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README.md

Use NVIDIA Parabricks on HiperGator

Update on 04/23/2023: the latest Parabricks v4.0.1 is pre-installed on HiperGator, refer to the doc here on how to use Parabricks on HiperGator instead of following this tutorial!

This directory is a tutorial on how to use tools provided by Clara Parabricks on HiperGator, adapted from the tutorial in Clara Parabricks v4.0.1 documentation.

Note

  1. This tutorial assumes you have downloaded the repository monai_uf_tutorials following this section.

  2. In all following commands and scripts, replace hju with your HiperGator username; change the path to files according to your own settings on HiperGator.

  3. In all following SLURM job scripts, alter the #SBATCH settings for your own needs.

    IMPORTANT: Refer to the doc on how to allocate adequate computing resources (e.g., number of gpu, number of cpu core, amounts of memory) for using Clara Parabricks.

  4. Please read the comments in each script to get a better understanding on how to tune the scripts to your own needs.

How to run

  1. Read the tutorial in Clara Parabricks v4.0.1 documentation to understand what will be done in this tutorial.

  2. Go to directory /monai_uf_tutorials/clara_parabricks/

    cd ~/monai_uf_tutorials/clara_parabricks/

  3. Build a Clara Parabricks container by submitting a SLURM job script build_container.sh

    sbatch build_container.sh

    Check if the job is done

    squeue -u $USER

    You can check it in multiple ways, refer to UFRC doc for more details.

    When the job is done, check the SLURM job output

    cat build_container.sh.job_id.output

    If the container was built successfully, there should be no error in the output file and the output file should look similar to build_container.sh.job_id.out.

  4. Download sample data by download_sample_data.sh

    sbatch download_sample_data.sh

    If the data was downloaded successfully, there should be no error in the SLURM job output file and the output file should look similar to download_sample_data.sh.job_id.out.

  5. Run alignment tool - FQ2BAM (FASTA + FASTQ ==> BAM) by fq2bam.sh

    sbatch fq2bam.sh

    Three output files should be generated (refer to fq2bam tutorial and fq2bam doc).

    The SLURM job output file should be similar to fq2bam.sh.job_id.out.

  6. Run the gold-standard GATK variant caller - HaplotypeCaller (BAM ==> VCF) by haplotype.sh

    sbatch haplotype.sh

    A .vcf file should be generated (refer to HaplotypeCaller tutorial and HaplotypeCaller doc).

    The SLURM job output file should be similar to haplotype.sh.job_id.out.

  7. Run the deep-learning-based germline variant caller - DeepVariant (BAM ==> VCF) by deepvariant.sh

    sbatch deepvariant.sh

    A .vcf file should be generated (refer to DeepVariant doc).

    The SLURM job output file should be similar to deepvariant.sh.job_id.out.