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Generate BAM/CRAM output given one or more pairs of FASTQ files by using Parabricks

Generate BAM/CRAM output given one or more pairs of FASTQ files by using Parabricks

fq2bam (FQ2BAM + BWA-MEM)

Parabricks fq2bam is a software tool that can generate BAM/CRAM output from one or more pairs of FASTQ files. This tool takes advantage of the parallel computing capabilities of GPUs to speed up the analysis process.

To use Parabricks, users can provide input files in FASTQ format and specify the reference genome they wish to use for alignment. The software uses a proprietary algorithm to perform read alignment, variant calling, and quality control. The output is then generated in BAM or CRAM format, depending on the user's preference.

In this case study, we use the following dataset:

Reference Genome: human_g1k_v37.fasta

Sample Data Source: SRA SRR7733443

Number Of Read: 2 x 5M bp

Read length: 150bp 

We followed the steps below to collect the performance results:

First we need to use the scratch directory, please prepare about 50GB free space for this experiments

If you face any difficult in downloading data or indexing fasta, we already prepare all the file in our share drive

use cmd:

cp /pfss/toolkit/parabricks_data $SCRATCH/parabracks

  1. Download Samples 1.
    1. Download the SRA toolkit from https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software#header-global
    2. tar xfzv sratoolkit.2.10.5-centos_linux64.tar.gz
    3. sratoolkit.2.10.5-centos_linux64/bin/fastq-dump -X 5000000 --split-files SRR9932168

  2. Download Reference 
    1. Download reference from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/human_g1k_v37.fasta.gz 
  3. Reference Indexing
    1. Leverage our Parabricks container which already includes samtools.
    2. singularity run --nv /pfss/containers/clara-parabricks.4.0.0-1.sif bash samtools faidx /human_g1k_v37.fasta 
    3. install bwa and use it to index human_g1k_v37.fasta
      1. #install bwa
        
        git clone https://github.com/lh3/bwa.git
        
        cd bwa
        
        make
        #this process take resource 
        srun --pty -p batch --cpus-per-task=32  --mem=100G bash
        
        ./bwa index ../human_g1k_v37.fasta
  4.  Download Known Site 
    1. download 00-common_all.vcf from https://ftp.ncbi.nih.gov/snp/organisms/human_9606_b150_GRCh37p13/VCF/
    2. singularity run /pfss/containers/gatk.sif gatk IndexFeatureFile -I /00-common_all.vcf 
  5. Run Parabricks
    1. We utilize one NVIDIA A100 here, be careful that Parabricks currently does not support MIG.
    2. srun --pty -p gpu --cpus-per-task=16 --gres=gpu:a100:1 --mem=128G bash
      singularity run --nv /pfss/containers/clara-parabricks.4.1.0-1.sif bash
      
      cd $SCRATCH/parabricks
      
      mkdir tmp
      
      NVIDIA_VISIBLE_DEVICES="0" pbrun fq2bam \
        --num-gpus 1 \
        --ref ./human_g1k_v37.fasta \
        --in-fq ./SRR9932168_1.fastq ./SRR9932168_2.fastq \
        --out-bam ./mark_dups_gpu.bam \
        --tmp-dir ./tmp \
        --knownSites ./00-common_all.vcf \
        --out-recal-file ./recal_gpu.txt

