Scientists Make DNA Sequencing A Safer Bet
Michael Harper for redOrbit.com — Your Universe Online
DNA sequencing can be extremely useful in the right situations, but according to two researchers from the University of Southern California, understanding how much DNA to sequence can be a risky and expensive gamble. In an attempt to make sequencing easier and affordable enough to be used in all fields of research, Andrew Smith, computational biologist at the USC Dornsife College of Letters and USC graduate student Timothy Daley have teamed up to develop a helpful algorithm. This new development will allow researchers to determine the value of a DNA sequence before it´s conducted, but the basic mathematical principles could also be used to predict other unknowns based on small samples of data. Their research has now been published in the journal Nature Methods.
“It seems likely that some clinical applications of DNA sequencing will become routine in the next five to 10 years,” explained Smith in a statement.
“For example, diagnostic sequencing to understand the properties of a tumor will be much more effective if the right mathematical methods are in place.”
The computational biologist claims most modern day sequencing is ineffectual because researchers aren´t sure where to start or how much DNA to sequence. With some mathematical know-how, these researchers could get a birds-eye view of where they are heading before they embark, a powerful resource applicable in many situations.
“This is one of those great instances where a specific challenge in our research led us to uncover a powerful algorithm that has surprisingly broad applications,” Smith said.
For example, public health officials could use the same mathematical principles found in this algorithm to predict the population of AIDS-positive patients based on a small sample. Astronomers will be able to predict how many exoplanets are out there based on the number of exoplanets they´ve already discovered and categorized.
To create this algorithm, Smith and Daley combined an old practice with a little bit of new technology. The basics of their math can be found in a type of modeling used in ecology called capture-recapture. Using capture-recapture, ecologists can study the population of a certain type of animal by capturing an individual member of a species and tagging it. Once tagged, ecologists are able to determine if they´ve captured the same individual and how many times they´ve captured it. Working with this sample of data, researchers are able to somewhat accurately predict how many of these animals exist in the wild.
While effective, Daley says this method doesn´t lend itself well to the fine-grained application 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,” said Daley. Thanks to some new advances in sequencing technology, Smith and Daley were able to apply some different mathematical properties to DNA sequencing and develop a new and beneficial approach. Now it is the duo´s hope their algorithm will be useful in a varied amount of fields and encourage the use of fine-grained research, such as DNA sequencing.