Researchers develop new self-training gene prediction program...( Researchers at the Georgia Institute...)

Researchers at the Georgia Institute of Technology have developed a computer program that trains itself to predict genes in the DNA sequences of fungi.

Fungi which range from yeast to mushrooms are important for industry and human health, so understanding the recently sequenced fungal genomes can help in developing and producing critical pharmaceuticals. Gene prediction can also help to identify potential targets for therapeutic intervention and vaccination against pathogenic fungi.

"While we previously showed that our unsupervised training program worked well to predict genes in many eukaryotes, it didn't work as well for various fungal genomes that carry a significant part of the information that facilitates accurate gene prediction in locations called branch point sites," said Mark Borodovsky, director of Georgia Tech's Center for Bioinformatics and Computational Genomics.

Branch point sites are located inside introns, which are non-coding regions of DNA located between genetic-code carrying regions called exons.

"Previously during the process of predicting the exon-intron structure of eukaryotic genes, we didn't search for branch point sites, but doing so in the new program helps to better delineate intron regions inside fungal genes," added Borodovsky, who is also a Regents' Professor in the Coulter Department of Biomedical Engineering and the Computational Science and Engineering Division of the College of Computing.

Borodovsky and his colleagues expanded the eukaryotic genome self-training software program they developed in 2005 to address the issue that fungal genes are more complex than other eukaryotes. The research team included graduate student Vardges Ter-Hovhannisyan, Wallace H. Coulter Department of Biomedical Engineering research scientist Alexandre Lomsadze and School of Biology professor Yury Chernoff.

Details of the new program, called GeneMark.hmm-ES (BP), are available online in
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