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Related Concept Videos

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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Chromatin Position Affects Gene Expression02:35

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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)
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Related Experiment Video

Updated: Oct 23, 2025

High-Resolution Mapping of Protein-DNA Interactions in Mouse Stem Cell-Derived Neurons using Chromatin Immunoprecipitation-Exonuclease ChIP-Exo
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Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions

Fan Cao1, Yu Zhang2, Yichao Cai1

  • 1Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.

Genome Biology
|August 17, 2021
PubMed
Summary

We developed a computational method, chromatin interaction neural network (ChINN), to predict gene expression regulation via chromatin interactions using DNA sequences. This tool reveals significant differences in these interactions across chronic lymphocytic leukemia (CLL) patient samples.

Keywords:
3D genome organizationBioinformaticsChIA-PETChromatin interactionsDNA sequenceHi-CLeukemiaMachine learning

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Last Updated: Oct 23, 2025

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Chromatin interactions are crucial for gene expression regulation.
  • Genome-wide chromatin interaction data is currently limited.
  • Predicting these interactions computationally is essential for advancing biological insights.

Purpose of the Study:

  • To develop a novel computational method for predicting chromatin interactions using only DNA sequences.
  • To assess the performance of the developed method in predicting various types of chromatin interactions.
  • To investigate the heterogeneity of chromatin interactions in chronic lymphocytic leukemia (CLL) patient samples.

Main Methods:

  • Development of a computational method named chromatin interaction neural network (ChINN).
  • ChINN utilizes DNA sequences to predict chromatin interactions between open chromatin regions.
  • The method was validated by predicting CTCF- and RNA polymerase II-associated interactions and Hi-C interactions.

Main Results:

  • ChINN demonstrated robust performance across different samples.
  • The model successfully captured sequence features relevant to chromatin interaction prediction.
  • Application of ChINN to CLL samples revealed extensive heterogeneity in chromatin interactions among patients.

Conclusions:

  • The developed ChINN method provides a valuable tool for predicting chromatin interactions from DNA sequences.
  • ChINN aids in understanding gene regulation and identifying sequence-based determinants of chromatin interactions.
  • Significant inter-patient heterogeneity in chromatin interactions was observed in CLL, suggesting potential for personalized medicine approaches.