San José State University
Computer Science 223
Bioinformatics
Course Code: 27757

Spring 2013
Tuesdays and Thursdays 9:00-10:15 in MH 422

Some Former CS 223 Students

Kate Isaacs (2011) Pursuing PhD in Computer Science at the University of California at Davis (2011).
Yulia Newton (2011) Pursuing PhD in Bioinformatics at the University of California at Santa Cruz (2011).
Edward Liaw (2010) Pursuing MS in Bioinformatics at UC Santa Cruz.
Tom Austin (2009) Pursuing PhD in Computer Science at the University of California at Santa Cruz (2009).
Wendy Lee (2008) Summer Internship at Cal Tech. Entering PhD program in Bioinformatics at UC Santa Cruz (2009).
Phil Heller (2008) Entering PhD program in Bioinformatics at UC Santa Cruz (2009).
Amie Radenbaugh (2007) Summer Internship at TAIR. Entering PhD program in Bioinformatics at UC Santa Cruz (2009).
Tom Chien (2006) MS in Biological Sciences at SJSU.
Khoi Ho (2005) Summer Internship at the University of Texas Medical Branch. Pursuing MS in CS at UTMB.
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. Entering MS program in CS at UCSC (2008).
Monika Jain (2003) Summer Internship in the Department of Plant Biology at the Carnegie Institution of Washington.

Interesting links

A brief introduction to molecular biology with emphasis on genomics and bioinformatics Good introduction of molecular biology for CS students.
What is bioinformatics? NCBI Tutorial.
Bioinformatics at the Swiss Institute of Bionformatics Good definition of Bioinformatics.
An Introduction to the Double Helix.
A Primer on Molecular Genetics from the U.S. Department of Energy.
The Human Genome - What's Next? from Current Science and Technology Center.
Cracking the Code of Life NOVA program. Requires Quick Time or Real Video Plug-in.
Additional slides with animations to strengthen your understanding of the central dogma of molecular biology, sickle cell anemia, etc..

Link to biotechnology companies in the Bay Area.

Information about the Instructor

Name: Sami Khuri
Office: 418 MacQuarrie Hall
Phone: 924-5081
Office Hours: Tuesdays and Thursdays: 1:15 to 2:15pm.

Catalogue 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.
Prerequisites

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.
Textbook

Textbook Reading Guidelines Last update: January 16, 2013.
Understanding Bioinformatics by Marketa Zvelebil and Jeremy Baum, Garland Science, 2008, ISBN 0-815-34024-9.
1. We shall cover most of the topics from chapters 7, 9, and 10, and some selected topics from chapters 8 and 12.
2. Before reaching the end of a chapter, I will announce which topics of the following chapter will be covered.
3. A copy of my powerpoint slides (6 per page) will be available to the students enrolled in the class.
Course Objectives
Use case studies in homework and in-class hands-on exercises to extract generalizable concepts.
To continue acquainting students with some of the most challenging problems in life science and show how computer science can be used to solve some of these problems.
Using Hidden Markov Models for gene prediction, multiple sequence alignment and for protein profile construction
Study various folding algorithms to predict RNA secondary structure
Study various techniques for identifying genes, promoters, transcription factor binding sites, etc..
Upon successful completion of this course, students should be able to assess and evaluate novel computational methods for use in bioinformatics, including machine learning techniques, mainly Hidden Markov Models, neural networks, genetic algorithms, and pattern recognition techniques.
Course Requirements
1. Problem Sets:
  Five homework assignments. The purpose of the assignments is to develop your understanding of the material and your skills in problem-solving. Only a subset of the assigned problems on any given problem set will be graded. You will get the homework back one week after submitting it. No late homework will be accepted. However, under exceptional circumstances, one problem set per student might be accepted late. It will need to be handed in prior to the following class meeting and will be graded with 30% off. Such an extension should be requested from the instructor. Any homework not picked up during class time will be left on the shelves near my office door. Assignments are due in the beginning of the lecture on the following dates:
  - HW One due on Thursday, February 7, 2013.
  - HW Two due on Thursday, February 21, 2013.
  - HW Three due on Thursday, March 14, 2013.
  - HW Four due on Thursday, April 4, 2013.
  - HW Five due on Thursday, April 25, 2013.
  Cover sheet for all assignments
2. Term Project Term Project (Updated on October 9, 2012).
  This is a group project. Each group consists of two students. As mentioned in the project description, the Team Formation is due by Tuesday, March 5, 2013. The Progress Report is due by Tuesday, April 9, 2013 at the begining of the class. The final project is due on Thursday, May 2, 2013 at the beginning of the lecture.
3. Exams:
  Exam One: Tuesday, March 5, 2013.
  Exam Two: Tuesday, April 16, 2013.
  Final Exam: Wednesday, May 15, 2013, from 7:15 to 9:30am.
  Exam One and Exam Two are each one hour and fifteen minutes long. All exams are in-class, closed-book and comprehensive. You will get back your exams within one week at which time we'll go over them in class. Exams will be collected and kept with me. There will be no make-up exams.
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 book nor in the notes - clarifications and changes made to assignments or the project, etc...).

Grading Policy

The final grade will be computed as shown below:
Assignments: 20%
Exam One: 20%
Exam Two: 20%
Project: 20%
Final: 20%
[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 February 11, 2013. The last day to drop with a full refund is February 4, 2013. According to University and Department guidelines, dropping after February 4, 2013, 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.)
Summer 2013 Internships and Research Programs
An excellent link to Co-op and internship opportunities in Bioinformatics, Genomics and Proteomics (2013)
Carnegie Undergraduate Summer Program, at Stanford University. Over the years, a number of SJSU students have had summer internships there.
Summer Research Program 2011 in Molecular and Cellular Biosciences (2013).
Summer Internship Program in Biomedical Research at the National Institutes of Health (2013).
Stanford Summer Research Program in Biomedical Sciences (2013).
Summer Experience in Biomedical Science (2013)
Educational opportunities at Roswell Park, Buffalo, NY (2013).

San José State University Computer Science 223 Bioinformatics Course Code: 27757

Spring 2013 Tuesdays and Thursdays 9:00-10:15 in MH 422

Some Former CS 223 Students

Interesting links

Information about the Instructor

Catalogue Description

Prerequisites

Remark

Textbook

Course Objectives

Course Requirements

Class Attendance

Grading Policy

San José State University
Computer Science 223
Bioinformatics
Course Code: 27757

Spring 2013
Tuesdays and Thursdays 9:00-10:15 in MH 422