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An optimized and flexible pipeline for Hi-C data processing

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Conda Singularity Docker

MultiQC Forum DOI

Find documentation and examples at

For any question about HiC-Pro, please contact or use the HiC-Pro forum

What is HiC-Pro ?

HiC-Pro was designed to process Hi-C data, from raw fastq files (paired-end Illumina data) to normalized contact maps. It supports the main Hi-C protocols, including digestion protocols as well as protocols that do not require restriction enzymes such as DNase Hi-C. In practice, HiC-Pro 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.
The pipeline is flexible, scalable and optimized. It can operate either on a single laptop or on a computational cluster. HiC-Pro is sequential and each step of the workflow can be run independantly.
HiC-Pro includes a fast implementatation of the iterative correction method (see the iced python package for more information). Finally, HiC-Pro can use phasing data to build allele-specific contact maps.

If you use HiC-Pro, please cite :

Servant N., Varoquaux N., Lajoie BR., Viara E., Chen CJ., Vert JP., Dekker J., Heard E., Barillot E. HiC-Pro: An optimized and flexible pipeline for Hi-C processing. Genome Biology 2015, 16:259 doi:10.1186/s13059-015-0831-x


Using HiC-Pro through conda

In order to ease the installation of HiC-Pro dependancies, we provide a yml file for conda with all required tools. In order to build your conda environment, first install miniconda and use :

conda env create -f MY_INSTALL_PATH/HiC-Pro/environment.yml -p WHERE_TO_INSTALL_MY_ENV
conda activate WHERE_TO_INSTALL_MY_ENV

Using the HiC-Pro Docker image

A docker image is automatically build and available on Docker Hub To pull a Docker image, simply use :

docker pull nservant/hicpro:latest

Note that the tag may depend on the HiC-Pro version.

You can also build your own image from the root folder using

docker build -t hicpro:3.1.0 .

Using HiC-Pro through Singularity

HiC-Pro provides a Singularity container to ease its installation process. A ready-to-use container is available here.

In order to build you own Singularity image;

1- Install singularity

2- Build the singularity HiC-Pro image using the 'Singularity' file available in the HiC-Pro root directory.

sudo singularity build hicpro_latest_ubuntu.img MY_INSTALL_PATH/HiC-Pro/envs/Singularity

3- Run HiC-pro

You can then either use HiC-Pro using the 'exec' command ;

singularity exec hicpro_latest_ubuntu.img HiC-Pro -h

Or directly use HiC-Pro within the Singularity shell

singularity shell hicpro_latest_ubuntu.img
HiC-Pro -h

How to install it ?

The HiC-Pro pipeline requires the following dependencies :

  • The bowtie2 mapper
  • Python (>3.7) with pysam (>=0.15.4), bx-python(>=0.8.8), numpy(>=1.18.1), and scipy(>=1.4.1) libraries.
    Note that the current version no longer supports python 2
  • R with the RColorBrewer and ggplot2 (>2.2.1) packages
  • g++ compiler
  • samtools (>1.9)
  • Unix sort (which support -V option) is required ! For Mac OS user, please install the GNU core utilities !

Note that Bowtie >2.2.2 is strongly recommanded for allele specific analysis.

To install HiC-Pro, be sure to have the appropriate rights and run :

tar -zxvf HiC-Pro-master.tar.gz
cd HiC-Pro-master
## Edit config-install.txt file if necessary
make configure
make install

Note that if some of these dependencies are not installed (i.e. not detected in the $PATH), HiC-Pro will try to install them.
You can also edit the config-install.txt file and manually defined the paths to dependencies.

PREFIX Path to installation folder
BOWTIE2_PATH Full path the bowtie2 installation directory
SAMTOOLS_PATH Full path to the samtools installation directory
R_PATH Full path to the R installation directory
PYTHON_PATH Full path to the python installation directory
CLUSTER_SYS Scheduler to use for cluster submission. Must be TORQUE, SGE, SLURM or LSF

Annotation Files

In order to process the raw data, HiC-Pro requires three annotation files. Note that the pipeline is provided with some Human and Mouse annotation files.
Please be sure that the chromosome names are the same than the ones used in your bowtie indexes !

  • A BED file of the restriction fragments after digestion. This file depends both of the restriction enzyme and the reference genome. See the FAQ and the HiC-Pro utilities for details about how to generate this file. A few annotation files are provided with the HiC-Pro sources as examples.
   chr1   0       16007   HIC_chr1_1    0   +
   chr1   16007   24571   HIC_chr1_2    0   +
   chr1   24571   27981   HIC_chr1_3    0   +
   chr1   27981   30429   HIC_chr1_4    0   +
   chr1   30429   32153   HIC_chr1_5    0   +
   chr1   32153   32774   HIC_chr1_6    0   +
   chr1   32774   37752   HIC_chr1_7    0   +
   chr1   37752   38369   HIC_chr1_8    0   +
   chr1   38369   38791   HIC_chr1_9    0   +
   chr1   38791   39255   HIC_chr1_10   0   +
  • A table file of chromosomes' size. This file can be easily find on the UCSC genome browser. Of note, pay attention to the contigs or scaffolds, and be aware that HiC-pro will generate a map per chromosomes pair. For model organisms such as Human or Mouse, which are well annotated, we usually recommand to remove all scaffolds.
   chr1    249250621
   chr2    243199373
   chr3    198022430
   chr4    191154276
   chr5    180915260
   chr6    171115067
   chr7    159138663
   chr8    146364022
   chr9    141213431
   chr10   135534747
  • The bowtie2 indexes. See the bowtie2 manual page for details about how to create such indexes.

