Strategies for Parallelizing Mutect2 for Efficient Somatic Mutation Calling

how to parallelise mutect2

Parallelizing Mutect2, a tool from the GATK suite used for calling somatic mutations, can significantly speed up the analysis of large datasets. Here are some strategies to parallelize Mutect2:

1. Scatter-Gather Approach

This is the most common method for parallelizing GATK tools, including Mutect2. The idea is to split the input data into smaller chunks (scatter), process each chunk in parallel, and then combine the results (gather).

Steps:

1. Scatter: Split the input BAM file into smaller regions.
2. Parallel Processing: Run Mutect2 on each region in parallel.
3. Gather: Combine the output VCF files from each region into a single VCF file.

Tools:

• GATK4: The ScatterIntervalsByNs or IntervalListTools can be used to create interval lists.
• GNU Parallel: A tool to run jobs in parallel.
• Snakemake or Nextflow: Workflow management systems that can handle parallel execution and dependencies.

Example Workflow:

1. Create Interval List:

   gatk BedToIntervalList -I regions.bed -O intervals.list
   

2. Split Interval List:

   gatk SplitIntervals -R reference.fasta -L intervals.list -O interval_files/ -scatter 20
   

3. Run Mutect2 in Parallel:

   parallel -j 20 gatk Mutect2 -R reference.fasta -I input.bam -L {} -O output_{}.vcf ::: interval_files/*.interval_list
   

4. Combine VCF Files:

   gatk GatherVcfs -I output_1.vcf -I output_2.vcf -I output_3.vcf -O final_output.vcf
   

2. Using a Workflow Management System

Workflow management systems like Snakemake or Nextflow can automate the scatter-gather process and handle parallel execution.

Snakemake Example:

rule all:
    input:
        "final_output.vcf"

rule split_intervals:
    input:
        "reference.fasta"
    output:
        expand("interval_files/{i}.interval_list", i=range(20))
    shell:
        """
        gatk SplitIntervals -R {input} -O interval_files/ -scatter 20
        """

rule mutect2:
    input:
        bam="input.bam",
        ref="reference.fasta",
        interval="interval_files/{i}.interval_list"
    output:
        vcf="output_{i}.vcf"
    shell:
        """
        gatk Mutect2 -R {input.ref} -I {input.bam} -L {input.interval} -O {output.vcf}
        """

rule gather_vcfs:
    input:
        expand("output_{i}.vcf", i=range(20))
    output:
        "final_output.vcf"
    shell:
        """
        gatk GatherVcfs -I {input} -O {output}
        """

3. Using a High-Performance Computing (HPC) Cluster

If you have access to an HPC cluster, you can submit multiple Mutect2 jobs to the cluster's job scheduler (e.g., SLURM, PBS, SGE).

SLURM Example:

1. Create a SLURM Job Script:

   #!/bin/bash
   #SBATCH --job-name=mutect2
   #SBATCH --ntasks=1
   #SBATCH --cpus-per-task=4
   #SBATCH --mem=16G
   #SBATCH --time=24:00:00
   #SBATCH --output=mutect2_%A_%a.out
   #SBATCH --array=1-20
   
   INTERVAL_FILE=$(ls interval_files/*.interval_list | sed -n "${SLURM_ARRAY_TASK_ID}p")
   gatk Mutect2 -R reference.fasta -I input.bam -L $INTERVAL_FILE -O output_${SLURM_ARRAY_TASK_ID}.vcf
   

2. Submit the Job:

   sbatch mutect2_job.sh
   

3. Gather VCF Files:

   gatk GatherVcfs -I output_1.vcf -I output_2.vcf -I output_3.vcf -O final_output.vcf
   

4. Cloud-Based Solutions

Cloud platforms like Google Cloud, AWS, or Azure can be used to run Mutect2 in parallel using their respective batch processing services (e.g., AWS Batch, Google Cloud Dataflow).

Tips:

• Ensure that the reference genome and BAM files are indexed.
• Monitor resource usage to optimize the number of parallel jobs.
• Use appropriate disk I/O and memory settings to avoid bottlenecks.

By following these strategies, you can effectively parallelize Mutect2 to handle large datasets more efficiently.