OAC Core: High Performance Computing Algorithms and Software for large-scale Mass Spectrometry based Omics

OAC Core：基于大规模质谱组学的高性能计算算法和软件

• 批准号: 2312599
• 负责人: Fahad Saeed
• 金额: $ 60万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312599&HistoricalAwards=false
• 关键词: OAC; Core; Performance; Computing; Algorithms

High dimensional Mass Spectrometry (MS) based omics allows systematic analysis of thousands of proteins with the promise of discovering new biomarkers for various disease conditions and better understanding of human systems biology. Meta-proteomics is also fundamental to studies of microorganisms in diverse environments, and has significant effect on human, agriculture, aquatic, terrestrial, energy, and atmospheric systems. In addition to advances in mass spectrometry hardware, effective and scalable analysis of the complex data produced by these high-throughput mass spectrometers requires increasingly sophisticated computational tools. This project will design and develop high-performance computational frameworks which will enable effective analysis of omics data produced from mass spectrometry machines. The proposed high-performance computing (HPC) techniques will enable identification of novel peptides/proteins, and insights into microbiome communities and their effects on human health, agriculture, and environments. The proposed research and teaching activities will introduce students to high-performance computing, big data computational biology, and data-intensive computing. The proposed work will also train PhD students, including those at a Hispanic Serving Institution.Millions of spectra generated from mass spectrometry machines are compared with tera-scale theoretical database for peptide deductions. Currently, the bulk of this deduction is accomplished using serial algorithms which may take weeks of computational time for large databases. The overarching technical objective of this study is to design, develop, and evaluate high performance computing (HPC) infrastructure for a variety of heterogenous architectures. Such communication-avoiding HPC algorithms that can run on heterogenous architectures will enable scalable MS data analysis for non-model tera-scale databases against which MS data is matched and is currently an insurmountable technical hurdle. The project will focus on the design and development of: (1) CPU-GPU based method for processing of large-scale MS-based omics with communication-avoiding parallel pipelines; (2) methods for exploiting multiple GPUs on single node which is extended to memory-distributed CPU-GPU nodes on supercomputing machines; (3) hardware/software co-designs using CPU-FPGA architectures. This computational infrastructure will allow scientists to use large heterogeneous supercomputers, and the development of hardware/software designs will enable us to incorporate semiconductor designs directly on mass spectrometry machines. The development of such semiconductors designed for end-use application (of MS omics data) will preserve US global economic competitiveness, and will accelerate urgently needed personalized nutrition studies, human gut microbiome research, and cancer therapeutics studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基于高维质谱（MS）的组学允许系统分析数千种蛋白质，有望发现各种疾病的新生物标志物，并更好地了解人类系统生物学。元蛋白质组学也是研究不同环境中微生物的基础，并对人类，农业，水生，陆生，能源和大气系统产生重大影响。除了质谱硬件的进步之外，对这些高通量质谱仪产生的复杂数据进行有效和可扩展的分析需要越来越复杂的计算工具。该项目将设计和开发高性能的计算框架，这将使从质谱仪产生的组学数据的有效分析。拟议的高性能计算（HPC）技术将能够识别新的肽/蛋白质，并深入了解微生物群落及其对人类健康，农业和环境的影响。拟议的研究和教学活动将向学生介绍高性能计算，大数据计算生物学和数据密集型计算。拟议的工作还将培训博士生，包括那些在西班牙裔服务机构。从质谱仪产生的数百万光谱与万亿级的理论数据库进行比较，肽扣除。目前，大部分的推导是使用串行算法完成的，对于大型数据库来说，这可能需要数周的计算时间。本研究的首要技术目标是为各种异构体系结构设计、开发和评估高性能计算（HPC）基础设施。这种可以在异构体系结构上运行的避免通信的HPC算法将使可扩展的MS数据分析能够用于MS数据匹配的非模型万亿级数据库，并且目前是一个不可逾越的技术障碍。该项目将专注于设计和开发：（1）基于CPU-GPU的方法，用于处理大规模基于MS的组学，具有避免通信的并行管道;（2）在单个节点上利用多个GPU的方法，该方法扩展到超级计算机器上的内存分布式CPU-GPU节点;（3）使用CPU-FPGA架构的硬件/软件协同设计。这种计算基础设施将允许科学家使用大型异构超级计算机，硬件/软件设计的发展将使我们能够将半导体设计直接纳入质谱仪。这类为最终用途（MS组学数据）设计的半导体的开发将保持美国的全球经济竞争力，并将加速急需的个性化营养研究、人类肠道微生物组研究和癌症治疗研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。