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  CSE 428Sp '19:  Computational Biology Capstone
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Lecture Notes
 1:  Intro
 NHGRI Talking Glossary
 Mol. Biol. Glossary

Lecture:  CSE2 287 (room info) TuTh 1000- 1120 
Office Hours Location Phone
Instructor:  Larry Ruzzo, ruzzocs  Drop-in or by appt.  CSE 554  206-543-6298
TA:  Daniel Jones, dcjonescs  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 the CSE428 Google Group Discussion Board.

Catalog Description: Designs and implements a software tool or software analysis for an important problem in computational molecular biology.

Prerequisites: CSE 312; CSE 331; CSE 332

Credits: 5

Learning Objectives: The availability of the complete genome sequences of humans and other organisms is one of the landmark achievements of science. Understanding this exceedingly complex, voluminous 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 CompBio Capstone is to give CSE students an integrative design experience immersed in this highly interdisciplinary field.

Typical projects will involve (1) accessing large, rich, and rapidly evolving public data sets such as GenBank, Ensembl, Gene Expression Omnibus, Sequence Read Archive and/or the sequence and annotation databases underlying the Genome Browsers, (2) exploiting existing bioinformatic tools and toolkits (e.g., BLAST, BioPython), (3) developing efficient, robust software tools to integrate data from these and other sources, (4) perform rigorous mathematical and/or statistical analyses of results and (5) deploy the resulting system in a suitably user-friendly form, e.g., via a web server interface, so that it is accessible to biologists or other user communities. Modern software engineering principles (e.g., version control, modular design) will be employed. Version tracking will also be incorporated into data analysis workflows so that scientific results of the analyses are reliable and reproducible.

Evaluation: Class discussion, progress reports, final oral and written project report, peer evaluation by teammates.

References: See Schedule & Reading

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