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Related Concept Videos

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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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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The Eukaryotic Promoter Region02:40

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The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences.  The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...
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Spreading of Chromatin Modifications02:25

Spreading of Chromatin Modifications

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The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
Writers
The writer...
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Chromatin Structure Regulates pre-mRNA Processing02:41

Chromatin Structure Regulates pre-mRNA Processing

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In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
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Related Experiment Video

Updated: Aug 18, 2025

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

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DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.

Aman Agarwal1, Li Chen2

  • 1Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.

Bioinformatics (Oxford, England)
|December 10, 2022
PubMed
Summary

We developed a deep learning model to predict gene regulatory interactions, specifically promoter-enhancer (PE) and promoter-promoter (PP) interactions, overcoming limitations of current sequencing methods for better gene regulation insights.

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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Promoter-centered chromatin interactions, including promoter-enhancer (PE) and promoter-promoter (PP) interactions, are crucial for understanding gene regulation and disease mechanisms.
  • While promoter capture Hi-C (pcHi-C) identifies these interactions, its high cost and potential for underpowering limit its accessibility and scope.
  • Existing computational methods, often based on in situ Hi-C, may not be optimal for predicting specific promoter-centered interactions.

Purpose of the Study:

  • To develop a computational method for accurately predicting tissue/cell type-specific promoter-centered chromatin interactions.
  • To improve upon existing methods by leveraging a multi-modal deep learning approach with diverse feature sets.
  • To enhance prediction performance through multi-task and transfer learning frameworks.

Main Methods:

  • A supervised multi-modal deep learning model was developed.
  • The model integrates features including genomic sequence, epigenetic signals, anchor distance, evolutionary data, and DNA structural properties.
  • Multi-task and transfer learning frameworks were employed to optimize prediction accuracy and generalize across tissues/cell types.

Main Results:

  • The proposed deep learning model accurately predicts tissue/cell type-specific PE and PP interactions.
  • The approach demonstrates superior performance compared to state-of-the-art deep learning methods.
  • Comparable prediction performance was achieved using predefined or computationally inferred biologically relevant tissues/cell types for pretraining, especially for PE interactions.

Conclusions:

  • The developed deep learning model offers a powerful and accessible tool for predicting promoter-centered chromatin interactions.
  • This method can overcome the limitations of experimental techniques like pcHi-C, enabling broader studies of gene regulation.
  • The findings highlight the potential of multi-modal deep learning and transfer learning in advancing the prediction of complex genomic interactions.