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  CSE 527Au '09:  Computational Biology
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 1   : Introduction; Bio Basics (1-up4-up)
 2-4: Alignment; DNA Replication (1-up4-up)
 4-5: BLAST, Scoring; Sequencing (1-up4-up)
 6-7: MLE & EM (1-up4-upnotes )
         EM Example (.xls)
         EM Notes (.pdf)
 7-9: Motifs; Gene Regulation (1-up4-up)
         MEME Example (.xls)
 10 : Parsimony & Footprinter (1-up4-up)
         Phylogenetic Footprinting (.ppt)
 11-12: HMMs (1-up4-up)
         HMM Example (.xls)
 13-14: Genes & Splicing (1-up4-up)
 15-16: RNA Folding; RNA Function (1-up4-up)
 17-20: RNA Motifs, Discovery, & Search (1-up4-up)
 NCBI Science Primer
 NHGRI Talking Glossary
 ORNL Genome Glossary
 A Molecular Biology Glossary

Lecture:  JHN 026 (schematic) MW 12:00- 1:20 
Office Hours Location Phone
Instructor:  Larry Ruzzo, ruzzo at cs  send email to schedule  CSE 554  (206) 543-6298

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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: Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.

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.

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 Amazon, etc.)  Errata.

References: See Schedule & Reading

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