Skip to main content

The Rice Oligonucleotide Array Database: an atlas of rice gene expression

Abstract

Background

Microarray technologies facilitate high-throughput gene expression analysis. However, the diversity of platforms for rice gene expression analysis hinders efficient analysis. Tools to broadly integrate microarray data from different platforms are needed.

Results

In this study, we developed the Rice Oligonucleotide Array Database (ROAD,http://www.ricearray.org) to explore gene expression across 1,867 publicly available rice microarray hybridizations. The ROAD’s user-friendly web interface and variety of visualization tools facilitate the extraction of gene expression profiles using gene and microarray element identifications. The ROAD supports meta-analysis of genes expressed in different tissues and at developmental stages. Co-expression analysis tool provides information on co-regulation between genes under general, abiotic and biotic stress conditions. Additionally, functional analysis tools, such as Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) Orthology, are embedded in the ROAD. These tools facilitate the identification of meaningful biological patterns in a list of query genes.

Conclusions

The Rice Oligonucleotide Array Database provides comprehensive gene expression profiles for all rice genes, and will be a useful resource for researchers of rice and other grass species.

Background

Rice (Oryza sativa) is a staple food for more than 50% of the human population. Because of the high level of genomic colinearity and conservation of gene function among grass species, rice serves as a useful research model in other grass studies (Devos and Gale[2000]; Jung et al.[2008a]). The complete sequencing of rice genome achieved in year 2005 (International Rice Genome Sequencing Project[2005]), has brought biological research to the genome scale and post-genome era, while assigning function to every rice gene is still an enormous challenge. Comprehensive annotations of rice genome sequence have revealed that more than half of the predicted genes do not have assigned biological functions (Yuan et al.[2005]; Itoh et al.[2007]; Tanaka et al.[2008]). Despite extensive efforts to characterize the function of rice genes, only a handful of biological functions have been identified, mostly through the laborious process of map-based cloning (Jung et al.[2008a]).

Microarray technologies are an important strategy for genome-wide expression pattern analysis and is becoming increasingly important for gene functional analysis (Schmid et al.[2005]). Several rice array platforms for the two rice subspecies (ssp. japonica and indica) have been reported and their characteristics are summarized in Table 1. The GeneChip rice genome array, designed by Affymetrix and produced using a direct synthesis method, contains 57,381 probesets covering approximately 48,564 and 1,260 transcripts from the japonica and indica cultivars, respectively. Agilent has constructed a 22K Rice Oligo Microarray Kit based on rice FLcDNAs and recently announced a 44K version (Shimono et al.[2007]). The Oryza sativa Genome Oligo Set (Version 1.0; 61K) was designed by the Beijing Genomics Institute and Yale University (BGI/Yale) and based on the draft indica and japonica sequences. The University of California, Davis, led a National Science Foundation (NSF) supported effort to design, print and validate 22K and 45K oligonucleotide arrays based on gene model predictions from TIGR’s osa1 version 3.0 release.

