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Related Concept Videos

Transcription Factors02:16

Transcription Factors

75.8K
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...
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General Transcription Factors01:30

General Transcription Factors

5.2K
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...
5.2K
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

11.1K
Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
11.1K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.4K
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...
6.4K

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Related Experiment Video

Updated: Jun 19, 2025

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

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TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Jin Wang1

  • 1School of Public Health, Suzhou Medical College, Soochow University, Suzhou 215123, China.

Biomolecules
|July 27, 2024
PubMed
Summary

This study introduces a new R package to predict transcription factor (TF) target gene interactions. It integrates multiple bioinformatics tools for robust gene regulatory network analysis.

Keywords:
Shiny appTF–target predictionbioinformaticsgene regulatory networkstranscription factors

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Transcription factors (TFs) regulate gene expression, influencing cellular and organismal phenotypes.
  • Identifying TF-target gene interactions is vital for understanding molecular pathways and diseases.
  • Traditional experimental methods for TF-target identification are often demanding and labor-intensive.

Purpose of the Study:

  • To develop a novel R package and web application for predicting transcription factor-target gene relationships.
  • To provide a user-friendly platform for analyzing gene regulatory mechanisms and networks.
  • To overcome limitations of traditional experimental approaches in TF-target interaction discovery.

Main Methods:

  • Integration of multiple bioinformatics tools for robust prediction.
  • Merging of biological databases to enhance prediction accuracy.
  • Utilizing gene expression correlation and pan-tissue correlation analysis for context-specific insights.
  • Development of an R package and web application for TF-target gene network analysis.

Main Results:

  • A novel R package and web application for predicting TF-target gene interactions (and vice versa).
  • The application integrates diverse data sources and analytical methods for comprehensive network analysis.
  • User data can be integrated with existing resources for customized network exploration.

Conclusions:

  • The developed tool offers a powerful and integrated approach for dissecting complex gene regulatory mechanisms.
  • It serves as an invaluable resource for researchers in genomics and molecular biology.
  • Future expansions are planned to include additional species beyond human data.