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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...

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Related Experiment Video

Updated: May 27, 2026

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

Discovering transcription factor regulatory targets using gene expression and binding data.

Mark Maienschein-Cline1, Jie Zhou, Kevin P White

  • 1Department of Chemistry, The University of Chicago, Chicago, IL 60637, USA.

Bioinformatics (Oxford, England)
|November 16, 2011
PubMed
Summary

EMBER, a new machine learning method, accurately identifies target genes for transcription factors (TFs) by integrating binding and expression data. This approach improves understanding of gene regulation, particularly in complex mammalian systems like breast cancer.

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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Last Updated: May 27, 2026

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
11:32

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Published on: March 27, 2020

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Identifying transcription factor (TF) target genes is crucial for understanding gene regulation.
  • Genome-wide TF binding site mapping via ChIP-seq has limitations in inferring functional targets, especially in mammals due to long-range enhancers.
  • Assigning TF binding sites to target genes is challenging due to complex regulatory mechanisms.

Purpose of the Study:

  • To develop a computational method for inferring transcription factor (TF) target genes by integrating TF binding and gene expression data.
  • To overcome limitations of proximity-based methods in assigning TF binding sites to target genes.
  • To identify TF regulatory modes and their roles in biological processes.

Main Methods:

  • EMBER (Expectation Maximization of Binding and Expression pRofiles) uses an unsupervised machine learning algorithm.
  • Integrates high-throughput TF binding data (ChIP-chip, ChIP-seq) with gene expression data (microarray).
  • Identifies target genes based on overrepresented expression patterns matching TF binding profiles.

Main Results:

  • EMBER successfully infers TF target genes by integrating binding and expression data.
  • Application to human breast cancer data confirmed roles for estrogen receptor alpha and retinoic acid receptors.
  • EMBER identified target genes missed by proximity-based methods and revealed multiple TF regulatory modes.

Conclusions:

  • EMBER provides a robust method for inferring TF target genes and regulatory modes.
  • The method enhances understanding of gene regulation in complex biological systems.
  • EMBER offers a powerful tool for analyzing genome-wide TF binding and expression data.