Table 1 Summary of rice microarray platforms available in ROAD

These rice microarray platforms have been successfully used in characterizing gene expression profiles from different tissues and organs (Wang et al.[2010]), different cell types (Jiao et al.[2009]), under biotic and abiotic treatment conditions (Jung et al.[2008b]; Swarbrick et al.[2008]; Jung et al.[2010]), identification of alternative splice (Jung et al.[2009]) and mutants (Bruce et al.[2009]). As a result, an increasing number of rice microarray datasets are being deposited in public repositories such as the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) (Barrett et al.[2009]), the ArrayExpress at the European Bioinformatics Institute (EBI) (Parkinson et al.[2007]) and the Center for Information Biology gene EXpression database (CIBEX) at the DNA Data Bank of Japan (DDBJ) (Ikeo et al.[2003]). There are also several databases that allow for efficient access and data mining of collections of microarray data for rice (Table 2). For example, the Rice Expression Profile Database (RiceXPro,http://ricexpro.dna.affrc.go.jp/), which is based on the Agilent 44K microarray, provides an overview of the spatiotemporal gene expression profiles of various organs and tissues (Sato et al.[2011]). Genevestigator (https://www.genevestigator.ethz.ch/) provides a meta-analysis toolbox to explore gene expressions across a wide variety of biological contexts for rice and other species, but it is commercial and not completely publicly available (Hruz et al.[2008]). Other databases including OryzaExpress (Hamada et al.[2011]), RicePLEX within the Plant Expression Database (PLEXdb) (Dash et al.[2012]), Bio-Array Resource for Plant Biology (BAR) (Toufighi et al.[2005]) and Yale Virtual Center for Cellular Expression Profiling of Rice (Jiao et al.[2009]) are useful for expression pattern analysis of rice genes. Although general agreement between different microarray platforms has been shown to be low, data derived from high signal intensities can correlate between different platforms as well as in replicates of the same platform, and that overlap between significant gene lists from different platforms was as high as 67% when low intensity values were removed from an Arabidopsis study (Pylatuik and Fobert[2005]). This suggests the potential application for the broad integration of microarray data from different platforms. Here we describe the Rice Oligonucleotide Array Database (ROAD,http://www.ricearray.org), which integrates the most comprehensive public microarray datasets and provides several functional analysis tools. With a user-friendly web interface, the ROAD is a useful reference for elucidating rice gene expression and function.

Table 2 Summary of databases for expression analysis of rice genes

Results and discussion

Microarray element search for multiple array platforms in rice

We collected information from six rice microarray platforms, including the Affymetrix, Agilent 22K and 44K, BGI/Yale, and the NSF 20K and 45K, to construct the ROAD. Probe sequences from each platform were extracted and mapped onto cDNAs to match the probes to the expressed genes; the cDNAs were drawn from the Rice Genome Annotation Project (RGAP, previously TIGR) V6 (Ouyang et al.[2007]), Rice Annotation Project (RAP) V3 (Tanaka et al.[2008]) and the Knowledge-based Oryza Molecular biological Encyclopedia (KOME) (Kikuchi et al.[2003]) (Table 1). Because microarray platforms use their own element IDs rather than common gene IDs, two search tools were developed to determine the relationship between microarray elements and rice genes. The ‘Single-platform Microarray Element Search’ and the ‘Multiple-platform Microarray Element’ search tools allow users to identify the specific platform probes that map to a common gene target. Users may choose between entering a list of IDs (genes or microarray elements) of interest and uploading a file to use these search tools. The search result can be returned as HTML format for browsing or txt format for download. The entire probe mapping matrix table is also available for download. ‘Single-platform Microarray Element Search’ tool in ROAD is similar with ID converter tool in OryzaExpress which provides the RAGP IDs, RAP IDs and Affymetrixprobe IDs for querying and cross-linking (Hamada et al.[2011]), while probe IDs from six array platforms corresponding to RAGP, RAP and KOME IDs are provided in ROAD.

Gene expression analysis

Raw rice microarray data from six platforms (including 105 experiments and 1,867 hybridizations from March 2012, electronic Additional file1: Table S1) were downloaded from public repositories and were normalized using the same method. One-channel and two-channel platforms were normalized separately because of differences in platform features or designs (MAS 5.0 for one-channel platform, Lowess and MAD for two-channel platforms). For two-channel platforms, the normalized expression ratios (log2(Cy3/Cy5)) were used and the color-swap hybridizations were manually corrected to make them comparable among other samples. Normalized data were integrated into the ROAD, thus simplifying the retrieval process of their gene expression profiles. After entering a list of genes or microarray element IDs into the ROAD, the user can then select the microarray platform and specific experiment to search against (Figure1a). The “Gene Expression Search” tool in ROAD provides a tabular list of microarray element IDs and of matching gene IDs from RGAP, RAP and KOME (Figure1b). Clicking on the gene IDs will redirect users to RGAP and RAP-DB databases to obtain detailed information on gene annotation. The expression profiles for query genes can be shown either as a heatmap (Figure1c) or a classic line plot (Figure1d). For one-channel platform Affymetrix, heatmap representation is generated by Blue-Black-Yellow color scheme, while Green-Black-Red for two-channel platforms (Agilent 22K and 44K, BGI/Yale, NSF 20K and 45K). The scale bar for the heatmap output can be adjusted using several options. Because there are multiple microarray elements matched for some genes, the heatmap and line plot are displayed according to microarray element IDs. Besides displaying expression profile from multiple biological or technical replicates within an experiment, the average expression of replicates can also be generated when checking “Show Average” checkbox. The download option allows users to easily transfer expression data into other databases or software for further analysis. The experiments integrated into ROAD can be searched by the “Experiment Search” tool and the “Highly Expressed Genes” tool allows users to quickly identify a set of genes that are highly expressed under a selected expression threshold in a specific tissue.

