| Description: |
Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography?mass spectrometry (GC-MS). To make the dataset available as an efficient public functional genomics tool for hypothesis generation, we developed our MeKO database. It allows evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Non-processed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by Metabolomics Standards Initiative (MSI) and are freely downloadable. Proof-of-concept analysis suggests that the MeKO database is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation.
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