Biowulf High Performance Computing at the NIH
HiC-Pro on HPC

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).


Important Notes

Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

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 '' 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 ~]$ \
                           --results_folder split_test/dixon_2M_split \
                           --nreads 50000 \
[user@cn3144 ~]$ \
                           --results_folder split_test/dixon_2M_split \
                           --nreads 50000 \

# 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:
The following command will merge the processed data and run the remaining steps per sample:

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

Batch job
Most jobs should be run as batch jobs.

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

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
Swarm of Jobs
A swarm of jobs is an easy way to submit a set of independent commands requiring identical resources.

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
-g # Number of Gigabytes of memory required for each process (1 line in the swarm command file)
-t # Number of threads/CPUs required for each process (1 line in the swarm command file).
--module Loads the module for each subjob in the swarm