- Open Access
RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina
BMC Genomicsvolume 9, Article number: 208 (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
The RETINOBASE home page presents a list of all experiments available to the user and also provides access to experimental details such as title, short description etc. The "Sample details" option (Figure 1) gives details about sample description, organism, tissue, treatment, strain specific information and the array used for hybridisation for a given experiment.
Querying the database
RETINOBASE has three different querying modules: "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis".
Gene information module
The "Gene Information" module offers three different query options – "Gene Query", "Ortholog Query" and "Blast Query". Using these, one can access information such as chromosomal location, linked retinal diseases, cellular localization, and gene ontologies for a given gene. Furthermore, gene details returned from these queries are linked to external databases such as GeneCards , NCBI , specifically to UniGene , ADAPT mapping viewer  and also to UCSC genome browser  that would yield more information (Figure 2).
"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
This module has "Data and Cluster Query" options and "Data visualization" which is both a query and visualization option. "Data Query" (Figure 3) provides gene expression information at the level of signal intensities for single or multiple genes in all experiments. "Cluster Query" (Figure 3) – unique to RETINOBASE, provides information about expression patterns of related genes across various conditions and genetic backgrounds. It also identifies any two given genes in the same cluster in one or more experiments. Apart from the above mentioned query options, RETINOBASE also provides a user-friendly transcriptomic data visualization tool that was developed to allow retinal biologists to graphically analyse gene expression profiles across all the experiments. A user can choose the experiment, chip, gene and analysis software to be used in a step-by-step process, following which the related samples can be labelled and organized for an easy comparison through histograms or radar-graph representations (Figure 4). This step-by-step process effectively increases querying speed, which in turn allows faster retrieval of specific data from large volumes of gene expression information. Additional information concerning the number of Probe Sets for a gene on a given chip, the normalization software used to obtain the signal intensities and the quality control report of the experiment are also provided.
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
In order to demonstrate the utility of RETINOBASE, we undertook a case study to identify novel genes that may have a potential role in retinal function. In the experiment "Targeting of GFP to newborn rods by Nrl promoter and temporal expression profiling of flow-sorted photoreceptors" (experiment 7 in RETINOBASE) it was elegantly demonstrated that Nrl (neural retina leucine zipper) is a key regulator of photoreceptor differentiation in mammals . We first performed cluster analysis using the "Signal intensity or Cluster query" tool in RETINOBASE by providing Nrl as the gene symbol and then retrieved the resulting clusters. In agreement with the original study by Akimoto et al.,, our "cluster query" found Rho (rhodopsin), Nr2e3 (nuclear receptor subfamily 2, group E, member 3) and Pde6b (phosphodiesterase 6B, cGMP-specific, rod, beta) in the same cluster as Nrl in 4 out of 5 possible combinations (1. RMA normalized data and K-means clustering with TMEV, 2. RMA normalized data, K-means clustering with FASABI, 3. dChip normalized data, K-means clustering with TMEV, 4. dChip normalized data, K-means clustering with FASABI and 5. dChip normalized data, clustering with mixture model), confirming that genes specific for rods are coregulated with Nrl. In addition, Gnat1 (guanine nucleotide binding protein (G protein), alpha transducing activity polypeptide 1), a gene implicated in congenital stationary night blindness , was also found in the same cluster in all 5 cluster combinations mentioned above, confirming its role in retinal function. This suggests that Gnat1 is also coregulated with Nrl in retina. Based on the similar coexpression profiles in wild type mouse retina at time points corresponding to embryonic day 16, post natal day 2, 6, 10 and 28 (Figure 5), we further identified a novel gene that is likely to be implicated in regulating retinal differentiation, namely D6Wsu176e (DNA segment, Chr 6, Wayne State University 176, expressed), described as being expressed in the outer nuclear layer of neural retina . The RETINOBASE "Ortholog query" for D6Wsu176e points to the human ortholog, FAM3C, that is involved in cell differentiation and proliferation during inner ear embryogenesis . With its known function in cell differentiation and its presence in the same cluster as Nrl in 3 out of 5 of the above mentioned clustering combinations, D6Wsu176e may be an interesting candidate for studying rod differentiation. We further went on to check whether Nrl, Rho, Gnat1 and D6Wsu176e genes are coexpressed (present in the same cluster) in other experiments present in RETINOBASE, in particular checking experiment 12 (Gene expression patterns in the retina of rds mice treated with CNTF/rAAV virus and non-treated after 60 days of injection) (GEO: GSE4756) and experiment 14 (Biological characterization of gene response in Rpe65-/- mouse model of Leber's congenital amaurosis during progression of the disease) . In these two experiments the four genes mentioned above were present in the same cluster indicating that they might be coregulated. This case study illustrates how RETINOBASE facilitates hypothesis testing for the biologist, and demonstrates how to generate novel hypotheses regarding retinal function and finally, how to identify potential novel targets for human retinopathies.
