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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...

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Updated: Jul 2, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

An integrated model for detecting significant chromatin interactions from high-resolution Hi-C data.

Mark Carty^1,2,3, Lee Zamparo¹, Merve Sahin^1,3

¹Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA.

Nature Communications

|May 18, 2017

Summary

HiC-DC is a new method that estimates the statistical significance of chromatin interactions in Hi-C experiments. It identifies structural and regulatory interactions, revealing insights into chromatin architecture.

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Last Updated: Jul 2, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Chromatin interactions are crucial for genome regulation.
Estimating statistical significance of Hi-C data is challenging due to biases and noise.
Identifying functional interactions requires robust statistical methods.

Purpose of the Study:

To present HiC-DC, a novel method for estimating the statistical significance of chromatin interactions from Hi-C data.
To account for systematic variations and model complex count distributions in Hi-C data.
To identify biologically relevant chromatin interactions at high resolution.

Main Methods:

Developed HiC-DC, a method employing hurdle negative binomial regression.
Modeled systematic sources of variation: distance-dependent ligation, GC content, and mappability bias.
Modeled zero inflation and overdispersion in Hi-C read counts.

Main Results:

HiC-DC detects significant chromatin interactions at the sub-topologically associating domain level.
Identified potential structural and regulatory interactions supported by CTCF binding, DNase accessibility, and histone marks.
CTCF-associated interactions are enriched at 700 kb-1.5 Mb; DNase-associated interactions at <500 kb and >1.5 Mb.
Observed enrichment of long-range interactions connecting histone genes on chromosome 6.

Conclusions:

HiC-DC provides a principled approach to assess the statistical significance of Hi-C interactions.
The method reveals novel insights into chromatin architecture and regulatory element interactions.
Findings highlight the complex organization of chromatin, including specific patterns around histone gene loci.