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  CSE P590BAu '14:  Computational Biology (Professional Masters Program)
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Lecture Notes
 1:  Introduction; Bio Basics
 2:  Alignment; DNA Replication
   Smith-Waterman Example (.xlsx)
 3:  BLAST; Scoring; DNA Sequencing
 4:  MLE & EM
   EM Example (.xls)
 5:  Motifs; Gene Regulation
 6:  HMMs
   HMM Example (.xls)
 7:  Gene Finding; Splicing
 8:  RNA Function & Structure
 9:  RNA Search & Discovery
 10:  RNAseq & Summary
Lecture Recordings
 All recordings
Previous Versions
 CSEP 590A, 2013
 CSEP 590B, 2011
 CSEP 590A, 2008
 CSEP 590A, 2006
 CSE 590TV, 2003
 NHGRI Talking Glossary
 ORNL Genome Glossary
 Molecular Biology Glossary

Lecture:  MGH 231 (schematic) M 6:30- 9:20 
Office Hours Location Phone
Instructor:  Larry Ruzzo, ruzzocs  By appt. CSE 554  (206) 543-6298
TA:  Kathryn Doroschak, kdoroschcs  By appt.

Course Email: Staff announcements and general interest student/staff Q&A about homework, lectures, etc. The instructor and TA 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: An introduction to the use of computational methods for the understanding of biological systems at the molecular level.

Prerequisite: None

Credits: 4

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-based (no exams). Homework will include programming, paper & pencil exercises and some online discussion of readings.

Textbook: Richard Durbin, Sean R. Eddy, Anders Krogh and Graeme Mitchison, Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids, Cambridge, 1998.  (Available from U Book Store, Amazon, etc.)  Errata.

References: See Schedule & Reading

Portions of the CSE P590B Web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly credited. The CSE P590B Web: © 1993-2014, the Authors and the Department of Computer Science and Engineering, University of Washington.

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