The University of Toledo

Fall Course Offering

Statistical Methods in Bioinformatics

UT course #: BIPG 5200/7200 - 3 cr
 
Prerequisites: PUBH532 - Statistical Methods I (or equivalent, or permission of course director)

Offered: Fall Semester, Tuesdays/Thursdays.  Lectures posted online/Blackboard.

Course director:  Dr. Sadik Khuder, Department of Medicine, (419) 383-6244, Sadik.Khuder@utoledo.edu

Summary: This course introduces students to statistical methods commonly used in bioinformatics, but which are not covered in depth in general introductory statistics courses. Students will learn to use statistical programs and related bioinformatics resources locally and over the internet. Lectures and lab discussion will emphasize on the statistical models and methods underlying the computational tools. The course will focus on the application of newer statistical methods and the reasoning behind these applications. Emphasis will be placed on the analysis of functional genomic experiments, and students will learn statistical techniques used to interpret microarray data. Specific course objectives include:

1. Have a strong understanding of fundamental concepts of the statistics that underlie bioinformatics;
2. Understand the formulation of stochastic models for genomic data;
3. Be able to apply statistical techniques to analyze microarray data and interpret the results generated;
4. Be able to use statistical tests commonly employed in bioinformatics;
5. Be familiar with statistical methods and software for solving complex problems in bioinformatics.

Grading: The course grade will be determined by performance on the weekly exercises (50%), semester project (20%), and final exam (30%).

Instruction: The course is primarily taught by the course director, with some lectures from other faculty at UT.

Text: Parmigiani, et al., The Analysis of Gene Expression Data, Springer, New York, 2003
http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2292983-0,00.html

Ewens & Grant, Statistical Methods in Bioinformatics, Springer, Verlag Press, New York, 2001

Link to course page for registered students: BIPG 5200/7200 Academic Intranet Page

Reference books (recommended):
Mount. Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, Cold Springs Harbor, New York, 2001.

Balding et al. Statistical Genetics, John Wiley Publishing Co, New York, 2001.

Gibson & Muse. A primer of Genomic Science, Sinauer, Massachusetts, 2002.

END-OF-COURSE STUDENT EVALUATION FORM:  please click here
After completion of the Statistical Methods course, please click on the above link, print out the Evaluation Form, complete and return anonymously to:
Jo Anne Gray
BPG Program
3rd Floor CCE Building
Mail Stop: 1034

 

EDUCATIONAL PROGRAM OBJECTIVES (EPO's) for Statistical Methods Course I.  A BPG Graduate Will Be Knowledgeable In the course of their educational program, students are provided the opportunity to gain knowledge through instruction by content experts and by supervised participation in research projects.  Knowledge will be assessed by the student’s ability to define, describe, and explain facts and concepts, as well as at higher levels of cognition that will be measured by the ability to apply, analyze, and integrate content.   Before graduation, a student will have demonstrated to the satisfaction of the faculty: K1. Knowledge of fundamental systems biology technologies, such as proteomics, genomics, and transcriptomics.   K2. Knowledge of algorithmic and statistical methods for analysis of nucleic acid and protein sequences, such as hidden Markov models and Bayesian statistics. II.  A BPG Graduate Will Be Skilled The BPG curriculum provides a training environment in which research and teaching skills are learned in concert with the correlated knowledge.  Students have the opportunity to gain these skills under the supervision of a faculty mentors with the advice and guidance of the student advisory committee, through direct contact with content and/or technical experts, and through direct participation in research projects. Before graduation, a student will have demonstrated to the satisfaction of the faculty: S1. The ability to perform procedures necessary for the completion of the student's thesis (MS) research project(s).   S2. The ability to assess statistical & biological significance of bioinformatic results & patterns.