In this way scientists can estimate, for example, the number of gorillas remaining in the wild. In DNA sequencing, the individuals are the various different genomic molecules in a sample. However, the mathematical models used for counting gorillas don't work on the scale of DNA sequencing.
"The basic model has been known for decades, but the way it has been used makes it highly unstable in most applications. We took a different approach that depends on lots of computing power and seems to work best in large-scale applications like modern DNA sequencing," Daley said.
Scientists faced a similar problem in the early days of the human genome sequencing project. A mathematical solution was provided by Michael Waterman of USC, in 1988, which found widespread use. Recent advances in sequencing technology, however, require thinking differently about the mathematical properties of DNA sequencing data.
"Huge data sets required a novel approach. I'm very please it was developed here at USC," said Waterman.
|Contact: Robert Perkins|
University of Southern California