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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...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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Related Experiment Video

Updated: May 11, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Mapping functional transcription factor networks from gene expression data.

Brian C Haynes1, Ezekiel J Maier, Michael H Kramer

  • 1Center for Genome Sciences and Systems Biology, Washington University, Saint Louis, Missouri 63108, USA.

Genome Research
|May 3, 2013
PubMed
Summary
This summary is machine-generated.

NetProphet, a new algorithm, accurately maps gene regulatory networks using gene expression data. It identifies direct transcription factor targets, offering a more efficient method than traditional binding assays for understanding genome function.

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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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Related Experiment Videos

Last Updated: May 11, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

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

Published on: February 11, 2019

Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Understanding genome function requires identifying gene-regulatory transcription factors (TFs).
  • Current methods like ChIP-chip and expression profiling in Saccharomyces cerevisiae show limited overlap between TF binding and gene expression changes, hindering definitive network mapping.
  • Existing approaches lack efficiency in mapping functional TF networks.

Purpose of the Study:

  • To introduce NetProphet, a novel algorithm designed to enhance the efficiency of mapping TF networks from gene expression data.
  • To demonstrate NetProphet's ability to predict direct, functional regulatory interactions using solely gene expression data.
  • To provide new functional insights into yeast TFs, including Cbf1 and Eds1.

Main Methods:

  • NetProphet algorithm leverages the principle that TF disruption/overexpression effects are strongest on direct targets and diminish rapidly through the network.
  • Utilizes gene expression data from Saccharomyces cerevisiae to predict regulatory interactions.
  • Compares NetProphet predictions with ChIP-identified targets for validation.

Main Results:

  • NetProphet predicts thousands of direct, functional regulatory interactions from gene expression data.
  • Predicted targets show strong concordance with TF binding specificity, similar to ChIP targets.
  • NetProphet targets exhibit functional regulation, unlike many ChIP targets.
  • The algorithm offers new insights into the functions of yeast TFs Cbf1 and Eds1.

Conclusions:

  • Gene expression data, analyzed by NetProphet, can effectively identify direct, functional TF-promoter interactions.
  • Measuring TF binding may not be the most efficient initial step for mapping functional TF networks.
  • NetProphet provides a powerful, data-driven approach to deciphering complex gene regulatory networks.