HSLS MolBio Workshops

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.

Target Audience

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.

PowerPoints

• Part-1 GEO Data Mining (03/30/2021)
• Part-2 Pathway Enrichment Analysis- g:Profiler and GSEA (10/05/2022)
• Part-3 Cytoscape - Data Visualization (04/13/2022)
• Hands-On Exercise - differential gene expression dataset for SARS-Cov2 infected vs. Mock infected samples from Calu3 cells

Lecture Videos

• Part-1 (GEO Data Mining) offered on March 30, 2021
• Part-2 Pathway Enrichment Analysis - gProfiler and GSEA (12/01/2021)
• Part-3 Cytoscape - data visualization (12/01/2021)

Data Download

Differentially Expressed Genes (DEG)

• DEG_biojupies (.xlsx) - differentially expressed human genes between AI-10-49 treatment vs. DMSO (GSE101788, RNA-seq) generated by BioJupies software
• DEG_geo2r (.xlsx) - DEG calculated by Geo2R using GSE11352 dataset (microarray); Filter used: Adj.p.val <0.1
• GSE147507_handson_GREIN - a DEG list from SARS-Cov2 infected vs. Mock infected Calu3 cells generated by GREIN 

g: Profiler

• AI-1049 vs. DMSO_DEG - differentially expressed human genes between AI-10-49 treatment vs. DMSO (GSE101788, RNA-seq); Filter used: FDR-P-value <.05, abs log FC >1
• Up_regulated_BioJupies (.txt) 
• Down_regulated_BioJupies (.txt) 
• Human_allpathways.gmt_Bader 

GSEA

• Human_allpathways_Bader - a GMT file available from Badder lab comprising pathway Genesets culled from various databases
• Preranked_DEG_BioJupies (.rnk): differentially expressed human genes between AI-10-49 treatment vs. DMSO (GSE101788, RNA-seq) generated by BioJupies software
• Preranked GSE30885(.rnk) - a mouse breast tumor vs. control differentially expressed genes
• Preranked DEG_geo2r (.rnk) : differentially expressed genes between  treatment vs. control generated by GEO2R  software  using GSE11352 dataset (microarray); Filter used: Adj.p.val <0.1
• GSE56192_handson_preranked - dataset for hands-on practice exercise
• GSE147507_handson_preranked (.rnk) - a DEG list from SARS-Cov2 infected vs. Mock infected Calu3 cells generated by GREIN 

Cytoscape

• Protein Interaction Network - a list of data sets for creating a protein interaction map
• Human_allpathways_Bader - a GMT file available from Badder lab comprising pathway Genesets culled from various databases
• AI_vs_DMSO_GSEA_pos - GSEA result for pos that is ready to upload to Cytoscape
• AI_vs_DMSO_GSEA_neg -- GSEA result for pos that is ready to upload to Cytoscape
• hands_on_GSEA_pos_sars-Cov2_vs_mock_calu3 - GSEA result for pos that is ready to upload to Cytoscape 
• hands_on_GSEA_neg_sars-Cov2_vs_mock_calu3 - GSEA result for neg that is ready to upload to Cytoscape 

ChIP-Seq

• RUNX1_AI_Treated Peaks - Downloaded from GSE101789 dataset

Software Download

• GSEA Software - geneset enrichment analysis software
• Cytoscape - network data integration, analysis, and visualization tool
• BioRender - Scientific illustrations creating tool

Tutorials

• Video 1. Uploading and aligning raw RNA-seq data using BioJupies
• Video 2. Generating a notebook using the BioJupies website
• Video 3. Exploring the contents of a BioJupies notebook
• Video 4. How to import metadata into Biojupies
• How to run GSEA in the pre-ranked mode
• Understanding the p-value of overlap statistics (18 min) 

Pre-analyzed Results

• biojupies result : differentially expressed genes between AI-10-49 treatment vs. DMSO 
• g:Profiler - Down-reg biojupies : enrichment analysis performed with gProfiler
• g:Profiler - Up-reg biojupies : enrichment analysis performed with gProfiler

GEO Data Mining

• NCBI GEO -a public functional genomics data repository supporting MIAME-compliant data submissions.
• Search Engine for Gene Expression Data
• BioJupies - BioJupies Automatically Generates RNA-seq Data Analysis Notebooks.
• GREIN - GREIN is an interactive web platform that provides user-friendly options to explore and analyze GEO RNA-seq data.
• GEO2R - Use GEO2R to compare two or more groups of samples in order to identify genes that are differentially expressed across experimental conditions.
• Correlation Engine* - Find pre-analyzed results from GEO hosted RNA-seq and microarray experiments.
• CistromeDB - Retrieve pre-analyzed results generated from GEO hosted ChIP-Seq experiments.
• CitromeDB ToolKit - Find factors that have a significant binding overlap with your ChIP-seq peak set.
• RaNA-Seq - An open bioinformatics tool for the quick analysis of RNA-Seq data.
• GSE101788 (RNA-Seq) - CBFb-SMMHC inhibition triggers apoptosis by disrupting MYC chromatin dynamics in acute myeloid leukemia [RNA-seq]
• GSE11352 (Microarray) - Timecourse of estradiol (10nM) exposure in MCF7 breast cancer cells.
• GSE101789 (ChIP-Seq) - CBFb-SMMHC inhibition triggers apoptosis by disrupting MYC chromatin dynamics in acute myeloid leukemia
• GSE147507 (RNA-seq) - GEO dataset ( SARS-C0v2 vs. mock-infected Calu3 Cells) for hands-on practice exercise
• BioRender - Scientific illustrations creating tool

Enrichment Analysis

• g:Profiler - a web server for functional enrichment analysis and conversions of gene lists
• Genesets (Bader Lab) - monthly updated gene sets available for downloading in GMT format
• Gene Ontology - a comprehensive, computational model of biological systems
• Molecular Signatures Database - GSEA - a collection of annotated gene sets for use with GSEA software

Featured Article

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.

Key References

• BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud
• Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
• g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments
• Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
• GREIN: An Interactive Web Platform for Re-analyzing GEO RNA-seq Data
• Pulikkan JA, Hegde M, Ahmad HM, et al. CBFβ-SMMHC Inhibition Triggers Apoptosis by Disrupting MYC Chromatin Dynamics in Acute Myeloid Leukemia. Cell. 2018;174(1):172-186.e21. doi:10.1016/j.cell.2018.05.048
• Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019). 

Feedback

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Support

• Contact HSLS MolBio for consultations