A computational/functional genomics approach for the enrichment of the retinal transcriptome and the identification of positional candidate retinopathy genes.

Grouping genes by virtue of their sequence similarity, functional association, or spatiotemporal distribution is an important first step in investigating function. Given the recent identification of >30,000 human genes either by analyses of genomic sequence or by derivation/assembly of ESTs, automated means of discerning gene function and association with disease are critical for the efficient processing of this large volume of data. We have designed a series of computational tools to manipulate the EST sequence database (dbEST) to predict EST clusters likely representing genes expressed exclusively or preferentially in a specific tissue. We implemented this tool by extracting 40,000 human retinal ESTs and performing in silico subtraction against 1.4 million human ESTs. This process yielded 925 ESTs likely to be specifically or preferentially expressed in the retina. We mapped all retinal-specific/predominant sequences in the human genome and produced a web-based searchable map of the retina transcriptome, onto which we overlaid the positions of all mapped but uncloned retinopathy genes. This resource has provided positional candidates for 42 of 51 uncloned retinopathies and may expedite substantially the identification of disease-associated genes. More importantly, the ability to systematically group ESTs according to their predicted expression profile is likely to be an important resource for studying gene function in a wide range of tissues and physiological systems and to identify positional candidate genes for human disorders whose phenotypic manifestations are restricted to specific tissues/organs/cell types.