Taking the Gamble out of DNA Sequencing

Two USC scientists have developed an algorithm that could help make DNA sequencing affordable enough for clinics -- and could be useful to researchers of all stripes.

Andrew Smith, a computational biologist at the USC Dornsife College of Letters, Arts and Sciences, developed the algorithm along with USC graduate student Timothy Daley to help predict the value of sequencing more DNA.

Extracting information from the DNA means deciding how much to sequence: sequencing too little and you may not get the answers you are looking for, but sequence too much and you will waste both time and money. That expensive gamble is a big part of what keeps DNA sequencing out of the hands of clinicians. But not for long, according to Smith.

The mathematical underpinnings of the algorithm rely on a model of sampling from ecology known as capture-recapture. In this model, individuals are captured and tagged so that a recapture of the same individual will be known -- and the number of times each individual was captured can be used to make inferences about the population as a whole. "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.