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Related Concept Videos

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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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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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.
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Master Transcription Regulators02:23

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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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Eukaryotic Transcription Activators02:42

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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.
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Predicting tissue specific transcription factor binding sites.

Shan Zhong, Xin He, Ziv Bar-Joseph1

  • 1Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. zivbj@cs.cmu.edu.

BMC Genomics
|November 19, 2013
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Summary
This summary is machine-generated.

Researchers developed PIPES, a computational method to predict tissue-specific transcription factor (TF) binding sites by integrating multiple data types. This approach improves accuracy over sequence-only methods for understanding gene regulation.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Genome-wide transcription factor (TF) binding site prediction is crucial for understanding gene regulation.
  • Existing methods often rely solely on sequence data, neglecting critical biological contexts like tissue type and developmental stage.
  • Experimental methods like ChIP-seq are limited in scalability for comprehensive network analysis.

Purpose of the Study:

  • To develop an improved computational approach for predicting tissue-specific TF binding.
  • To integrate diverse biological data for more accurate TF binding predictions.

Main Methods:

  • Developed PIPES (Prediction of Interactions by PIPES), a novel computational tool.
  • Integrated in vitro protein binding microarray (PBM) data, sequence conservation, and tissue-specific epigenetic information (DNase I hypersensitivity).

Main Results:

  • PIPES demonstrated improved performance in distinguishing in vivo bound from unbound sequences compared to existing methods, validated using ChIP-seq data for 11 mouse TFs.
  • The predictions align well with established knowledge of tissue-specific TF regulation.

Conclusions:

  • A comprehensive computational map of tissue-specific TF binding targets for 284 mouse TFs across 55 tissue/cell types was generated.
  • This resource facilitates research into mammalian transcriptional regulatory networks.