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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...

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...

General Transcription Factors

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...

Conserved Binding Sites

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...

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Related Experiment Video

Updated: Jul 18, 2026

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

Integrating transcription factor binding site information with gene expression datasets.

Ian B Jeffery¹, Stephen F Madden, Paul A McGettigan

¹UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland. Ian.Jeffery@ucd.ie

Bioinformatics (Oxford, England)

|November 28, 2006

Summary

This study introduces a novel method to link gene expression patterns to specific DNA motifs. The approach successfully identifies transcription factor motifs associated with tissue-specific gene expression and disease states.

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Last Updated: Jul 18, 2026

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

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

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
Molecular Biology

Background:

Microarrays are crucial for analyzing gene expression differences across biological samples.
Transcription factor activity significantly influences observed gene expression variations.
Directly associating promoter motifs with gene expression changes presents practical challenges.

Purpose of the Study:

To develop a computational method for associating DNA motifs with gene expression differences.
To identify transcription factor binding sites that correlate with specific biological conditions or tissue types.
To enhance the interpretation of gene expression data by linking it to regulatory elements.

Main Methods:

A novel approach combining correspondence analysis, between-group analysis, and co-inertia analysis was employed.
The method integrates databases of promoter motifs with gene expression data from microarrays.
A ranked list of motifs associated with predefined sample groups is generated.

Main Results:

The method successfully identified known motifs associated with central nervous system (CNS) and muscle tissue expression.
Analysis of a prostate cancer dataset revealed distinct transcriptional pathways linked to metastatic progression.
The approach provides a clear and effective way to find motifs associated with specific biological distinctions.

Conclusions:

The developed method offers a powerful tool for linking DNA motifs to gene expression patterns.
This approach facilitates the discovery of regulatory mechanisms underlying tissue specificity and disease.
The freely available source code promotes broader application in genomic research.