OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice

Abstract

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.

HapRice, an SNP Haplotype Database and a Web Tool for Rice

“…In addition to providing a greater understanding of evolution in rice, this information should help us to select candidate regions associated with economically important traits, particularly when combined with genetic dissection by QTL analysis and genomewide association studies. In recent years, omics information such as transcriptome data has been organized into databases (Hamada et al 2011, Kudo et al 2013, Sakai et al 2013. The integration of our haplotype data with these omics databases would lead to more comprehensive understanding of rice genomics.…”

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of <i>Arabidopsis thaliana</i> and model crop plants

“…Thus, one of the CK2A genes may also be suitable as a reference gene in these plants, depending on experimental conditions, suggesting the potency of ortholog-based selection. While a number of plant databases, such as PODC (Ohyanagi et al, 2015), ATTED-II (Obayashi et al, 2014), OryzaExpress (Hamada et al, 2011), eFP Browsers (Winter et al, 2007), UniVIO (Kudo et al, 2013) and qTeller (http:// www.qteller.com/), are dealing with plant transcriptomic data, direct information on reference gene candidates has not been provided in the databases. A database implemented with a search function for reference genes, combined with information on orthologs and experimental conditions, would be helpful to explore reference gene candidates in species with insufficient accumulation of transcriptomic data.…”

Expression Analysis and Genome Annotations with RNA Sequencing

“…To globally interpret the results obtained from CA, a graphical and intuitive viewer is also required, as large-scale expression analysis frequently contains more than tens of thousands of genes. This viewing software should allow graphical adjustments such as rotation, zooming in and out, and panning of the image and searches for genes with functional annotations (descriptions) (Hamada et al 2011 ;Manickavelu et al 2012 ;Nishida et al 2012 ). For this purpose, a GUI tool and viewer for CA calculation "CA Plot Viewer" has been developed and distributed ( http://bioinf.mind.meiji.ac.…”