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Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites

Vinsensius B Vega1,2*†, Chin-Yo Lin3,4,1†, Koon S Lai1, Say Li Kong3,1, Min Xie3,1, Xiaodi Su5, Huey F Teh5, Jane S Thomsen1, Ai Li Yeo3,1, Wing K Sung2, Guillaume Bourque2 and Edison T Liu1*

Author Affiliations

1 Estrogen Receptor Biology Program, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672

2 Information and Mathematical Sciences Group, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672

3 Microarray and Expression Genomics Laboratory, Genome Institute of Singapore, 60 Biopolis Street, Republic of Singapore 138672

4 Department of Microbiology and Molecular Biology, Brigham Young University, 753 WIDB, Provo, UT 84602, USA

5 Institute of Materials Research and Engineering, 3, Research Link, Republic of Singapore 117602

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Genome Biology 2006, 7:R82 doi:10.1186/gb-2006-7-9-r82

Published: 9 September 2006

Abstract

Background

Transcription factor binding sites (TFBS) impart specificity to cellular transcriptional responses and have largely been defined by consensus motifs derived from a handful of validated sites. The low specificity of the computational predictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requirements for binding. We posited that the inadequacy is due to limited input of empirically verified sites, and demonstrated a multiplatform approach to constructing a robust model.

Results

Using the TFBS for the estrogen receptor (ER)α (estrogen response element [ERE]) as a model system, we extracted EREs from multiple molecular and genomic platforms whose binding to ERα has been experimentally confirmed or rejected. In silico analyses revealed significant sequence information flanking the standard binding consensus, discriminating ERE-like sequences that bind ERα from those that are nonbinders. We extended the ERE consensus by three bases, bearing a terminal G at the third position 3' and an initiator C at the third position 5', which were further validated using surface plasmon resonance spectroscopy. Our functional human ERE prediction algorithm (h-ERE) outperformed existing predictive algorithms and produced fewer than 5% false negatives upon experimental validation.

Conclusion

Building upon a larger experimentally validated ERE set, the h-ERE algorithm is able to demarcate better the universe of ERE-like sequences that are potential ER binders. Only 14% of the predicted optimal binding sites were utilized under the experimental conditions employed, pointing to other selective criteria not related to EREs. Other factors, in addition to primary nucleotide sequence, will ultimately determine binding site selection.