image University of Washington Computer Science & Engineering
  CSE 427Au '17:  Computational Biology
  CSE Home   About Us    Search    Contact Info 

 Schedule & Reading
Course Email/BBoard
 Class List Archive
 E-mail Course Staff
 GoPost BBoard
 1:  Assignment
 2:  Assignment
 3:  Assignment
 4:  Assignment
     Electronic Turnin
Lecture Notes
 1:  Intro
 2:  Alignment & Scoring
   Smith-Waterman Example (.xlsx)
 3:  BLAST & More Scoring
   Bio Notes - PCR & Seq
 4:  Genes
 5:  Markov Models
   HMM Example (.xls)
   Reading (.pdf)
 6:  MLE & EM
   EM Example (.xls)
 7:  Motifs
 8:  Noncoding RNA
 9:  CMs: ncRNA Models
 10:  RNAseq and Bias
Lecture Recordings
 NHGRI Talking Glossary
 ORNL Genome Glossary
 A Molecular Biology Glossary

Lecture:  EEB 045 TuTh 1030- 1150 
Office Hours Location Phone
Instructor:  Larry Ruzzo, ruzzocs  M 4:30- 5:30  CSE 554  (206) 543-6298
TA :  Katie Doroschak, kdoroschcs  Tu 1:30- 2:20  CSE 220 
TA :  Daniel Jones, dcjonescs  Th 1:00- 2:00  CSE 220 
TA :  David Wadden, dwaddencs  W 4:00- 5:00  CSE 007 

Course Email: Staff announcements and general interest student/staff Q&A about homework, lectures, etc. The instructor and TAs are subscribed to this list. Enrolled students are as well, but probably should change their default subscription options. Messages are automatically archived. 

Discussion Board: Also feel free to use Catalyst GoPost to discuss homework, etc.

Catalog Description: Algorithmic and analytic techniques underlying analysis of large-scale biological data sets such as DNA, RNA, and protein sequences or structures, expression and proteomic profiling. Hands-on experience with databases, analysis tools, and genome markers. Applications such as sequence alignment, BLAST, phylogenetics, and Markov models.

Prerequisites: CSE 312; CSE 332

Credits: 3

Learning Objectives: The availability of the complete genome sequences of humans and other organisms is one of the landmark achievements of science. Understanding this enormous volume of data is a problem that will challenge scientists for decades to come, and the nature and scope of the problem means that computer scientists will play a vital role. The primary objective of the course is for students to understand the variety of computational problems and solutions that arise in this interdisciplinary field. Students will learn enough of the basic concepts of molecular biology to understand the context for the computational problems presented in the rest of the course. They will learn how some of the computational methods they have encountered in other courses can be applied to solve problems in modern molecular biology. An important component is to learn the nature and capabilities of some of the key public databases available for the solution of these problems, as well as publicly available computational analysis tools and the algorithmic principles underlying them.

Grading: Homework, possibly including a small project: 90%; class participation: 10%.

Late Policy: We will deduct 20% per day (business day, e.g., Monday for Friday due dates) for late homework.

Textbooks: None.

Portions of the CSE 427 Web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly credited. The CSE 427 Web: © 1993-2017, the Authors and the Department of Computer Science and Engineering, University of Washington.

CSE logo Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA  98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX