Teaching

With the goal of giving students a solid foundation in Data Science for Genomics I teach two complementary courses in R (Human Genome Analysis) and Python (Evolutionary Genomics and Bioinformatics), project clubs in evaluating current methods and and honor colloquium. The R and Python course are available on our GitHub site https://github.com/jeffreyblanchard

Evolutionary Genomics and Bioinformatics The course deals with evolutionary processes on a molecular and genetic level and provides training in analytical methods related to detecting genetic variation, phylogenetics, recombination, horizontal gene transfer, comparative genomics, and the analysis of microbiomes. The course includes weekly evolutionary genomics and bioinformatics lab sessions which provide training in analytical methods related to genetic variation, detecting selection, phylogenetics, genome annotation, comparative genomics, RNAseq and microbiomes. The course will include learning to program in Python for genomic data analysis.

Human Genome Analysis  Practical skills for analyzing genomic and RNA-seq data are taught through weekly bioinformatic lab sessions in the R statistical programming language. A complementary discussion section covers social, ethical and legal issues surrounding genetic technology, genomic technology, human population genomics, the human microbiome and disease genomics. Students will have the opportunity to submit their DNA for genome-wide SNP determination.

Microbial Genomics Journal/Project Club Each year the topical focus of this 1 credit seminar varies, but always includes hands-on investigation of new computational methods for analyzing genomic data.

Gut Feelings: Learning to Foster Better Microbiome Relationships (Honors Colloquium) Microbes...are they good or are they bad? Do they control your thoughts and actions? We are realizing the collection of microbes in and on our body, the human microbiome, acts like an organ affecting our health and behavior. This seminar will help you understand the science that is discovering who is in a microbiome and how they are influencing the world around us. By learning methods for generating and analyzing microbial data you will be able make sense of the scientific literature and develop your own hypotheses.