Professors Jeremy Buhler and Roger Chamberlain and Senior Professor Mark Franklin, Computer Science & Engineering, have received a one-year, $99,653 grant from the National Science Foundation for research titled "CI-P: A flexible platform for accelerating biological sequence analysis."
Recent declines in the cost of DNA sequencing have enabled biologists to conduct experiments that produce very large DNA and protein sequence data sets. Understanding this data requires computational analyses to recognize known sequences and group new ones by similarity. As data sets grow, these analyses become a serious bottleneck to progress. Computer scientists have therefore tried to accelerate sequence analysis using hybrid computing architectures that combine multicore CPUs with accelerators, such as field-programmable gate arrays and graphics engines, whose performance equals that of tens or hundreds of CPU cores. To more effectively accelerate biosequence analysis tasks, new infrastructure is needed to facilitate both development of accelerated analytical tools and their deployment to biologists.
This project is a planning effort to create development and deployment infrastructure for accelerated biosequence analysis applications. The PIs are developing design criteria for a preferred hardware platform and set of software tools to speed the creation, validation, and deployment of biosequence accelerators. Key activities include qualifying hardware platforms, developing prototype software and firmware, and consulting developer and user communities for accelerated sequence analysis tools to guide the planning effort. In particular, the PIs are organizing a special track at a major accelerator design conference to solicit input on proposed infrastructure.
Developing the proposed infrastructure will stimulate creation of biosequence analysis accelerators with low cost, rapid deployment, and a large supporting developer and user community. More agile development will boost adoption of accelerators by biologists, empowering labs to analyze massive biosequence data sets and speeding discovery.
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