Concepts in RNAseq

This four-class course introduces bulk RNAseq analysis for biomedical research, and is designed for research scientists (lab, clinical, computational) who have no prior experience working with genomic data. This course requires participants have a general understanding of the central dogma of molecular biology (DNA->RNA->protein), but assumes no experience handling genomic data or performing computational analyses. This course, or equivalent background knowledge, is a pre- or co-requisite for the skills course described below.

Information about RNAseq is available on the Fred Hutch Biomedical Data Science Wiki.

By the end of this course, you should be able to:

• Identify data types and applications for bulk RNAseq analysis in biomedical research
• Design statistically robust RNAseq experiments
• Choose appropriate analytical approaches for RNAseq data
• Interpret common visualizations and hypothesis tests associated with RNAseq
• Connect data types, experimental design, and analysis methods to appropriately frame research questions and understand technical limitations of RNAseq analyses.

Please see each set of class materials for specific learning objectives.

[[links to other materials that have been adapted in this lesson]]

Concepts schedule

When taught by an instructor, each of the four classes is scheduled for one hour. The HackMD (interactive page used for sharing links and information) for instructor-led courses is here.

1. Introduction to RNAseq data and experimental design

2. Read mapping and quantification

3. Hypothesis and visualization

4. Contextualizing results, and mods to RNAseq

For curriculum contributors and instructors

Please see the RNAseq course series information for teaching and contributing to these materials.