Concepts and Techniques in Computational Biology

4.0 credits · Graduate

This course is geared towards graduate students and advanced biology undergraduates seeking a better understanding of computational biology. Lectures will be a combination of presentations, paper discussions and hands-on sessions. Labs and paper discussions will have a significant component of plant science, but students from non-plant fields are also encouraged to register. Students will learn to work in a Unix environment, code using Python/R, and deploy tools for genome assembly, RNA-seq data analysis, local and global sequence alignment, protein domain searching using Hidden Markov Models, phylogenetic reconstruction, metabolomic analysis, and machine learning. Lectures will cover the algorithmic concepts underlying popular tools. The students will also learn practical aspects of implementing these tools in their own research using facilities available at Cornell.