|
GRAIL
will increasingly provide clues
about gene regulation by generating
comparative information for closely
related genomes such as human
and mouse. |
The human genome contains information
that could be used to prevent birth
defects and treat or cure devastating
diseases, but it is written in a language
that scientists are only beginning
to understand. To help decipher the
code, Ed Uberbacher and colleagues
at Oak Ridge National Laboratory combined
cutting-edge computer technology with
their knowledge of human biology to
develop GRAIL (for Gene Recognition
Analysis Internet Link), a "thinking"
computer program that imitates the
human learning process as it searches
for genetic meaning. GRAIL and successor
software programs can rapidly identify
key instructions in genes from within
vast stretches of DNA that appear
to be meaninglessa critical
contribution to the massive, international
effort to record and understand the
billions of bits and pieces of DNA
that make up the human genome, which
contains an estimated 30,000 genes.
Like humans, GRAIL learns by observing.
To train the program, the scientists
developed seven statistical rules
differentiating genes from other parts
of DNA. As it examines more and more
bits of DNA, the program learns when
and how well the rules work and adjusts
them accordingly.
Scientific Impact:
About 1,000 biotechnology companies
and laboratories now use GRAIL to
track down genes that play a role
in human disease. The program has
become increasingly productive over
time and greatly accelerated the process
of locating human genes.
Social Impact: In
a recent application, GRAIL located
the gene that causes adrenoleukodystrophy,
an often-fatal disease of the nervous
system that affects young boys. Identification
and understanding of such genes could
lead to treatments or cures for conditions
ranging from sickle-cell anemia to
muscular dystrophy and cystic fibrosis.
Reference: E.C.
Uberbacher and R.J. Mural, "Locating
Protein Coding Regions in Human DNA
Sequences Using a Multiple Sensor-Neural
Network Approach," Proc. Natl.
Acad. Sci. USA, Vol. 88, pp.
11261-11265 (1991).
Edward C. Uberbacher, Ying Xu, and
Richard J. Mural, "Discovering and
Understanding Genes in Human DNA Sequence
Using GRAIL," Comput. Methods
Macromol. Sequence Anal., Vol.
266, pp. 259-281, 1996.
Ying Xu and Edward C. Uberbacher,
"Automated Gene Identification in
Large-Scale Genomic Sequences," Journal
of Computational Biology, Vol.
4.3, pp. 325-338, 1997.
URL:
http://compbio.ornl.gov
Technical Contact:
Dr. Marvin Stodolsky, Life Sciences
Division, Office of Biological and
Environmental Research, 301-903-4742
Press Contact: Jeff
Sherwood, DOE Office of Public Affairs,
202-586-5806
SC-Funding Office:
Office of Biological and Environmental
Research |