RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina.
BACKGROUND
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.


METHODS
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.


CONCLUSIONS
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.
Kalathur
Ravi Kiran Reddy
RK
Laboratoire de Bioiformatique et de Genomique Integratives, Institut de Génétique et de Biologie Moléculaire et Céllulaire, CNRS/INSERM/ULP, BP 163, 67404 Illkirch Cedex, France. ravi@igbmc.u-strasbg.fr
Gagniere
Nicolas
N
Berthommier
Guillaume
G
Poidevin
Laetitia
L
Raffelsberger
Wolfgang
W
Ripp
Raymond
R
Léveillard
Thierry
T
Poch
Olivier
O
D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D016247:Information Storage and Retrieval; D020407:Internet; D012160:Retina