The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Both support for preprocessing of raw datasets and also means to visualize and integrate the results of higher level statistical analyses within a functional genomics context are required.

To facilitate the systematic storage, analysis and integration of metabolomics experiments, MeltDB was designed, implemented, and applied. MeltDB is a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. The software supports open and standardized file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The newly developed system covers the process from raw data management to the visualization of results in a knowledge-based background and is integrated into the existing software platforms of genomics, proteomics, and transcriptomics at Bielefeld University.

This work demonstrates the functionality of MeltDB by means of several application examples where e.g. the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris or the differences between healthy and disease human blood plasma samples are dissected. Novel visualization and analysis methods based on the MeltDB API have been developed and evaluated in the context of the extensible software platform.