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The Department of Computer Science and Engineering is involved in
several major initiatives in
bioinformatics,
specifically in the area of computational life science.
These interdisciplinary research projects with faculty in biology
and medicine represent a continued commitment by the department
to apply expertise in computational technology to the solution of
real problems in science and engineering. The goal in many of
these projects is to combine in joint research projects the talents
of the faculty in computer science and engineering in the analysis,
presentation, and manipulation of data with the knowledge of discipline
scientists in the interpretation of that data and to impart the blend of both
skill sets to both graduate and undergraduate students.
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Graduate Work in Bioinformatics at the University of South Carolina
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Arrangements are being formalized between the Department of Computer
Science and Engineering and the Department of Biological Sciences for
coordinated graduate work in bioinformatics through both departments.
Prospective students are encouraged to contact Professor Duncan Buell
(address 'buell' at domain 'cse.sc.edu')
or Professor John Rose
(address 'rose' at domain 'cse.sc.edu')
for details on graduate degrees in Computer Science and Engineering with a
focus on problems in computational biology.
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Statistical Genomics
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Comparative genomics is likely to offer some of the most significant
advances in understanding the functional role of the approximately
30,000 genes comprising the human genome.
Evolutionary trees are essential to both systematics and comparative genomics,
yet use of such methods continues to pose major computational problems.
For example, the most powerful methods for detecting positive
selection at sites in a protein use a tree and an explicit model of
molecular evolution. Unfortunately, the running time of even the best
code for a single gene with a 61-codon model may exceed a week even
on the fastest single processors.
The problem becomes even more difficult when it is realized that the
optimal tree may need to be searched for; this is an exponential
problem as the number of sequences increases.
Given the large number of genes, the need to search among trees,
and the rapidly increasing complexity of the models, it is clear
that there is going to be a strong and persistent need for much
more powerful computational approaches to statistical genomics.
Professors
Duncan Buell
and
John Rose
are collaborating with faculty
in statistics, biology, and mathematics on improvements to algorithms
for constructing phylogenetic trees.
The
phylogenetics
and statistical genomics web page in the Department of Computer Science
and Engineering.
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PeroBase: A Biological Database for Peroymyscus
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Professor
John Rose
has undertaken significant research projects
in computational biology during his several years at USC. In
collaboration with Wallace Dawson and Michael Dewey of the Department
of Biological Sciences, and with the support of the National Science
Foundation, he is developing a comprehensive database of information
on the biology and evolution of Peromyscus (deer mice) and other
related species (Peromyscus is the mouse associated through the deer
tick with Lyme disease, it is also the primary carrier of the virus
responsible for hantaviral pulmonary syndrome). As the most populous
mammal in north America, Peromyscus plays a significant ecological role.
The information is accessible to both researchers and laypersons via
the PeroBase site on the Internet. PeroBase is an outreach of the
Peromyscus Genetic Stock Center, a deer mouse colony located at USC,
to provide genetically characterized types of peromyscus to scientific
investigators.
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Understanding Cancer through Differential Gene Expression
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Professors
Duncan Buell
and
John Rose
are collaborating with researchers
at the South Carolina Cancer Center on the analysis of differential gene
expression data. Newly funded research and equipment at the Cancer
Center will supply the researchers with significant problems in finding
correlations between gene expression in sample populations and the
clinical nature of the cancer common to the different populations.
This is an area ripe for increased computational power and improved
statistical and analytical algorithms, which must be combined with
the knowledge and intuition of the medical research specialists to
understand and interpret patterns found in the data.
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Genetic Linkage Maps
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Professors
Michael Huhns
and
Larry Stephens
are being funded by the
US Department of Agriculture to investigate and develop tools and
methodologies for assisting researchers in constructing linkage maps
among genetic and phenotypic information from different mammalian species.
The genetic data that is the target in this project is the bovine,
sheep, and swine data of the Meat Animals Research Center in Hastings,
Nebraska. A combination of software agents and ontologies will promote
the interchange of information among heterogeneous systems and will
lead to the development of tools for discovering, manipulating, and
viewing aggregated information from the different systems.
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