Statistics 115 -- Introduction to Computational Biology and Bioinformatics
Course Description: Basic problems, algorithms and data analysis approaches in computational biology. Topics include sequence alignment, genome sequencing and gene finding, gene expression microarray analysis, transcription regulation and sequence motif finding, comparative genomics, RNA/protein structure prediction, proteomics and SNP analysis. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.

This class is at the Undergraduate level
Instructor: Xiaole Shirley Liu (Public Health)
Prerequisites: Good quantitative skills, strong interest in biology, willingness and diligence to learn programming.

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