About Workshop
Hands-on PPI Network Analysis for Biomarker Identification using Cytoscape & STRING is a 3-day mentor-led bioinformatics workshop designed to help participants understand how protein-protein interaction networks can be used to identify hub genes, key proteins, and potential biomarkers for disease-related research.
The workshop provides practical exposure to widely used bioinformatics tools such as STRING Database, Cytoscape, CytoHubba, NetworkAnalyzer, and GO/pathway enrichment tools. Participants will learn how to construct PPI networks, visualize biological interactions, analyze network topology, identify important molecular targets, and interpret results for biomarker discovery and systems biology applications.
Aim
The aim of this workshop is to equip participants with practical knowledge and analytical skills to explore biological interaction networks for biomarker discovery using modern bioinformatics tools and network biology approaches. The workshop focuses on developing the ability to interpret protein interaction data, identify biologically significant targets, and apply systems biology concepts in translational and disease-related research workflows.
What Participants Will Learn
- Understand the fundamentals of Protein-Protein Interaction (PPI) networks and systems biology.
- Learn to construct and analyze PPI networks using STRING Database and Cytoscape.
- Identify hub genes and key proteins using CytoHubba and NetworkAnalyzer.
- Perform GO and pathway enrichment analysis for biological interpretation.
- Explore biomarker identification workflows for disease-related research.
- Develop practical skills in biological network visualization and analysis.
- Interpret interaction data for bioinformatics and translational research applications
Structure
📅 Day 1: Introduction to Protein-Protein Interaction (PPI) Networks and STRING Database
Focus
Understanding the fundamentals of systems biology, network biology, and the role of PPI databases in studying biological interactions and disease mechanisms.
Topics Covered
- Introduction to systems biology and network biology
- Understanding complex biological systems and molecular interactions
- Overview of PPI networks, gene networks, and signaling pathways
- Importance of PPI databases in biological research
- Introduction to STRING Database for protein interaction search
- Searching protein-protein interactions using STRING
- Filtering interaction data based on confidence score and organism
- Visualizing PPI networks in Cytoscape
Tools / Practical Platforms
- STRING Database
- Cytoscape
Hands-on Session
- PPI Search using STRING: Participants will search for interactions of a target protein, such as a cancer-related protein, using the STRING Database.
- Network Import and Visualization: Participants will download interaction data from STRING and import it into Cytoscape for network visualization.
- Basic Network Exploration: Participants will explore degree centrality and understand how protein interactions are represented in Cytoscape.
📅 Day 2: Identifying Biomarkers Using Cytoscape, NetworkAnalyzer, and CytoHubba
Focus
Learning how to analyze PPI networks and identify hub proteins or key molecular targets that may serve as potential biomarkers.
Topics Covered
- Introduction to biomarker discovery using PPI networks
- Role of network biology in identifying disease-associated proteins
- Understanding centrality measures: degree, betweenness, and closeness
- Importance of hub proteins in biological networks
- Using Cytoscape for PPI network analysis
- Introduction to NetworkAnalyzer for network topology analysis
- Using CytoHubba to identify hub proteins
- Shortlisting potential biomarkers based on network centrality
Tools / Practical Platforms
- Cytoscape
- NetworkAnalyzer
- CytoHubba
Hands-on Session
- PPI Network Visualization: Participants will visualize a PPI network associated with a specific disease, target protein, or drug-related pathway using Cytoscape.
- Hub Protein Identification: Participants will use NetworkAnalyzer and CytoHubba to identify central proteins with high degree centrality.
- Biomarker Interpretation: Participants will discuss and shortlist potential biomarkers based on identified hub proteins and network properties.
📅 Day 3: Gene Ontology, Pathway Identification, and Transcription Factor Analysis
Focus
Applying functional enrichment, pathway analysis, and regulatory network interpretation to understand the biological relevance of identified biomarkers.
Topics Covered
- Introduction to Gene Ontology and functional annotation
- GO analysis for biological process, molecular function, and cellular component interpretation
- Pathway enrichment analysis using KEGG and Reactome
- Understanding the biological relevance of identified biomarkers
- Introduction to transcription factors and their role in gene regulation
- Using Gen-TF for transcription factor identification
- Overview of miRNA-gene interactions related to biomarkers
- Integrating PPI, GO, pathway, TF, and miRNA results for biomarker interpretation
Tools / Practical Platforms
- gProfiler
- KEGG
- Reactome
- Gen-TF
- Public miRNA-gene interaction databases
Hands-on Session
- GO Enrichment Analysis: Participants will perform GO enrichment analysis for biomarkers identified on Day 2 using gProfiler.
- Pathway Identification: Participants will identify enriched biological pathways using KEGG or Reactome.
- TF and miRNA Interaction Exploration: Participants will identify transcription factors using Gene-TF and explore potential miRNA interactions linked to the selected biomarkers.
Important Dates
Registration Ends
5:30 PM
Workshop Dates
2026-05-22
6.30 PM
6.30 PM
What You Will Gain
Outcomes
- Build and visualize Protein-Protein Interaction (PPI) networks using Cytoscape and STRING.
- Analyze biological interaction networks and identify hub genes/proteins.
- Perform pathway and functional enrichment analysis for biological interpretation.
- Apply network biology approaches for biomarker discovery and disease research.
- Use Cytoscape plugins such as CytoHubba and NetworkAnalyzer effectively.
- Interpret biological datasets for systems biology and translational research applications.
- Gain hands-on experience in practical bioinformatics workflows and data-driven analysis.