使用NVIDIA Parabricks加速基因组工作流
Kyle A O'Connell1, Zelaikha B Yosufzai1, Ross A Campbell1
1Health Data and AI, Deloitte Consulting LLP, VA, 22009, Arlington, USA.
BMC bioinformatics
|May 31, 2023
概括
图形处理单元 (GPU) 显著加速基因组数据分析,减少生殖系变种的运行时间,调用最多65倍. 虽然GPU为生殖线分析提供了成本节省,但体质呼叫器的性能有所不同,需要特定于平台的基准测试.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因组数据分析带来了重大的计算挑战.
- 图形处理单元 (GPU) 为基因组工作流提供了可观的加速.
- NVIDIA Parabricks是一款用于基因组分析的GPU加速软件套件.
研究的目的:
- 在不同的计算平台 (AWS,GCP,NVIDIA DGX) 上对NVIDIA Parabricks的性能进行比较.
- 使用GPU评估六种变异调用管道 (两个生殖系,四个体质) 的加速.
主要方法:
- 基准测试 NVIDIA Parabricks 软件套件. 这是一个很好的例子.
- 使用亚马逊网络服务 (AWS),谷歌云平台 (GCP) 和一个NVIDIA DGX集群.
- 评估了两个生殖系变异调用者 (HaplotypeCaller,DeepVariant) 和四个体质调用者 (Mutect2,Muse,LoFreq,SomaticSniper).
主要成果:
- 对于生殖系变体调用器,实现了高达65倍的加速,将HaplotypeCaller的运行时间从36小时减少到35分钟以下.
- 在GPU和平台上,Somatic呼叫者的性能各不相同.
- 云平台上的GPU加速生殖线调用器比CPU运行节省了成本.
结论:
- 生殖系变异调用者在平台上与GPU展示了强大的可扩展性.
- 实体呼叫者显示出可变的性能,表明需要针对特定平台的优化和基准测试.
- GPU 加速可以显著加快基因组工作流程,推进生物监控和个性化医学等领域.
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