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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 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.
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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: Sep 25, 2025

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq
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PeakVI: A deep generative model for single-cell chromatin accessibility analysis.

Tal Ashuach1, Daniel A Reidenbach2, Adam Gayoso1

  • 1Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.

Cell Reports Methods
|April 27, 2022
PubMed
Summary
This summary is machine-generated.

PeakVI is a new deep learning framework for analyzing single-cell ATAC sequencing (scATAC-seq) data. It addresses challenges like noise and sparsity, enabling robust identification of regulatory elements and cell types.

Keywords:
deep learningsingle-cell ATAC-seqsingle-cell chromatin accessibilitysingle-cell genomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Single-cell ATAC sequencing (scATAC-seq) is vital for studying cellular heterogeneity.
  • scATAC-seq data presents challenges due to high noise, sparsity, and large scale.
  • Existing analysis methods struggle with these data complexities.

Purpose of the Study:

  • To introduce PeakVI, a novel probabilistic framework for scATAC-seq data analysis.
  • To develop a method that handles technical variations and preserves biological signals.
  • To enable precise identification of differential chromatin accessibility and regulatory elements.

Main Methods:

  • Utilized deep neural networks within a probabilistic framework (PeakVI).
  • Developed a latent space representation to correct for batch and technical effects (library size, biases).
  • Implemented differential accessibility analysis at single-region resolution.

Main Results:

  • PeakVI demonstrates scalability, stability, and robustness on public scATAC-seq datasets.
  • The framework outperforms existing methods in critical analysis tasks.
  • PeakVI effectively identifies differential accessibility for cell-type annotation and cis-regulatory element discovery.

Conclusions:

  • PeakVI offers a powerful solution for analyzing challenging scATAC-seq data.
  • The framework enhances the discovery of regulatory elements and cell-type specific chromatin accessibility.
  • PeakVI is publicly available within the scvi-tools framework.