Syllabus

Course Number and Title: CS 640 Bioinformatics
Location: Harney Science Center 235 Kudlick Classroom
Schedule:M,W 4:45 - 6:25pm

Instructor: Patricia Francis-Lyon
Email: pfrancislyon (at) cs (dot) usfca (dot) edu
Phone: (415) 422-2810
Office: Harney Science Center 526
Office Hours M 9:15-10:15 am, 2:10-3:10 pm; 6:30-9 pm, W 2:10-3:10 pm, 6:30-7:30 pm; F 9:15-10:15 am; Also by appointment
Class email list: go to groups.cs.usfca.edu, list is: CS 640 Bioinformatics

Teaching Assistant:
TA: Julien Dubeau
Email: jdubeau(at) dons (dot) usfca (dot) edu
Office Hours in PC lab HR 530:TBA
Intro CS TA Office Hours (for Biotech students) in HR 530: William: T Th 12:30 - 2:30 pm; Jordan: MW 3:30 - 5:30 pm

Course Goals and Objectives
This course will prepare students to enter the field of bioinformatics working either in research or in industry. Assuming expertise in either Biology or Computer Science but not both, this course will bring you up to speed so you can do research involving proteins, DNA, and RNA. Cancer research, machine learning applied to discover how genes are related to disease, and research on flu are areas you may wish to explore.

Approach
This is a lab-based and projects-based class. After an introduction to the field and the tools of the field in the first half of the class, students will choose a project of their interest to implement. (I can provide code to read protein data bank and force field files, etc.)

Lectures
Lectures

List of Topics

Grading:

Assignments and Assessments
Unannounced quizzes will be given approximately weekly.
Lab-based assignments will be due about once per week during the first half of the semester, less frequently in the second half.
Each student will present one bioinformatics research paper on a topic of their choosing.
All students are expected to participate in class discussions, including presentations by fellow students.
An in-class midterm will be administered. A make-up exam will be given only in the case of a medical emergency documented by a doctor's note.
Each student will implement a bioinformatics project of their choosing. For example, a student interested in AI can do gene-finding, or work with the Buck Institute on one of their machine learning projects.

Mapping to Letter Grade
100 - 93.0 - A
92.9 - 90.0 - A-
89.9 - 87.0 - B+
86.9 - 83.0 - B
82.9 - 80.0 - B-
79.9 - 77.0 - C+
76.9 - 73.0 - C
72.9 - 70.0 - C-
69.9 - 67.0 - D+
66.9 - 63.0 - D
62.9 - 60.0 - D-
59.9 - 0 - F

Lectures
Lectures

Bioinformatics Resources

Text: Understanding Bioinformatics by Zvelebil and Baum
Other Bioinformatics texts are listed in Bioinformatics Resources

Academic Honesty We will adhere to the University's Student Academic Honesty Policy In this course you must do your own work on exams and homework, unless explicitly specified otherwise.

Note: This syllabus is subject to change.