Syllabus | MLBio+ Laboratory

Syllabus

**Class Information**
Instructor:	Huzefa Rangwala, Room #345 ST II, rangwala@cs.gmu.edu
Class Time & Location:	W: 4:30pm-7:10pm Krug Hall 5
Text Book:	Understanding Bioinformatics by Zvelebil & Baum
Teaching Assistant:	Rashmi Kumar: rkumar5@gmu.edu
Office Hours:	Instructor: W 3-4pm, 345 ST II, TA: 330 ST II, Th 3-4 pm, OB 321 Tue 3-4 pm

**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 Conceptualize and implement sequence alignment algorithm methods which use adynamic programming solution. Study the working of large genomic sequence database search tools like FASTAand BLAST. Analyze the vast amount of genomic and proteomic data using machine learningand data mining tools (discriminative and generative models). Understand the theoretical aspects of Markov chains and hidden Markov models and their application to gene prediction, protein sequence annotation and multiple sequence alignment. Read research papers pertaining to bioinformatic and computational biology. 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.

Assignment/Exam Schedule

Type	Date Due	Weight
Assignment 1	02.18.2009	15
Mid-Term 1	02.25.2009	20
Project Proposals	03.25.2009	5
Assignment 2	03.25.2009	15
Assignment 3	04.08.2009	20
Presentations	04.15/22/29	10
Final Report Due	05.06.2009 (Mid-night)	15

**Grade Distribution**
Grade	Score Range
A	94-100
A-	90-94
B+	84-90
B	80-84
B-	76-80
C+	72-76
C	68-72
C-	64-68
F	< 64

**Policies:**
Attendance
Attendance is not compulsory but highly recommended for doing well in the class. This class has lots of active learning exercises, and they will be a lot of fun.
Assignment Submission
Please ensure that the assignments are submitted on-time. No late submissions.
Make-Up Exams & Incompletes
Make up exams and incompletes will not be given for this class.
Academic Honesty and GMU Honor Code
Please visit the University's Academic Honesty Page and GMU Honor Code .
Disability Statement
If you have a documented learning disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Office of Disability Services (SUB I, Rm. 222; 993-2474; www.gmu.edu/student/drc) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.