Accelerating genomic workflows using NVIDIA Parabricks

BMC Bioinformatics

Research

Thad B. Carlson¹  Josh J. Catana²  Haley T. Engelken²  Kyle A. O’Connell²  Collin J. Lobb²  Laura M. Gorrell²  Zelaikha B. Yosufzai²  Ross A. Campbell²  Juergen A. Klenk²  Dina Mikdadi²  Vivien R. Bonazzi² 

[1] Cloud Managed Services, Deloitte Consulting LLP, 48226, Detroit, MI, USA;Health Data and AI, Deloitte Consulting LLP, 22009, Arlington, VA, USA;

关键词: GPU acceleration;    NVIDIA Parabricks;    Cloud computing;    Amazon Web Services;    Google Cloud Platform;   

DOI  :  10.1186/s12859-023-05292-2

 received in 2022-07-20, accepted in 2023-04-15,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAs genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).ResultsWe achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost.ConclusionsGermline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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