RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina
© Kalathur et al; licensee BioMed Central Ltd. 2008
Received: 30 October 2007
Accepted: 05 May 2008
Published: 05 May 2008
The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina.
RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated.
The results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.
The retina is a thin and highly structured layer of neuronal cells that lines the back of eye. Its main function is to convert light energy into an interpretable signal for cortical cells in the brain. The retina has two components – an inner neurosensory retina and an outer retinal pigment epithelium (RPE), which together form the structural and functional basis for visual perception.
The retina consists of several cell types, of which neural cells predominate. Photoreceptors, bipolar and ganglion cells are three principal neuron cell types whose activity is modulated by other groups of cells, such as horizontal and amacrine cells . Defects in any of the above-mentioned cell types can lead to a variety of retinal diseases, including age-related macular degeneration (AMD), retinitis pigmentosa (RP), Leber congenital amaurosis (LCA) and glaucoma. These diseases may cause partial visual loss or complete blindness, depending on the severity.
The recent progress in genomic approaches has now led to an increase in the number of transgenic and knockout animal models that can be used to investigate the role of specific genes in retinal function and related disorders in humans, e.g., rd1 is a mouse model for RP , Nr2e3 for the Human Enhanced S-cone syndrome (ESCS) , Rds for macular dystrophy and RPE65-/- for LCA . Experimental information from the above mentioned models, combined with high-throughput technologies, has led to an increase in the number of experiments related to retinal gene expression.
The recent development of high-throughput technologies has resulted in an enormous volume of gene expression data. General repositories such as GEO  and ArrayExpress  operate as central data distribution centres encompassing gene expression data from different organisms and from various conditions. In contrast, resources like CGED , SIEGE  and GeneAtlas  are specialized databases that address specific problems; CGED concentrates on gene expression in various human cancer tissues, SIEGE focuses on epithelial gene expression changes induced by smoking in humans and Gene Atlas provides the expression profiles of genes in various mouse and human tissues.
In order to address specific issues related to retina and to meet the needs of retinal biologists in their analysis of gene expression data, we have developed RETINOBASE, a microarray gene expression database for retina. RETINOBASE combines simplified querying, analysis and data visualization options, plus specifically developed meta analysis tools. The integration of gene expression data from various development stages of wild type retina and from diverse conditions and genetic backgrounds will hopefully, not only increase our understanding of the physiological mechanisms involved in normal retinal tissue, but also facilitate studies of gene expression patterns under diverse conditions. Furthermore, RETINOBASE provides a platform for the comparison of different analysis scenarios based on various normalization methods, such as RMA , dChip , MAS5 , and clustering methods, such as the K-means  and mixture models methods .
Construction and content
RETINOBASE is designed to store gene expression profiles from microarray experiments. We downloaded all publicly available retina-related expression profiles from Gene Expression Omnibus (GEO) yielding 21 experiments [17–32], GEO datasets (GSE 1816, 4756, 1835, 3791, 2868). In addition, 8 proprietary experiments have been incorporated that can be accessed with permission from the owner of the experiment. These experiments were performed under different conditions, including knockout models, treatments and time series experiments performed on different organisms such as drosophila, zebra fish, rat, mice and human. All experiments have complete data, except for one experiment  that has partial data at the level of fold change, due to the unavailability of raw data (.CEL) or signal intensity data. Currently, RETINOBASE contains approximately 27 million gene expression values resulting from 509 hybridizations. In future releases of the database, we plan to include data from other studies associated with retina, including the SAGE , datasets from Diehn and coworkers  who used cDNA array to study human eye tissues, and/or datasets from Blackshaw and coworkers  who used SAGE to study mouse retinal development.
In RETINOBASE, the gene annotation information obtained from Affymetrix  is linked to information about genes and loci causing inherited retinal diseases, obtained from the Retinal information network (RETNET) . RETINOBASE also provides information obtained from literature about expression of approximately 200 retinal genes specific to certain types of cell, such as photoreceptors, Muller cells or retinal sphere cells.
Raw data was obtained in two different formats, either as .CEL files (20 experiments) or at the level of signal intensities (8 experiments). Data obtained at the level of .CEL files are first analysed with three different normalization programs – RMA , dChip  and MAS5  and then processed using the R statistical package  and Bioconductor ; after preprocessing, the resulting background-corrected and normalized signal intensities are automatically uploaded to RETINOBASE using SQL scripts via pgAdminIII.
