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Related Concept Videos

Transcription Factors02:16

Transcription Factors

76.1K
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...
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Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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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...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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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...
8.4K
General Transcription Factors01:30

General Transcription Factors

5.4K
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...
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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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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...
9.2K
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

7.4K
Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Updated: Jul 16, 2025

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

Published on: June 27, 2020

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Computational tools for inferring transcription factor activity.

Dennis Hecker1,2,3, Michael Lauber4, Fatemeh Behjati Ardakani1,2,3

  • 1Goethe University Frankfurt, Frankfurt am Main, Germany.

Proteomics
|September 14, 2023
PubMed
Summary
This summary is machine-generated.

Computational tools estimate transcription factor activity (TFA) to understand cell regulation. This study reviews these tools, their applications, and limitations for researchers.

Keywords:
bioinformatic toolsgene regulationgene regulatory networkstranscription factor activity

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Transcription factors (TFs) are crucial regulators of cellular processes, but their precise functions and interactions are complex and difficult to study experimentally.
  • The cell-type-specific nature and unique characteristics of each TF make experimental elucidation of their regulatory networks laborious.
  • Computational approaches are increasingly vital for inferring TF activity (TFA) and regulatory mechanisms.

Purpose of the Study:

  • To provide a comprehensive overview of computational tools available for estimating transcription factor activity (TFA).
  • To illustrate the practical applications of these TFA estimation tools with relevant examples.
  • To discuss common strategies for validating computational results and the inherent assumptions and limitations of these methods.

Main Methods:

  • Review and synthesis of existing computational tools for TFA estimation.
  • Analysis of diverse data modalities used by these tools (e.g., TF-target gene mapping, genome-wide approaches).
  • Examination of result validation techniques and critical discussion of underlying assumptions.

Main Results:

  • Identification and categorization of various computational tools for TFA inference.
  • Demonstration of how these tools can provide insights into TF regulatory roles.
  • Highlighting the importance of careful validation and awareness of methodological limitations.

Conclusions:

  • Computational tools significantly aid in understanding transcription factor activity and regulatory networks.
  • These tools offer valuable insights for prioritizing experimental validation of TF functions.
  • A critical understanding of tool assumptions and limitations is essential for reliable biological interpretation.