This flipped class covers bioinformatics concepts and software used to interpret a gene list using pathway and network information. It will focus on software demonstration using HSLS-licensed tools: Correlation Engine, Ingenuity Pathway Analysis (IPA), and MetaCore. Upon registration, you will receive links to workshop materials, including PowerPoint slides, lecture videos, and practice exercises, that you can view on your schedule. During the class, we will learn how to solve the exercise problems.
Participants will learn how to
- retrieve a list of differentially expressed genes (DEG) associated with a genome-scale experiment such as an RNA-Seq gene expression study ("treatment vs. control," "tumor vs. normal" or "infected vs. Mock") by searching gene expression data repositories (NCBI GEO)
- glean mechanistic insights by finding statistically overrepresented terms (biological functions, molecular processes, diseases, etc.) and pathways present in that DEG list
- predict upstream causal regulators (transcription factors, miRNA, etc.)
- retrieve datasets from GEO that show similar or opposite gene expression profiles
Experimental biologists working with human, mouse, or rat tissues and seeking to interpret gene lists generated through omics experiments such as gene expression, protein interactions, and SNP arrays. The software covered in the workshop operates through a user-friendly, point-and-click graphical user interface, so neither programming experience nor familiarity with the command line interface is required.