolome represents a vast number of compounds, estimations range from 2000 in human to 200.000 in plants.

Since these compounds differ in their chemical and physical properties and occur in wide concentration ranges, no single analytical instrumentation can cover the complete metabolome quantitatively. Nevertheless, the ability to identify and quantify a growing number of metabolites in biological samples using high-throughput gas or liquid chromatography (GC/LC) coupled to mass spectrometery (MS) provides the basis for the understanding and generation of quantitative models of cellular processes.

The MeltDB open source framework is designed to provide analysis methods for raw GC- and LC-MS datasets and offers methods to combine the respective results. A flexible tool pipeline implemented in MeltDB allows both the import of preprocessed data as well as the integration of existing open source analysis packages such as XCMS, MassSpecWavelet or metaB. For the identification of metabolites based on mass spectra the freely available GMD database is queried. Additionally, user defined libraries in the NIST format can be imported.

To facilitate the sound statistical analysis of preprocessed metabolite quantities, MeltDB supports normalization of metabolite quantities by internal standards (e.g. Ribitol) and also dry weight or cell volumina. The integration of the R statistics software allows to apply standard methods (T-Tests, AOV, Hierachical Clustering) and generate explorative visualizations (PCA, ICA, Heatmaps Clustering results) on the normalized datasets.

The integration of genomic and transcriptomic datasets originating from GenDB or EMMA is achieved via SOAP based web services. Thus, interactive visualizations of metabolite concentrations together with transcript meassurements mapped on e.g. KEGG pathways can easily be generated.

Neuweger, H., S. Albaum, M. Dondrup, M. Persicke, T. Watt, K. Niehaus, J. Stoye, & A. Goesmann. 2008. “MeltDB: a software platform for the analysis and integration of metabolomics experiment data”.
Bioinformatics, 24(23), 2726 - 2732.
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Persicke, M., C. Rückert, J. Plassmeier, L. Stutz, N. Kessler, J. Kalinowski, A. Goesmann, & H. Neuweger. 2011. “MSEA: metabolite set enrichment analysis in the MeltDB metabolomics software platform: metabolic profiling of Corynebacterium glutamicum as an example”. Metabolomics, 8(2), 310 - 322.
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