Figure 1
figure 1

Process of gene expression profiling analysis in ROAD. (a) A snap shot of the web interface for gene expression search in ROAD. Gene expression search can be initiated by entering one (or multiple) gene(s) or microarray element ID(s), such as RGAP ID, RAP ID, KOME ID, Affymetrixprobeset ID, or by uploading a file containing them. Then select the microarray platform and specific experiment to search against and the display format. (b) Tabular list of microarray elements mapped onto the example gene IDs when selecting Affymetrix platform. (c) Heatmap representation of expression profiles retrieved by the example genes from Affymetrix experiment GSE6737. (d) The classical line plot representation of the same data with (c).

Meta-analysis on anatomy and development from Affymetrix and Agilent 44K platforms

Because many factors such as RNA isolation, labeling and hybridization methods affect the quality of microarray data, pooling data from different experiments does not allow for a rigorous expression profiling analysis. Genevestigator developed a novel approach (meta-analysis) to assemble microarray data from different experiments into context-related profiles (meta-profiles). This large-scale combination and analysis of expression data from a single organism using a single platform allows the identification of biologically meaningful expression patterns of individual genes (Hruz et al.[2008]). One drawback to Genevestigator, however, is that the public has limited access to the platform’s functions. As the open access version only supports the meta-analysis of a maximum of 50 genes at one time and does not allow data downloading, further analysis of the gene expression data is hindered. Therefore, we constructed a meta-analysis tool based on the 1,155 Affymetrix hybridizations and 209 Agilent 44K hybridizations found in the ROAD. This construction was possible because both the Affymetrix and Agilent 44K platforms provide standardized systems with a high degree of reproducibility. We have also developed four meta-profiles for genes expressed in different tissues and at different developmental stages for each of the two platforms. In case of the meta-profiles for the developmental stages, the Affymetrix meta-profile provides the average gene expression levels in all tissues during one developmental stage. The Agilent 44K provides the average gene expression levels in leaf blades during 17 developmental stages. Through analyzing Affymetrix and Agilent 44K anatomic meta-profiles, 19 root-preferential genes were identified and their anatomic (Figure2a and2c; Electronic Additional file2: Figure S1a and c) and developmental expression patterns (Figure2b; Electronic Additional file2: Figure S1b, d and e) on both platforms are shown in Figure2 and electronic Additional file2: Figure S1. This meta-analysis allowed us to evaluate root-preferential expression patterns. The meta-profile developmental stage analysis from the Affymetrix array platform indicates that these 19 genes were preferentially expressed during seedling stages (Figure2b and electronic Additional file2: Figure S1b). Using the Agilent 44K platform, we found that expression levels in leaf blades were lower than those in the root (Electronic Additional file2: Figure S1d and e). These results show that by integrating data for different anatomic tissues and developmental stages, the meta-analysis tool provides straightforward information about where and when genes of interest are expressed.

Figure 2
figure 2

Screenshots of a ROAD meta-analysis using Affymetrix (a and b) and Agilent 44K (c) array platforms. Nineteen genes that are preferentially expressed in root were selected and used for the three meta-analyses. (a) Meta-analysis query of diverse tissues. (b) Meta-analysis query of diverse developmental stages. (c) Meta-analysis query of diverse floral and vegetative organs.

