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Related Concept Videos

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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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

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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.
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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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Related Experiment Video

Updated: Mar 12, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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Evaluating tools for transcription factor binding site prediction.

Narayan Jayaram1, Daniel Usvyat1, Andrew C R Martin2

  • 1Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK.

BMC Bioinformatics
|November 4, 2016
PubMed
Summary
This summary is machine-generated.

This study identifies the top computational tools for predicting transcription factor binding sites (TFBSs). rGADEM excels at creating TFBS models from ChIP-Seq data, while FIMO and MCAST are best for scanning DNA sequences.

Keywords:
Motif discoveryMotif scanning toolsPWMsPerformance evaluation

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology and Genetics

Background:

  • Transcription factor binding sites (TFBSs) are crucial for regulating gene transcription.
  • Accurate TFBS prediction is essential for gene annotation and understanding disease-causing genetic variations.
  • Current methods for TFBS identification often rely on position weight matrices (PWMs) and computational scanning.

Purpose of the Study:

  • To evaluate and identify the best locally installable computational tools for generating PWMs from high-throughput ChIP-Seq data.
  • To assess the performance of different TFBS prediction tools for scanning DNA sequences using these PWMs.
  • To enhance the accuracy of TFBS prediction for large-scale genomic analyses.

Main Methods:

  • Evaluated de novo motif discovery tools using ENCODE ChIP-Seq data.
  • Compared TFBS prediction tools, categorizing them into individual TFBS predictors and cluster identification tools.
  • Focused on tools suitable for local installation and large-scale analysis.

Main Results:

  • rGADEM was identified as the top-performing tool for de novo motif discovery from ChIP-Seq data.
  • FIMO demonstrated superior performance in predicting individual TFBSs.
  • MCAST showed the best performance for identifying clusters of TFBSs.

Conclusions:

  • Selecting optimal tools for PWM generation (rGADEM) and TFBS scanning (FIMO, MCAST) can significantly improve prediction accuracy.
  • Enhanced TFBS prediction aids gene finding, understanding gene regulation, and evaluating the impact of single nucleotide variations on disease.
  • The findings support the use of these validated tools for robust TFBS prediction in genomic studies.