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)

Analysis of differential gene expression of bulk RNA-seq data using “DESeq2” in RStudio.

The detection of differentially expressed genes is one of the basic goals of RNAseq experiments. This can be done within a specific sample or between samples, each analysis demanding different mathematical approaches. “DESeq2” is an established computational tool developed to find changes in gene expression between conditions (e.g. wild type versus mutant). DESeq2 is a free tool or package developed using R command language and can be run in RStudio, a free and open-source software that provides a friendly environment to run R packages.

In this session you will learn how to use DESeq2 in RStudio to re-analyze publicly available bulk RNA-seq data deposited in the database GREIN - GEO RNA-seq Experiments Interactive Navigator.

This workshop is designed for: biomedical researchers with none to minimal knowledge of command language or R programming. Limited to 25 seats.

You will learn how to:

• Import data files into RStudio
• Load R packages
• Run “DESeq2”
• Save processed data files into your computer (e.g. normalized counts, DEX genes table)
• Perform basic data visualization (e.g. PCA plot, MA plot)

Requirements:

• Bring a laptop with R and RStudio installed and running.
• Prework documentation with instructions on how to install R and RStudio will be sent to you a week prior to the workshop to help you get ready.
• We will also provide you with the files of an RNA-seq dataset publicly available on NCBI’s GEO Database