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Related Experiment Videos

Computational method for discovery of estrogen responsive genes.

Suisheng Tang1, Sin Lam Tan, Suresh Kumar Ramadoss

  • 1Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613.

Nucleic Acids Research
|December 4, 2004
PubMed
Summary

This study introduces a cost-effective computational method to identify estrogen-responsive genes. The approach successfully predicted hundreds of potential estrogen target genes, significantly aiding in understanding gene function.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Estrogen significantly influences human physiology by regulating numerous genes.
  • Classical estrogen signaling involves estrogen receptors (ERs) binding to estrogen response elements (EREs) in gene promoters.
  • Identifying all estrogen-responsive genes is challenging due to experimental limitations.

Purpose of the Study:

  • To develop an economical and effective computational method for predicting estrogen-responsive genes.
  • To identify a subclass of human genes that respond to estrogen treatment.
  • To provide functional insights for genes lacking detailed annotation.

Main Methods:

  • A computational approach based on the similarity of estrogen response element (ERE) frames in human gene promoters.

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  • Tested a set of 60 known estrogen-responsive genes against over 18,000 human promoters.
  • Validated predictions using existing microarray data and scientific literature.

    • Main Results:

    • Identified 604 candidate estrogen-responsive genes.
    • Over half (53.6%) of the predicted candidates were confirmed as estrogen-responsive through validation.
    • The method demonstrated high efficiency in identifying potential estrogen targets.

    Conclusions:

    • The proposed computational method is a valuable tool for efficiently identifying estrogen-responsive genes.
    • This approach can reduce the cost and effort of experimental testing for potential estrogen targets.
    • The findings contribute to a better understanding of estrogen's role in gene regulation and functional genomics.