Registration Required

The Bioinformatics Support Hub has designed this teaching series for researchers interested in learning the basics steps for re-analysis of publicly available bulk-RNAseq data. We will introduce you to basic genomic data annotation concepts, including where to find data to re-analyze, how to perform differential gene analysis, how to add gene annotations, and how to visualize your results. Finally, we'll demonstrate different approaches to gene perform gene pathway analysis. Importantly, we have introduced interspersed coached-practice sessions to which attendees will be invited to participate. In these sessions, you will be able to try things for yourself and get immediate support if needed. (Please see below for overall schedule).  

• Session 1 - Intro to annotation of gene expression data (Tue, August 2^nd)
• Session 2 - Where in the World is the Data You Need? How to Find and Reuse Data (e.g. GREIN) – (Thurs, August 4^th)
• Coached-practice 1 - Exercise sources and genomic information format (annotations and gene ID) of datasets (Tue, August 9^th)

• Session 3 - Re-analyze public dataset with DESeq2 package (Thurs, August 11^th)
• Session 4 - biomaRt using R package (Tue, August 16^th)
• Coached-practice 2 - Differential Gene expression, annotation (Thurs, August 18^th)

• Session 5 - Visualization with Qlucore (Tue, August 23^rd)
• Coached-practice 3 - Visualization with Qlucore (Thurs, August 25^th)

• Session 6 – Functional analysis of omics data: Overrepresentation Analysis (ORA) (Tue, August 30^th)
• Session 7 – Gene Set Enrichment Analysis GSEA (Thurs, September 1^st)
• Coached-practice 4 – Functional Pathway Analysis (Tue, August 6^th)

Functional analysis of omics data: Overrepresentation Analysis (ORA)

Attendance in person

High-throughput technologies (e.g., genomics, transcriptomics, proteomics) are widely used to monitor the activity of thousands of genes in a single experiment. These result in hundreds or thousands of differentially expressed genes leaving the researcher with the challenging task of understanding their biological relevance. Due to its simplicity, well-established statistical model, and ease of implementation, enrichment through Overrepresentation Analysis (ORA) is commonly used to interpret lists of differentially expressed genes and it is also available through many online tools. The goals of the session are:

• Learn what are the components of an ORA: gene list, cutoffs, background, or reference set, etc.
• Learn how to use three online tools: DAVID, WebGestalt, gProfiler
• Learn how to visualize and present the results