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Pushing Statistics to the Limit

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  • Title: Group Leader, European Bioinformatics Institute
  • Education: PhD, University of Freiburg, 1998
  • Recommended by: Janet Thornton

One might wonder how Wolfgang Huber, a trained theoretical physicist, got into bioinformatics. Blame it on math — and the serious amount of coding that physics PhDs have to do in the course of getting their doctorates. Programming "is pretty much part of [the] skills you have to pick up when you do physics," Huber says. And in reality, he adds, what he's doing with biological data isn't so far from what he originally trained to do. "In a way, physics is describing nature with numbers, with mathematics, and I'm still doing that," he says.

Huber, whose PhD degree is in quantum stochastics, moved into computational biology during a postdoc at the German Cancer Research Center in Heidelberg, and he now heads up a bioinformatics group at the European Bioinformatics Institute. There, he applies statistical computing and analysis to everything from microarrays to automated microscopy. "We apply [it] to emerging technologies in genomics, in particular new sequencing, like Solexa and 454 … and then automated phenotyping of cells and possibly model organisms using automated high-throughput microscopy." On one hand, sequencing can yield a huge amount of genotype information, while on the other, imaging can give "very subtle and interesting phenotype variation," he says. "In both cases, we're really interested in the relationship between genotype and phenotype."

Huber's interest in biology co-incided, he says, with the emergence of the genomic era in the '90s. With the Human Genome Project underway and microarrays hitting the scene as one of the first affordable ways to examine genomic information on a large scale, Huber took his part-time interest to the next level. Prior to his postdoc, Huber worked as a programmer at the clinic affiliated with the University of Freiburg, which led him to start taking classes and learning about the new technology. He began to see biology as "a much more dynamic and exciting field," which he compares to the state of physics in the early 20th century. "Completely surprising discoveries are being made," he says.

Looking ahead

As for the future, Huber hopes tools will continue to improve to allow him to see images across four dimensions, including space and time. Viewing cells in real time is the ultimate goal, he says. "It gives us the possibility to watch single cells and single molecules do their job. Right now most of the data that we have is population averages," he says. Another tool that would help is deep sequencing. But managing and analyzing all that new data is a formidable challenge; after all, finding people who are good at math and also interested in biology isn't easy. "These people are very rare, very precious," he says.

Publications of note

In a paper published in Nature, Huber and colleagues looked at genome-wide recombination events in S. cerevisiae, mapping crossovers, crossover-associated gene conversion, and non-crossover gene conversion across 56 yeast meioses. Their maps are the first high-resolution, genome-wide characterizations of the multiple outcomes of recombination for any organism.

"We used tiling arrays to detect the combination events at very, very high resolution — an unprecedented resolution," Huber says. Achieving this near-base pair resolution required Huber to bring in the big guns, statistically speaking. Like all experimental tools, he says, stats can be optimized, or tweaked, to make the tool experimentally better and stronger. "We had to push the limits of statistical data analysis much further than had been done before to achieve this high accuracy of our genotype calls. If we had used existing methods, the data would have been much too noisy," he says. "It's almost like building a new telescope or a new microscope. We can suddenly see things that we couldn't see before."

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