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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: Jun 21, 2026

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

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Published on: February 7, 2019

A knowledge-based method to predict the cooperative relationship between transcription factors.

Lingyi Lu1, Ziliang Qian, XiaoHe Shi

  • 1Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.

Molecular Diversity
|July 11, 2009
PubMed
Summary

This study introduces a computational framework to predict transcription factor cooperation by integrating protein annotation data. The new method achieves high accuracy, outperforming existing approaches for understanding gene expression.

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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Published on: February 7, 2019

Chromatin Interaction Analysis with Paired-End Tag Sequencing (ChIA-PET) for Mapping Chromatin Interactions and Understanding Transcription Regulation
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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Understanding transcription factor (TF) cooperation is vital for deciphering complex gene expression patterns.
  • Existing computational methods for inferring TF cooperation can be improved by integrating protein knowledge.
  • Protein functional and structural annotations offer valuable insights into TF interactions.

Purpose of the Study:

  • To develop an information-integrative computational framework for inferring transcription factor cooperation.
  • To leverage protein annotation data to enhance the accuracy of TF interaction prediction.
  • To provide a novel computational approach for predicting TF cooperation.

Main Methods:

  • Proposed an information-integrative computational framework for TF cooperation inference.
  • Employed a hybridization-space method to integrate protein annotation information.
  • Utilized function domain annotations of proteins in computational experiments.

Main Results:

  • Achieved an overall prediction accuracy of 84.3% using function domain annotations.
  • Reached a specificity of 76.9% on the testing dataset.
  • Outperformed traditional methods like amino acid composition-based and BLAST-based approaches.

Conclusions:

  • The proposed information-integrative framework effectively predicts transcription factor cooperation.
  • Integrating protein annotation data significantly improves TF interaction prediction accuracy.
  • The developed TFIPS (Transcription Factor Interaction Prediction System) offers a valuable tool for researchers.