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Conserved Binding Sites

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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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Updated: Feb 26, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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An efficient algorithm for improving structure-based prediction of transcription factor binding sites.

Alvin Farrel1, Jun-Tao Guo2

  • 1Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.

BMC Bioinformatics
|July 19, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient pentamer algorithm for predicting transcription factor binding sites. The new method improves accuracy and significantly reduces computation time, especially for longer DNA sequences.

Keywords:
Binding motifFragment-based methodIntegrative energy functionPentamerStructure-based predictionTranscription factor binding site

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene expression is regulated by transcription factors binding to DNA.
  • Accurate prediction of transcription factor binding sites is crucial for understanding gene regulation.
  • Previous structure-based methods were computationally inefficient for long binding sites.

Purpose of the Study:

  • To develop a more efficient and accurate method for predicting transcription factor binding sites.
  • To address the computational limitations of previous structure-based prediction models.

Main Methods:

  • Developed a fragment-based pentamer algorithm splitting DNA sequences into overlapping 5-base pair fragments.
  • Utilized a simplified integrative energy function for calculating transcription factor-pentamer interactions.
  • Employed Kmer-Sum and Position Weight Matrix (PWM) stacking for full-length binding motif prediction.

Main Results:

  • The pentamer algorithm significantly reduced computation time, particularly for longer binding sites.
  • Both Kmer-Sum and PWM stacking methods improved prediction accuracy.
  • The combination of the pentamer approach and simplified energy function enhanced overall performance.

Conclusions:

  • The novel fragment-based pentamer algorithm offers improved efficiency and accuracy in structure-based transcription factor binding site prediction.
  • This represents the first fragment-based approach for structure-based prediction of these sites.