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Chromatin Immunoprecipitation- ChIP

Chromatin Immunoprecipitation- ChIP

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...

Spreading of Chromatin Modifications

Spreading of Chromatin Modifications

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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Updated: Aug 17, 2025

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Published on: November 13, 2017

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model.

Seong Kyu Han^1,2, Yoshiharu Muto³, Parker C Wilson⁴

¹Department of Pediatrics, Division of Nephrology, Boston Children's Hospital, Boston & Harvard Medical School, Boston, MA 02115.

Proceedings of the National Academy of Sciences of the United States of America

|December 12, 2022

Summary

This summary is machine-generated.

We developed gkmQC, a machine learning tool to assess chromatin accessibility data quality. This method identifies high-quality samples, improving the accuracy of regulatory element identification and heritability analysis.

Keywords:

chromatin accessibility gkmQC quality control sequence-based model

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Last Updated: Aug 17, 2025

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Published on: November 13, 2017

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Formaldehyde-assisted Isolation of Regulatory Elements to Measure Chromatin Accessibility in Mammalian Cells

Formaldehyde-assisted Isolation of Regulatory Elements to Measure Chromatin Accessibility in Mammalian Cells

Published on: April 2, 2018

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Chromatin accessibility assays are crucial for identifying gene regulatory elements involved in transcriptional regulation.
Data quality in these assays can be highly variable due to biological and technical factors, impacting downstream analyses.
Existing quality assessment methods may not adequately capture the nuances of chromatin accessibility data.

Purpose of the Study:

To develop a novel sequence-based machine learning method for evaluating and refining chromatin accessibility data quality.
To introduce gapped k-mer SVM quality check (gkmQC) as a robust framework for assessing data quality and identifying high-quality samples.
To demonstrate the utility of gkmQC in improving the biological interpretation of chromatin accessibility data.

Main Methods:

Developed gapped k-mer SVM quality check (gkmQC), a machine learning framework utilizing sequence information to predict data quality.
Trained and tested gkmQC on 886 DNase-seq samples from the ENCODE/Roadmap projects.
Evaluated the performance of gkmQC in identifying high-quality (HQ) samples and optimizing peak-calling thresholds.

Main Results:

gkmQC effectively identifies HQ samples, even those with low conventional quality scores due to marginal read depths.
Peaks from HQ samples identified by gkmQC align more accurately with functional regulatory elements.
HQ samples demonstrate greater enrichment of regulatory elements with functional variants and explain more phenotype heritability.

Conclusions:

gkmQC provides reliable quality metrics for chromatin accessibility data, enhancing the identification of functional regulatory elements.
The method improves the biological relevance of peak calling, particularly for datasets with limited read depth or from rare cell types.
gkmQC offers a valuable tool for optimizing experimental design and data analysis in epigenomic studies.