SE45:/DS01
From Metabolonote
Sample Set Information
| ID | SE45 |
|---|---|
| Title | Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout 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. 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/. |
| Authors | Atsushi Fukushima, Miyako Kusano, Ramon Francisco Mejia, Mami Iwasa, Makoto Kobayashi, Naomi Hayashi, Akiko Watanabe-Takahashi, Tomoko Narisawa, Takayuki Tohge, Manhoi Hur, Eve Syrkin Wurtele, Basil J. Nikolau, Kazuki Saito, RIKEN CSRS |
| Reference | Fukushima et al. (2014) Plant Physiology 165:948-961 (PMID: 24828308) |
| Comment | The raw data files are available at DropMet web site in PRIMe database, MetaboLights (Salek et al., 2013) (accession no. MTBLS47), and The MetabolomeExpress (Carroll et al., 2010). |
The raw data files are available at DROP Met web site in PRIMe database of RIKEN.
Related data are deposited in MetaboLights.
Data Analysis Details Information
| ID | DS01 |
|---|---|
| Title | Data processing |
| Description | Non-processed MS data from GC-TOF/MS analysis were exported in NetCDF format generated by chromatography processing and mass spectral deconvolution software, Leco ChromaTOF version 3.22 (LECO, St. Joseph, MI, USA) to MATLAB 6.5 (Mathworks, Natick, MA, USA), where all data-pretreatment procedures, such as smoothing, alignment, time-window setting, and peak deconvolution, were carried out by using hyphenated data analysis (HDA) (Jonsson et al., 2004; Jonsson et al., 2006). The resolved MS spectra were matched against reference mass spectra using the NIST mass spectral search program for the NIST/EPA/NIH mass spectral library (version 2.0) and our custom software for peak annotation written in JAVA. Peaks were identified or annotated based on their RIs and a reference mass spectra comparison to the Golm Metabolome Database (GMD) and our in-house spectral library. The metabolites were identified by comparison with the RIs from library databases (GMD and our own library) and with the mass spectra of authentic standards, and the metabolites were defined as annotated metabolites upon comparison with the mass spectra and RIs from these two libraries. The 5 batches of metabolite profiles were combined using the HDA method (Jonsson et al., 2004; Jonsson et al., 2006). To correct the “batch effect” we applied COMBAT normalization (Johnson et al., 2007) to our quality samples consisting of Col-0 wild-type plants for each batch. Data were normalized using the CCMN algorithm (Redestig et al., 2009). |
| Comment_of_details |
The raw data files are available at DROP Met web site in PRIMe database of RIKEN.
