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Software to Reveal Secrets of DNA Regulatory Signals

May 16, 2007

A software suite being developed by computer scientists at the University of Texas at Arlington’s College of Engineering will provide biologists and chemists with clues to medically relevant motifs in long DNA sequences. These bioinformatics tools will greatly improve our understanding of diseases and help with the development of treatments.

Raw sequences carry only a part of a gene’s regulatory signal, which is often too short and subtle to be detected by existing software. Many short motifs appear to be over-represented in any segment of DNA, impeding the reliable discovery of these which are functionally significant. Computer Science & Engineering Assistant Professor Nikola Stojanovic’s software, developed together with Ph.D. student Abanish Singh, will be based on an adaptation of classic string processing algorithms to isolate statistically and compositionally significant signals in DNA.

In order to fine-tune this software and use it in driving genetic discovery, Dr. Stojanovic will work with a number of collaborators, including Assistant Professors of Biology Esther Betrán and Cédric Feschotte and Assistant Professor of Biochemistry Subhrangsu Mandal, all with UT Arlington’s College of Science, and Assistant Professor of Pediatrics-Nutrition Monique Rijnkels with the Baylor College of Medicine in Houston. Dr. Betrán is investigating gene activation, Dr. Feschotte is studying repetitive DNA sequences, Dr. Mandal is searching for genes that lead to the onset of leukemia, and Dr. Rijnkels is performing comparative studies on genomes related to milk production in mammals. Dr. Stojanovic’s software will have the potential to significantly cut the amount of laboratory work necessary in these diverse studies.

The project is being conducted on behalf of the National Library of Medicine. “This is a complex project,” said Dr. Stojanovic. “It may take us a while to integrate these software tools into medically relevant research. They will need substantial fine tuning and, while they are not foolproof, they will provide better odds for success in the laboratory.”

Once completed, the programs will be placed in the public domain, along with the other software that his group has already developed and published, inviting other investigators in life sciences to use them on their own data.

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