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Related Concept Videos

Transcription Factors02:16

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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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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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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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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Position dependencies in transcription factor binding sites.

Andrija Tomovic1, Edward J Oakeley

  • 1Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland.

Bioinformatics (Oxford, England)
|February 20, 2007
PubMed
Summary
This summary is machine-generated.

Transcription factor binding site prediction models can be improved by accounting for sequence dependencies. Some factors exhibit dependencies, leading to lower conformational energy and better predictions when modeled.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Current transcription factor binding site prediction tools often assume independence between base positions.
  • Investigating the statistical basis of sequence dependence is crucial for improving prediction accuracy.

Purpose of the Study:

  • To statistically assess sequence dependence in transcription factor binding sites.
  • To develop improved scoring functions for binding-site prediction based on dependency analysis.

Main Methods:

  • Application of three statistical tests to analyze binding site position dependence.
  • Examination of transcription factor-DNA crystal structures for evidence of dependence.
  • Analysis of conformational energy (Z-score) in relation to sequence dependency.

Main Results:

  • Identified that some transcription factors exhibit binding site dependencies, while others do not.
  • Observed significantly lower conformational energy (Z-score) in dependent sequences compared to independent ones (P < 0.02).
  • Demonstrated that modeling dependencies improves binding-site predictions where evidence exists.

Conclusions:

  • Sequence dependencies in transcription factor binding sites are factor-specific.
  • Incorporating dependency modeling enhances prediction accuracy for relevant factors.
  • A web tool implementing a dependency-corrected algorithm is available.