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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
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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions take up more dye, appearing darker, while the less-compact areas take up less dye and appear lighter. Based on the compaction level, chromatins are classified into two primary forms – euchromatin and heterochromatin.
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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. 
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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Haitham Ashoor1, Xiaowen Chen1, Wojciech Rosikiewicz1

  • 1The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.

Nature Communications
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

Sub-Compartment Identifier (SCI) is a new computational algorithm that accurately predicts genome sub-compartments using Hi-C data. SCI outperforms traditional methods and reveals distinct epigenetic features, improving our understanding of nuclear organization.

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Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • Chromatin interaction studies are crucial for understanding genome organization within the nucleus.
  • Accurately identifying nuclear sub-compartments from chromatin interaction data presents a significant computational challenge.

Purpose of the Study:

  • To introduce Sub-Compartment Identifier (SCI), a novel algorithm for predicting genome sub-compartments.
  • To evaluate SCI's performance against existing methods like hidden Markov models (HMM).
  • To explore the biological significance of SCI-predicted sub-compartments.

Main Methods:

  • Developed SCI, an algorithm employing graph embedding and unsupervised learning on Hi-C data.
  • Compared SCI's sub-compartment predictions with HMM using network topological centrality and clustering metrics.
  • Validated SCI predictions using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data.
  • Developed a deep neural network for sub-compartment prediction utilizing epigenome, replication timing, and sequence data.

Main Results:

  • SCI demonstrated superior network topological centrality and clustering performance compared to HMM.
  • ChIA-PET data confirmed SCI's improved accuracy over HMM.
  • SCI-predicted sub-compartments exhibited distinct epigenetic marks, transcriptional activities, and transcription factor enrichment.
  • A deep neural network trained with SCI-derived labels achieved more accurate sub-compartment predictions.

Conclusions:

  • SCI is a robust and accurate algorithm for identifying genome sub-compartments from Hi-C data.
  • SCI predictions offer insights into the functional and epigenetic characteristics of nuclear organization.
  • The integration of SCI with deep learning models enhances predictive accuracy for nuclear sub-compartments.