Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Human Lymph Node Cellular Senescence Atlas Reveals Age-Dependent Alteration in Germinal Center B Cell Function and Niches.

bioRxiv : the preprint server for biology·2026

Same author

Locat: Joint enrichment and depletion testing identifies localized marker genes in single-cell transcriptomics.

bioRxiv : the preprint server for biology·2026

Same author

Sharp cell type boundaries emerge from coordinated morphogen signaling.

bioRxiv : the preprint server for biology·2026

Same author

CONFIDENT-HFpEF: A Machine Learning-Based Risk Stratification for Mortality and Hospitalization Using Multimodal Real-World Data.

ESC heart failure·2026

Same author

Immunometabolic resistors of aging in long-lived golden spiny mice.

Science advances·2026

Same author

MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics.

bioRxiv : the preprint server for biology·2026

Same journal

Correction to 'New origin firing is inhibited by APC/CCdh1 activation in S-phase after severe replication stress'.

Nucleic acids research·2026

Same journal

VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.

Nucleic acids research·2026

Same journal

Accessibility of telomeric overhangs to stabilizing small-molecule ligands.

Nucleic acids research·2026

Same journal

Multivalent interactions mediate SNAIL transcription factor stimulation of the nucleosome deacetylase activity of the CoREST complex.

Nucleic acids research·2026

Same journal

Genome-wide mapping of DNA G-quadruplexes in Trypanosoma brucei chromatin reveals enrichment in coding regions and transcription start sites.

Nucleic acids research·2026

Same journal

Correction to 'The Gene Ontology knowledgebase in 2026'.

Nucleic acids research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 25, 2026

Isolation and Cultivation of Neural Progenitors Followed by Chromatin-Immunoprecipitation of Histone 3 Lysine 79 Dimethylation Mark

Isolation and Cultivation of Neural Progenitors Followed by Chromatin-Immunoprecipitation of Histone 3 Lysine 79 Dimethylation Mark

Published on: January 26, 2018

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments.

Mariann Micsinai¹, Fabio Parisi, Francesco Strino

¹Yale University School of Medicine, Department of Pathology, New Haven, CT 06520, USA.

Nucleic Acids Research

|February 7, 2012

Summary

This summary is machine-generated.

We developed Qeseq, a new algorithm for analyzing ChIP-Seq data, to identify enriched genomic regions. Our study also provides a robust method for comparing ChIP-Seq peak callers and suggests using default parameters for stable performance.

More Related Videos

The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin

The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin

Published on: April 11, 2014

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Published on: December 10, 2014

Related Experiment Videos

Last Updated: May 25, 2026

Isolation and Cultivation of Neural Progenitors Followed by Chromatin-Immunoprecipitation of Histone 3 Lysine 79 Dimethylation Mark

Isolation and Cultivation of Neural Progenitors Followed by Chromatin-Immunoprecipitation of Histone 3 Lysine 79 Dimethylation Mark

Published on: January 26, 2018

The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin

The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin

Published on: April 11, 2014

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Automating ChIP-seq Experiments to Generate Epigenetic Profiles on 10,000 HeLa Cells

Published on: December 10, 2014

Area of Science:

Genomics
Bioinformatics
Epigenetics

Background:

ChIP-Seq (Chromatin Immunoprecipitation Sequencing) is crucial for studying epigenetic marks.
Analyzing diverse ChIP-Seq signal patterns requires advanced algorithms.
Objective comparisons of existing ChIP-Seq peak callers are needed.

Purpose of the Study:

To develop a novel algorithm, Qeseq, for detecting ChIP-Seq enriched regions.
To objectively assess Qeseq's performance against 14 other peak finders.
To establish a reproducible methodology for benchmarking ChIP-Seq algorithms.

Main Methods:

Developed Qeseq using iterative recalibration and neighbor joining for read analysis.
Designed a Validation Discriminant Analysis (VDA) protocol for selecting validation sites.
Generated two comprehensive ChIP-Seq validation datasets for key epigenetic marks.

Main Results:

Qeseq effectively identifies enriched regions of varying lengths.
Parameter optimization is dataset-specific; default parameters offer stable performance across datasets.
A systematic exploration of 315 parameter configurations revealed generalization challenges.

Conclusions:

Qeseq offers a novel approach to ChIP-Seq data analysis.
Default parameters are recommended for robust ChIP-Seq peak calling.
The developed methodology facilitates unbiased comparison and development of high-throughput sequencing tools.