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Related Concept Videos

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...
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...
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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...

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Related Experiment Video

Updated: May 18, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Uncovering transcription factor modules using one- and three-dimensional analyses.

Xun Lan1, Peggy J Farnham, Victor X Jin

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.

The Journal of Biological Chemistry
|September 7, 2012
PubMed
Summary
This summary is machine-generated.

Understanding transcription factor (TF) interactions in 3D nuclear space is crucial for deciphering gene regulation in health and disease. New methods integrating experimental and computational approaches enhance TF association network analysis.

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

Last Updated: May 18, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

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

Area of Science:

  • Molecular Biology
  • Genomics
  • Computational Biology

Background:

  • Transcriptional regulation governs cellular processes and disease.
  • Transcription factors (TFs) bind DNA and form complexes to regulate genes.
  • Current methods often analyze TF interactions in a one-dimensional context.

Purpose of the Study:

  • To describe methods for identifying transcription factor modules.
  • To highlight the importance of considering TF interactions in three-dimensional (3D) nuclear space.
  • To promote integrated experimental and computational approaches for understanding TF networks.

Main Methods:

  • Review of existing machine learning and Bayesian approaches for 1D TF module identification.
  • Discussion of high-throughput technologies revealing 3D TF interactions.
  • Proposal of integrated experimental and computational strategies.

Main Results:

  • Current 1D methods for TF module identification are limited.
  • High-throughput data indicate TF interactions occur in 3D nuclear space.
  • A 3D paradigm offers a more comprehensive view of TF association networks.

Conclusions:

  • Moving beyond 1D analysis to a 3D perspective is essential for understanding TF networks.
  • Integrated experimental and computational methods are key to advancing this field.
  • Improved understanding of TF association networks can elucidate mechanisms of gene regulation and disease.