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.
Masland RH: The fundamental plan of the retina. Nat Neurosci. 2001, 4 (9): 877-886. 10.1038/nn0901-877.
Pittler SJ, Baehr W: Identification of a nonsense mutation in the rod photoreceptor cGMP phosphodiesterase beta-subunit gene of the rd mouse. Proc Natl Acad Sci USA. 1991, 88 (19): 8322-8326. 10.1073/pnas.88.19.8322.
Akhmedov NB, Piriev NI, Chang B, Rapoport AL, Hawes NL, Nishina PM, Nusinowitz S, Heckenlively JR, Roderick TH, Kozak CA: A deletion in a photoreceptor-specific nuclear receptor mRNA causes retinal degeneration in the rd7 mouse. Proc Natl Acad Sci USA. 2000, 97 (10): 5551-5556. 10.1073/pnas.97.10.5551.
Pang JJ, Chang B, Hawes NL, Hurd RE, Davisson MT, Li J, Noorwez SM, Malhotra R, McDowell JH, Kaushal S: Retinal degeneration 12 (rd12): a new, spontaneously arising mouse model for human Leber congenital amaurosis (LCA). Mol Vis. 2005, 11: 152-162.
Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R: NCBI GEO: mining tens of millions of expression profiles – database and tools update. Nucleic Acids Res. 2007, D760-765. 10.1093/nar/gkl887. 35 Database
Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, Holloway E, Kolesnykov N, Lilja P, Lukk M: ArrayExpress – a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 2007, D747-750. 10.1093/nar/gkl995. 35 Database
Kato K, Yamashita R, Matoba R, Monden M, Noguchi S, Takagi T, Nakai K: Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues. Nucleic Acids Res. 2005, D533-536. 33 Database
Shah V, Sridhar S, Beane J, Brody JS, Spira A: SIEGE: Smoking Induced Epithelial Gene Expression Database. Nucleic Acids Res. 2005, D573-579. 33 Database
Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci USA. 2002, 99 (7): 4465-4470. 10.1073/pnas.012025199.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4 (2): 249-264. 10.1093/biostatistics/4.2.249.
Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA. 2001, 98 (1): 31-36. 10.1073/pnas.011404098.
Hubbell E, Liu WM, Mei R: Robust estimators for expression analysis. Bioinformatics. 2002, 18 (12): 1585-1592. 10.1093/bioinformatics/18.12.1585.
Hartigan JAWM: A K-Means Clustering Algorithm. Applied Statistics. 1979, 28 (1): 100-108. 10.2307/2346830.
Medvedovic M, Sivaganesan S: Bayesian infinite mixture model based clustering of gene expression profiles. Bioinformatics. 2002, 18 (9): 1194-1206. 10.1093/bioinformatics/18.9.1194.
Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg A, Peterson C: BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol. 2002, 3 (8): SOFTWARE0003-10.1186/gb-2002-3-8-software0003.
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001, 29 (4): 365-371. 10.1038/ng1201-365.
Akimoto M, Cheng H, Zhu D, Brzezinski JA, Khanna R, Filippova E, Oh EC, Jing Y, Linares JL, Brooks M: Targeting of GFP to newborn rods by Nrl promoter and temporal expression profiling of flow-sorted photoreceptors. Proc Natl Acad Sci USA. 2006, 103 (10): 3890-3895. 10.1073/pnas.0508214103.
