Masaaki Kobayashi
,
School of Agri., Meiji University, Kanagawa, JST, CREST, Japan
Hajime Ohyanagi
,
School of Agri., Meiji University, Kanagawa, JST, CREST, Japan
Maasa Kanno
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Kyoko Morimoto
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Hiromi Toyoshima
,
School of Agri., Meiji University, Kanagawa, JST, CREST, Japan
Tomoyuki Takano
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Yohei Sasaki
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Shin Terashima
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Hayato Matsumura
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Hiroyoshi Iwata
,
Agricultural and Life Sci., The Univ. of Tokyo, JST, CREST, Japan
Takashi Sazuka
,
Nagoya Univ. JST, CREST, Japan
Tsuyoshi Tokunaga
,
Earth Note Co. Ltd., JST, CREST, Japan
Nobuhiro Tsutsumi
,
Agricultural and Life Sci., The Univ. of Tokyo, JST, CREST, Japan
Kentaro Yano
,
School of Agri., Meiji University, Kanagawa, JST, CREST, Japan
The large-scale gene expression data from next generation sequencers (NGS) leads to detailed classifications of genes according to the similarities in expression profiles. We have developed a statistical method and GUI software “CA Plot Viewer” based on correspondence analysis to quickly classify genes and construct gene expression networks in a personal computer. “CA Plot Viewer” is available from our web-site (
http://bioinf.mind.meiji.ac.jp/lab/). The information on gene expression networks are available from databases PODC (Plant Omics Data Center,
http://bioinf.mind.meiji.ac.jp/podc/), OryzaExpress (
http://bioinf.mind.meiji.ac.jp/OryzaExpress/) and TOMATOMICS (
http://bioinf.mind.meiji.ac.jp/tomatomics/).
We employed Illumina sequence data (NCBI SRA) to show the theoretical advantage of this method in classification of genes according to expression profiles. To obtain digital gene expression profiles for Arabidopsis, rice, tomato, grape and sorghum, the number of reads mapped onto each cDNA was used as the expression level. Our method allows us to efficiently detect genes with similar expression profiles, and construct gene expression networks. The comparisons of expression networks among species can be easily performed by using information on orthologues in the five species in the PODC. Information on expression networks calculated from microarray data in rice and tomato have been provided from the OryzaExpress and TOMATOMICS, respectively.
With the large-scale NGS data, we have developed a pipeline for detection of genome-wide SNPs by using the RAD-Seq method. With the wealth of SNP data, the high-throughput method for large-scale expression profiles facilitates the understanding of biological functions of genes.
This work is partially supported by Resarch Funding for Computational Software Supporting Program in Meiji University.