How to use it ?

First have a look at the help message !

  HiC-Pro --help
  usage : HiC-Pro -i INPUT -o OUTPUT -c CONFIG [-s ANALYSIS_STEP] [-p] [-h] [-v]
  Use option -h|--help for more information

  HiC-Pro 3.1.0

   -i|--input INPUT : input data folder; Must contains a folder per sample with input files
   -o|--output OUTPUT : output folder
   -c|--conf CONFIG : configuration file for Hi-C processing
   [-p|--parallel] : if specified run HiC-Pro on a cluster
   [-s|--step ANALYSIS_STEP] : run only a subset of the HiC-Pro workflow; if not specified the complete workflow is run
      mapping: perform reads alignment - require fast files
      proc_hic: perform Hi-C filtering - require BAM files
      quality_checks: run Hi-C quality control plots
      merge_persample: merge multiple inputs and remove duplicates if specified - require .validPairs files
      build_contact_maps: Build raw inter/intrachromosomal contact maps - require .allValidPairs files
      ice_norm : run ICE normalization on contact maps - require .matrix files
   [-h|--help]: help
   [-v|--version]: version
  • Copy and edit the configuration file 'config-hicpro.txt' in your local folder. See the manual for details about the configuration file

  • Put all input files in a rawdata folder. The input files have to be organized with one folder per sample, such as;

     + sample1
       ++ file1_R1.fastq.gz
       ++ file1_R2.fastq.gz
       ++ ...
     + sample2
       ++ file1_R1.fastq.gz
       ++ file1_R2.fastq.gz
  • Run HiC-Pro on your laptop in standalone model
  • Run HiC-Pro on a cluster (TORQUE/SGE/SLURM/LSF)

In the latter case, you will have the following message :

  Please run HiC-Pro in two steps :
  1- The following command will launch the parallel workflow through 12 torque jobs:
  2- The second command will merge all outputs to generate the contact maps:

Execute the displayed command from the output folder:


Once executed succesfully (may take several hours), run the step using:


Test Dataset

The test dataset and associated results are available here. Small fastq files (2M reads) extracted from the Dixon et al. 2012 paper are available for test.

 ## Get the data. Will download a test_data folder and a configuration file
 wget && tar -zxvf HiCPro_testdata.tar.gz

 ## Edit the configuration file and set the path to Human bowtie2 indexes

 ## Run HiC-Pro
 time HICPRO_INSTALL_DIR/bin/HiC-Pro -c config_test_latest.txt -i test_data -o hicpro_latest_test

Run HiC-Pro 3.1.0
Thu Mar 19, 12:18:10 (UTC+0100)
Bowtie2 alignment step1 ...
Logs: logs/dixon_2M_2/mapping_step1.log
Logs: logs/dixon_2M/mapping_step1.log

Thu Mar 19, 12:18:57 (UTC+0100)
Bowtie2 alignment step2 ...
Logs: logs/dixon_2M_2/mapping_step2.log
Logs: logs/dixon_2M/mapping_step2.log

Thu Mar 19, 12:19:08 (UTC+0100)
Combine R1/R2 alignment files ...
Logs: logs/dixon_2M_2/mapping_combine.log
Logs: logs/dixon_2M/mapping_combine.log

Thu Mar 19, 12:19:13 (UTC+0100)
Mapping statistics for R1 and R2 tags ...
Logs: logs/dixon_2M_2/mapping_stats.log
Logs: logs/dixon_2M/mapping_stats.log

Thu Mar 19, 12:19:15 (UTC+0100)
Pairing of R1 and R2 tags ...
Logs: logs/dixon_2M_2/mergeSAM.log
Logs: logs/dixon_2M/mergeSAM.log

Thu Mar 19, 12:19:25 (UTC+0100)
Assign alignments to restriction fragments ...
Logs: logs/dixon_2M_2/mapped_2hic_fragments.log
Logs: logs/dixon_2M/mapped_2hic_fragments.log

Thu Mar 19, 12:20:10 (UTC+0100)
Merge chunks from the same sample ...
Logs: logs/dixon_2M/merge_valid_interactions.log
Logs: logs/dixon_2M_2/merge_valid_interactions.log

Thu Mar 19, 12:20:11 (UTC+0100)
Merge stat files per sample ...
Logs: logs/dixon_2M/merge_stats.log
Logs: logs/dixon_2M_2/merge_stats.log

Thu Mar 19, 12:20:11 (UTC+0100)
Run quality checks for all samples ...
Logs: logs/dixon_2M/make_Rplots.log
Logs: logs/dixon_2M_2/make_Rplots.log

Thu Mar 19, 12:20:22 (UTC+0100)
Generate binned matrix files ...
Logs: logs/dixon_2M/build_raw_maps.log
Logs: logs/dixon_2M_2/build_raw_maps.log

Thu Mar 19, 12:20:22 (UTC+0100)
Run ICE Normalization ...
Logs: logs/dixon_2M/ice_500000.log
Logs: logs/dixon_2M/ice_1000000.log
Logs: logs/dixon_2M_2/ice_500000.log
Logs: logs/dixon_2M_2/ice_1000000.log

real	2m15,736s
user	4m3,277s
sys	0m24,423s