This page contains resources - PowerPoint slides, lecture videos, datasets for a software demonstration, etc. - for HSLS Molbio Information Service offered workshops on Gene expression data mining and pathway enrichment analysis.
Part 1 (GEO Data Mining) teaches how to retrieve a list of differentially expressed genes (DEG) associated with a gene expression study (RNA-seq / microarray) by searching the Gene Expression Omnibus (GEO) database using BioJupies (RNA-Seq), GREIN (RNA-Seq), and Geo2R (Microarray).
Part 2 (Enrichment Analysis with g:Profiler and GSEA) focuses on uncovering the biology hidden behind the extracted differentially expressed gene list by searching publicly available pathway enrichment analysis resources, including Gene Ontology (GO), Molecular Signature Database (MsigDB), Reactome, Panther, KEGG, PathwayCommons, and WikiPathways using GSEA and g: Profiler.
Part 3 (Cytoscape - Data Visualization) entails the visualization of gene expression using Cytoscape. We cover how to upload gene lists into the publicly available protein interaction databases such as StringDB to retrieve relevant interaction networks and import them into Cytoscape and analyze gene ontology terms and pathways. We also cover how to generate enrichment maps using GSEA and g: Profiler results in Cytoscape.
Experimental biologists seeking to analyze gene lists through omics experiments. 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.
Chaparala S, Iwema CL, Chattopadhyay A. SARS-CoV-2 Infections - Gene Expression Omnibus (GEO) Data Mining, Pathway Enrichment Analysis,
and Prediction of Repurposable Drugs/Compounds. Preprints.org; 2020. DOI: 10.20944/preprints202009.0459.v1.