HiC-Pro was designed to process Hi-C data, from raw fastq files (paired-end Illumina data) to the normalized contact maps. Since version 2.7.0, HiC-Pro supports the main Hi-C protocols, including digestion protocols as well as protocols that do not require restriction enzyme such as DNase Hi-C. In practice, HiC-Pro can be used to process 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 library for more information).

References:

• http://www.genomebiology.com/2015/16/1/259

• http://nservant.github.io/HiC-Pro/

• Module Name: hicpro (see the modules page for more information)
• Test files: /usr/local/apps/hicpro/test
• The --cpur-per-task should match the N_CPU set in the config.txt. For example, if it's 'N_CPU = 4' in the config.txt, then one should use --cpus-per-task=4 when submit the job.

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive --cpus-per-task=4 --mem=40g
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load hicpro
[user@cn3144 ~]$ cp -r /usr/local/apps/hicpro/test /data/$USER/hicpro/test
[user@cn3144 ~]$ cd /data/$USER/hicpro/test
[user@cn3144 ~]$ tar xvfz HiCPro_testdata.tar.gz

# Modify the config.txt and change 'username@hpc.nih.gov' to your email address

[user@cn3144 ~]$ HiC-Pro -i test_data -o output -c config.txt

# Running the aligment step of HiC-Pro in parallel.
[user@cn3144 ~]$ module load hicpro/3.1.0_v2

# First, split reads in small chunks, by default --nreads would be 2,000,000:
[user@cn3144 ~]$ split_reads.py \
                           --results_folder split_test/dixon_2M_split \
                           --nreads 50000 \
                           ./dixon_2M/SRR400264_00_R1.fastq.gz 
[user@cn3144 ~]$ split_reads.py \
                           --results_folder split_test/dixon_2M_split \
                           --nreads 50000 \
                           ./dixon_2M/SRR400264_00_R2.fastq.gz 

# Then, generate two sbatch jobs:
[user@cn3144 ~]$ HiC-Pro \
                          -i split_test \
                          -o split_out \
                          -c config.txt -p

Run HiC-Pro 3.1.0 parallel mode
The following command will launch the parallel workflow through 5 torque jobs:
sbatch HiCPro_step1_apptest1.sh
The following command will merge the processed data and run the remaining steps per sample:
sbatch HiCPro_step2_apptest1.sh 

# Last, submit sbatch jobs:
[user@cn3144 ~]$ cd split_out
[user@cn3144 ~]$ sbatch --dependency=afterok:$(sbatch HiCPro_step1_apptest1.sh) HiCPro_step2_apptest1.sh 
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. sbatch.sh). For example:

#!/bin/bash
set -e
module load hicpro
cd /data/$USER/hicpro/test
HiC-Pro -i test_data -o output -c config.txt

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=4 --mem=40g sbatch.sh

Create a swarmfile (e.g. job.swarm). For example:

cd dir1; HiC-Pro -i test_data -o output -c config.txt
cd dir2; HiC-Pro -i test_data -o output -c config.txt
cd dir3; HiC-Pro -i test_data -o output -c config.txt

Submit this job using the swarm command.

swarm -f job.swarm -g 40 -t 4 --module hicpro