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Related Concept Videos

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...
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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...
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...
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...
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...

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Related Experiment Video

Updated: Jul 15, 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

Bayesian model-based inference of transcription factor activity.

Simon Rogers1, Raya Khanin, Mark Girolami

  • 1Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK. srogers@dcs.gla.ac.uk

BMC Bioinformatics
|May 12, 2007
PubMed
Summary

This study introduces a Bayesian approach using nonlinear Michaelis-Menten kinetics to infer transcription factor activity from microarray data, improving accuracy over linear models, especially with limited data.

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Last Updated: Jul 15, 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

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene expression levels of transcription factors (TFs) are often used as a proxy for their activity, but post-translational modifications can cause inaccuracies.
  • Existing methods for inferring TF activity from target gene expression predominantly use linear models, which fail to capture the nonlinear nature of transcription.
  • Nonlinear models are needed to accurately reflect the biological processes governing gene regulation.

Purpose of the Study:

  • To extend a nonlinear Michaelis-Menten kinetics-based approach for inferring transcription factor activity.
  • To transition from maximum likelihood inference to a fully Bayesian inference framework.
  • To demonstrate the advantages of Bayesian inference and nonlinear modeling in analyzing microarray data.

Main Methods:

  • Developed a fully Bayesian inference framework for inferring transcription factor activity.
  • Utilized nonlinear Michaelis-Menten kinetics to model the transcription process.
  • Extended the model to incorporate gene and replicate-specific delays.

Main Results:

  • Presented findings from analyses of both synthetic and real microarray datasets.
  • Demonstrated the successful incorporation of time delays into the inference model.
  • Showcased the model's performance on diverse biological data.

Conclusions:

  • Full Bayesian inference is suitable for inferring transcription factor activity and offers advantages over maximum likelihood, particularly with limited datasets.
  • Nonlinear models provide superior accuracy compared to linear models for transcription factor activity inference, especially in cases of gene repression.
  • The developed Bayesian nonlinear approach enhances the understanding of gene regulatory networks.