CS 795/BINF 730
(Biological Sequence Analysis) [Spring 2009]
Class
Information
Class
Link: http://www.cs.gmu.edu/~hrangwal/drupal/drupal-6.3/?q=node/20
Instructor: |
Huzefa Rangwala, Room #345 ST II, rangwala@cs.gmu.edu |
Class
Time & Location: |
TBD |
Text
Book: |
Understanding
Bioinformatics by Zvelebil & Baum |
Teaching
Assistant: |
TBD |
Office
Hours: |
TBD |
About
the Course
Course
Description |
CS
795 (Biological Sequence Analysis) is an inter-disciplinary course aimed at
bridging the gap between biology and computer science, by exposing students
to the widely used algorithms and methods playing a key role in
bioinformatics and computational biology. The human genome project and
advances in sequencing technologies have left us with a wealth of DNA, RNA, protein sequence data. Its
important to infer key characteristics of biological systems using sequence
analysis methods. The first half of the course will help students understand
basic sequence alignment algorithms, hidden Markov models, classification
and prediction methods. The second half will be an application of the
concepts and ideas learned to some of the current bioinformatics applications
motivated with a fair biological understanding. |
Course
Prerequisites |
Programming
in language of your choice. The class will cover the needed biology. |
Course
Outcomes |
As
an outcome of taking this class, a student will be able to á
Conceptualize
and implement sequence alignment algorithm methods which
use a dynamic programming solution. á Study the working of large genomic
sequence database search tools like FASTAand BLAST. á Analyze the vast amount of genomic and
proteomic data using machine learningand data
mining tools (discriminative and generative models). á Understand the theoretical aspects of
Markov chains and hidden Markov models and their application to gene
prediction, protein sequence annotation and multiple sequence alignment. á Read research papers pertaining to bioinformatic and computational biology. á Learn about new sequencing technologies
along with development of short-read assembly algorithms |
Course
Format |
Lectures
will be given by the instructor.
Besides material from the textbook, topics not discussed in the book may also
be covered. Research papers and handouts of material not covered in the book
will be made available. Grading will be based on homework assignments, exams,
and a project. Homework assignments will require some programming. Exams and
homework assignments must be done on an individual basis. Any deviation from
this policy will be considered a violation of the GMU Honor Code. |
Tentative
Class Topics |
Sequence
Alignment, Sequence Assembly, Markov Models, Genome Annotation, Short-Read
Sequencing, Protein Structure and Function Prediction. |
Grading |
2-3
Programming Assignments (40 %)
2
Exams (30%)
Final
Project (30%) |