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Related Concept Videos

General Transcription Factors01:30

General 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...
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...
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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

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Related Experiment Video

Updated: Jun 4, 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

G =  MAT: linking transcription factor expression and DNA binding data.

Konstantin Tretyakov1, Sven Laur, Jaak Vilo

  • 1Institute of Computer Science, University of Tartu, Tartu, Estonia.

Plos One
|February 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a computational method to identify transcription factor-motif associations, reducing the need for costly lab experiments. The approach combines DNA sequence and gene expression data for reliable biological discoveries.

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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation

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Last Updated: Jun 4, 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

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation
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Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation

Published on: April 30, 2012

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcription factors regulate gene expression by binding to DNA motifs.
  • Understanding these interactions is crucial for biological mechanism elucidation.
  • Experimental identification of transcription factor-binding sites is resource-intensive.

Purpose of the Study:

  • To develop a computational method for predicting transcription factor-motif associations.
  • To offer an alternative to expensive wet lab experiments for discovering regulatory interactions.

Main Methods:

  • A linear model integrating DNA sequence information and gene expression data was employed.
  • Various model parameter estimation techniques were explored.
  • The method's performance was evaluated using simulated and biological datasets.

Main Results:

  • The proposed computational method reliably identifies putative transcription factor-motif associations.
  • Experiments on simulated data demonstrated the robustness of the parameter estimation methods.
  • Analysis of biological data confirmed the method's ability to uncover meaningful regulatory links.

Conclusions:

  • The developed computational approach provides an efficient and cost-effective way to discover transcription factor-motif associations.
  • This method aids in understanding gene expression regulation.
  • The associated software is publicly available as a web tool.