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Related Experiment Video

Updated: Mar 26, 2026

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

HiCUP: pipeline for mapping and processing Hi-C data.

Steven Wingett¹, Philip Ewels², Mayra Furlan-Magaril³

¹Bioinformatics Group, The Babraham Institute, Cambridge, CB22 3AT, UK; Nuclear Dynamics Programme, The Babraham Institute, Cambridge, CB22 3AT, UK.

|February 3, 2016

Summary

This summary is machine-generated.

HiCUP is a new pipeline for processing Hi-C and Capture Hi-C data to study 3D genome organization. It maps genomic data, removes artifacts, and provides quality control reports for improved experimental design.

Keywords:

Bioinformatics CHi-C Chromatin Epigenetics Genomics Hi-C Pipeline Structure

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Last Updated: Mar 26, 2026

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

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

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Investigating three-dimensional genomic organization is crucial for understanding cellular functions.
Hi-C and Capture Hi-C (CHi-C) are key experimental techniques for studying genome architecture.
Processing the complex data from these experiments requires specialized bioinformatics tools.

Purpose of the Study:

To introduce HiCUP, a comprehensive bioinformatics pipeline for processing Hi-C and CHi-C sequence data.
To enable accurate mapping of sequencing data to reference genomes and removal of experimental artifacts.
To provide detailed quality control (QC) reports to aid in refining future experimental protocols.

Main Methods:

The HiCUP pipeline processes raw sequence data from Hi-C and CHi-C experiments.
It maps sequencing reads to a user-defined reference genome.
Artifacts commonly found in Hi-C/CHi-C data are identified and removed.

Main Results:

HiCUP successfully maps sequence data and removes artifacts, ensuring cleaner datasets for analysis.
The pipeline generates an interpretable and detailed quality control (QC) report.
The software is freely available and has been successfully applied in multiple peer-reviewed publications.

Conclusions:

HiCUP provides a robust and user-friendly solution for processing Hi-C and CHi-C data.
The tool enhances the reliability of 3D genome organization studies through artifact removal and QC.
Its availability and proven utility make it a valuable resource for the research community.