Computational method for discovery of estrogen responsive genes

• Published in: Nucleic acids research Vol. 32; no. 21; pp. 6212 - 6217
• Main Authors: Tang, Suisheng, Tan, Sin Lam, Ramadoss, Suresh Kumar, Kumar, Arun Prashanth, Tang, Man-Hung Eric, Bajic, Vladimir B.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2004; Oxford Publishing Limited (England)
• Subjects: Computational Biology - methods; Estrogens - pharmacology; Gene Expression Profiling; Gene Expression Regulation; Genome, Human; Genomics - methods; Humans; Promoter Regions, Genetic; Response Elements
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkh943

Estrogen has a profound impact on human physiology and affects numerous genes. The classical estrogen reaction is mediated by its receptors (ERs), which bind to the estrogen response elements (EREs) in target gene's promoter region. Due to tedious and expensive experiments, a limited number of human genes are functionally well characterized. It is still unclear how many and which human genes respond to estrogen treatment. We propose a simple, economic, yet effective computational method to predict a subclass of estrogen responsive genes. Our method relies on the similarity of ERE frames across different promoters in the human genome. Matching ERE frames of a test set of 60 known estrogen responsive genes to the collection of over 18 000 human promoters, we obtained 604 candidate genes. Evaluating our result by comparison with the published microarray data and literature, we found that more than half (53.6%, 324/604) of predicted candidate genes are responsive to estrogen. We believe this method can significantly reduce the number of testing potential estrogen target genes and provide functional clues for annotating part of genes that lack functional information.