Undergraduate

I teach “Quantitative Methods in Human Genetics” for undergraduates in the Spring.  You can access the class syllabus here.

Deciphering the information encoded in the human genome is one of the greatest (and most exciting) challenges of the 21st century.  This course will provide an introduction to studying and interpreting the human genome with a focus on the statistical methods required for its study.  Fundamental concepts in human genetics will be introduced including inheritance of mendelian disease, population genetics, multifactorial disease and functional genomics.  Accompanying each topic will be an introduction to the statistical concepts and tools that are required to study inheritance, genes and gene function.  These include probability, hypothesis testing, ANOVA, regression, correlation and likelihood.  Hands on experience will be provided through weekly assignments using the statistical programming language, R.  Prior experience with statistics and genetics is not required.

Other Undergraduate level teaching:

Microbiology and Microbial Genomics, 2011, 2013, 2014.  Guest lecture: “Microbial metabolism”

Molecular and Cell Biology I, 2010. Guest lectures: “Sequencing the Human Genome I” and “Sequencing the Human Genome II”

Graduate

I teach “Applied Genomics” for graduate students with Ken Birnbaum in the Fall.  In 2016, I am teaching Applied Genomics with Manny Katari. You can access the class syllabus here.

This course provides a comprehensive introduction to the analysis of next generation DNA sequence (NGS) data. Through a combination of lectures, hands-on computational training, discussions of scientific papers, and assignments using real data, students will learn the foundations of analytical methods, the computational skills to implement those methods, and the reasoning skills to critically assess the primary literature in genomics. The course will cover all commonly used NGS methods including genome sequence analysis, gene expression analysis and protein-nucleic acid interactions. To gain practical expertise in executing bioinformatic analyses, students will undertake a series of assignments using real data. Students will also complete an individual project that integrates skills and concepts covered during the class and that is tailored to meet their background and training.

Other Graduate level teaching:

Biocore I, 2009. Genomics and systems biology I: methods.

Biocore II, 2010, 2011, 2013, 2014. (One paper-based discussion lecture).

Biocore III, 2009, 2010, 2011 (Student paper assessment).

Genome Biology, 2014.  Guest lecture: “eQTL mapping

Principles of Evolution, 2009.  Guest lecture: “Experimental evolution”

Systems Biology, 2009. Guest lecture: “Towards quantitative reverse genetics”