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hardware-based optimizations for the pairhmm algorithm in haplotypecaller  (Broad Institute Inc)

 
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    Broad Institute Inc hardware-based optimizations for the pairhmm algorithm in haplotypecaller
    Churchill enables rapid secondary analysis and variant calling with GATK <t>HaplotypeCaller</t> using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.
    Hardware Based Optimizations For The Pairhmm Algorithm In Haplotypecaller, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/hardware-based+optimizations+for+the+pairhmm+algorithm+in+haplotypecaller/hardware+based+optimizations+for+the+pairhmm+algorithm+in+haplotypecaller/pmc04333267-151-19-5
    Average 90 stars, based on 1 article reviews
    hardware-based optimizations for the pairhmm algorithm in haplotypecaller - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics"

    Article Title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics

    Journal: Genome Biology

    doi: 10.1186/s13059-014-0577-x

    Churchill enables rapid secondary analysis and variant calling with GATK HaplotypeCaller using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.
    Figure Legend Snippet: Churchill enables rapid secondary analysis and variant calling with GATK HaplotypeCaller using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.

    Techniques Used: Variant Assay, Sequencing

    Related Articles

    Variant Assay:

    Article Title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics
    Article Snippet: due to errors in short read alignment, but are considerably more computationally expensive than methods which operate on a single position at a time. .. In collaboration with Intel®, the Broad Institute recently developed a set of hardware-based optimizations for the PairHMM algorithm in HaplotypeCaller enabling them to reduce the time to analyze a single genome from three days to one day (a three-fold speedup). .. Utilization of Churchill’s balanced parallelization approach, in combination with AWS EC2 instances equipped with Intel Xeon® processors that can util

    Sequencing:

    Article Title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics
    Article Snippet: due to errors in short read alignment, but are considerably more computationally expensive than methods which operate on a single position at a time. .. In collaboration with Intel®, the Broad Institute recently developed a set of hardware-based optimizations for the PairHMM algorithm in HaplotypeCaller enabling them to reduce the time to analyze a single genome from three days to one day (a three-fold speedup). .. Utilization of Churchill’s balanced parallelization approach, in combination with AWS EC2 instances equipped with Intel Xeon® processors that can util

    Software:

    Article Title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics
    Article Snippet: due to errors in short read alignment, but are considerably more computationally expensive than methods which operate on a single position at a time. .. In collaboration with Intel®, the Broad Institute recently developed a set of hardware-based optimizations for the PairHMM algorithm in HaplotypeCaller enabling them to reduce the time to analyze a single genome from three days to one day (a three-fold speedup). .. Utilization of Churchill’s balanced parallelization approach, in combination with AWS EC2 instances equipped with Intel Xeon® processors that can util



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    hardware-based optimizations for the pairhmm algorithm in haplotypecaller  (Broad Institute Inc)
    90
    Broad Institute Inc hardware-based optimizations for the pairhmm algorithm in haplotypecaller
    Churchill enables rapid secondary analysis and variant calling with GATK <t>HaplotypeCaller</t> using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.
    Hardware Based Optimizations For The Pairhmm Algorithm In Haplotypecaller, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/hardware-based+optimizations+for+the+pairhmm+algorithm+in+haplotypecaller/hardware+based+optimizations+for+the+pairhmm+algorithm+in+haplotypecaller/pmc04333267-151-19-5
    Average 90 stars, based on 1 article reviews
    hardware-based optimizations for the pairhmm algorithm in haplotypecaller - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Churchill enables rapid secondary analysis and variant calling with GATK HaplotypeCaller using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.

    Journal: Genome Biology

    Article Title: Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics

    doi: 10.1186/s13059-014-0577-x

    Figure Lengend Snippet: Churchill enables rapid secondary analysis and variant calling with GATK HaplotypeCaller using cloud computing resources. Analysis of raw sequence data for a single human genome sequence dataset (30× coverage) was compared using Churchill and bcbio-nextgen, with both pipelines utilizing BWA-MEM for alignment and GATK HaplotypeCaller for variant detection and genotyping. (A) CPU utilization on a single r3.8xlarge AWS EC2 instance (32 cores) was monitored throughout the analysis process and demonstrated that Churchill improved resource utilization (94%) when compared with bcbio-nextgen (57%), enabling the entire analysis to be completed in under 12 hours with a single instance. (B) Unlike bcbio-nextgen, Churchill enables all steps of the analysis process to be efficiently scaled across multiple compute nodes, resulting in significantly reduced run times. With 16 AWS EC2 instances the entire analysis could be completed in 104 minutes, with the variant calling and genotyping with GATK HaplotypeCaller stage taking only 24 minutes of the total run time.

    Article Snippet: In collaboration with Intel®, the Broad Institute recently developed a set of hardware-based optimizations for the PairHMM algorithm in HaplotypeCaller enabling them to reduce the time to analyze a single genome from three days to one day (a three-fold speedup).

    Techniques: Variant Assay, Sequencing