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Combinatorial Gene Control

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...

Transcription Factors

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 Factors

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...

RNA Polymerase II Accessory Proteins

RNA Polymerase II Accessory Proteins

Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...

Cooperative Binding of Transcription Regulators

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 Regulators

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 10, 2026

Single-Molecule Imaging of EWS-FLI1 Condensates Assembling on DNA

Single-Molecule Imaging of EWS-FLI1 Condensates Assembling on DNA

Published on: September 8, 2021

Predicting and understanding transcription factor interactions based on sequence level determinants of combinatorial

A D J van Dijk¹, C J F ter Braak, R G Immink

¹Applied Bioinformatics, PRI, Wageningen UR, Droevendaalsesteeg 1, Wageningen, The Netherlands.

Bioinformatics (Oxford, England)

|November 21, 2007

Summary

Predicting transcription factor interactions is challenging due to sequence similarity. This study developed a method using Random Forest and motif analysis to accurately predict these interactions, identifying key sequence elements and potential partners.

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Last Updated: Jul 10, 2026

Single-Molecule Imaging of EWS-FLI1 Condensates Assembling on DNA

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Published on: September 8, 2021

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Published on: June 15, 2016

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

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

Published on: February 11, 2019

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Transcription factor (TF) interactions are vital for gene regulation.
Predicting TF interactions from sequence alone is difficult due to high sequence identity within TF families.
Limited experimental interaction data necessitates computational prediction methods.

Purpose of the Study:

To develop an accurate computational method for predicting transcription factor interactions based on sequence data.
To identify sequence motifs and regions critical for interaction specificity.
To apply the developed method for genome-wide prediction of TF interaction partners.

Main Methods:

Utilized a correlated motif search algorithm to identify potential motifs.
Employed a Random Forest-based feature selection to select relevant motifs.
Validated prediction accuracy across multiple transcription factor families (bZIP, MADS, homeobox, forkhead).

Main Results:

Achieved prediction accuracies ranging from 60% to 90% for various TF families.
Identified specific sequence regions crucial for interaction specificity, consistent with existing data.
Successfully performed genome-wide scans, identifying known and novel putative TF interaction partners.

Conclusions:

The developed Random Forest-based method effectively predicts transcription factor interactions using sequence motifs.
The approach can identify key sequence determinants of interaction specificity.
This method facilitates genome-wide discovery of transcription factor interaction networks.