Digital Symbiosis: Undergraduate Research Collaboration between Plant Pathology and Computer Science at the University of Kentucky
Grade Level at Time of Presentation
Sophomore
Major
Computer Science
Minor
Mathematics
Institution
University of Kentucky
KY House District #
2
KY Senate District #
22
Faculty Advisor/ Mentor
Dr. Jerzy W Jaromczyk; Dr. Neil Moore
Department
Computer Science
Abstract
Bioinformatics, the use and study of computational techniques for collecting and analyzing biological data (for example, DNA sequences), is a field of great and increasing importance in the life sciences, including biology, agriculture, and medicine. Our collaborative team of faculty members and undergraduate student researchers in Plant Pathology and Computer Science came together to conduct research and make advancements in the field of bioinformatics. Our team of researchers used a wide range of tools, from biology “wet labs” to computer processing tools, to investigate fungi in the family Clavicipitaceae, many of which enter into mutually beneficial relationships with agriculturally significant grasses such as tall fescue.
In order to help answer questions about the distribution and prevalence of toxin-producing genes in the Clavicipitaceae, we analyzed the genomes of several dozen strains of fungus. Beginning with genetic samples prepared and sequenced by plant science researchers, and using virtual machines in a “cloud computing” environment in the Department of Computer Science, we applied software including MAKER and OrthoMCL to identify and classify genes in those organisms; to find relationships among genes in different fungal strains; and to identify genes specific to only one or a few fungal strains.
As part of the research, we developed shell scripts—small programs that control the execution of other programs—to run on the virtual machines and execute the software required for the analysis in a consistent and reproducible manner, ensuring that the results of future DNA sequencing work can easily be integrated into the existing analysis. Likewise, the easily configurable and reproducible nature of virtual machines ensures that future analyses will not be adversely affected by upgrades to the analysis software we have used.
Abstract in .doc form
Digital Symbiosis: Undergraduate Research Collaboration between Plant Pathology and Computer Science at the University of Kentucky
Bioinformatics, the use and study of computational techniques for collecting and analyzing biological data (for example, DNA sequences), is a field of great and increasing importance in the life sciences, including biology, agriculture, and medicine. Our collaborative team of faculty members and undergraduate student researchers in Plant Pathology and Computer Science came together to conduct research and make advancements in the field of bioinformatics. Our team of researchers used a wide range of tools, from biology “wet labs” to computer processing tools, to investigate fungi in the family Clavicipitaceae, many of which enter into mutually beneficial relationships with agriculturally significant grasses such as tall fescue.
In order to help answer questions about the distribution and prevalence of toxin-producing genes in the Clavicipitaceae, we analyzed the genomes of several dozen strains of fungus. Beginning with genetic samples prepared and sequenced by plant science researchers, and using virtual machines in a “cloud computing” environment in the Department of Computer Science, we applied software including MAKER and OrthoMCL to identify and classify genes in those organisms; to find relationships among genes in different fungal strains; and to identify genes specific to only one or a few fungal strains.
As part of the research, we developed shell scripts—small programs that control the execution of other programs—to run on the virtual machines and execute the software required for the analysis in a consistent and reproducible manner, ensuring that the results of future DNA sequencing work can easily be integrated into the existing analysis. Likewise, the easily configurable and reproducible nature of virtual machines ensures that future analyses will not be adversely affected by upgrades to the analysis software we have used.