The researchers plan to develop new algorithms in an open-source software framework that will utilize the capabilities of parallel, petascale computing platforms to infer ancestral rearrangement events. The starting point for developing 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.
"GRAPPA is currently the most accurate method for determining genome rearrangement, but it has only been applied to small genomes with simple events because of the limitation of the algorithms and the lack of computational power," explained Bader, who is also executive director of high-performance computing at Georgia Tech.
On a dataset of a dozen bellflower genomes, the latest version of GRAPPA determined the flowers' evolutionary relatedness one billion times faster than the original implementation that did not utilize parallel processing or optimization.
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.
The researchers believe these new algorithms will make genome rearrangement analysis more reliable and efficient, while potentially revealing new evolutionary patterns. In addition, the algorithms will enable a better understanding of the mechanisms and rate of gene re
|Contact: Abby Vogel|
Georgia Institute of Technology Research News