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 unravelled
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.
Lecture: MWF 10:00 - 10:50, Butler 104
Instructor: Susan Bridges, bridges@cse.msstate.edu
- Office: Butler 312, tel. 662-325-7505
- Office hours: Tuesday 1:30 - 2:30, Thursday 3:30 - 5:00
Textbooks:
There is no text for this course. Course materials will be posted on WebCT.
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.
Jones, Neil C., Pavel A. Pevzner. 2004, An Introduction to Bioinformatics Algorithms
(Computational Molecular Biology). Cambridge, MA: MIT Press.
Mount, David W. 2001. Bioinformatics: Sequence and Genome
Analysis. Cold Spring Harbor, NY: Cold Spring Harbor Press.
Pevzner, Pavel A. 2001. Computational Molecular Biology: An
Algorithmic Approach , Cambridge, MA: MIT Press.
Setubal, Joao,
and Joao Meidanis 1997. Introduction to Computational Molecular
Biology, Boston: PWS Publishing Company.
Schwartz, Randall L., Tom Phoenix Learning Perl 3rd Edition, July 2001, O'Reilly
& Associates.
Prerequisites: Junior standing or higher in one of the computational
or life sciences.
Course schedule: See schedule
Grading:
- 20% weekly quizzes (drop 2 lowest scores)
Grading is on a 10 point scale.
Additional requirements for CSE 6990 students: Students taking the
course for graduate credit will have additional questions on exams not required
of undergraduate students. There will also be an additional requirement on the
final project for students taking the course for graduate credit.
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 homework, 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
