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

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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.