Computational Bio With Lab

4.0 credits

The course will provide an introduction to computational approaches to biological problems, including the theory, algorithms and methods used in the analysis and interpretation of genomes. The course will cover alignment, motifs, maximum likelihood, Markov models (HMM and MCMC), expectation maximization and machine learning methods used to interpret genomes and address problems in comparative genomics, population genomics and metagenomics. The lab BIO 253L/453L is required and will provide an introduction to Python and implementing methods in computational biology. Prior programming experience (any language) is recommended but not required.