Course Description:
This course is designed for students from both the life sciences and computational sciences who want to learn the major issues concerning representation and analysis of biological sequences and structures. Biology has undergone a transformation since the structure of DNA was unraveled and massive data sets representing the sequences of both DNA and protein have been made available in public databases. Computational techniques have been the key to both "constructing" the data from fragments and to "mining" the data stored in these databases. This course will explore the computational techniques that are the basis for the field of bioinformatics. Students are not expected to have advanced knowledge in both computer science and molecular biology, but should be knowledgeable in one of these fields and ready to learn about the other. Major class projects will be done as teams consisting of students from both computer science and the biological sciences. Topics to be covered include sequence alignment, phylogenetic prediction, sequence similarity searches, gene prediction, protein classification and structure prediction, and genome analysis. An introduction to Perl programming will also be taught in the class and programming assignments will be done in Perl.
Lecture:
Monday Wednesday 2:00 - 3:15, Butler 104
Instructor:
Susan Bridges,
bridges@cse.msstate.edu
- Office: Butler 312, tel. 325-7505
- Office hours: Tuesday 3:00 - 4:00, Thursday 1:30 - 2:30
Textbooks:
Mount, David W. 2001. Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor, NY: Cold Spring Harbor Press.
Tisdall, James. 2001. Beginning Perl for Bioinformatics. O'Reilly & Associates.
Additional References:
Baldi, Pierre, and Soren Brunak. 2000. Bioinformatics: The Machine Learning Approach. MIT Press.
Baxevanis, Andreas D. and B. F. Francis Ouellette (editors) 2001. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, John Wiley & Sons.
Durbin, R. S. Eddy, A. Krough, G. Mitchison. 1998. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
Setubal, Joao, and Joao Meidanis 1997. Introduction to Computational Molecular Biology, Boston: PWS Publishing Company.
Schwartz, Randal L. and Tom Phoenix. 2001. Learning Perl, Third Edition , Sebastopol, CA: O'Reilly & Associates, Inc.
Prerequisites: Junior standing or higher in one of the computational or life sciences.
Course schedule: See schedule
Grading:
- 20% daily quizzes (best 6 of 8 grades on quizzes)
Grading is on a 10 point scale.
Attendance:
Students are expected to attend all classes. If you must be absent, you should notify the instructor ahead of time if possible. More than 3 unexcused absences will result in a reduction of one letter grade in the course. Students who are auditing the course must attend all lectures. An audit will not be awarded if a student has more than 3 unexcused absences.
Academic honesty policy:
Cheating on homework or a quiz will result in an F on the assignment and Cheating on an exam will result in an F for the course as well as possible university disciplinary measures. A student must cite any references used in or resources used to complete projects and term papers. Failure to properly cite references can result in a grade of 0 on the assignment.
All assignments are individual endeavors unless otherwise specified. Students may consult only with the instructor for help on individual assignments unless specifically told otherwise on the assignment. Cheating on the assignments will result in a grade of F for the course as well as possible university disciplinary measures.
See the Computer
Science Departmental Policy Regarding Professional Conduct. The University policy regarding academic honesty applies to all MSU students.
Add/drop policy: See the
Mississippi
State University Add/Drop Policy
