GATK MARKDUPLICATESSPARK

Spark implementation of Picard MarkDuplicates that allows the tool to be run in parallel on multiple cores on a local machine or multiple machines on a Spark cluster while still matching the output of the non-Spark Picard version of the tool. Since the tool requires holding all of the readnames in memory while it groups read information, machine configuration and starting sort-order impact tool performance.

URL:

Example

This wrapper can be used in the following way:

rule mark_duplicates_spark:
    input:
        "mapped/{sample}.bam"
    output:
        bam="dedup/{sample}.bam",
        metrics="dedup/{sample}.metrics.txt"
    log:
        "logs/dedup/{sample}.log"
    params:
        extra="--remove-sequencing-duplicates",  # optional
        java_opts="", # optional
        #spark_runner="",  # optional, local by default
        #spark_v1.1.0="",  # optional
        #spark_extra="", # optional
    resources:
        mem_mb=1024
    threads: 8
    wrapper:
        "v1.1.0/bio/gatk/markduplicatesspark"

Note that input, output and log file paths can be chosen freely.

When running with

snakemake --use-conda

the software dependencies will be automatically deployed into an isolated environment before execution.

Software dependencies

  • gatk4=4.2
  • snakemake-wrapper-utils==0.1.3

Input/Output

Input:

  • bam file
  • reference file

Output:

  • bam file with marked or removed duplicates

Notes

  • The java_opts param allows for additional arguments to be passed to the java compiler, e.g. “-Xmx4G” for one, and “-Xmx4G -XX:ParallelGCThreads=10” for two options.
  • The extra param allows for additional program arguments for markduplicatesspark.
  • The spark_runner param = “LOCAL”|”SPARK”|”GCS” allows to set the spark_runner. Set the parameter to “LOCAL” or don’t set it at all to run on local machine.
  • The spark_master param allows to set the URL of the Spark Master to submit the job. Set to “local[number_of_cores]” for local execution. Don’t set it at all for local execution with number of cores determined by snakemake.
  • The spark_extra param allows for additional spark arguments.
  • For more information see, https://gatk.broadinstitute.org/hc/en-us/articles/360050814112-MarkDuplicatesSpark

Authors

  • Filipe G. Vieira

Code

__author__ = "Fillipe G. Vieira"
__copyright__ = "Copyright 2021, Filipe G. Vieira"
__license__ = "MIT"

import tempfile

from snakemake.shell import shell
from snakemake_wrapper_utils.java import get_java_opts

extra = snakemake.params.get("extra", "")
spark_runner = snakemake.params.get("spark_runner", "LOCAL")
spark_master = snakemake.params.get(
    "spark_master", "local[{}]".format(snakemake.threads)
)
spark_extra = snakemake.params.get("spark_extra", "")
java_opts = get_java_opts(snakemake)

tmpdir = tempfile.gettempdir()

metrics = snakemake.output.get("metrics", "")
if metrics:
    metrics = f"--metrics-file {metrics}"

log = snakemake.log_fmt_shell(stdout=True, stderr=True)

shell(
    "gatk --java-options '{java_opts}' MarkDuplicatesSpark "
    "{extra} "
    "--input {snakemake.input} "
    "--tmp-dir {tmpdir} "
    "--output {snakemake.output.bam} "
    "{metrics} "
    "-- --spark-runner {spark_runner} --spark-master {spark_master} {spark_extra} "
    "{log}"
)