HiCExplorer

Set of programs to process, analyze and visualize Hi-C, Micro-C and cHi-C data

Sequencing techniques that probe the 3D organization of the genome generate large amounts of data whose processing, analysis and visualization is challenging. HiCExplorer is a set of tools for the analysis and visualization of chromosome conformation data. It builds contact matrices from Hi-C, Micro-C and capture Hi-C reads, corrects them, checks their quality and reproducibility, calls domains, loops, compartments and stripes, compares conditions, scores capture Hi-C interactions and converts between the cool, mcool, h5 and .hic formats. It visualizes contact matrices together with genes, ChIP-seq coverage and other genomic tracks, long-range contacts and viewpoints.

HiCExplorer 4.0

HiCExplorer 4.0 (November 2026) keeps the names, command lines, file formats and outputs of every tool of 3.7.6 and the Python API, and runs them on a new compiled core. On real data its analysis tools run three to eight times faster than in 3.7.6 with two to four times less memory, hicBuildMatrix needs about a twelfth of the CPU time, and most tools run multithreaded (--threads):

Task	3.7.6	4.0	Speedup	Peak memory
hicBuildMatrix, 92.7M read pairs, CPU time	2,245 s	197 s	11.4x	15.3 GB to 6.3 GB
hicDetectLoops, GM12878 chr1, 10 kb	20.0 s	6.3 s	3.2x	636 MB to 263 MB
hicFindTADs, GM12878 chr1, 10 kb	32.3 s	4.0 s	8.2x	6.25 GB to 1.52 GB
hicPCA, GM12878 chr1, 100 kb	11.1 s	4.0 s	2.8x	554 MB to 166 MB

Stripe calling with hicDetectStripes gives the stripes of Stripenn on 22 GM12878 autosomes at 10 kb in 210 s instead of 3,015 s. The benchmarks page of the documentation gives the data, the threads and the CHiCAGO runs.

New in version 4:

• Capture Hi-C with CHiCAGO: chicChicagoBackgroundModel, chicChicagoScores, chicChicagoSignificantInteractions and chicChicagoPlotViewpoint call the same interactions as R CHiCAGO, from CHiCAGO's input files or directly from contact matrices.
• Domains: hicArrowhead (the Arrowhead algorithm of Juicer tools), nested domains and a CTCF motif filter in hicFindTADs.
• Stripes: hicDetectStripes calls the same stripes as Stripenn.
• Differential analysis: hicDifferentialAnalysis tests domains, loops and compartments between conditions with replicates.
• Quality control: hicMatrixQC reports on finished cool and mcool matrices, and hicCorrelate computes the reproducibility measures SCC, GenomeDISCO, HiC-Spector, QuASAR-Rep and ENT3C.
• Formats: hicConvertFormat reads .hic files of versions 6 to 9 and writes versions 8 and 9, and hicBuildMatrix reads the 4DN .pairs format.

The full list is in the release notes, and the data and all measurements are on the benchmarks page.

Single-cell Hi-C data

We provide the scHiCExplorer to create, manipulate, analyse and visualize single-cell Hi-C data in its own software: The scHiCExplorer.

Citation:

Joachim Wolff, Rolf Backofen, Björn Grüning. Loop detection using Hi-C data with HiCExplorer, GigaScience, Volume 11, 2022, giac061, https://doi.org/10.1093/gigascience/giac061

Joachim Wolff, Leily Rabbani, Ralf Gilsbach, Gautier Richard, Thomas Manke, Rolf Backofen, Björn A Grüning. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization, Nucleic Acids Research, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W177–W184, https://doi.org/10.1093/nar/gkaa220

Joachim Wolff, Vivek Bhardwaj, Stephan Nothjunge, Gautier Richard, Gina Renschler, Ralf Gilsbach, Thomas Manke, Rolf Backofen, Fidel Ramírez, Björn A Grüning. "Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization", Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W11–W16, doi: https://doi.org/10.1093/nar/gky504

Fidel Ramirez, Vivek Bhardwaj, Jose Villaveces, Laura Arrigoni, Bjoern A Gruening, Kin Chung Lam, Bianca Habermann, Asifa Akhtar, Thomas Manke. "High-resolution TADs reveal DNA sequences underlying genome organization in flies". Nature Communications, Volume 9, Article number: 189 (2018), doi: https://doi.org/10.1038/s41467-017-02525-w

Availability

HiCExplorer is available as a command line suite of tools on this very GitHub repository and also on other platforms (detailed in Installation below).

A Galaxy HiCExplorer version is directly available to users at http://hicexplorer.usegalaxy.eu. Training material is available at the Galaxy Training Network, while a Galaxy Tour is available here for users not familiar with this platform. Galaxy HiCExplorer is also available as a Docker image at the Docker Galaxy HiCExplorer GitHub repository. Finally, this Galaxy version is available on the Galaxy Tool Shed and on the corresponding GitHub repository.

Installation

HiCExplorer runs on Linux (x86_64 and arm64) and macOS (arm64). We recommend installing it with conda from Bioconda:

$ conda create --name hicexplorer -c conda-forge -c bioconda hicexplorer=4.0
$ conda activate hicexplorer

The package contains the command-line tools and the Python API. To learn more about conda environments, see the conda documentation.

Build from source

Building from source needs a C++20 compiler, CMake 3.24 or newer and the native libraries listed in cpp/BUILDING.md, most easily taken from a conda environment:

$ git clone https://github.com/deeptools/HiCExplorer.git
$ cd HiCExplorer
$ pip install .

The installation documentation describes the details.

Galaxy version

Install with Docker

Installation instructions as a Docker image can be followed at https://github.com/deeptools/docker-galaxy-hicexplorer.

Install with Tool Shed

Galaxy HiCExplorer is part of the Galaxy Tool Shed and can be installed from there to any Galaxy server following this link.

Documentation:

Please visit our complete documentation Here. This documentation is also available directly within Galaxy.