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To Capture Better Human Genomic Data, UCSC Team Builds a Mouse Trap

SANTA CRUZ, Calif., March 21 - Researchers at the University of California, Santa Cruz have added a mouse genome assembly to their popular web-based browser that features the human genome assembly.

 

"Ninety-five percent of the genes in humans have an ortholog in mouse," said UCSC project director David Haussler. "So for almost every gene you might want to look at there's a corresponding gene in the other species."

 

The assembly on the Santa Cruz site is from the November freeze produced at the Sanger Center. Jim Kent, a research scientist at UCSC, led the mouse genome browser effort, which was rolled out last week and became fully functional this week.

 

"For pharma, this is in some ways more important than the human genome because this is where you can really do your experiments," Haussler told GenomeWeb in a recent interview. "And having both the mouse and the human is what accelerates usage of these resources because by comparing genomes you can start understanding a lot more about what's actually going on."

 

The researchers have already found that previous models used to compare the sequences of different species were off the mark.

 

"We're in the process of looking at patterns of molecular evolution based on huge data sets now that we can compare all of the mouse to all of the human," said Haussler. For example, researchers can ask whether models of molecular evolution used compare sequences up to now have been adequate.

 

That answer is no, Haussler contends. "The answer is that those models were naïve. They didn't take into account the context effects and complex effects that strongly influence the outcome."

 

"From a scientific point of view comparative genomics is a lot harder than people might have expected," he suggests. "It's very challenging because you're looking for patterns of conservation and patterns of change between the species. In biology you learn a lot by comparison, but comparison is a tricky thing when you're doing it on the genome-wide level.

 

"So if you don't have very strong statistical models, it's like trying to find a needle in a haystack," Haussler went on. Scientists risk getting confused with "things that don't really matter, or you'll miss the things that do matter."

 

"There is an enormous opportunity in bioinformatics at this point to develop more sophisticated bioinformatics software and statistical models that will really capture the promise of comparative genomics," he said.

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