San José State University
Computer Science 223
Bioinformatics

Fall 2009
Tuesdays and Thursdays: 12:00 to 1:15 in MH225
Course Code: 45270

Some Former CS 286/223 Students

Wendy Lee (2008) Summer Internship at Cal Tech. Entering PhD program in Bioinformatics at the University of California at Santa Cruz (2009).
Phil Heller (2008) Entering PhD program in Bioinformatics at the University of California at Santa Cruz (2009).
Amie Radenbaugh (2007) Summer Internship at TAIR. Entering PhD program in Bioinformatics at the University of California at Santa Cruz (2009).
Tom Chien (2006) MS in Biological Sciences at SJSU.
Ryan Pham (2005) Summer Internship at TAIR. MS in CS at SJSU (2008). Employed at IBM.
Christopher Wilks (2004) Summer Internship at TAIR, Programmer at TAIR. Entered MS program in CS at UCSC (2008).

Helpful links

For those of you who would like to have a head-start:

Computer Applications in Molecular Biology by Russ B. Altman.
What is bioinformatics? NCBI Tutorial.
Bioinformatics at the Swiss Institute of Bionformatics Good description of how the role of Bioinformatics has evolved over the years.
An Introduction to the Double Helix.
A Primer on Molecular Genetics from the U.S. Department of Energy.
Good introduction to Molecular Biology with a lot of details on cells, molecules, genes, functional genomics, and microarrays from EMBL-EBI.
Cracking the Code of Life NOVA program. Requires Quick Time or Real Video Plug-in. Recommended segments: 1, 2, 6, 9, 10, 12, 13, 14, and 16.
Additional slides with animations to strengthen your understanding of the central dogma of molecular biology, sickle cell anemia, etc..

Information about the Instructor

Name: Sami Khuri
Office: 418 MacQuarrie Hall
Phone: 924-5081
Office Hours: Tuesdays and Thursdays: 10:20 to 10:45 and 13:20 to 15:00.

Catalog Description

The course investigates the main algorithms for solving computational problems in bioinformatics. Methods will include Hidden Markov Models for gene prediction and protein profiling, and Genetic Algorithms for biological sequence analysis and structure prediction. The course is self contained and does not assume any background knowledge in biology. Students will be given programming projects.

Prerequisite: CS123A or CS155 or permission of the instructor.

Remark

Biology is not a prerequisite for this course. But an interest in Biology is. The following list of biology terms will be covered throughout the semester.

Required Textbook

Exploring Bioinformatics by Jonathan Visick, Jones and Bartlett, 2010.

We shall cover chapters 1, 2, 3, 4 and 5, and parts of the remaining chapters from the textbook.
Additional topics will be covered with material distributed in class.
Please bring the book to every class meeting.
A copy of my powerpoint slides (2 per page and 6 per page) will be available at the Blackboard Learning System (blackboard) to the students enrolled in the class.

Course Objectives

Become familiar with problems from computational biology and their solutions. The main emphasis is on algorithms. Most problems are computationally very difficult, therefore, we will examine exact, approximation and heuristic algorithms.

Become proficient in bioinformatics software and public WWW resources.

Acquire the ability to read scientific papers related to the bioinformatics field.
Upon successful completion of this course, students should be able to use dynamic programming for pairwise alignment and RNA structure prediction, to know various algorithms for multiple sequence alginment, to have a clear understanding of how hidden Markov models are used for gene prediction, for protein profile construction, and for pattern recognition techniques in general, to understand various phylogenetic tree algorithms, and to know various databases for DNA and protein sequences.

Course Requirements

Tentative Schedule:

Weeks one and two: Introduction to Molecular Biology and to Bioinformatics
Weeks three and four: Pairwise and Multiple Sequence Alignments
Week five: DNA Fragment Assembly including NP-Completeness
Weeks six and seven: Hidden Markov Models
Weeks eight and nine: Protein Profiling, Gene Prediction
Weeks ten and eleven: Phylogenetic Inference
Week twelve: Genome Rearrangement
Week thirteen: RNA Secondary Structure Analysis
Week fourteen: Genetic Algorithms for Protein Structure Prediction.

Problem Sets:

Five homework assignments. Only a subset of the assigned problems will be graded (per homework), and you will get back the homework a week after submitting it. No late homework will be accepted. The assignments are due in the beginning of the lecture, on the following dates:

HW One due on Thursday, September 10, 2009.
HW Two due on Thursday, September 24, 2009.
HW Three due on Thursday, October 15, 2009.
HW Four due on Thursday, November 5, 2009.
HW Five due on Thursday, November 19, 2009.

Term Project

There will be a group project. Each group consists of two students. Information on the project is posted at the Blackboard Learning System. The group chooses a topic, writes a term-project, and gives an in-class, 20 minute presentation (10 minutes per student) at the end of the semester during class time (dates to be announced later in the semester). The Team Formation is due by Tuesday, September 29, 2009. The Progress Report is due by Tuesday, November 3, 2009 at the begining of the class. The term-project is due on Tuesday, December 1, 2009.

Exams:

Midterm Exam: Thursday, October 22, 2009.
Final Exam: Wednesday, December 16, 2009 from 9:45 to 12:00.

The Midterm is one hour and 15 minutes long, and all exams are in-class, closed-book and comprehensive. You will get back your exam one week later.

Class Attendance

Class attendance is strongly encouraged. In class, we shall cover many topics and examples that are neither in the class notes nor in the textbook. If you miss a lecture, it is your responsibility to find out what was covered in class (this includes: handouts given out during your absence, corrected typos and errors, examples discussed in class - that are neither in the books nor in the notes - clarifications and changes made to assignments or the project, etc...).
You are required to turn off and not use your cell phone in class. You are also required to turn off your computer and put it away since you will not need it during class.

Grading Policy

The final grade will be computed as shown below:

Assignments 25%
Term-project 20%
In-class Presentation 5%
Midterm Exam 25%
Final Exam 25%

[97, 100] A+
[90, 97) A
[87, 90) A-
[85, 87) B+
[80, 85) B
[77, 80) B-
[75, 77) C+
[70, 75) C
[65, 70) C-
[56, 65) D+
[53, 56) D
[50, 53) D-
[0, 50) F

Add/Drop Policy

For those wishing to add this course, the deadline is September 11, 2009. The last day to drop with a full refund is September 4, 2009. According to University and Department guidelines, dropping after September 4 requires a serious and compelling reason to drop a course. Grades alone do not constitute reason to drop a course. Students who stop attending without officially dropping will be issued a U at the end of the semester which is counted as an F in calculations of GPA. See University Catalog.

Academic Integrity

Your own commitment to learning, as evidenced by your enrollment at San José State University, and the University's Academic Integrity Policy requires you to be honest in all your academic course work. Faculty members are required to report all infractions to the Office of Student Conduct and Ethical Development. The "Policy on Academic Integrity" can be found here.

Disability Resource Center

If you need course adaptations or accommodations because of a disability, or if you need special arrangements in case the building must be evacuated, please make an appointment with us as soon as possible, or see us during office hours. Presidential Directive 97-03 requires that students with disabilities requesting accommodations must register with DRC to establish a record of their disability. (Please let us know as soon as possible in order to more effectively accommodate your needs.)

San José State University Computer Science 223 Bioinformatics

Fall 2009 Tuesdays and Thursdays: 12:00 to 1:15 in MH225 Course Code: 45270