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

Convergence and divergence of DNA methylation and gene expression patterns in neopolyploid Arabidopsis kamchatica.

Nature communications·2026

Same author

Elucidating the toxicity of the plasticizer DPhP: Evidence of neurotoxicity and developmental impairment from <i>in vitro</i> to <i>in vivo</i> models.

Current research in toxicology·2026

Same author

Mapping the functional connectome between grey matter and white matter.

Communications biology·2026

Same author

Photoelectric Therapy Needle-Combined ICG-Cu(II)-AIBI Infinite Coordination Polymer Nanoparticles for Free Radical Spatiotemporal-Matched Photodynamic-Thermodynamic Tumor Synergistic Therapy.

ACS applied materials & interfaces·2026

Same author

Cell-to-cell variability and gain of methylation at polycomb CpG islands as a hallmark of aging.

Nature communications·2026

Same author

Hybrid untargeted short-read and targeted long-read RNA sequencing facilitates genotype-phenotype associations at single-cell resolution.

Genome biology·2026

Same journal

Somatic mobility of transposons is explosive and shaped by distinct integration biases in Arabidopsis thaliana.

Genome biology·2026

Same journal

UK Biobank whole-genome sequencing reveals robust contributions of rare variants to complex-trait heritability.

Genome biology·2026

Same journal

A one-week automated genome-wide optical pooled screen using OttoSeq.

Genome biology·2026

Same journal

Integrated lipidomic and transcriptomic profiling of the host response in human malaria.

Genome biology·2026

Same journal

Centromeric satellite expansion drives genome evolution in the snowy owl.

Genome biology·2026

Same journal

Mapping the landscape of allele-specific expression in porcine genomes.

Genome biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2025

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Published on: February 1, 2022

Benchmarking computational methods for single-cell chromatin data analysis.

Siyuan Luo^1,2, Pierre-Luc Germain^2,3,4, Mark D Robinson^5,6

¹Laboratory of Nutrition and Metabolic Epigenetics, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

|August 16, 2024

Summary

This summary is machine-generated.

Benchmarking single-cell ATAC-seq (scATAC-seq) analysis methods reveals optimal feature engineering pipelines for cell type identification. SnapATAC and SnapATAC2 excel in discerning complex cell types and scaling with large datasets.

Keywords:

Benchmark Clustering Dimensional reduction Feature engineering ScATAC-seq

More Related Videos

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Published on: April 5, 2018

Related Experiment Videos

Last Updated: Jun 16, 2025

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Single-Cell Factor Localization on Chromatin using Ultra-Low Input Cleavage Under Targets and Release using Nuclease

Published on: February 1, 2022

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies

Published on: August 13, 2020

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Published on: April 5, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell chromatin accessibility assays like scATAC-seq are vital for multi-omic profiling.
Analyzing sparse, noisy, high-dimensional scATAC-seq data presents significant challenges.
Efficiently extracting cellular heterogeneity information is crucial for cell type identification.

Purpose of the Study:

To benchmark feature engineering pipelines for scATAC-seq data analysis.
To evaluate methods for discovering and discriminating cell types.
To provide guidelines for selecting appropriate analysis tools.

Main Methods:

Benchmarking 8 feature engineering pipelines from 5 recent methods.
Utilizing 10 metrics at cell embedding, graph, and partition levels.
Assessing method performance across different data processing stages.

Main Results:

Feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing methods.
SnapATAC and SnapATAC2 are preferred for datasets with complex cell-type structures.
SnapATAC2 and ArchR demonstrate superior scalability for large datasets.

Conclusions:

Guidelines are provided for selecting scATAC-seq analysis methods based on dataset characteristics.
Specific methods are recommended for complex cell types and large-scale data analysis.
Understanding method strengths and weaknesses optimizes single-cell data interpretation.