BIOCB 4840

Computational Genetics and Genomics

Course Description

Course information provided by the 2025-2026 Catalog.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.Undergrads will not need to do research but will do a final project that involves a sizeable amount of programming, comprehensive literature review of a topic, or similar.

Prerequisites REF-FA25/Corequisites REF-FA25 BTRY 3010 and CS 2110 or equivalents. Corequisites: None.

Distribution Requirements (OPHLS-AG), (BIO-AS, SDS-AS)

Last 4 terms offered (None)

Outcomes REF-FA25

• Understand computational algorithms used for the analysis of genetic and genomic data
• Formulate computational approaches for solving problems in computational genomics
• Understand challenges and limitations in inference methods used in computational genetics and genomics

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