Course Description
Course focuses on stochastic models in bioinformatics, including Hidden Markov models, continuous-time Markov chain (Poisson process, birth and death process, coalescent process) and their applications in sequence alignment, genome assembly, gene prediction, and phylogenetic tree reconstruction. Hands-on experience using innovative bioinformatics software for alignment, gene prediction, phylogenetic tree reconstruction, and network building.
Athena Title
Stat Inference Bioinformatics
Prerequisite
STAT 6220 or STAT 6315 or STAT 6315E or BINF 8441E
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
Students will learn concepts of statistical inference, particularly maximum likelihood and Bayesian inference, and apply techniques to biological data. Students will write and run computer simulations that will strengthen their knowledge of statistical concepts.
Topical Outline
Alignment, genome assembly, gene prediction, phylogenetic tree reconstruction, gene duplication/loss, and transmission network.
Syllabus