Kentaro Yano
,
School of Agri., Meiji University, Kawasaki, Japan
Shin Terashima
,
School of Agri., Meiji University, Kawasaki, Japan
Yukino Nakamura
,
School of Agri., Meiji University, Kawasaki, Japan
Minami Katayama
,
School of Agri., Meiji University, Kawasaki, Japan
Yuno Takaki
,
School of Agri., Meiji University, Kawasaki, Japan
Kataru Onosato
,
School of Agri., Meiji University, Kawasaki, Japan
Tomoyuki Takano
,
School of Agri., Meiji University, Kawasaki, Japan
Yohei Sasaki
,
School of Agri., Meiji University, Kawasaki, Japan
Maasa Kanno
,
School of Agri., Meiji University, Kawasaki, Japan
Misa Saito
,
School of Agriculture, Meiji University, Kawasaki, Japan
Noriko Matsuda
,
School of Agri., Meiji University, Kawasaki, Japan
Satomi Asano
,
School of Agriculture, Meiji University, Kawasaki, Japan
Koji Yokoyama
,
School of Agri., Meiji University, Kawasaki, Japan
Yoshifumi Tada
,
TOHOKU CHEMICAL CO., LTD., Morioka, Japan
Hiroshi Chiba
,
TOHOKU CHEMICAL CO., LTD., Morioka, Japan
Hajime Ohyanagi
,
School of Agri., Meiji University, Kawasaki, Japan
Toru Kudo
,
School of Agri., Meiji University, Kawasaki, Japan
Masaaki Kobayashi
,
School of Agri., Meiji University, Kawasaki, Japan
High-throughput RNA sequencing technologies with next generation sequencers (NGS) facilitate identification of 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. 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. 'CA Plot Viewer' is available from our web-site (
http://bioinf.mind.meiji.ac.jp/lab/).
With the large-scale analysis method, we have maintained databases providing information on GENs in plants such as OryzaExpress (http://bioinf.mind.meiji.ac.jp/OryzaExpress/) for rice and TOMATOMICS (http://bioinf.mind.meiji.ac.jp/tomatomics/) for tomato. A database PODC (http://bioinf.mind.meiji.ac.jp/podc/) also provides information on GENs for Arabidopsis and crops. PODC allows us to compare GENs among species with orthologues, and detect a species-specific gene module (a gene set showing similar expression profiles). To efficiently detect genes through GENs in PODC, we have accumulated highly-reliable functional annotations for genes and proteins on the basis of both natural language processing techniques and manual curation processes. The large-scale omics data and knowledge-based information in the databases facilitate the improvement of key agronomic traits in crops.
This work is supported by Research Funding for Computational Software Supporting Program in Meiji University. Computations were partially performed on the NIG supercomputer at ROIS National Institute of Genetics.
