The algorithm takes advantage of the fact that there is often extensive overlap in the genomes of closely related species, and even some overlap in distantly related ones. Berger, Michael Baym, Ph.D., and Po-Ru Loh, her former and current grad students respectively, developed a way to mathematically represent different species’ genomes so that overlapping data is only stored once. This allows searches of multiple genomes to focus on the differences between the genomes and save time.  

 

When the team experimented on a database of 36 yeast genomes, they compared their new algorithm to BLAST, the Basic Local Alignment Search Tool, which is one of the most commonly used genomic-search algorithms in biology. The experiment saw the new algorithm performing twice as fast as BLAST in a search of 10 genomes, and four times as fast in a search of all 36 genomes.

 

“If I want to run a computation on my genome, it takes a certain amount of time,” said Baym in a press release. “If I then want to run the same computation on your genome, the fact that we’re so similar means that I’ve already done most of the work.”  

 

The new algorithm is expected to have potential in a variety of applications in which researchers are looking to identify similar genomic sequences, such as the identification of microbes. It could aid researchers in determining the causes of infections, to characterize “microbiomes,” to characterize microbes in fertile or infertile soil and possibly even in the study of physical evidence in forensics.  

 

Berger, Baym and Loh are now working to extend their technique to also work with information on proteins and RNA sequences.  

 

 

SOURCE: MIT press release