CS 697F: Introduction to Bioinformatics (Fall 2007)

ANNOUNCEMENTS

10/31. Take home exam. We don't meet on 10/31 and you can get the exam from webct6. You need to bring the finished exam, project proposal and assignment 4, and prepared a short presentation to describe your project topic, when we meet again on 11/7. The student paper presentations have been rescheduled. Please check the link to the student presentations. 

10/4. You don't need to do question 3 (textbook problem 6.35) on assignment 3. We will move it to next assignment.

9/17. Please go to webct6 to signup for paper presentations. First come, first choose.

STUDENT LEARNING OBJECTIVES

After taking this course, students should be able to:

• understand the types and sources of data and the fundamental computational problems in molecular biology and genomics
• understand a set of widely used algorithms in bioinformatics
• become familiar with some important tools in bioinformatics research.
• develop basic research skills by researching and reading related bioinformatics literatures.
• improve writing skill through writing assignments and projects.
• improve presentation and critical thinking skills.

COURSE DESCRIPTION

Bioinformatics is becoming one of the most exciting application fields of the computer science and is central to the biological research. More and more molecular biology data, e.g. the sequences and structures of DNA, RNA and proteins, have become available at explosion rate in the last decade. The vast amount of genomic data poses great challenges and opportunities to both computer science and biology.

Computational methods play an important role in the creation, analysis, and management of the data. This course will cover the underlying computational models and the most significant algorithms in computational biology. The topics include introduction to molecular biology, sequence assembly, sequence alignment, motif searching, pattern matching, DNA microarray analysis, clustering and evolutionary trees, and Probability modeling and Hidden Markov Model (HMM).

PREREQUISITE

No prior knowledge on molecular biology is assumed. All students should have knowledge of basic data structures, programming experience and understanding of college-level probability and statistics. For CSUSM students, it means that you should have taken Math 241 and CS 311. Contact the instructor for more questions prerequisites.

TEXTBOOKS

“An Introduction to Bioinformatics Algorithms” , by N. C. Jones and P. A. Pevzner, MIT Press, 2004 (ISBN 0262101068)

More reading materials will be posted online.