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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...
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...

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Related Experiment Video

Updated: Jul 6, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ 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

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Probabilistic inference of transcription factor binding from multiple data sources.

Harri Lähdesmäki1, Alistair G Rust, Ilya Shmulevich

  • 1Institute for Systems Biology, Seattle, Washington, United States of America.

Plos One
|March 28, 2008
PubMed
Summary
This summary is machine-generated.

We developed a probabilistic framework to predict transcription factor (TF) binding sites using multiple data sources. This approach improves accuracy and integrates TF binding predictions into gene network inference.

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Last Updated: Jul 6, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
12:29

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

Published on: April 16, 2018

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

Published on: February 7, 2019

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

Published on: February 11, 2019

Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genomics

Background:

  • Understanding transcriptional regulation is crucial for molecular biology.
  • Current methods for predicting transcription factor (TF) binding sites often rely on hypothesis testing and scanning.
  • Integrating diverse data sources for TF binding prediction remains a challenge.

Purpose of the Study:

  • To develop a flexible, probabilistic framework for predicting TF binding from multiple data sources.
  • To improve the accuracy and interpretability of TF binding predictions.
  • To enable systematic integration of TF binding predictions into other probabilistic models, such as gene network inference.

Main Methods:

  • Developed a probabilistic modeling framework to estimate the probability of TF binding.
  • Incorporated multiple evidence sources including TF motifs, evolutionary conservation, CpG islands, and ChIP-chip data.
  • Implemented both likelihood and Bayesian methods, with the latter using Markov chain Monte Carlo (MCMC).
  • Extended the framework to model combinatorial regulation by multiple TFs and predict binding at nucleotide resolution.

Main Results:

  • Demonstrated significant improvement in TF binding prediction performance through principled data fusion on a mouse genome test set.
  • Applied the framework to all mouse promoters, revealing sparse connectivity between transcriptional regulators and their target promoters.
  • Developed an online web tool, ProbTF, to facilitate TF binding prediction using multiple data sources.

Conclusions:

  • Probabilistic modeling offers a powerful and flexible approach for predicting TF binding and integrating this information into broader biological network analyses.
  • Principled data fusion significantly enhances the accuracy of TF binding prediction.
  • The developed framework and tool (ProbTF) provide valuable resources for researchers studying transcriptional regulation.