RiceArrayNet: A Database for Correlating Gene Expression from Transcriptome Profiling, and Its Application to the Analysis of Coexpressed Genes in Rice      

Abstract

Microarray data can be used to derive understanding of the relationships between the genes involved in various biological systems of an organism, given the availability of databases of gene expression measurements from the complete spectrum of experimental conditions and materials. However, there have been no reports, to date, of such a database being constructed for rice (Oryza sativa). Here, we describe the construction of such a database, called RiceArrayNet (RAN; http://www.ggbio.com/arraynet/), which provides information on coexpression between genes in terms of correlation coefficients (r values). The average number of coexpressed genes is 214, with sd of 440 at r ≥ 0.5. Given the correlation between genes in a gene pair, the degrees of closeness between genes can be visualized in a relational tree and a relational network. The distribution of correlated genes according to degree of stringency shows how each gene is related to other genes. As an application of RAN, the 16-member L7Ae ribosomal protein family was explored for coexpressed genes and gene expression values within and between rice and Arabidopsis (Arabidopsis thaliana), and common and unique features in coexpression partners and expression patterns were observed for these family members. We observed a correlation pattern between Os01g0968800, a drought-responsive element-binding transcription factor, Os02g0790500, a trehalose-6-phosphate synthase, and Os06g0219500, a small heat shock factor, reflecting the fact that genes responding to the same biological stresses are regulated together. The RAN database can be used as a tool to gain insight into a particular gene by examining its coexpression partners.

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

“…Coexpression networks have been proven to be effective to identify sets of candidate genes underlying specific phenotypes (Lee et al, 2009;Mutwil et al, 2010). Here, we investigated if MatNet could be used as a tool to identify gene modules linked to longevity.…”

“…Networks constructed from mixed sample sets represent a "global" or meta-analysis view of gene coexpression and have been obtained for many species (Faccioli et al, 2005;Lee et al, 2009;Ficklin et al, 2010;Schaefer et al, 2014). However, targeted networks, focusing on a specific condition, organ, or developmental phase, allow the dissection of fine regulatory switches that will otherwise be obscured through global comparisons (Usadel et al, 2009).…”

“…Therefore, clusters of genes with related functions often exhibit expression patterns that are correlated under a large number of diverse conditions. Based on the so-called "guilt by association" principle, coexpression networks have been proven to be very effective to identify relevant gene interactions (Ficklin et al, 2010;Amrine et al, 2015;Li et al, 2015) and to identify sets of candidate genes underlying specific phenotypes (Lee et al, 2009;Mutwil et al, 2010). Horvath and colleagues defined a "gene significance measure" as a means to provide a simple geometric interpretation of coexpression network analysis (Horvath and Dong, 2008).…”

Characterizing gene coexpression modules in Oryza sativa based on a graph-clustering approach

“…For example, Ͼ200 microarray data are deposited in NCBI GEO (Barrett et al 2009). Despite the development of several coexpression databases such as RiceArrayNet (Lee et al 2009), Oryza_Express (http://riceball.lab.nig.ac.jp/oryzaexpress/), and the Rice Coexpression Database (http://www.ricearray.org/rice_ coexpression.shtml), the entire gene expression organization in the rice genome remains largely uncharacterized. With the accumulation of rice microarray data the meta-analysis approach can now be applied to this model crop plant.…”

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks

“…Examples include those constructed to identify functional gene modules in humans (Lee et al, 2004), identification of genes involved with cellulose synthase in Arabidopsis (Arabidopsis thaliana; Persson et al, 2005), identification of biomarkers for glycerol kinase-deficient mice (MacLennan et al, 2009), identification of cis-regulatory elements in gene clusters for budding yeast (Mariñ o-Ramírez et al, 2009), construction of a regulatory network of iron response in Shewanella oneidensis , and identification of conserved gene clusters across several species (Stuart et al, 2003). For plants, global coexpression networks have been constructed for Arabidopsis (Persson et al, 2005;Wei et al, 2006;Mentzen et al, 2008;Atias et al, 2009;Mao et al, 2009;Wang et al, 2009), barley (Hordeum vulgare; Faccioli et al, 2005), rice (Jupiter et al, 2009;Lee et al, 2009), and tobacco (Nicotiana tabacum; Edwards et al, 2010).…”