Co-expression analysis

Similarity of gene expression profiles (co-expression) can provide powerful information to identify new genes functionally related. The rapid accumulation of microarray data in past decade allows the creation of co-expression networks by examining the co-expression patterns of genes over a large number of experimental conditions. Several online co-expression analysis tools have been developed for rice, including OryzaExpress (Hamada et al.[2011]), RiceArrayNet (Lee et al.[2009]) and ATTED-II (Obayashi et al.[2011]). During co-expression analysis, more microarray data will generate better reliability. Based on the collection of the most comprehensive microarray data in ROAD (Table 2), we also developed co-expression analysis tools in ROAD. After filtering out of 74 outliers and selecting hybridizations involved in abiotic and biotic stresses, three kinds of co-expression relationships between rice genes were calculated, including general co-expression, abiotic and biotic stress co-expression, based on 1,081, 329 and 181 Affymetrix microarray hybridizations individually. The widely used Pearson correlation coefficient (PCC) index was selected to evaluate the similarities of expression profiles for gene pairs. We selected default values of the PCC cutoff as 0.75 and 0.8, for general and abiotic and biotic stresses, respectively. At this relatively stringent cutoff value, the general or abiotic and biotic stress co-expression networks can be constructed by using online network drawing tool. After entering a list of guide-genes and selecting the network type, a co-expression network will be generated by Cytoscape Web, an interactive web-based network browser (Lopes et al.[2010]). The network can be easily zoomed in/out and external RGAP database link is provided for each node. The download option allows users to export the network into local file with SIF format which can be used in local Cytoscape or other network analysis software. Lower co-expression under the selected PCC cutoff values and negative co-expression may still be meaningful for some genes, so we developed another tool to extract positively and negatively co-expressed genes with query gene under a user entered PCC cutoff, not limited to the cutoff values used in network construction. All the current rice co-expression analysis tools in Table 2 use the whole microarray dataset to construct the network except ROAD and RiceXPro. Of them, RiceXPro provides two co-expression profiles such as anatomy (spatiotemporal gene expression of various tissues/organs across entire life cycle) and development of leaves (Sato et al.[2011]). The abiotic and biotic co-expression network tools in ROAD will provide useful information for elucidating the relationship of stress related genes. It has been proven to be reasonable to calculate the co-expression for each set of diverse experiment conditions with a clear biological meaning in ATTED-II (Obayashi et al.[2011]), supporting the possible functionality of rice abiotic and biotic co-expression network tools.

Functional analysis using gene ontology or KEGG orthology

Gene Ontologies (GO) provide controlled vocabulary to describe the biological process, molecular function, and component of the cell to which a gene product putatively contributes (Ashburner et al.[2000]; Berardini et al.[2004]; The Gene Ontology Consortium[2008]). KEGG Orthology (KO) consisting of manually defined ortholog groups that correspond to KEGG pathway nodes and BRITE hierarchy nodes, is the basis for the representation of KEGG reference pathway maps and BRITE functional hierarchies (Kanehisa et al.[2010]). GO and KO analyses are useful for identifying biological patterns in a list of genes, microarray datasets, or cDNA collection. For example, GO enrichment analysis has been successfully applied to assessment of rice light-responsive genes (Jung et al.[2008b]). To facilitate GO and KO analyses of query genes, we developed online tools to identify the enriched or depleted GO/KO terms within a query gene list based on a hypergeometric distribution. These tools provide a tabular list of GO/KO terms mapped onto the query genes with detailed information for each term. Next, a hypergeometric p value is calculated for each GO/KO term, whose value is based on comparisons of the observed number from the queried gene list and the expected number from the genome scale.

Conclusions

The Rice Oligonucleotide Array Database is designed to provide a comprehensive gene expression profile for all rice genes. Our current meta-analysis tool focuses on expressions specific to tissue and developmental stages. This analysis will be expanded to include meta-profiles of genes expressed during the rice response to abiotic and biotic stresses and to hormone treatment. New microarray data will be normalized and imported into ROAD semi-automatically on a regular. We anticipate that this database will be useful to researchers of rice and other grass species and that it will accelerate the identification of gene function in monocotyledonous species.

