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Understanding the language of our genes

Matt Hibbs

Our genomes, which include all of our genetic material, are huge. If you started reading a genome sequence aloud with all the letters representing DNA bases in the genetic code ("C, A, T, G, G, T, C, G, A," etc.) and didn't stop, it would take you about nine and half years to reach the end, 3.2 billion letters later.

But what do all those letters mean, and how do our genomes affect our health? Assistant Professor Matt Hibbs, Ph.D., is in the business of figuring that out. It's like decoding a language starting with just the letters.

In a paper published in PLoS Computational Biology, Hibbs demonstrates that it's possible to predict genes associated with a particular disease, in this case osteoporosis, using existing data. He used large datasets—essentiallyequivalent to huge collections of letters and sometimes sentence fragments—and applied computing algorithms of his design to find patterns and identify gene function. He then tested and confirmed his predictions using traditional experimental methods.

"Two of the genes we experimentally confirmed predicted were not even candidates in previous studies," says Hibbs. "The results show that we can apply our methodology to identify gene networks—multiple genes that work together in the same process or pathway—and pinpoint individual genes associated with disease." Hibbs' work will greatly speed gene discovery and add meaning to the language of life and health that we're only beginning to understand.


Guan et al.: Functional genomics complements quantitative genetics in identifying disease-gene associations. PLoS Computational Biology, November 2011, doi:10.1371/journal.pcbi.1000991