OryzaExpress: An integrated database of gene expression networks and omics annotations in rice

Kazuki Hamada, Kohei Hongo, Keita Suwabe, Akifumi Shimizu, Taishi Nagayama, Reina Abe, Shunsuke Kikuchi, Naoki Yamamoto, Takaaki Fujii, Koji Yokoyama, Hiroko Tsuchida, Kazumi Sano, Takako Mochizuki, Nobuhiko Oki, Youko Horiuchi, Masahiro Fujita, Masao Watanabe, Makoto Matsuoka, Nori Kurata, Kentaro Yano

    Research output: Contribution to journalArticlepeer-review

    97 Citations (Scopus)


    Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology.

    Original languageEnglish
    Pages (from-to)220-229
    Number of pages10
    JournalPlant and Cell Physiology
    Issue number2
    Publication statusPublished - 2011 Feb


    • Correspondence analysis
    • Database
    • Gene expression network
    • Microarray
    • Oryza sativa
    • Systems biology

    ASJC Scopus subject areas

    • Physiology
    • Plant Science
    • Cell Biology


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