Software is a new addition to the family of GeneMark gene prediction
programs developed at Georgia Tech
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 the journal Genome Research and will be included in the journal's
December print edition. The software will also be freely available for
academic researchers.

Borodovsky developed the first version of GeneMark in 1993. In 1995, this
program was used to find genes in the first completely sequenced genomes of
bacteria and archea. The research team then developed self-training versions
of the gene finding program for prokaryotic (organisms that lack a cell
nucleus) and eukaryotic (organisms that contain a cell nucleus) genomes in
2001 and 2005, respectively. Development of these programs has been
supported by the National Institutes of Health.

Unlike other programs that require a pre-determined training set along with
the genome sequence, GeneMark.hmm-ES (BP) only requires the genome sequence.
The program is able to iteratively identify the correct algorithm parameters
from the anonymous sequence. The program uses a probabilistic mathematical
model called the Hidden Markov Model to pinpoint the boundaries between
coding sequences (exons) and non-coding sequences (introns and intergenic
regions).

Most introns start from the dinucleotide guanine-thymine (abbreviated GT)
and end with the dinucleotide adenine-guanine (abbreviated AG). However,
finding these dinucleotides is not sufficient to signal the presence of an
intron. Several nucleotides that surround GT and AG are also important, but
the similarity of the pattern is not deterministic. Locating the branch
site ? which is nine nucleotides in length, almost always contains an
adenine and is located 20-50 bases upstream of the acceptor site ? helps to
accurately identify an intron.

An initial run of the program with a reduced model containing heuristically
defined parameters breaks the sequence into coding and non-coding regions.
With this information, the researchers apply machine-learning techniques to
refine the parameters of the recognition algorithm with respect to the
specific patterns found in the newly identified protein-coding and
non-coding sequences as well as the border sites.

The prediction and training steps are repeated, each time detecting a larger
set of true coding and non-coding sequences that are used to further improve
the model employed in statistical pattern recognition. When the new sequence
breakdown coincides with the previous one, the researchers record their
final set of predicted genes.

To test the algorithm, the researchers selected 16 fungal species from the
phyla Ascomycota, Basidiomycota and Zygomycota and compiled sets of genome
sequences containing previously validated genes. The species spanned large
evolutional distances and exhibited significant variability in genome size,
gene number and average number of introns per gene. The results showed that
by including branch site information in the model, the researchers could
more accurately predict exon-intron structures of fungal genes.

"The enhanced program predicted fungal genes with higher accuracy than
either the original self-training algorithm or known algorithms with
supervised training," noted Borodovsky. "And because we didn't need any
additional training information for our program, the sequencing teams could
immediately proceed with gene annotation right after the genomic sequence
was in hand without spending time and effort to extract a set of validated
genes necessary for estimating parameters of traditional algorithms."

Researchers at the U.S. Department of Energy Joint Genome Institute and the
Broad Institute of the Massachusetts Institute of Technology and Harvard
University have already realized the advantages of the new algorithm. They
have already used the new program to annotate about 20 novel fungal genomes.
In addition, hundreds of fungal genome sequencing projects currently in
progress should benefit from the new method as well, according to
Borodovsky.

With the fungal software completed, Borodovsky and his team are already
looking to expand their gene prediction algorithms to accurately interpret
even more complex eukaryotic genomes.

"There are genome sequencing projects where large repeat populations, a
significant number of pseudogenes or substantial sequence inhomogeneity
hamper ab initio gene prediction and we're ready to tackle them next," added
Borodovsky.

www.checkbiotech.org