Parabricks Hands-on

Objectives

Run Basic Parabricks pipeline
Time: 30 minutes

Step 01: Checklist

Checklist

Login to the VM assigned to you (Follow the instructions given previously)
Check for docker image - nvcr.io/nvidia/clara/clara-parabricks:4.3.0-1

Commands:

# List Docker images in the VM
docker images
# Move the home directory
cd $HOME
# Check current working-directory (check if you are in $HOME)
echo current working directory: $(pwd)

Output

REPOSITORY                              TAG       IMAGE ID       CREATED         SIZE
nvcr.io/nvidia/clara/clara-parabricks   4.3.0-1   1a84efd2eedf   14 months ago   3.23GB

current working directory: /home/ubuntu

Step 02: Generate Parabricks run-scripts

Step 02.1: fq2bam

Note

echo '#!/bin/bash

FASTA="/data/ngs/ref/Homo_sapiens_assembly38.fasta"
KNOWN_SITES="/data/ngs/ref/Homo_sapiens_assembly38.known_indels.vcf.gz"

READ1="/data/ngs/fastq/dw_sample_R1.fastq.gz"
READ2="/data/ngs/fastq/dw_sample_R2.fastq.gz"

pbrun fq2bam \
--ref ${FASTA} \
--in-fq ${READ1} ${READ2} \
--num-gpus 1 \
--knownSites ${KNOWN_SITES} \
--out-bam pbrun_fq2bam_GPU.bam \
--out-recal-file pbrun_recal_gpu.txt \
--logfile fq2bam.log \
--tmp-dir .'  > fq2bam.sh

Step 02.2: applybqsr

Note

echo '#!/bin/bash

FASTA="/data/ngs/ref/Homo_sapiens_assembly38.fasta"

pbrun applybqsr \
--ref ${FASTA} \
--in-bam pbrun_fq2bam_GPU.bam \
--num-gpus 1 \
--in-recal-file pbrun_recal_gpu.txt \
--logfile applybqsr.log \
--out-bam  pbrun_fq2bam_GPU_applybqsr.bam ' > applybqsr.sh 

Step 02.3: haplotypecaller

Note

echo '#!/bin/bash

FASTA="/data/ngs/ref/Homo_sapiens_assembly38.fasta"

pbrun haplotypecaller \
--ref ${FASTA} \
--num-gpus 1 \
--in-bam pbrun_fq2bam_GPU_applybqsr.bam \
--logfile hc.log \
--out-variants  pbrun_fq2bam_GPU.bam_applybqsr.vcf ' > hc.sh

Step 3: Run docker in interactive mode

Run docker in interactive mode and enter the docker container

Note

docker run \
-it \
--rm \
--gpus all \
-v /data:/data \
-v $PWD:$PWD \
-w $PWD \
nvcr.io/nvidia/clara/clara-parabricks:4.3.0-1 bash

Step 4: Execute run_scripts

Now you are inside the docker container

# If you are inside docker; you'll see `root@712558e5c91f:/home/ubuntu#`
# Execute bash scripts you created

# Run fq2bam.sh 
bash fq2bam.sh

# Run applybqsr.sh 
bash applybqsr.sh 

# Run hc.sh
bash hc.sh

Exit the docker container with exit command

Step 5: Inspect results

Note

ls -l
total 1041856
-rw-r--r-- 1 root root      1707 Jun  5 21:42 applybqsr.log
-rw-rw-r-- 1 1000 1000       255 Jun  5 21:16 applybqsr.sh
drwxrwxr-x 3 1000 1000      4096 Jun  2 10:49 data
-rw-r--r-- 1 root root   2551016 Jun  5 13:37 deepvariant.vcf
-rw-r--r-- 1 root root      1893 Jun  5 13:37 dv.log
-rw-rw-r-- 1 1000 1000        33 Jun  4 13:25 file-with-numbers.txt
-rw-r--r-- 1 root root      4976 Jun  5 21:42 fq2bam.log
-rw-rw-r-- 1 1000 1000       443 Jun  5 21:15 fq2bam.sh
-rw-r--r-- 1 root root      1941 Jun  5 21:44 hc.log
-rw-rw-r-- 1 1000 1000       235 Jun  5 21:17 hc.sh
-rw-r--r-- 1 root root 516112463 Jun  5 21:42 pbrun_fq2bam_GPU.bam
-rw-r--r-- 1 root root   5447024 Jun  5 21:42 pbrun_fq2bam_GPU.bam.bai
-rw-r--r-- 1 root root   4719876 Jun  5 21:44 pbrun_fq2bam_GPU.bam_applybqsr.vcf
-rw-r--r-- 1 root root 532038424 Jun  5 21:42 pbrun_fq2bam_GPU_applybqsr.bam
-rw-r--r-- 1 root root   5447024 Jun  5 21:42 pbrun_fq2bam_GPU_applybqsr.bam.bai
-rw-r--r-- 1 root root     86706 Jun  5 21:42 pbrun_fq2bam_GPU_chrs.txt
-rw-r--r-- 1 root root    392014 Jun  5 21:42 pbrun_recal_gpu.txt
-rw-r--r-- 1 root root         0 Jun  4 16:59 test