About Workshop
Aim
What Participants Will Learn
- Understand the fundamental principles of computational systems biology and network biology.
- Retrieve and analyse publicly available gene-expression datasets from GEO.
- Perform gene-expression preprocessing, normalisation, and differential-expression analysis using R and Bioconductor.
- Identify significantly upregulated and downregulated genes.
- Generate and interpret volcano plots, heatmaps, and gene-expression visualisations.
- Construct protein–protein interaction networks using STRING and Cytoscape.
- Analyse network topology using degree, betweenness, closeness, and MCC centrality measures.
- Identify hub genes using cytoHubba and functional molecular modules using MCODE.
- Perform Gene Ontology, KEGG, and Reactome pathway-enrichment analysis.
- Interpret disease-associated pathways and biological functions of candidate genes.
- Integrate gene-expression, network-centrality, and pathway evidence for biomarker prioritisation.
Structure
📅 Day 1: Gene-Expression Analysis & DEG Identification
Core Objective: Identify significant disease-associated genes from gene-expression data.
Topics Covered
- Introduction to computational systems biology
- Gene-expression datasets and GEO
- Dataset retrieval and sample understanding
- Data preprocessing and normalization
- Differential-expression analysis
- Fold change, p-values, and adjusted p-values
- Identification of upregulated and downregulated genes
- Volcano plots and heatmaps
- Preparing gene lists for network analysis
🛠️ Hands-on Session
Participants will retrieve a public GEO dataset, perform differential-expression analysis in R, filter significant genes, and generate a volcano plot and heatmap.
Hands-on Workflow: GEO Dataset → Preprocessing → Differential Expression → DEG Filtering → Volcano Plot → Heatmap
Tools: R, RStudio, GEOquery, Bioconductor, limma/DESeq2, ggplot2
📅 Day 2: PPI Networks & Hub-Gene Discovery
Core Objective: Construct biological networks and identify important hub genes and molecular modules.
Topics Covered
- Introduction to protein–protein interaction networks
- Nodes, edges, connectivity, and network topology
- Constructing PPI networks using STRING
- Importing networks into Cytoscape
- Network-topology analysis
- Degree, betweenness, closeness, and MCC centrality
- Hub-gene identification using cytoHubba
- Molecular-module detection using MCODE
- Biological interpretation of hub genes and clusters
🛠️ Hands-on Session
Participants will use the significant genes identified on Day 1 to construct a STRING PPI network, analyze it in Cytoscape, identify top hub genes, and detect significant molecular modules.
Hands-on Workflow: DEGs → STRING → Cytoscape → Network Analysis → cytoHubba → MCODE → Hub Genes
Tools: STRING, Cytoscape, NetworkAnalyzer, cytoHubba, MCODE
📅 Day 3: Pathway Analysis & Biomarker Prioritization
Core Objective: Integrate hub genes and pathway evidence to prioritize candidate biomarkers and therapeutic targets.
Topics Covered
- Introduction to functional-enrichment analysis
- Gene Ontology enrichment
- KEGG and Reactome pathway analysis
- Functional interpretation of hub genes
- Disease-associated pathway identification
- Gene- pathway interaction networks
- Biomarker prioritization
- Therapeutic-target identification
- Integrating differential-expression, centrality, and pathway evidence
- Publication-quality network visualization
🛠️ Hands-on Session
Participants will perform enrichment analysis on hub genes, identify important disease-associated pathways, construct a gene–pathway network, and develop a candidate biomarker shortlist.
Hands-on Workflow: Hub Genes → GO/KEGG/Reactome → Pathway Interpretation → Gene–Pathway Network → Biomarker Prioritization
Tools: Enrichr, g, KEGG, Reactome, ClueGO, CytoscapeImportant Dates
Registration Ends
Workshop Dates
What You Will Gain
Who Should Attend
- Ph.D. scholars and researchers
- Biotechnology and bioinformatics students
- Molecular biology and genomics researchers
- Biomedical and pharmaceutical researchers
- Faculty and academicians
- Wet-lab researchers interested in computational biology
- Researchers working on transcriptomics, disease mechanisms, biomarkers, or therapeutic targets