Syllabus | MLBio+ Laboratory

Syllabus

SYLLABUS

**Class Information**
Instructor:	Huzefa Rangwala, Room 4423 EB, rangwala@cs.gmu.edu
Class Time & Location:	W: 4:30pm-7:10pm Innovation Hall 136
Text Book:	Understanding Bioinformatics by Zvelebil & Baum

**About the Course**
Course Description
CS 499 (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. CS majors will need CS 310.
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 FASTA and 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.

**Tentative Class Schedule**
Date	Topics Covered
09.02.2009	Welcome, Introduction to Bio-informatics Class Semantics, Policies, Syllabus
09.09.2009	Sequence Comparison and Alignments
09.16.2009	Scoring Matrices
09.23.2009	Multiple Sequence Alignment Algorithms
09.30.2009	Database Search
10.07.2009	Statistical Significance of Alignment Scores
10.14.2009	Markov Models
10.21.2009	Protein Structure Prediction I
10.28.2009	Mid-Term 1
11.04.2009	Gene Prediction
11.11.2009	Next Generation Assembly
11.10.2009	Bioinformatics Applications
11.17.2009	Data Mining in Bioinformatics
11.25.2009	Thanksgiving Holidays (No Classes)
12.02.2009	Data Mining in Bioinformatics
12.09.2009	Project Presentations
12.16.2009	No Final :)

**Assignments/Exams**
Deadline	Type	% Weight
09.23.2009	Assignment 1	15
10.14.2009	Assignment 2	15
10.28.2009	Mid-Term	20
11.17.2009	Assignment 3	15
12.16.2009	Final Project* (See Below for Milestones)	30
11.11.2009	Project Proposal (2 pages)	5
Signup	Project Presentations	10
12.16.2009	Final Report	15

* Note: There will be no extensions offered for the projects or any of the assignments. I would encourage you to start early.

Grading
3 Assignments (45 %) Final Project (30%) Mid-Term (20%) Classroom Participation (5%)

**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.

** Please note syllabus is subject to change to aid student learning **