One 80 minute lecture, 2 x 40 minute student presentations each week (3 credits).
Prerequisites: graduate standing in the natural sciences and/or engineering, OR permission of instructor.
This course will introduce biologists to computational considerations, and computational scientists to biological considerations, in the context of modern biological "grand challenges". Likely topics include genome-scale annotation, comparative and regulatory genomics, metagenomics, large-scale analysis of experimental data, phylogeny, gene and protein interaction networks, and machine learning techniques. The intention is to cross-fertilize interests and expertise, as well as expose students to considerations in large-scale data analysis and scientific inference.
The course will be graded on reading, attendance, participation, and presentation.
Additional potential topics: genome-scale alignments; RNAi/ncRNA; gene finding; assembly issues; whole-genome phylogenetics; protein structure; databases, data integration, and data warehousing.
Lab of Genomics, Evolution and Development / firstname.lastname@example.org