Bioinformatics

Catalog description

Detailed introduction to bioinformatics, including reproducibility in scientific computing, elements of data science and machine learning models, nonparametric and Bayesian approaches to statistical inference, analysis of frequencies and compositions, models of molecular evolution, BLAST theory, alignment and motif-finding algorithms, distance-based methods, linear modeling and error control for high-throughput omics experiments. This is an R/RStudio- and UNIX-command-line-based course that also teaches some coding skills in Python and Perl. Previous programming experience not assumed or required.