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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...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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...

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Updated: Jun 12, 2026

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

A parallel scheme for comparing transcription factor binding sites matrices.

Solenne Carat1, Rémi Houlgatte, Jérémie Bourdon

  • 1Institut du thorax, INSERM U 915, Université de Nantes, France. Solenne.Carat@univ-nantes.fr

Journal of Bioinformatics and Computational Biology
|June 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for comparing and clustering gene regulatory motifs. The approach enhances transcription factor identification and reduces false positives in motif discovery.

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Related Experiment Videos

Last Updated: Jun 12, 2026

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06:38

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

Published on: February 7, 2019

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
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PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Published on: April 16, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene regulation involves complex mechanisms crucial for understanding cellular processes and diseases.
  • Identifying regulatory elements, particularly transcription factors, is essential for constructing gene regulatory networks.
  • Existing motif discovery tools generate numerous results that require efficient comparison and clustering to reduce redundancy and identify true transcription factors.

Purpose of the Study:

  • To develop an integrated method for generating, comparing, and clustering DNA motifs.
  • To improve the accuracy of transcription factor identification by reducing false positives.
  • To apply the developed method to analyze ChIP-chip data in eukaryotic organisms.

Main Methods:

  • A novel global motif comparison method is proposed, differing from traditional independent column comparisons.
  • The method generates, compares, and clusters a set of motifs.
  • An original graph motif model is introduced, generalizing classical position-specific matrices.

Main Results:

  • The developed method effectively compares and clusters motifs, reducing redundancy.
  • The global comparison approach minimizes false positive motif identifications.
  • The method successfully identifies motifs similar to those in databases like JASPAR and Transfac.
  • Application to ChIP-chip data demonstrates its utility in eukaryotic gene regulation studies.

Conclusions:

  • The new method offers an improved approach for analyzing gene regulatory motifs.
  • It enhances the identification of transcription factors and aids in understanding gene regulation.
  • The global comparison and graph motif model represent advancements in motif analysis.