CSE 427: Computational Biology

Algorithmic and analytic techniques underlying analysis of large-scale biological data sets such as DNA, RNA, and protein sequences or structures, expression and proteomic profiling. Hands-on experience with databases, analysis tools, and genome markers. Applications such as sequence alignment, BLAST, phylogenetics, and Markov models.

either CSE 32 or CSE 332.

The goal of this course is to give students enough exposure to Computational Biology that they can make informed choices about further exploration such as graduate programs. Another goal is to give the students hands-on experience with databases, analysis tools, and genome browsers.

(a) an ability to apply knowledge of mathematics, science, and engineering
(b) an ability to design and conduct experiments, as well as to analyze and interpret data
(c) an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
(j) knowledge of contemporary issues
(l) knowledge of probability and statistics

Basics of Molecular Biology: proteins, DNA, RNA; DNA replication, transcription, translation, regulation of expression; gene structure; genome organization
Pairwise sequence alignment: optimal global and local alignment, affine gap penaltie
The algorithms and statistics of BLAST
Amino acid substitution matrice
Multiple sequence alignment: definition and NP-completeness, comparative sequence analysis and the UCSC Human
Genome Browser, progressive alignment, methods for whole-genome multiple sequence alignmen
Inference of phylogenetic trees
Markov chains and hidden Markov models
Challenges in Computational Molecular Biology