This pipeline was originally set up from the HiC-Pro workflow. It was designed to process Hi-C data from raw FastQ files (paired-end Illumina data) to normalized contact maps. The current version supports most protocols, including digestion protocols as well as protocols that do not require restriction enzymes such as DNase Hi-C. In practice, this workflow was successfully applied to many data-sets including dilution Hi-C, in situ Hi-C, DNase Hi-C, Micro-C, capture-C, capture Hi-C or HiChip data.

Contact maps are generated in standard formats including HiC-Pro, and cooler for downstream analysis and visualization. Addition analysis steps such as compartments and TADs calling are also available.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible.

Pipeline summary

  1. HiC-Pro data processing (HiC-Pro)
    1. Mapping using a two steps strategy to rescue reads spanning the ligation sites (bowtie2)
    2. Detection of valid interaction products
    3. Duplicates removal
    4. Generate raw and normalized contact maps (iced)
  2. Create genome-wide contact maps at various resolutions (cooler)
  3. Contact maps normalization using balancing algorithm (cooler)
  4. Export to various contact maps formats (HiC-Pro, cooler)
  5. Quality controls (HiC-Pro, HiCExplorer)
  6. Compartments calling (cooltools)
  7. TADs calling (HiCExplorer, cooltools)
  8. Quality control report (MultiQC)

Quick Start

  1. Install nextflow (>=20.04.0)

  2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

  3. Download the pipeline and test it on a minimal dataset with a single command

    nextflow run nf-core/hic -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

    Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.

  4. Start running your own analysis!

    nextflow run nf-core/hic -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input '*_R{1,2}.fastq.gz' --genome GRCh37


The nf-core/hic pipeline comes with documentation about the pipeline: usage and output.

For further information or help, don’t hesitate to get in touch on Slack. You can join with this invite.


nf-core/hic was originally written by Nicolas Servant.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #hic channel (you can join with this invite).


If you use nf-core/hic for your analysis, please cite it using the following doi: 10.5281/zenodo.2669513

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

In addition, references of tools and data used in this pipeline are as follows:

HiC-Pro: An optimized and flexible pipeline for Hi-C processing.

Nicolas Servant, Nelle Varoquaux, Bryan R. Lajoie, Eric Viara, Chongjian Chen, Jean-Philippe Vert, Job Dekker, Edith Heard, Emmanuel Barillot.

Genome Biology 2015, 16:259 doi: 10.1186/s13059-015-0831-x