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New Algorithm Could Automate Comparative Gene Mapping

NEW YORK, Dec 26 – Cornell University researchers have developed a comparative gene mapping algorithm that will soon be available in a software implementation, according to a university statement.  

The algorithm draws on natural language processing techniques to remember the labels of genes and make decisions about what sequences go together on the basis of an overall trend.

The automated method should significantly speed the comparative gene mapping process, according to the university. Such maps are currently compiled by hand, using data collected in wet labs and analyzed with software that can only interpret one map at a time, a process that can take months.

Early tests of the method took only hours, according to the university. A computer-generated comparison of the genomes of rice and maize closely resembled a similar map made by hand, and revealed a small " footprint" of an ancestral chromosome in maize that did not turn up in the handmade map, the university said.

A Cornell spokesperson was not immediately available for comment on the university’s plans to commercialize the software.

Debra Goldberg, a graduate student in applied mathematics, developed the method in collaboration with Susan McCouch, professor of plant breeding, and Jon Kleinberg, assistant professor of computer science. Goldberg will present the work at the Plant and Animal Genome IX conference in San Diego in January.

The algorithm has also been tested on a comparison between the mouse and human genomes. " It appears to work well in both cases," McCouch said in a statement.  

" It is certainly our intention to present this algorithm as a replacement for the construction of hand-crafted comparative maps," McCouch said.

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