The planned North Carolina Bioinformatics Grid, one of the nation's first networks to offer computing resources and data storage for biological sciences, aims to ensure that North Carolina is at the forefront of genetic research.
IBM and MCNC, a locally-based information technology provider, announced a collaboration in November to provide the vast software and hardware technologies that will eventually house more than a terabyte of genomic data.
The grid is being developed with and will be used by the North Carolina Genomics and Bioinformatics Consortium, a collection of more than 60 organizations ranging from universities to pharmaceutical companies. Both Duke and the University of North Carolina System are members of the Consortium.
"As an outcome of the Human Genome Project and studies of other animals..., there has been an explosion in the amount of data required to study these species," said Tom Dunning, vice president of High-Performance Computing and Communications at MCNC. "To actually use this coming avalanche of data requires high-performance computing."
With that in mind, each member of NCGBC linked to the grid will be allowed to use the massive amounts of stored genomic and proteomic data, as well as advanced analytical software.
The network is designed to be so quick and easy to use that it will appear the data is contained on the researchers' own hard drives, Dunning said.
The project is currently in its test-bed phase, in which software is evaluated and machines are constructed. Dunning hopes the grid will be up and running within three years.
"North Carolina has an enormous advantage here," Dunning said, citing the state's extensive supercomputing resources and already impressive genetics expertise. He also noted there has been interest in expanding the grid throughout the Southeast.
Elizabeth Hauser, assistant research professor at the Duke Center for Human Genetics, accomplishes what she calls "gene-hunting" by studying the statistical genetic relationships within families. She explained the grid could be useful in searching for "sequence motifs where I think I have evidence for a gene."
"It could be tremendously valuable when I have results I need to verify," Hauser said, comparing the ease of using a computerized database with searching through published resources.
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