NSF grant aims to develop algorithms to analyze genomic evolution

University Park, Pa. -- Technological advances in DNA sequencing make it possible to determine how living things are related by analyzing the ways in which their genes have been rearranged on chromosomes. However, inferring these evolutionary relationships from rearrangement events requires massive computing impossible even on the most advanced computing systems available today.

A four-year $1 million project, funded by the National Science Foundation's PetaApps program, aims to develop computational tools that will use next-generation petascale computers to understand genomic evolution. A team of universities received the grant, including the Georgia Institute of Technology, the University of South Carolina and Penn State. The funding is part of the American Recovery and Reinvestment Act.

"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, 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. So, the research team -- which also includes Jijun Tang, associate professor, department of computer science and engineering, University of South Carolina, and Stephen Schaeffer, associate professor of biology, Penn State -- is focusing on future generations of petascale machines, which will be able to process more than a thousand trillion calculations per second. Today, most personal computers can only process a few hundred thousand calculations per second.

The researchers plan to develop new algorithms in an open-source software framework that will use the capabilities of parallel, petascale computing platforms to infer ancestral rearrangement events. The starting point to develop these new algorithms will be GRAPPA, an open-source code co-developed by Bader and initially released in 2000 that reconstructed the evolutionary relatedness among species.

The researchers will test the performance of their new algorithms by analyzing a collection of fruit fly genomes.

"Fruit flies -- formally known as Drosophila -- are an excellent model system for studying genome rearrangement because the genome sizes are relatively small for animals, the mechanism that alters gene order is reasonably well understood and the evolutionary relationships among the 12 sequenced genomes are known," said Schaeffer.

The analysis of genome rearrangements in Drosophila will provide a relatively simple system to understand the mechanisms that underlie gene order diversity, which can later be extended to more complex mammalian genomes, such as primates.
 

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Last Updated May 31, 2011