GNOM560: Statistics for Genome Scientists
The data-intensive nature of the 21st-century biology makes it important for scientists to have a basic proficiency in statistics. Whether it is thousands of gene expression levels as measured by a RNA-Seq, millions of polymorphisms that have been genotyped in a case-control study or more general questions of how to properly design an experiment, you will constantly be confronted with how to collect, analyze and interpret data. This course provides the key statistical concepts and methods necessary for extracting biological insights from data.
A common misconception about "doing" statistics is that it is useful only for analyzing data after an experiment has been performed. In fact, statistical methods are an integral part of designing experiments as well. How small of an effect size do you want to be able to detect? What sample size will you need? What is the power of your experiment?
GNOM373: Genome Informatics
This course is intended to introduce students to the breadth of problems and methods in computational analysis of genomes, arguably the single most important new area in biological research. The specific subjects will include large-scale comparative genome structure, sequence alignment and search methods, gene prediction, evolutionary relationships among genes, and high-throughput sequencing. Programming experience is not required. Students will learn rudimentary Python in the discussion section of the course.