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Related Concept Videos

Transcription Factors02:16

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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Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

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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.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
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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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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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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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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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Related Experiment Video

Updated: Jun 26, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

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A statistical approach for identifying single nucleotide variants that affect transcription factor binding.

Nina Baumgarten1,2,3,4, Laura Rumpf1,2,3,4, Thorsten Kessler5,6

  • 1Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany.

Iscience
|May 13, 2024
PubMed
Summary

This study introduces a faster computational method to predict how DNA sequence variants affect transcription factor (TF) binding, improving accuracy for gene expression analysis.

Keywords:
Computational bioinformaticsComputational mathematicsGenomics

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Related Experiment Videos

Last Updated: Jun 26, 2025

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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Genetics

Background:

  • Non-coding genetic variants can impact gene expression by altering transcription factor (TF) binding sites.
  • Current computational models for assessing TF binding alterations by DNA variants are often slow and lack statistical significance evaluation.
  • Single nucleotide variants (SNVs) are a key focus for understanding functional genetic consequences.

Purpose of the Study:

  • To develop and validate a computationally efficient and statistically robust method for assessing the impact of DNA variants on TF binding.
  • To investigate the distribution of differential TF binding scores for general computational models.
  • To provide a practical tool for analyzing genetic variants in regulatory elements.

Main Methods:

  • Investigated the distribution of absolute maximal differential TF binding scores using computational models.
  • Utilized a modified Laplace distribution to approximate empirical score distributions.
  • Benchmarked the new approach against existing methods using in vitro and in vivo datasets.

Main Results:

  • A modified Laplace distribution accurately approximates TF binding score distributions.
  • The developed approach demonstrates improved performance and speed compared to existing methods.
  • Applications on eQTLs and GWAS highlight cell type-specific regulatory elements and target genes.

Conclusions:

  • The novel statistical approach provides a faster and more accurate assessment of TF binding alterations due to DNA variants.
  • This method enhances the analysis of genetic variations in non-coding regions, aiding in the understanding of gene regulation.
  • Freely available implementation facilitates broader application in genetic research and disease association studies.