This introductory course provides a hands-on experience in bioinformatics data analysis with a focus on biomedical research.
Following an introduction to the R statistical programming environment and UNIX, students will perform a complete analysis of a human gene expression data set including quality control, read alignment, identification of differentially expressed genes and biological interpretation of a recent biomedical experiment.
Topics covered will include:
- Introduction to R and UNIX command line environments
- Command line tools such as FastQC, HISAT2 and HTSeq for dealing with RNA-seq data
- R Bioconductor tools like edgeR for analysis of differential gene expression
- Web-based tools such as Galaxy, UCSC Genome Browser and IGV for general genomics
- A survey of publicly available experimental data and gene annotation resources
- R packages for dimensional reduction, visualization, gene ontology and pathway analysis
- Introduction to cloud computing technology relevant to biomedical research
- An introduction to machine learning using CART models, random forests and support vector machines
Additionally, the course will include evening workshops to address topics of interest to course participants.
- Britton Goodale, Ph.D.Postdoctoral FellowGeisel School of Medicine, Dartmouth College
- Joel H. Graber, Ph.D.Senior Staff Scientist, Director of Computational Biology and Bioinformatics CoreMDI Biological Laboratory
- Thomas H. Hampton, Ph.D.Senior Bioinformatics AnalystGeisel School of Medicine at Dartmouth
- Katja Koeppen, Ph.D.Research ScientistGeisel School of Medicine, Dartmouth College
- Steven Munger, Ph.D.Assistant ProfessorThe Jackson Laboratory
- W. Kelley Thomas, Ph.D.Director, Hubbard Center for Genome StudiesUniversity of New Hampshire
Schedule details will be provided to course registrants as we approach the start date.
Double occupancy on-campus housing is included in tuition. Single occupancy housing, if available, may be purchased for an additional fee.