Methods

Microarray data and database construction

As of March 2012, microarray data from 105 rice microarray experiments (1,867 hybridizations) were collected from NCBI GEO (Barrett et al.[2009]), EBI ArrayExpress (Parkinson et al.[2007]) and PLEXdb (Dash et al.[2012]). The raw data was downloaded and experiments without raw data were discarded. For one-channel array (Affymetrix), MAS 5.0 method provided by the R package, affy, for the Affymetrix rice array was used to conduct background correction, normalization, probe specific background correction, probe summarization and convert probe level data to expression values (Affymetrix[2012]). The trimmed mean target intensity of each array was arbitrarily set to 500. The data were then log2 transformed. For two-channel arrays (Agilent 22K and 44K, BGI/Yale, NSF 20K and 45K), R package marray in Bioconductor was used to do the normalization with within-array Lowess and between-array MAD scale normalization methods (Cleveland[1979]; Wang et al.[2002]). The color-swap hybridizations were manually corrected to make them comparable among other samples. In case of Agilent 44K array data used for meta-profiling analyses, we converted the median signal intensities of Cy3 to log2 median intensities and then normalized the log2 intensities using the quantile normalization method (Bolstad et al.[2003]). The sequences of probes were extracted from each platform website and then mapped onto RGAP V6, RAP V3 and KOME cDNAs using NCBI Blast with 100% identity over 100% coverage (Altschul et al.[1990]). Regarding Affymetrixprobeset which have 11 probe pairs, the probeset with at least half perfect-match (PM) probes matched onto cDNA sequence was considered as mapped.

The ROAD database was constructed with PHP (Hypertext Preprocessor) and MySQL, run on a Windows 2003 server. The http address ishttp://www.ricearray.org. Heatmap and classic line plots were generated by the PHP library JpGraph (http://jpgraph.net/).

Co-expression analysis

After MAS normalization of all Affymetrix microarray samples, outliers were detected using the arrayQualityMetricsBioconductor package, which uses three different statistical tests to identify outliers (Gentleman et al.[2004]; Kauffmann et al.[2009]). Seventy-four samples failed at least one test and were considered as outliers and removed from the dataset. As a result, a total of 1,081 Affymetrix samples remained for co-expression analysis. For genes with multiple Affymetrixprobesets matched, the probeset with highest expression profile was used. There are several kinds of methods to evaluate the strength of co-expression, such as Pearson correlation coefficient (PCC), mutual rank (MR) based on rank transformations of the weighted PCC (Obayashi and Kinoshita[2009]) and correspondence analysis (CA) (Yano et al.[2006]). Although PCC takes a long-calculation time and was considered to contain many false-positives (Hamada et al.[2011]; Obayashi et al.[2011]), it has been widely used as an index in the co-expression analysis, such as RiceArrayNet (Lee et al.[2009]), RiceXPro (Sato et al.[2011]) and Gene Co-expression Network Browser (Ficklin et al.[2010]). The success in functional study of plant genes using PCC has also been reported (Fujii et al.[2010]; Matsuura et al.[2010]; Soeno et al.[2010]). Therefore, we adopted PCC to measure tendency of co-expression between genes based on these 1,081 Affymetrix samples. To choose an appropriate PCC cutoff value to construct co-expression network, we examined the changes in the node number, edge number, and network density as a function of PCC cutoff values. As the cutoff value increased, both the node number and edge number decreased; however, as the cutoff reached a relatively high value, the decreasing rate of edges became slower than that of nodes, which might lead to an increase in the network density. Indeed, the network density showed minima around 0.75 (general) and 0.8 (abiotic and biotic stresses) PCC cutoff values and increased thereafter. Therefore, we selected default values of the PCC cutoff as 0.75 and 0.8, for general and abiotic and biotic stresses, respectively. Cytoscape Web, an interactive web-based network browser, was used as the network viewer (Lopes et al.[2010]).