Identification of control samples in an experiment facilitated incorporation of data at the level of fold change in RETINOBASE. The fold-changes in gene expression were calculated as the ratio between the signal intensities of a given gene in the treated (or knockout) model and the control. In the case of experiments performed in replicate, signal intensities were averaged before calculation of the ratios. All the experiments in RETINOBASE were clustered using 3 independent methods: (i) the density of points clustering (DPC) method  which is implemented in the in-house FASABI (Functional And Statistical Analysis of Biological Data) software, (ii) the dot product K-means method  used in TM4 Multiexperiment Viewer (MeV) a free, open-source system for microarray data management and analysis , (iii) the mixture model method implemented in FASABI. Although cluster analyses often provide useful insights into the data, biological interpretation of the results is recommended, since alternative algorithms generally produce different cluster outputs and no single clustering algorithm is best suited for clustering genes into functional groups for all data sets . We chose the DPC, K-means and mixture models methods because of their robustness in clustering large datasets. Although the K-means method generally requires the user to choose the number of clusters to be calculated, the TMEV system uses figure of merit (FOM) graphs  to make an appropriate suggestion. Other clustering algorithms, such as a graph-theoretic approach , and a neural network based method SOM , as well as different parameter options, will be incorporated in future releases of the database. Storing both the normalized and analyzed data in our relational model allows flexible comparisons across different chips at the level of individual genes.
Quality control reports are generated using affyQCReport – an R package that generates quality control reports for Affymetrix array data  and RReportGenerator  for all experiments, where .CEL files are available. In addition, we also calculate a coefficient of variation for individual Probe Sets between the replicates, which provides a direct estimate of the quality between replicates.
Experiment and sample details
Querying the database
RETINOBASE has three different querying modules: "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis".
Gene information module
"Gene Query" and "Ortholog Query" accept as input the gene name, symbol, Affymetrix Probe Set ID, Refseq or Unigene IDs, whereas "Blast Query" accepts sequences in FASTA format. "Ortholog Query" is useful in cross-referencing probe sets between different Affymetrix GeneChip arrays. The data based on reference sequence similarity is taken from HomoloGene and cross-referenced. In addition, the raw data and cluster information for a given gene (cluster number, software used for clustering and information about other genes present in the same cluster) for all experiments can be obtained through the "Gene Query" (Figure 2).
Raw data system analysis module
Fold change system analysis module
Gene expression information at the level of fold change is provided for single or multiple genes in one or more experiments. In addition, "Ratio Query" supports a specialized query that permits retrieval of all genes from one or more experiments having a fold change greater and/or less than a given criteria.
Downloading results and user manual
In order to allow users to further compare and interpret data, the results from all querying modules available in RETINOBASE can be downloaded in the comma separated value (.CSV) file format using the "Download results" option.
A user manual is also available on the home page of RETINOBASE and it would provide a detailed description of the utilities.
Case study: Use of meta-analysis tools in RETINOBASE
RETINOBASE is under constant development, including addition of new experiments when available. In addition, data from proprietary experiments can be accessed on approval by individual researchers and will be made generally available after publication. Several functional enhancements are also planned for the future. We will continue to refine and update RETINOBASE with respect to data retrieval, mining and visualization options. Direct upload and meta-analysis options will also be provided.
RETINOBASE has been developed to store, analyse, visualize and compare retinal-related data in order to provide insights into retinal gene expression in various mouse models and other organisms under diverse conditions. Our database, with different types of query options and powerful visualization tools, allows comprehensive analysis of biological mechanisms/pathways of the retina in normal and diseased conditions. We demonstrated by means of a case study how novel genes such as D6Wsu176e (which potentially play an important role in retinal differentiation and development) can be identified using the meta analysis tools incorporated in RETINOBASE. With the addition of new experiments the variety of hypothesis testing options will continuously increase, providing biologists with a valuable tool to gain a better understanding of the retina.
Availability and requirements
The RETINOBASE can be accessed at . All users must register (name and email address) to obtain a username and password.
We would like to thank Naomi Berdugo for valuable suggestions, as well as "beta tester" users of the RETINOBASE, for their valuable suggestions. We thank Julie Thompson for proofreading the manuscript. This work was supported by the European Retinal Research Training Network (RETNET) MRTN-CT-2003-504003, EVI-GENORET LSHG-CT-2005-512036, CNRS, INSERM and University of Louis Pasteur (ULP), Strasbourg, France.
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