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Related Concept Videos

Transcription Factors02:16

Transcription Factors

83.1K
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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General Transcription Factors01:30

General Transcription Factors

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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

12.9K
Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These...
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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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Bioinformatics approaches to predict target genes from transcription factor binding data.

Alexandra Essebier1, Marnie Lamprecht1, Michael Piper1

  • 1The University of Queensland, Brisbane 4072, Australia.

Methods (San Diego, Calif.)
|September 12, 2017
PubMed
Summary

Identifying transcription factor target genes is challenging. This study reviews computational methods, finding that combining approaches improves the biological relevance of identified target genes.

Keywords:
BioinformaticsCancerEnhancerPromoterRegulatory interactionsTranscription factor

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Area of Science:

  • Molecular Biology
  • Genetics
  • Bioinformatics

Background:

  • Transcription factors (TFs) are crucial regulators of gene expression, essential for development, cellular differentiation, and cell fate determination.
  • Identifying the specific genes regulated by TFs, especially those acting over long distances, presents a significant challenge.
  • Existing experimental methods for detecting distal regulatory interactions have limitations, driving the need for computational solutions.

Purpose of the Study:

  • To review and evaluate computational approaches for identifying transcription factor target genes.
  • To assess the data dependency, cell type specificity, and usability of current computational methods.
  • To determine the effectiveness of applying these methods to experimental TF datasets.

Main Methods:

  • Systematic review of existing computational approaches for TF target gene identification.
  • Application of selected computational methods to typical TF experimental datasets.
  • Comparative analysis of method performance based on data requirements, specificity, and ease of use.

Main Results:

  • Computational approaches vary in their ability to annotate all TF binding sites.
  • TF binding sites often require disparate treatment for accurate analysis.
  • Combining multiple computational approaches enhances the biological relevance of identified TF target genes.

Conclusions:

  • No single computational approach is universally sufficient for TF target gene identification.
  • A critical evaluation of TF binding site characteristics is necessary for effective analysis.
  • Integrating diverse computational strategies offers a more robust and biologically meaningful method for identifying TF targets.