BIOCB 6840 - Computational Genetics and Genomics

Fall. 4 credits. Student option grading.

Prerequisite: BTRY 3010 and CS 2110 or equivalents. Co-meets with BIOCB 4840/CS 4775.

J. Kim.

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.

Outcome 1: Understand computational algorithms used for the analysis of genetic and genomic data

Outcome 2: Formulate computational approaches for solving problems in computational genomics

Outcome 3: Understand challenges and limitations in inference methods used in computational genetics and genomics