  6.  Result 
    1. The output should be something like this:
    2. main] CMD: /usr/local/parabricks/binaries//bin/bwa mem -Z ./pbOpts.txt /pfss/scratch02/appcara/parabricks/parabricks_sample/Ref/human_g1k_v37.fasta /pfss/scratch02/appcara/parabricks/sratoolkit.3.0.6-centos_linux64/SRR9932168_1.fastq /pfss/scratch02/appcara/parabricks/sratoolkit.3.0.6-centos_linux64/SRR9932168_2.fastq @RG\tID:SRR9932168.1.1\tLB:lib1\tPL:bar\tSM:sample\tPU:SRR9932168.1.1
      [main] Real time: 105.672 sec; CPU: 1343.564 sec
      [PB Info 2023-Aug-01 12:07:41] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:41] ||        Program:                      GPU-BWA mem, Sorting Phase-I        ||
      [PB Info 2023-Aug-01 12:07:41] ||        Version:                                           4.0.0-1        ||
      [PB Info 2023-Aug-01 12:07:41] ||        Start Time:                       Tue Aug  1 12:05:55 2023        ||
      [PB Info 2023-Aug-01 12:07:41] ||        End Time:                         Tue Aug  1 12:07:41 2023        ||
      [PB Info 2023-Aug-01 12:07:41] ||        Total Time:                            1 minute 46 seconds        ||
      [PB Info 2023-Aug-01 12:07:41] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:41] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:41] ||                 Parabricks accelerated Genomics Pipeline                 ||
      [PB Info 2023-Aug-01 12:07:41] ||                              Version 4.0.0-1                             ||
      [PB Info 2023-Aug-01 12:07:41] ||                             Sorting Phase-II                             ||
      [PB Info 2023-Aug-01 12:07:41] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:41] progressMeter - Percentage
      [PB Info 2023-Aug-01 12:07:41] 0.0       0.00 GB
      [PB Info 2023-Aug-01 12:07:51] Sorting and Marking: 10.001 seconds
      [PB Info 2023-Aug-01 12:07:51] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:51] ||        Program:                                  Sorting Phase-II        ||
      [PB Info 2023-Aug-01 12:07:51] ||        Version:                                           4.0.0-1        ||
      [PB Info 2023-Aug-01 12:07:51] ||        Start Time:                       Tue Aug  1 12:07:41 2023        ||
      [PB Info 2023-Aug-01 12:07:51] ||        End Time:                         Tue Aug  1 12:07:51 2023        ||
      [PB Info 2023-Aug-01 12:07:51] ||        Total Time:                                     10 seconds        ||
      [PB Info 2023-Aug-01 12:07:51] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:51] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:51] ||                 Parabricks accelerated Genomics Pipeline                 ||
      [PB Info 2023-Aug-01 12:07:51] ||                              Version 4.0.0-1                             ||
      [PB Info 2023-Aug-01 12:07:51] ||                         Marking Duplicates, BQSR                         ||
      [PB Info 2023-Aug-01 12:07:51] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:07:51] progressMeter -  Percentage
      [PB Info 2023-Aug-01 12:08:01] 0.0       4.38 GB
      [PB Info 2023-Aug-01 12:08:11] 0.0       4.38 GB
      [PB Info 2023-Aug-01 12:08:21] 0.0       4.38 GB
      [PB Info 2023-Aug-01 12:08:31] 0.0       4.38 GB
      [PB Info 2023-Aug-01 12:08:41] 0.0       4.38 GB
      [PB Info 2023-Aug-01 12:08:51] 3.4       4.28 GB
      [PB Info 2023-Aug-01 12:09:01] 100.0     0.00 GB
      [PB Info 2023-Aug-01 12:09:01] BQSR and writing final BAM:  70.031 seconds
      [PB Info 2023-Aug-01 12:09:01] ------------------------------------------------------------------------------
      [PB Info 2023-Aug-01 12:09:01] ||        Program:                          Marking Duplicates, BQSR        ||
      [PB Info 2023-Aug-01 12:09:01] ||        Version:                                           4.0.0-1        ||
      [PB Info 2023-Aug-01 12:09:01] ||        Start Time:                       Tue Aug  1 12:07:51 2023        ||
      [PB Info 2023-Aug-01 12:09:01] ||        End Time:                         Tue Aug  1 12:09:01 2023        ||
      [PB Info 2023-Aug-01 12:09:01] ||        Total Time:                            1 minute 10 seconds        ||
      [PB Info 2023-Aug-01 12:09:01] ------------------------------------------------------------------------------


fq2bam performs the following steps.

fastq2bam_steps.png

Compatible CPU-based BWA-MEM, GATK4 Commands
srun --pty -p batch --cpus-per-task=32  --mem=100G bash
singularity run /pfss/containers/gatk.4.4.0.0.sif bash

cd $SCRATCH
cd bwa

./bwa mem -t 32 -K 10000000 -R '@RG\tID:SRR9932168.1.1 \tLB:lib1\tPL:bar\tSM:sample\tPU:SRR9932168.1.1 ' \
../human_g1k_v37.fasta \
../SRR9932168_1.fastq \
../SRR9932168_2.fastq | \
gatk SortSam \
--java-options -Xmx30g \
--MAX_RECORDS_IN_RAM 5000000 \
-I /dev/stdin \
-O ../cpu.bam \
--SORT_ORDER coordinate

# for max spot id 5000000 spent 2.72 mins for sorting, 2.2 mins for convert to BAM

[main] CMD: ./bwa mem -t 32 -K 10000000 -R @RG\tID:SRR9932168.1.1 \tLB:lib1\tPL:bar\tSM:sample\tPU:SRR9932168.1.1  /pfss/scratch02/appcara/parabricks/parabricks_sample/Ref/human_g1k_v37.fasta /pfss/scratch02/appcara/parabricks/sratoolkit.3.0.6-centos_linux64/SRR9932168_1.fastq /pfss/scratch02/appcara/parabricks/sratoolkit.3.0.6-centos_linux64/SRR9932168_2.fastq
[main] Real time: 151.130 sec; CPU: 3594.553 sec
INFO    2023-08-01 05:58:33     SortSam Finished reading inputs, merging and writing to output now.
INFO    2023-08-01 05:58:47     SortSam Wrote    10,000,000 records from a sorting collection.  Elapsed time: 00:02:43s.  Time for last 10,000,000:   14s.  Last read position: */*
[Tue Aug 01 05:58:47 GMT 2023] picard.sam.SortSam done. Elapsed time: 2.72 minutes.
Runtime.totalMemory()=1409286144
Tool returned:
0
# generate .dict file 
cd $SCRATCH
gatk CreateSequenceDictionary -R human_g1k_v37.fasta

# Mark duplicates.

gatk MarkDuplicates \
--java-options -Xmx30g \
-I ./cpu.bam \
-O ./mark_dups_cpu.bam \
-M metrics.txt

# spend 1 mins

# Generate a BQSR report.

gatk IndexFeatureFile -I ./00-common_all.vcf

gatk BaseRecalibrator \
--java-options -Xmx30g \
--input ./mark_dups_cpu.bam \
--output ./recal_cpu.txt \
--known-sites ./00-common_all.vcf \
--reference ./human_g1k_v37.fasta

# spend 1.68 mins

One of the main advantages of Parabricks is its speed. The software can analyze large datasets in a fraction of the time it would take traditional tools to complete the same task. Parabricks is also highly scalable and can analyze datasets of varying sizes without sacrificing performance. It is ideal for researchers and scientists who need to process large amounts of genomic data quickly and efficiently.