Generation of a foveomacular transcriptome

• Type of Material: Article
• Authors: Alison Ziesel, Emory University; Steven Bernstein, University of Maryland; Paul Wong, Emory University
• Language: English
• Date: 2014
• Publisher: Molecular Vision
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Molecular Vision.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://www.molvis.org/molvis/v20/947/
• Title of Journal or Parent Work: Molecular Vision
• ISSN: 1090-0535
• Volume: 20
• Start Page: 947
• End Page: 955
• Grant/Funding Information: This study was also funded via grants from the V. Kann Rasmussen Foundation and NEI R01EY015304 and R01EY019529 to SLB.; This work was supported in part with funding from RPB, NIH (NEI) P30EY006360, FFB (PW), the Knights Templar of Georgia (PW) and NSERC (PW and ACZ)

Abstract

Abstract Purpose: Organizing molecular biologic data is a growing challenge since the rate of data accumulation is steadily increasing. Information relevant to a particular biologic query can be difficult to extract from the comprehensive databases currently available. We present a data collection and organization model designed to ameliorate these problems and applied it to generate an expressed sequence tag (EST)–based foveomacular transcriptome. Methods: Using Perl, MySQL, EST libraries, screening, and human foveomacular gene expression as a model system, we generated a foveomacular transcriptome database enriched for molecularly relevant data. Results: Using foveomacula as a gene expression model tissue, we identified and organized 6,056 genes expressed in that tissue. Of those identified genes, 3,480 had not been previously described as expressed in the foveomacula. Internal experimental controls as well as comparison of our data set to published data sets suggest we do not yet have a complete description of the foveomacula transcriptome. Conclusions: We present an organizational method designed to amplify the utility of data pertinent to a specific research interest. Our method is generic enough to be applicable to a variety of conditions yet focused enough to allow for specialized study.