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Related Concept Videos

Transcription Factors02:16

Transcription Factors

83.1K
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 Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
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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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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...
7.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

12.6K
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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Related Experiment Video

Updated: Feb 26, 2026

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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Differential chromatin profiles partially determine transcription factor binding.

Rujian Chen1, David K Gifford1

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

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|July 14, 2017
PubMed
Summary

A new method, DeltaBind, accurately predicts how genetic changes affect regulatory factor binding using DNase-seq data. These changes in chromatin accessibility do not always alter nearby factor binding, requiring broader context for causality.

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Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation
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Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation

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Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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Chromatin Interaction Analysis with Paired-End Tag Sequencing ChIA-PET for Mapping Chromatin Interactions and Understanding Transcription Regulation
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Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass
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Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Genomic variants can alter gene regulation by changing chromatin accessibility.
  • Understanding how these variants affect regulatory factor binding is crucial for deciphering gene expression and cellular function.
  • Existing methods for predicting factor binding from accessibility data have limitations.

Purpose of the Study:

  • To develop and validate a novel method, DeltaBind, for accurately predicting condition-specific regulatory factor binding.
  • To investigate the relationship between genomic variants, chromatin accessibility, and proximal factor binding.
  • To assess the predictive power of DNase-seq/ATAC-seq Quantitative Trait Loci (dsQTLs) in establishing causality.

Main Methods:

  • Development of DeltaBind, a computational method for predicting regulatory factor binding from DNase-seq data.
  • Application of DeltaBind to analyze differential factor binding in K562 and GM12878 cell lines.
  • Comparison of DeltaBind's predictive accuracy against existing methods.
  • Analysis of the correlation between variants affecting chromatin accessibility and proximal factor binding.

Main Results:

  • DeltaBind demonstrates improved accuracy in predicting condition-specific factor binding compared to other methods using DNase-seq data.
  • Differential binding of 18 factors was predicted in K562 and GM12878 cells with an average precision of 28% at 10% recall.
  • Individual factor prediction precision ranged from 5% to 65%.
  • Genomic variants altering chromatin accessibility were not consistently predictive of changes in proximal factor binding.

Conclusions:

  • DeltaBind offers a more accurate approach to predicting regulatory factor binding from accessibility data.
  • The link between altered chromatin accessibility and proximal factor binding is complex and not always direct.
  • DNase-seq/ATAC-seq Quantitative Trait Loci (dsQTLs) are valuable but require integration with broader genomic and functional data to establish causal relationships for phenotypic changes.