With the striking success of genome sequencing in the past four decades, biological and biomedical data are accumulating exponentially. Efficiently curating and analyzing these data in a manner that maximizes their value and accessibility is critical for realizing the scientific advances and social benefits genome sequencing will enable. Yet the scale of databases has become increasingly difficult to process using on-hand database management tools and traditional processing applications, creating a continuing demand for innovative approaches. Investigators at the University of Michigan (UM) have recently developed a number of biomedical methods and databases to help facilitate protein folding and drug discovery, gene mutation and human disease diagenesis, cardiovascular disease and surgery treatment, complex human disease and human health-driven medicine. Some of the methods have been recognized as the world?s best and widely used in the biological and medical communities. However, limited computing resources critically constrain the scope and scale of developments, as well as broad application, of these studies. In this proposal, we propose to acquire a new, hybrid high-performance computing (HPC) cluster with multiple CPU and GPU nodes, to serve the computational need of a group of 24 UM biomedical research laboratories. Due to the nature of the biomedical studies which involve large-scale and dynamic genomic databases, the simulation work often has special requirements in memory, storage and backup, input/output (I/O) setting, network speed, and internet connections, which make it difficult ? and sometimes infeasible ? to implement by the university-wide and public computing resources. To address these issues, new database and library allocation strategies are being developed and integrated with high-speed NVMe flash drives and EDR InfiniBand networks to improve the performance for the data-intensive biomedical studies. The new HPC cluster will reside in the Michigan Academic Computing Center (MACC) associated with the campus-wide Great Lakes cluster system and be managed by the professional team of the Advanced Research Computing Technology Services (ARC-TS) who have over 20 years of operational experience supporting HPC environments. Substantial infrastructure and technical support will also be provided by the ARC-TS and the Department of Computational Medicine and Bioinformatics (DCM&B), which will significantly reduce startup costs and improve the operational efficiency of the proposed HPC system. Overall, the acquisition of this equipment will immediately benefit 39 independent NIH-funded research projects led by the HPC User team, as well as 31 other NIH projects in which the Users participate. All bioinformatics and biomedical software tools and databases developed on this infrastructure will be made publicly and freely available to academic individuals and institutions. In this manner, the impact of the proposed computer resource will be multiplied and significantly enhanced by the product dissemination through the broader biomedical communities outside the University of Michigan.

Public Health Relevance

In contemporary medicine and drug discovery industries, scientists need to mine large-scale biomedical databases to identify possible compounds and drug leads, which often require high-performance computing system with ultra-speed searching abilities. This project is to develop new computing infrastructure to help computational biologists and medical scientists to improve their research and speed up the procedures of finding new drugs and treatments to fight against human diseases including cancer, cardiovascular and Parkinson?s diseases.

National Institute of Health (NIH)
Office of The Director, National Institutes of Health (OD)
Biomedical Research Support Shared Instrumentation Grants (S10)
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Special Emphasis Panel (ZRG1)
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Horska, Alena
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Medicine
Ann Arbor
United States
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