Technological advances in high-throughput DNA sequencing have opened up the possibility of determining how living things are related by analyzing the ways in which their genes have been rearranged on chromosomes. However, inferring such evolutionary relationships from rearrangement events is computationally intensive on even the most advanced computing systems available today.
Research recently funded by the American Recovery and Reinvestment Act of 2009 aims to develop computational tools that will utilize next-generation petascale computers to understand genomic evolution. The four-year $1 million project, supported by the National Science Foundation's PetaApps program, was awarded to a team of universities that includes the Georgia Institute of Technology, the University of South Carolina and The Pennsylvania State University.
"Genome sequences are now available for many organisms, but making biological sense of the genomic data requires high-performance computing methods and an evolutionary perspective, whether you are trying to understand how genes of new functions arise, why genes are organized as they are in chromosomes, or why these arrangements are subject to change," said lead investigator David A. Bader, a professor in the Computational Science and Engineering Division of Georgia Tech's College of Computing.
Even on today's fastest parallel computers, it could take centuries to analyze genome rearrangements for large, complex organisms. That is why the research team -- which also includes Jijun Tang, an associate professor in the Department of Computer Science and Engineering at the University of South Carolina; and Stephen Schaeffer, an associate professor of biology at Penn State -- is focusing on future generations of petascale machines, which will be able to process more than a thousand trillion, or 10^15, calculations per second. Today, most personal computers can only process a few hundred thousand calculations per s
|Contact: Abby Vogel|
Georgia Institute of Technology Research News