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

You might also read

Related Articles

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

Sort by

Same author

The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.

Development (Cambridge, England)·2026

Same author

KAT6A is essential for developmental control gene expression in neural stem and progenitor cells.

PLoS genetics·2026

Same author

Acetyl-carnitine improves hyperactivity and learning deficits in <i>KAT6A</i> haploinsufficient mice.

Life science alliance·2026

Same author

Interleukin 4 selectively expands functional type 1 conventional dendritic cells from bone marrow progenitors.

Cell reports·2025

Same author

Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR.

Nucleic acids research·2025

Same author

MORC2 is a phosphorylation-dependent DNA compaction machine.

Nature communications·2025

Search research articles

Related Experiment Video

Updated: Apr 5, 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

diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data.

Aaron T L Lun^1,2, Gordon K Smyth^3,4

¹The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Melbourne, Australia. alun@wehi.edu.au.

BMC Bioinformatics

|August 19, 2015

Summary

The diffHic software package identifies significant changes in genome interactions from Hi-C data. This tool offers a statistically rigorous approach for differential interaction analysis between biological conditions.

More Related Videos

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: Apr 5, 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

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Chromatin conformation capture with high-throughput sequencing (Hi-C) measures in vivo genome interactions.
Conventional Hi-C analysis focuses on detecting significant interactions.
Identifying differential interactions between conditions offers a more rigorous and biologically relevant strategy.

Purpose of the Study:

Introduce the diffHic software package for detecting differential interactions in Hi-C data.
Provide a robust computational tool for analyzing changes in genome-wide interactions across biological conditions.

Main Methods:

diffHic offers methods for read pair alignment, processing, and counting into bin pairs.
Includes filtering of low-abundance events and normalization for biases (trended, CNV-driven).
Utilizes the edgeR package's statistical framework for modeling variability and testing differences.

Main Results:

diffHic successfully detects significant differential interactions in real Hi-C data.
The software includes options for visualizing results.
Evaluated performance against existing methods using simulated data.

Conclusions:

diffHic effectively identifies significant differences in interaction intensity between biological conditions.
The package demonstrates favorable performance compared to existing tools on simulated data.
diffHic is a viable approach for differential analyses of Hi-C data.