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NC State Launches Bioinformatics Center; New Initiative Puts Emphasis on Statistics


Joining the ranks of universities stepping into bioinformatics, North Carolina State University formally opened its Bioinformatics Research Center March 7 with a ceremony marked by a lecture from Eric Lander.

The NC State center hopes to set itself apart from the rest, however, by focusing on data analysis rather than data management, said Bruce Weir, the director of the center, located on NC State’s campus in Raleigh. “The thing that might distinguish us is that we have more interest in statistics than computer science,” said Weir.

Weir has also sought to build a program with strong support from businesses in the Research Triangle Park, NC, area with an interest in bioinformatics.

Companies such as GlaxoSmithKline, Paradigm Genetics, and the SAS Institute will support student and faculty research, and in some cases take on students as interns. “We might be a little ahead [of other bioinformatics programs] in terms of training,” Weir said.

The bioinformatics program at NC State may not be one of the most prestigious in the US, said Josh Baker, CEO of PPGx, a pharmacogenomics company based in Morrisville, NC, but it has one of the largest training programs and is well known for focusing on applied research.

At the moment, the center runs a cluster of Sun machines and PCs with access to the North Carolina Supercomputing Center in Research Triangle Park, and is in the process of buying a Beowulf system. The center currently occupies about 6,000 square feet of space, with an additional 4,000 square feet likely to become available next year, said Weir.

The most obvious applications of NC State’s statistics-based approach would be in the analysis of data from expression array and SNP association studies, added Weir. “The question is, how to organize all this SNP data — there’s hundreds of thousands of SNPs. How do you tease the data out from the noise?”

Baker said he hopes collaborating with the NC State program will generate new methodologies for analyzing microarray data, since the statistical methods are not well defined.


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