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Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. Learn more

GATK4 - Parallelizing genotypegvcfs

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    Bhanu Gandham

    Hi ,

    The GATK support team is focused on resolving questions about GATK tool-specific errors and abnormal results from the tools. For all other questions, such as this one, we are building a backlog to work through when we have the capacity.

    Please continue to post your questions because we will be mining them for improvements to documentation, resources, and tools.

    We cannot guarantee a reply, however, we ask other community members to help out if you know the answer.

    For context, check out our support policy.

     

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    Pietro Mello

    Hi Bhanu,

    Fair enough. I understand. Just so that you know I am running GATK4's genotypegvcfs one chromosome at a time as of now. I have also been able to use MergeVcfs from Picard that is available through gatk4 to preliminarily merge smaller chromosomes for which genotypegvcfs have already finished. It feels to me that this is the best way to speed up the process (I was able to cut it from ~ 15 days in a single threaded processor into 3 days using multiple processors, one per job, in a cluster).

    P

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    Ivan

    Hi,

    I am having a similar issue where GenotypeGVCF is taking a long time to finish. Is there a way to get it to use more than a single core? It is also not using all the memory provided to it.

     

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    Genevieve Brandt (she/her)

    Hi Ivan,

    We have a few other forum discussions where we provide ideas for speeding up GenotypeGVCFs, please look for those. In addition, I would recommend using the new option in GenotypeGVCFs, --genomicsdb-shared-posixfs-optimizations, if you are using a shared cluster and a GenomicsDB workspace.

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    Vinod Kumar

    Ivan,

    I was also struggling with this but then according to suggestions from other threads, I divided the whole chromosome.interval_list into different chromosomes(1 to 8).interval_list and rerun the analysis parallely with each chromosome using -L as a different script. And now it is working fine and fast.

    Thanks,

      

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    Genevieve Brandt (she/her)

    Thanks for posting your solution Vinod Kumar

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