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Identifying estrogen receptor alpha target genes using integrated computational genomics and chromatin
Victor X Jin1, Yu-Wei Leu, Sandya Liyanarachchi
1Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA.
Nucleic Acids Research
|December 21, 2004
Summary
Researchers identified direct and indirect estrogen receptor alpha (ERalpha) gene targets using ChIP-on-chip and computational analysis. This method accurately predicts ERalpha promoter sequences, advancing our understanding of gene regulation.
Area of Science:
- Molecular Biology
- Genomics
- Cancer Research
Background:
- Estrogen receptor alpha (ERalpha) is a key regulator of gene expression.
- ERalpha influences gene transcription through direct DNA binding or protein interactions.
- Identifying ERalpha target genes is crucial for understanding its role in various biological processes, including cancer.
Purpose of the Study:
- To identify genome-wide promoter sequences targeted by ERalpha.
- To differentiate between direct and indirect ERalpha transcriptional targets.
- To develop and validate a computational model for predicting ERalpha target promoters.
Main Methods:
- Genome-wide screening using ChIP-on-chip to identify ERalpha binding sites.
- Statistical pattern recognition and comparative genomics for promoter sequence analysis.
- Classification and Regression Tree (CART) model utilizing position weight matrices and sequence similarity for target classification.
- Experimental validation using ChIP-on-chip to detect active ERalpha promoters and chromatin modifications.
Main Results:
- 70 candidate ERalpha loci were identified.
- 63 loci had mouse counterparts, with 42 (67%) classified as direct ERalpha targets by the CART model.
- The CART model accurately predicted 20 out of 27 upregulated ERalpha targets in a breast cancer cell line.
- ChIP-on-chip analysis confirmed active ERalpha promoters and associated acetylated chromatin components.
Conclusions:
- An integrated approach combining computational prediction and experimental validation effectively identifies ERalpha target promoters.
- The developed CART model demonstrates high accuracy in predicting direct ERalpha targets.
- This iterative strategy of model refinement and verification provides a robust method for accurate promoter target prediction.