Special Topics in Computer Science:
Bioinformatics
CIS 595 – Spring 2003
Meeting days:
Monday, 7:25P - 9:55P, TL404 Instructor: Slobodan
Vucetic, 304 Wachman Hall, phone: 204-5773,
www.ist.temple.edu/~vucetic Office Hours: Wednesday
2:00 pm - 3:00 pm, or by appointment Objective: Bioinformatics
is an interdisciplinary field that involves developing and applying
computational methods for managing and analyzing information about the sequence,
structure and function of biological molecules and systems. Main goals of this
course are to provide an understanding of the types and sources of biological
data, the fundamental computational problems in molecular biology and genomics,
and a core set of widely used algorithms in computational biology. Existing
methods will be critically described and the strengths and limitations of each
will be discussed. Future directions for development of new methods will also be
discussed. Students will get hands-on experience through using different
bioinformatics software and Matlab. audience: The course is designed to introduce bioinformatics at a level
appropriate for computer science students, although it could be useful to
biology, chemistry, and statistics students.
prerequisites:
CIS501 and CIS510 (they were CIS542 and CIS573) or permission from the instructor. Knowledge of molecular biology and statistics is a plus.
textbooks:
Required:
1. David W. Mount, Bioinformatics: Sequence and Genome Analysis, 2001.
2. Greg Gibson, Spencer V. Muse, A Primer of Genome Science, 2001.
Recommended:
1. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, 1999.
Additional papers and handouts relevant to presented topics will be
distributed as needed.
Topics:
Pairwise Sequence Alignment.
Multiple Sequence Alignment.
Database Searching for Similar Sequences.
Phylogenetic Analysis.
Machine Learning Algorithms for Bioinformatics.
Protein Classification, Structure, and Function Prediction.
Gene Prediction.
Genome Analysis, Analysis of Microarray Data.
Modeling Cellular Systems.
Biomedical Text Analysis.
Emerging
Directions in Bioinformatics Research.
Grading:
Class Participation 10%, Homework (exercises, reading assignments) 25%, Class Presentation 15%, Midterm 20%, Individual Project 30%.
LATE POLICY AND ACADEMIC HONESTY:
The projects and homework assignments are due in class, on the specified due date. NO LATE SUBMISSIONS will be accepted. For fairness, this policy will be strictly enforced. Academic honesty is taken seriously. You must write up your own solutions and code. For homework problems or projects you are allowed to discuss the problems or assignments verbally with other class members or instructor. You MUST acknowledge the people with whom you discussed your work. Any other sources (e.g. Web, research papers, books) used for solutions and code MUST also be acknowledged.