Yoshida S, Mears AJ, Friedman JS, Carter T, He S, Oh E, Jing Y, Farjo R, Fleury G, Barlow C: Expression profiling of the developing and mature Nrl-/- mouse retina: identification of retinal disease candidates and transcriptional regulatory targets of Nrl. Hum Mol Genet. 2004, 13 (14): 1487-1503. 10.1093/hmg/ddh160.
Chen J, Rattner A, Nathans J: The rod photoreceptor-specific nuclear receptor Nr2e3 represses transcription of multiple cone-specific genes. J Neurosci. 2005, 25 (1): 118-129. 10.1523/JNEUROSCI.3571-04.2005.
Liu J, Huang Q, Higdon J, Liu W, Xie T, Yamashita T, Cheon K, Cheng C, Zuo J: Distinct gene expression profiles and reduced JNK signaling in retinitis pigmentosa caused by RP1 mutations. Hum Mol Genet. 2005, 14 (19): 2945-2958. 10.1093/hmg/ddi325.
Cottet S, Michaut L, Boisset G, Schlecht U, Gehring W, Schorderet DF: Biological characterization of gene response in Rpe65-/- mouse model of Leber's congenital amaurosis during progression of the disease. Faseb J. 2006, 20 (12): 2036-2049. 10.1096/fj.06-6211com.
Vazquez-Chona F, Song BK, Geisert EE: Temporal changes in gene expression after injury in the rat retina. Invest Ophthalmol Vis Sci. 2004, 45 (8): 2737-2746. 10.1167/iovs.03-1047.
Cheng H, Aleman TS, Cideciyan AV, Khanna R, Jacobson SG, Swaroop A: In vivo function of the orphan nuclear receptor NR2E3 in establishing photoreceptor identity during mammalian retinal development. Hum Mol Genet. 2006, 15 (17): 2588-2602. 10.1093/hmg/ddl185.
Gerhardinger C, Costa MB, Coulombe MC, Toth I, Hoehn T, Grosu P: Expression of acute-phase response proteins in retinal Muller cells in diabetes. Invest Ophthalmol Vis Sci. 2005, 46 (1): 349-357. 10.1167/iovs.04-0860.
Steele MR, Inman DM, Calkins DJ, Horner PJ, Vetter ML: Microarray analysis of retinal gene expression in the DBA/2J model of glaucoma. Invest Ophthalmol Vis Sci. 2006, 47 (3): 977-985. 10.1167/iovs.05-0865.
Cameron DA, Gentile KL, Middleton FA, Yurco P: Gene expression profiles of intact and regenerating zebrafish retina. Mol Vis. 2005, 11: 775-791.
Abou-Sleymane G, Chalmel F, Helmlinger D, Lardenois A, Thibault C, Weber C, Merienne K, Mandel JL, Poch O, Devys D: Polyglutamine expansion causes neurodegeneration by altering the neuronal differentiation program. Hum Mol Genet. 2006, 15 (5): 691-703. 10.1093/hmg/ddi483.
Kirwan RP, Leonard MO, Murphy M, Clark AF, O'Brien CJ: Transforming growth factor-beta-regulated gene transcription and protein expression in human GFAP-negative lamina cribrosa cells. Glia. 2005, 52 (4): 309-324. 10.1002/glia.20247.
Zhang J, Gray J, Wu L, Leone G, Rowan S, Cepko CL, Zhu X, Craft CM, Dyer MA: Rb regulates proliferation and rod photoreceptor development in the mouse retina. Nat Genet. 2004, 36 (4): 351-360. 10.1038/ng1318.
Leung YF, Ma P, Dowling JE: Gene expression profiling of zebrafish embryonic retinal pigment epithelium in vivo. Invest Ophthalmol Vis Sci. 2007, 48 (2): 881-890. 10.1167/iovs.06-0723.
Carter TA, Greenhall JA, Yoshida S, Fuchs S, Helton R, Swaroop A, Lockhart DJ, Barlow C: Mechanisms of aging in senescence-accelerated mice. Genome Biol. 2005, 6 (6): R48-10.1186/gb-2005-6-6-r48.
