I enjoy teaching classes and mentoring students and postdocs, and consider it to be one of the unique privileges of my profession. My greatest pleasure as a teacher comes during those moments when students transition from simply listening and reading the material to actually understanding it, and thereby advancing to independently solving problems relevant for their future research career.
[Syllabus]
Introduction to algorithms, statistical methods and data analysis skills for genome biology. The lecture topics cover databases, sequence (NGS) analysis, phylogenetics, comparative genomics and network biology. The hands-on data analysis components include homework assignments and course projects focusing on data analysis programming of next generation genome data using Linux command-line tools and the programming environment R. 4 units, 4 hours lecture, 2 hours discussion.
[Syllabus]
Introduction to the science of genomics and bioinformatics, including genome integrated sequencing, database techniques, comparative and evolutionary genomics, and microarray analysis. 4 units, 3 hours lecture; 1 hour computer lab. Prerequisites: BIOL 005A, BIOL 05LA, BIOL 005B, BIOL 005C, BIOL 102, CHEM 001C or CHEM 01HC, CHEM 112C, MATH 009B or MATH 09HB, PHYS 002C, PHYS 02LC, BCH 100 or BCH 110A, one course in statistics.