MSCBIO 2020 : Bioinformatics of Gene Regulation

Spring Term 2006

Instructors	Takis Benos, Ph.D. Suite 3064 BST3 (412)648-3315 takis.benos@hgen.pitt.edu	Eleanor Feingold, Ph.D. Crabtree Hall A310B (412)383-8599 eleanor.feingold@hgen.pitt.edu
Office Hours	By appointment
Time	Tuesday, 11:00 am - 12:50 pm
Location	3073 BST-3 (directions to the classroom)
Schedule	2006 schedule
Web page	http://www.hgen.pitt.edu/~benos/LESSONS/MSCBIO2020/
Textbook	literature-based
Course Description This is a graduate level course designed primarily for students who want to learn about the computational methods and tools that are used in the analysis of promoter regions and transcription regulation data. Students with a biological background and knowledge of introductory level statistics can participate as well as students of quantitative background. The course will primarily focus on the methods that are used in the identification of transcription factor binding sites in the promoter regions of the genes. Both sequence-based and structure-based methods will be discussed. Various technologies for data collection will also be presented, including DNA arrays, SELEX, ChIP, and their derivatives.
Prerequisites An introductory Statistics course or permission of the instructors is required. Basic programming skills are also helpful.
Assignments and Grading There will be two homework assignments during the course of the semester to give students hands-on experience with the types of data that are discussed in the lectures. One assignment will introduce students to the existing methods for analyzing promoter data and discover DNA regulatory "signals". Students will have the opportunity to run and compare different computational algorithms on artificial or real (yeast) data. In the second assignment, the students will learn to analyze high-throughput protein-DNA association data, discover DNA "signals" and build gene association maps. There will be a small final project consisting of a paper critique or an analysis of a dataset. The students that will chose the latter should be able to write a small program implementing one of the algorithms discussed in the class and apply it in experimental data collected from the literature. Students are encouraged to work cooperatively on assignments, but must turn in final written work that is their own. Grading will be 30% for each homework assignment and 40% for the final project.
Disabilities If you have a disability that requires special accommodation, you need to notify both the instructor and the Disability Resources and Services no later than the 2nd week of the term. You may be asked to provide documentation of your disability to determine the appropriateness of accommodations. To notify Disability Resources and Services, call 648-7890 (Voice or TDD) to schedule an appointment. The Office is located in 216 William Pitt Union.
Academic Integrity Students in this course will be expected to comply with the University of Pittsburgh's Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process, initiated at the instructor level, as outlined in the University Guidelines on Academic Integrity.