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OPTIMAS-DW: A comprehensive transcriptomics, metabolomics, ionomics, proteomics and phenomics data resource for maize

Christian Colmsee1, Martin Mascher1, Tobias Czauderna1, Anja Hartmann1, Urte Schlüter2, Nina Zellerhoff3, Jessica Schmitz3, Andrea Bräutigam4, Thea R Pick45, Philipp Alter6, Manfred Gahrtz6, Sandra Witt7, Alisdair R Fernie7, Frederik Börnke2, Holger Fahnenstich8, Marcel Bucher3, Thomas Dresselhaus6, Andreas PM Weber4, Falk Schreiber19, Uwe Scholz1* and Uwe Sonnewald2

Author affiliations

1 Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Corrensstr. 3

2 Department of Biology, Friedrich-Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Staudtstr. 5, Germany

3 University of Cologne, Botanical Institute, 50923 Köln, Albertus-Magnus-Platz, Germany

4 Plant Biochemistry, Heinrich-Heine-University, Universitätsstr. 1, 40225 Düsseldorf, Germany

5 International Graduate Program for Plant Science (iGrad-plant), Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany

6 Cell Biology and Plant Biochemistry, University of Regensburg, Universitätsstr. 31, 93040 Regensburg, Germany

7 Department of Molecular Physiology, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Am Mühlenberg 1, Germany

8 metanomics GmbH, 10589 Berlin, Tegeler Weg 33, Germany

9 Martin Luther University Halle-Wittenberg, Institute of Computer Science, 06120 Halle, Von-Seckendorff-Platz 1, Germany

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Citation and License

BMC Plant Biology 2012, 12:245  doi:10.1186/1471-2229-12-245

Published: 29 December 2012

Abstract

Background

Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world’s population or the world’s climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments.

Description

The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided.

Conclusions

With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw webcite.

Keywords:
Maize; Zea mays; Database; WGCNA; Biomass; Yield; Data integration; Transcriptomics; Metabolomics; Phenomics