The Department of Crop Sciences offers a concentration within the campus-wide M.S. program in Bioinformatics that focuses on the agricultural and life sciences.
The discipline of Bioinformatics addresses the need to manage and interpret the data that is being massively generated by genomic and proteomic research. This discipline represents the convergence of biology, computer and information technology sciences, and encompasses analysis and interpretation of biomolecular data, modeling of biological phenomena, and development of algorithms and statistical approaches. With current technology, scientific discovery occurs in a global arena and data are stored and archived massively in databases, disseminated through cable or wireless conduits, and analyzed. This includes information on genomes, biomolecules, biomolecular circuitry, and biological processes at the molecular, cellular, organismal and population levels. Our world expects substantial pay-offs from the analysis of multi-dimensional data structures, including proactive control and clear understanding of chemical, biological and cosmological processes. Ultimately, we expect a better life. The College of Agricultural, Consumer and Environmental Sciences (ACES) and the Department of Crop Sciences have a comprehensive mission that relates to agriculture, food, and environment, and is driven mainly by a human-community dimension. This involves addressing important issues in biology. Within this framework, bioinformatics plays an important role in the management and exploitation of microbial, plant and animal genomic resources.
Students interested in our Bioinformatics program may come with undergraduate training in one of the following areas: (a) biological and agricultural sciences, (b) statistical, mathematical and computer sciences, (c) informatics and engineering sciences. Graduates from the bioinformatics program will be able to integrate basic and applied concepts in the three areas and applied them to biotechnology and medical research.
The Crop Sciences concentration within the M.S. in Bioinformatics is offered in both thesis and non-thesis versions.
The thesis option, requires a minimum of 32 hours, including 28 hours of coursework with at least 12 hours at the 500-level and 8 hours within the Department of Crop Sciences. Of the 32 hours, a minimum of 12 hours must be within a General core, equally distributed between Fundamental Bioinformatics, Biology, and Computer Science courses. The General core complies with the requirements of the campus-wide Master of Science in Bioinformatics. In addition, a minimum of 7 hours of courses in Computational, Quantitative and Statistical Biology must be completed, together with a minimum of 5 hours of electives. Within the Computational, Quantitative and Statistical Biology core, the students must take CPSC 440 (Applied Statistical Methods I) or CPSC 540 (Applied Statistical Methods II). No double counting is possible; the same course cannot be used to satisfy the General core and the Computational, Quantitative and Statistical Biology core requirements simultaneously. The courses approved for the General core and the Computational, Quantitative and Statistical Biology core are listed below. Electives can be satisfied with any graduate-level course; however, students must select elective courses in consultation with their departmental advisor and are strongly encouraged to select from among courses offered by the Department of Crop Sciences. Students must also complete a minimum of 4 hours of thesis within Crop Sciences research (CPSC 599). Students are required to register each semester for 1 hr of seminar in one of the sections of Crop Sciences. A student may be exempted from seminar for the semester or register for a seminar in another department upon the recommendation of his/her advisor and approval of the graduate coordinator. Students are required to present a seminar on their thesis research during the last semester of their study program.
With the permission of their advisor, students in the Department of Crop Sciences may choose to pursue a non-thesis option within the M.S. in Bioinformatics. The non-thesis option requires a minimum of 36 hours with the same course requirements specified in the thesis option. Supplementary requirements towards satisfying the 36 hours include an additional minimum of 3 hours of General Core courses, an additional minimum of 3 hours of Computational, Quantitative and Statistical Biology core courses, and an additional minimum of 3 additional hours of elective courses, for a minimum total of 9 hours. The student may incorporate supervised research experiences including internships and projects to complete the remaining required hours of the non-thesis option. No course can be used to satisfy more than one requirement.
ANSC 441 - Human Genetics
ANSC 444 - Applied Animal Genetics
ANSC 445 - Statistical Methods
ANSC 446 - Population Genetics
ANSC 447 - Quantitative Genetics
ANSC 543 - Bioinformatics
ANSC 545 - Statistical Genomics
CPSC 432 - Genetic Toxicology
CPSC 440 - Applied Statistical Methods I
CPSC 540 - Applied Statistical Methods II
CPSC 541 - Regression Analysis
CPSC 452 - Genetics of Higher Organisms
CPSC 453 - Principles of Plant Breeding
CPSC 454 - Plant Breeding Methods
CPSC 558 - Quantitative Plant Breeding
CPSC 563 - Chromosomes
CPSC 564 - Molecular Marker Data Analyses
CPSC 567 - Bioinformatics and Systems Biology
CPSC 569 - Applied Bioinformatics
LIS 451 - Introduction to Network Systems
LIS 501 - Info Org and Access
BIOP - 420 Molecular Biophysics
BIOP - 541 Macromolecular Modeling
CHEM 470 - Computational Chemical Biology
CHEM 574 - Genomics, Proteomics, and Bioinformation
CS 400 - Data Structures, Non-CS Majors
CS 411 - Database Systems
CS 413 - Introduction to Combinatorics
CS 418 - Computer Graphics
CS 420 - Intro to Parallel Programming
CS 446 - Machine Learning & Pattern Recognition
CS 450 - Introduction to Numerical Analysis
CS 473 - Algorithms
CS 484 - Computer Data Acquisition Sys.
CS 512 - Data Mining
CS 519 - Scientific Visualization
CS 542 - Artificial Neural Networks
CS 571 - Information Theory
STAT 424 - Analysis of Variance
STAT 425 - Applied Regression and Design
STAT 429 - Time Series Analysis
STAT 525 - Computational Statistics
STAT 571 - Multivariate Analysis
VP 554 - Molecular and Evolutionary Epidemiology
MCB 421 - Microbial Genetics
MCB 432 - Computing in Molecular Biology
IB 402 - Molecular Evolution
IB 405 - Ecological Genetics
IB 504 - Genomic Analysis of Insects