Michaut L, Flister S, Neeb M, White KP, Certa U, Gehring WJ: Analysis of the eye developmental pathway in Drosophila using DNA microarrays. Proc Natl Acad Sci USA. 2003, 100 (7): 4024-4029. 10.1073/pnas.0630561100.
Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene expression. Science. 1995, 270 (5235): 484-487. 10.1126/science.270.5235.484.
Diehn JJ, Diehn M, Marmor MF, Brown PO: Differential gene expression in anatomical compartments of the human eye. Genome Biol. 2005, 6 (9): R74-10.1186/gb-2005-6-9-r74.
Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H, Kuo WP, Weber G, Lee K, Fraioli RE, Cho SH: Genomic analysis of mouse retinal development. PLoS Biol. 2004, 2 (9): E247-10.1371/journal.pbio.0020247.
The Affymetrix website. [http://www.affymetrix.com/]
The Retinal information network. [http://www.sph.uth.tmc.edu/Retnet/]
The R statistical package. [http://www.r-project.org]
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.
Wicker N, Dembele D, Raffelsberger W, Poch O: Density of points clustering, application to transcriptomic data analysis. Nucleic Acids Res. 2002, 30 (18): 3992-4000. 10.1093/nar/gkf511.
Soukas A, Cohen P, Socci ND, Friedman JM: Leptin-specific patterns of gene expression in white adipose tissue. Genes Dev. 2000, 14 (8): 963-980.
Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, Howe EA, Li J, Thiagarajan M, White JA, Quackenbush J: TM4 microarray software suite. Methods Enzymol. 2006, 411: 134-193. 10.1016/S0076-6879(06)11009-5.
Datta S, Datta S: Evaluation of clustering algorithms for gene expression data. BMC Bioinformatics. 2006, 7 (Suppl 4): S17-10.1186/1471-2105-7-S4-S17.
Yeung KY, Haynor DR, Ruzzo WL: Validating clustering for gene expression data. Bioinformatics. 2001, 17 (4): 309-318. 10.1093/bioinformatics/17.4.309.
Sharan R, Maron-Katz A, Shamir R: CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics. 2003, 19 (14): 1787-1799. 10.1093/bioinformatics/btg232.
Kohonen T: Self-Organizing Maps. 2001, Springer-Verlag Berlin Hiedelberg New York, 3
Wilson CL, Miller CJ: Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics. 2005, 21 (18): 3683-3685. 10.1093/bioinformatics/bti605.
Raffelsberger W, Krause Y, Moulinier L, Kieffer D, Morand AL, Brino L, Poch O: RReportGenerator: Automatic reports from routine statistical analysis using R. Bioinformatics. 2007
The GeneCards. [http://www.genecards.org]
The NCBI. [http://www.ncbi.nlm.nih.gov]
The UniGene. [http://www.ncbi.nlm.nih.gov/sites/entrez?db=unigene]
Leong HS, Yates T, Wilson C, Miller CJ: ADAPT: a database of affymetrix probesets and transcripts. Bioinformatics. 2005, 21 (10): 2552-2553. 10.1093/bioinformatics/bti359.
The UCSC genome browser. [http://genome.ucsc.edu/cgi-bin/hgGateway]
Szabo V, Kreienkamp HJ, Rosenberg T, Gal A: p.Gln200Glu, a putative constitutively active mutant of rod alpha-transducin (GNAT1) in autosomal dominant congenital stationary night blindness. Hum Mutat. 2007, 28 (7): 741-742. 10.1002/humu.9499.
Blackshaw S, Fraioli RE, Furukawa T, Cepko CL: Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes. Cell. 2001, 107 (5): 579-589. 10.1016/S0092-8674(01)00574-8.
Pilipenko VV, Reece A, Choo DI, Greinwald JH: Genomic organization and expression analysis of the murine Fam3c gene. Gene. 2004, 335: 159-168. 10.1016/j.gene.2004.03.026.
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.
RK is involved in database design and development, data analysis, design of the user interface and prepared the manuscript. NG, GB and RR developed the web services and database back end. LP is involved in testing various querying tools. WR is involved in data analysis and helped to draft the manuscript. TL participated in the design of the user interface. OP was involved in overall design of the project and in drafting the manuscript.