GO and KO enrichment analysis

The GO terms and assignments for rice genes were downloaded from Gramene database (http://www.gramene.org/) (Jaiswal[2011]) and KO from KEGG database (http://www.genome.jp/kegg/) (Kanehisa et al.[2010]). The RAP rice gene locus IDs in KEGG database were converted to RGAP IDs using RAP-DB ID converter tool (http://rapdb.dna.affrc.go.jp/tools/converter) (Tanaka et al.[2008]). Then hypergeometric distribution was used to calculate the p value for GO and KO enrichment analyses.

References

  • Affymetrix (2012). Affymetrix Expression Console Software 1.2 User Manual. Technote.http://media.affymetrix.com/support/downloads/manuals/expression_console_userguide.pdf Technote.

  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol 1990,215(3):403–410.

    Article  CAS  PubMed  Google Scholar 

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000,25(1):25–29. 10.1038/75556

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al.: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 2009,37(Database issue):D885-D890.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Berardini TZ, Mundodi S, Reiser L, Huala E, Garcia-Hernandez M, Zhang PF, et al.: Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiol 2004,135(2):745–755. 10.1104/pp.104.040071

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003,19(2):185–193. 10.1093/bioinformatics/19.2.185

    Article  CAS  PubMed  Google Scholar 

  • Bruce M, Hess A, Bai JF, Mauleon R, Diaz MG, Sugiyama N, et al.: Detection of genomic deletions in rice using oligonucleotide microarrays. BMC Genomics 2009.,10(129): 10.1186/1471-2164-10-129

  • Cleveland W: Robust Locally Weighted Regression and Smoothing Scatterplots. J Am Stat Assoc 1979,74(368):829–836. 10.1080/01621459.1979.10481038

    Article  Google Scholar 

  • Dash S, Van Hemert J, Hong L, Wise RP, Dickerson JA: PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res 2012,40(Database issue):D1194-D1201.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Devos KM, Gale MD: Genome relationships: the grass model in current research. Plant Cell 2000,12(5):637–646.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ficklin SP, Luo F, Feltus FA: The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks. Plant Physiol 2010,154(1):13–24. 10.1104/pp.110.159459

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Fujii S, Yamada M, Fujita M, Itabashi E, Hamada K, Yano K, et al.: Cytoplasmic-nuclear genomic barriers in rice pollen development revealed by comparison of global gene expression profiles among five independent cytoplasmic male sterile lines. Plant Cell Physiol 2010,51(4):610–620. 10.1093/pcp/pcq026

    Article  CAS  PubMed  Google Scholar 

  • Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al.: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004,5(10):R80. 10.1186/gb-2004-5-10-r80

    Article  PubMed Central  PubMed  Google Scholar 

  • Hamada K, Hongo K, Suwabe K, Shimizu A, Nagayama T, Abe R, et al.: OryzaExpress: An Integrated Database of Gene Expression Networks and Omics Annotations in Rice. Plant Cell Physiol 2011,52(2):220–229. 10.1093/pcp/pcq195

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Hruz T, Laule O, Szabo G, Wessendorp F, Bleuler S, Oertle L, et al.: Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv Bioinformatics 2008.,2008(420747): 10.1155/2008/420747

  • Ikeo K, Ishi-i J, Tamura T, Gojobori T, Tateno Y: CIBEX: center for information biology gene expression database. C R Biol 2003,326(10–11):1079–1082.

    Article  CAS  PubMed  Google Scholar 

  • International Rice Genome Sequencing Project: The map-based sequence of the rice genome. Nature 2005,436(7052):793–800. 10.1038/nature03895

    Article  Google Scholar 

  • Itoh T, Tanaka T, Barrero RA, Yamasaki C, Fujii Y, Hilton PB, et al.: Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana. Genome Res 2007,17(2):175–183. 10.1101/gr.5509507

    Article  PubMed Central  PubMed  Google Scholar 

  • Jaiswal P: Gramene database: a hub for comparative plant genomics. Methods Mol Biol 2011, 678: 247–275. 10.1007/978-1-60761-682-5_18

    Article  CAS  PubMed  Google Scholar 

  • Jiao Y, Tausta SL, Gandotra N, Sun N, Liu T, Clay NK, et al.: A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nat Genet 2009,41(2):258–263. 10.1038/ng.282

    Article  CAS  PubMed  Google Scholar 

  • Jung KH, An G, Ronald PC: Towards a better bowl of rice: assigning function to tens of thousands of rice genes. Nat Rev Genet 2008,9(2):91–101.

