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  CSE 527Au '06:  Computational Biology
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Lecture Slides
 1  : Introduction; Bio Basics (1-up4-up)
 2-3: Alignment; DNA Replication (1-up4-up)
 4-5: BLAST, Scoring; Sequencing (1-up4-up)
 6-7: MLE & EM; Gene Expression (1-up4-up)
         EM Example (.xls)
         EM Notes (.pdf)
 8-9: Motifs; Gene Regulation (1-up4-up)
 10 : Parsimony & Footprinter (1-up4-up)
         Phylogenetic Footprinting (.ppt)
 11-12: HMMs (1-up4-up)
 13-14: Genes & Splicing (1-up4-up)
 15-16: RNA Folding; RNA Function (1-up4-up)
 17-19: RNA Motifs, Discovery, & Search (1-up4-up)
 20-21: Phylogeny & RNA (1-up4-up)
Lecture Notes
 1. Overview
 2. Global Alignment
 3. Local Alignment; DNA replication
 4. Blast; Statistics
 5. Alignment Scores; PCR
 6. MLE; Sequencing
 8. TF Binding; Weight Matrices
 9. MEME & Gibbs Sampler
 10. Motif Comparison; Phylogeny
 11. Phylogenetic Footprinting; HMMs
 12. Hidden Markov Models, II
 13. Pfam; Genes & Splicing
 14. Gene Finding, II
 15. RNA Roles & Structure
 16. RNA Secondary Structure; CMs
 17. Covariance Models, II
 19. CMs IV: CMfinder
 20. Phylogeny & Pfold
 21. Pfold
 NCBI Science Primer
 NHGRI Talking Glossary
 ORNL Genome Glossary
 A Molecular Biology Glossary

Lecture:  EEB 026 (schematic) MW 12:00- 1:20 
Office Hours Phone
Instructor:  Larry Ruzzo, ruzzo at cs  M 1:30- 2:20  CSE 554  (206) 543-6298
TA:  Zizhen Yao, yzizhen at cs  By appt.

Course Email: Use this list to ask and/or answer questions about homework, lectures, etc. The instructor and TA are subscribed to this list. All messages are automatically archived.  Questions not of general interest may be directed to the instructor and TA: cse527-staff or just to the instructor: ruzzo at cs. You can (and perhaps should) change your subscription options.

Catalog Description: Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, regulatory analysis. Techniques such as dynamic programming, Markov models, expectation-maximization, local search. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.

Prerequisite: None.

Credits: 3

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

References: See Schedule & Reading.

Portions of the CSE 527 Web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly credited. The CSE 527 Web: © 1993-2006, Department of Computer Science and Engineering, University of Washington.

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