COMPSCI 261 - Computational Sequence Biology

Institution:

Duke University

Subject:

Computer Science

Description:

Introduction to algorithmic and computational issues in analysis of biological sequences: DNA, RNA, and protein. Emphasizes probabilistic approaches and machine learning methods, e.g. Hidden Markov models. Explores applications in genome sequence assembly, protein and DNA homology detection, gene and promoter finding, motif identification, models of regulatory regions, comparative genomics and phylogenetics, RNA structure prediction, post-transcriptional regulation. Prerequisites: basic knowledge algorithmic design (Computer Science 230 or equivalent), probability and statistics (Statistics 213 or equivalent), molecular biology (Biology 118 or equivalent). Alternatively, consent instructor. Instructor: Hartemink or Ohler

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

WWW.DUKE.EDU

Phone Number:

(919) 684-8111

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

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