Omics- and Knowledge-Based Approaches for New Advances in the Use of Valuable Genetic Resources of Crops

Advanced computational study design with bioinformatics tools and knowledge-based information provides valuable hints for identification of new genes controlling important agronomic traits in crops. Large-scale gene expression data, which have been accumulated with high-throughput RNA sequencing technologies, also assist us to identify novel genes and their biological functions. The large-scale gene expression data from NGS leads to more detailed classifications of genes according to similarities in expression profiles. The similarities of gene expression profiles suggest the similar biological functions and/or the same mechanism of gene expression regulation. However, the current method, such as hierarchical clustering, for a large-scale dataset requires long calculation time and large-scale computer system. To overcome this problem, we have developed a statistical method based on correspondence analysis (CA). For even large-scale data, it permits us to quickly classify genes and construct gene expression networks (GENs) in a personal computer. We have also developed the GUI software 'CA Plot Viewer' which assists us to quickly perform the calculations and detect genes with similar expression profiles by the 3D graphical viewer (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

We have constructed GENs and integrated the knowledge-based information on GENs in model plants and crops so far. To correctly assign high-quality functional annotations (the knowledge-based information) to genes, we have applied natural language processing (NLP) to published literature, then knowledge-based information has been obtained through manual curation of the draft annotations from NLP. The information including GENs, orthologous relationships and literature-based functional annotations are accessible from our database Plant Omics Data Center (PODC;http://bioinf.mind.meiji.ac.jp/podc/).