Dr. Fahad Saeed is harnessing the power of new high-performance computational frameworks to enable effective analysis of biomolecules and molecular processes that contribute to the form and function of cells and tissues, through data produced from mass spectrometry machines. His work offers the promise of identifying novel peptides, proteins, and insights into microbiome communities and their effects on human health, agriculture, and environments.
Informally known as “omics” for studies in proteomics, transcriptomics, genomics, metabolomics, lipidomics, and epigenomics, which correspond to global analyses of proteins, RNA, genes, metabolites, lipids, and methylated DNA, research in these areas is unlocking new insights into microbiome communities. Dr. Fahad’s research team will design and develop high-performance computational frameworks that will enable effective analysis of omics data produced from mass spectrometry machines by incorporating high-performance computing (HPC) techniques to enable the identification of novel peptides/proteins, and insights into microbiome communities and their effects on human health and the environment.
Dr. Saeed’s current project is focused on the design and development of CPU-GPU-based methods for processing large-scale mass spectrometry-based omics. The research will incorporate communication avoiding parallel pipelines and methods for exploiting multiple GPUs on a single node which will be extended to memory-distributed CPU-GPU nodes on supercomputing machines. In addition, the hardware and software co-designs will be incorporated to use CPU-FPGA architectures. This computational infrastructure will allow scientists to use large heterogeneous supercomputers, while the development of advanced hardware and software designs will enable the team to incorporate semiconductor designs directly on mass spectrometry machines.
Related project work will include research and teaching activities that will introduce students to high-performance computing, big data computational biology, and data-intensive computing. The project is funded by a $600,000 National Science Foundation grant and is titled, “OAC Core: High Performance Computing Algorithms and Software for large-scale Mass Spectrometry-based Omics.” The project’s full abstract can be found on the NSF website at: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312599.