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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.
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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 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. 
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Histone Modification02:32

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
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Inheritance of Chromatin Structures03:17

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Epigenetics is the study of inherited changes in a cell's phenotype without changing the DNA sequences. It provides a form of memory for the differential gene expression pattern to maintain cell lineage, position-effect variegation, dosage compensation, and maintenance of chromatin structures such as telomeres and centromeres. For example, the structure and location of the centromere on chromosomes are epigenetically inherited. Its functionality is not dictated or ensured by the underlying...
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Chromatin Modification in iPS Cells01:32

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Chromatin modification alters gene expression; therefore, scientists can add histone-modifying enzymes, histone variants, and chromatin remodeling complexes to somatic cells to aid reprogramming into pluripotent stem (iPS) cells.
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Related Experiment Video

Updated: Aug 9, 2025

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq
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Deep Learning on Chromatin Accessibility.

Daniel S Kim1

  • 1Biomedical Informatics Program, Stanford University School of Medicine, Stanford, CA, USA. dskim89@stanford.edu.

Methods in Molecular Biology (Clifton, N.J.)
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models can now predict regulatory DNA function. Our new framework simplifies using these models for genomic analysis, offering insights into DNA sequence logic.

Keywords:
ATAC-seqDNA accessibilityDNase-seqDeep learningMachine learning

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • DNA accessibility assays identify active regulatory elements.
  • Understanding the combinatorial logic of these elements remains challenging.
  • Deep learning models show promise in predicting regulatory DNA function.

Purpose of the Study:

  • To provide a user-friendly deep learning framework for genomics.
  • To enable versatile and compatible analysis of DNA sequence regulatory logic.
  • To facilitate new biological insights into regulatory syntax.

Main Methods:

  • Development of a deep learning framework for genomic sequence analysis.
  • Implementation of best practices for model usability and compatibility.
  • Focus on ease of use, versatility, and integration with existing tools.

Main Results:

  • The framework offers a robust approach for deep learning in genomics.
  • It enhances the predictive power of models for regulatory DNA.
  • It aids in dissecting the combinatorial logic within regulatory elements.

Conclusions:

  • The proposed framework simplifies and enhances deep learning applications in genomics.
  • It provides a valuable tool for uncovering regulatory syntax and logic in DNA sequences.
  • This approach facilitates biological discovery in gene regulation.