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Database Commons

a catalog of worldwide biological databases

Database Profile

MeKO

General information

URL: http://prime.psc.riken.jp/meko
Full name: Metabolomic Characterization of Knock-Out Mutants in Arabidopsis - Development of a Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis
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.
Year founded: 2014
Last update: 2014
Version:
Accessibility:
Accessible
Country/Region: Japan

Classification & Tag

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Contact information

University/Institution: Chiba University
Address: Graduate School of Pharmaceutical Sciences, Chiba University, Chiba-shi, Chiba 263-8522, Japan
City:
Province/State:
Country/Region: Japan
Contact name (PI/Team): Saito K
Contact email (PI/Helpdesk): kazuki.saito@riken.jp.

Publications

24828308
Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis. [PMID: 24828308]
Fukushima A, Kusano M, Mejia RF, Iwasa M, Kobayashi M, Hayashi N, Watanabe-Takahashi A, Narisawa T, Tohge T, Hur M, Wurtele ES, Nikolau BJ, Saito K.

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. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the 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. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Plant Physiol. 2014:165(3) | 30 Citations (from Europe PMC, 2026-04-04)

Ranking

All databases:
3543/6932 (48.904%)
Pathway:
218/454 (52.203%)
Literature:
305/577 (47.314%)
3543
Total Rank
29
Citations
2.417
z-index

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Record metadata

Created on: 2018-01-27
Curated by:
Syed Sardar [2018-04-10]
Syed Sardar [2018-04-08]
Qi Wang [2018-01-27]