    CAS  PubMed  Google Scholar 

  • Jung KH, Dardick C, Bartley LE, Cao P, Phetsom J, Canlas P, et al.: Refinement of light-responsive transcript lists using rice oligonucleotide arrays: evaluation of gene-redundancy. PLoS One 2008,3(10):e3337. 10.1371/journal.pone.0003337

    Article  PubMed Central  PubMed  Google Scholar 

  • Jung KH, Bartley LE, Cao PJ, Canlas PE, Ronald PC: Analysis of Alternatively Spliced Rice Transcripts Using Microarray Data. Rice. 2009,2(1):44–55. 10.1007/s12284-008-9020-9

    Article  Google Scholar 

  • Jung KH, Seo YS, Walia H, Cao P, Fukao T, Canlas PE, et al.: The submergence tolerance regulator Sub1A mediates stress-responsive expression of AP2/ERF transcription factors. Plant Physiol 2010,152(3):1674–1692. 10.1104/pp.109.152157

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Jung KH, Jeon JS, An G: Web Tools for Rice Transcriptome Analyses. Journal of Plant Biology. 2011,54(2):65–80. 10.1007/s12374-011-9146-y

    Article  CAS  Google Scholar 

  • Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010,38(Database issue):D355-D360.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kauffmann A, Gentleman R, Huber W: arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 2009,25(3):415–416. 10.1093/bioinformatics/btn647

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kikuchi S, Satoh K, Nagata T, Kawagashira N, Doi K, Kishimoto N, et al.: Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice. Science 2003,301(5631):376–379. 10.1126/science.1081288

    Article  PubMed  Google Scholar 

  • Lee TH, Kim YK, Pham TT, Song SI, Kim JK, Kang KY, et al.: RiceArrayNet: a database for correlating gene expression from transcriptome profiling, and its application to the analysis of coexpressed genes in rice. Plant Physiol 2009,151(1):16–33. 10.1104/pp.109.139030

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics 2010,26(18):2347–2348. 10.1093/bioinformatics/btq430

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Matsuura H, Ishibashi Y, Shinmyo A, Kanaya S, Kato K: Genome-wide analyses of early translational responses to elevated temperature and high salinity in Arabidopsis thaliana. Plant Cell Physiol 2010,51(3):448–462. 10.1093/pcp/pcq010

    Article  CAS  PubMed  Google Scholar 

  • Mutwil M, Klie S, Tohge T, Giorgi FM, Wilkins O, Campbell MM, et al.: PlaNet: combined sequence and expression comparisons across plant networks derived from seven species. Plant Cell 2011,23(3):895–910. 10.1105/tpc.111.083667

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Obayashi T, Kinoshita K: Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression. DNA Res 2009,16(5):249–260. 10.1093/dnares/dsp016

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Obayashi T, Nishida K, Kasahara K, Kinoshita K: ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications to a broad range of flowering plants. Plant Cell Physiol 2011,52(2):213–219. 10.1093/pcp/pcq203

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, et al.: The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res 2007,35(Database issue):D883-D887.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, et al.: ArrayExpress--a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 2007,35(Database issue):D747-D750.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Pylatuik JD, Fobert PR: Comparison of transcript profiling on Arabidopsis microarray platform technologies. Plant Mol Biol 2005,58(5):609–624. 10.1007/s11103-005-6506-3

    Article  CAS  PubMed  Google Scholar 

  • Sato Y, Antonio BA, Namiki N, Takehisa H, Minami H, Kamatsuki K, et al.: RiceXPro: a platform for monitoring gene expression in japonica rice grown under natural field conditions. Nucleic Acids Res 2011,39(Database issue):D1141-D1148.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, et al.: A gene expression map of Arabidopsis thaliana development. Nat Genet 2005,37(5):501–506. 10.1038/ng1543

