Methodology
A flexible representation of omic knowledge for thorough analysis of microarray data
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* Corresponding author: Tetsuro Toyoda toyop@gsc.riken.jp
1 Phenome Informatics Team, Functional Genomics Research Group, Genomic Sciences Center, RIKEN, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
2 Plant Functional Genomics Research Team, Functional Genomics Research Group, Genomic Sciences Center, RIKEN, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
3 NEC Infomatec Systems Ltd, Sakato, Takatsu, Kawasaki, Kanagawa, Japan
4 Integrated Genome Informatics Research Unit, Metabolomics Group, Plant Science Center, RIKEN, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
5 Faculty of Bio-Science, Nagahama Institute of Bio-Science and Technology, Tamura, Nagahama, Shiga, Japan
6 IT technology research institute, Taehung Telcom co., Ltd., Dangsan-dong 3-ga 402, Youngdungpo-gu, Seoul, South Korea
7 Laboratory of Comparative Genomics, Department of Bioinformatics and Genomics, Graduate School of Information Science, NARA Institute of Science and Technology, Takayama, Ikoma, Nara, Japan
8 Morphoregulation Research Team, Plant Productivity Systems Research Group, Plant Science Center, RIKEN, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
9 Plant Science Center, RIKEN, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
10 Advanced Genome Information Technology Research Group, Genomic Sciences Center, RIKEN Suehiro, Tsurumi, Yokohama, Kanagawa, Japan
Plant Methods 2006, 2:5 doi:10.1186/1746-4811-2-5
Published: 2 March 2006Abstract
Background
In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data.
Results
In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi.
Conclusion
GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site http://omicspace.riken.jp webcite.