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Related Concept Videos

Master Transcription Regulators02:23

Master Transcription Regulators

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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...
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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...
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Cis-regulatory Sequences02:02

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cooperative Binding of Transcription Regulators02:13

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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...
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Related Experiment Video

Updated: May 5, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data.

David T W Tzeng1, Yu-Ting Tseng, Matthew Ung

  • 1Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan, Department of Computer Science and Engineering, National Chung Hsing University, Taichung 402, Taiwan, Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA, Agricultural Biotechnology Center, National Chung Hsing University, Taichung 402, Taiwan, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.

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|December 5, 2013
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Summary

This study introduces a database (DPRP) integrating gene expression and transcription factor (TF) binding data. It helps identify regulatory programs and TFs driving biological and clinical conditions.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Gene expression profiling generates vast public data, crucial for understanding biological and clinical conditions.
  • Identifying transcription factors (TFs) from gene expression alone is challenging due to post-transcriptional regulation.
  • ChIP-seq experiments now enable systematic determination of TF target genes.

Purpose of the Study:

  • To develop a database for identifying phenotype-specific transcriptional regulatory programs.
  • To integrate TF binding data with gene expression data for hypothesis generation.

Main Methods:

  • Constructed the Database of Phenotype-specific Regulatory Programs (DPRP).
  • Utilized integrative analysis of TF binding data (ChIP-seq) and gene expression data.
  • Implemented Fisher's Exact Test, Kolmogorov-Smirnov test, and the BASE algorithm.

Main Results:

  • DPRP facilitates the identification of regulatory programs underlying gene expression profiles.
  • The database enables hypothesis generation for transcriptional regulation in biological and clinical studies.

Conclusions:

  • The DPRP database provides a valuable resource for dissecting complex gene regulatory networks.
  • Integrative analysis of TF binding and expression data is key to understanding transcriptional regulation.