    Article  CAS  PubMed  Google Scholar 

  • Shimono M, Sugano S, Nakayama A, Jiang CJ, Ono K, Toki S, et al.: Rice WRKY45 plays a crucial role in benzothiadiazole-inducible blast resistance. Plant Cell 2007,19(6):2064–2076. 10.1105/tpc.106.046250

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Soeno K, Goda H, Ishii T, Ogura T, Tachikawa T, Sasaki E, et al.: Auxin biosynthesis inhibitors, identified by a genomics-based approach, provide insights into auxin biosynthesis. Plant Cell Physiol 2010,51(4):524–536. 10.1093/pcp/pcq032

    Article  CAS  PubMed  Google Scholar 

  • Swarbrick PJ, Huang K, Liu G, Slate J, Press MC, Scholes JD: Global patterns of gene expression in rice cultivars undergoing a susceptible or resistant interaction with the parasitic plant Strigahermonthica. New Phytol 2008,179(2):515–529. 10.1111/j.1469-8137.2008.02484.x

    Article  CAS  PubMed  Google Scholar 

  • Tanaka T, Antonio BA, Kikuchi S, Matsumoto T, Nagamura Y, Numa H, et al.: The Rice Annotation Project Database (RAP-DB): 2008 update. Nucleic Acids Res 2008,36(Database issue):D1028-D1033.

    CAS  PubMed  Google Scholar 

  • The Gene Ontology Consortium: The Gene Ontology project in 2008. Nucleic Acids Res 2008,36(Database issue):D440-D444.

    PubMed Central  Google Scholar 

  • Toufighi K, Brady SM, Austin R, Ly E, Provart NJ: The Botany Array Resource: e-Northerns, Expression Angling, and promoter analyses. Plant J 2005,43(1):153–163. 10.1111/j.1365-313X.2005.02437.x

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Nygaard V, Smith-Sorensen B, Hovig E, Myklebost O: MArray: analysing single, replicated or reversed microarray experiments. Bioinformatics 2002,18(8):1139–1140. 10.1093/bioinformatics/18.8.1139

    Article  CAS  PubMed  Google Scholar 

  • Wang L, Xie W, Chen Y, Tang W, Yang J, Ye R, et al.: A dynamic gene expression atlas covering the entire life cycle of rice. Plant J 2010,61(5):752–766. 10.1111/j.1365-313X.2009.04100.x

    Article  CAS  PubMed  Google Scholar 

  • Yano K, Imai K, Shimizu A, Hanashita T: A new method for gene discovery in large-scale microarray data. Nucleic Acids Res 2006,34(5):1532–1539. 10.1093/nar/gkl058

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Yuan Q, Ouyang S, Wang A, Zhu W, Maiti R, Lin H, et al.: The Institute for Genomic Research Osa1 Rice Genome Annotation Database. Plant Physiol 2005,138(1):18–26. 10.1104/pp.104.059063

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This research was supported in part by National Basic Research Program of China (2011CB109306), Next-Generation BioGreen 21 Program (PJ008079 and PJ008173) and BioGreen 21 Program (20080401) of Korea. This work was also supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jun Zhu or Pamela C Ronald.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PC and KJ did the microarray data analysis and developed ROAD. DC and DH wrote script for microarray data normalization. JZ and PCR proposed the project idea, evaluated the database, pointing out errors and improvements. All authors read and approved the final manuscript.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Electronic supplementary material

Additional file 1: Table S1. Detailed information of rice microarray experiments available in ROAD. (XLS 204 KB)

12284_2012_27_MOESM2_ESM.jpeg

Additional file 2: Figure S1. Screenshots of meta-analysis in ROAD queried with 19 root-preferential genes for anatomy (a) and developmental stages (b) of Affymetrix array platform, and anatomy (c) and developmental stages (d, e) of Agilent 44K array platform. (JPEG 5 MB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Cao, P., Jung, KH., Choi, D. et al. The Rice Oligonucleotide Array Database: an atlas of rice gene expression. Rice 5, 17 (2012). https://doi.org/10.1186/1939-8433-5-17

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1939-8433-5-17

Keywords