| Attribute | Detail |
|---|---|
| Format | Online, Live + LMS |
| Level | Beginner |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $99 |
| Tools | Collect, clean, and standardise disease-associated gene lists. Construct and export protein-interaction networks using STRING. Import networks into Cytoscape and calculate centrality measures. Identify hub genes and densely connected functional modules. Develop miRNA–gene or transcription factor–gene networks. Prepare publication-quality network figures and biological interpretations. STRING Cytoscap NetworkAnalyze cytoHubba MCODE ClueGO and CluePedia GeneMANIA Enrichr miRTarBase-Compatible Resources TRRUST-Compatible Resources Curated disease-gene database and standardised gene list STRING interaction network and exported interaction tables Cytoscape project file and centrality-analysis table Hub-gene ranking and MCODE module analysis Integrated miRNA or transcription factor regulatory network Publication-quality figures, GitHub repository, and final technical report Network Biology-Based Identification of Hub Genes in Colorectal Cancer Protein Interaction Network Analysis of Alzheimer’s Disease miRNA–Gene Regulatory Network in Breast Cancer Network Analysis of Antimicrobial Resistance Genes Hub-Gene and Pathway Analysis of Rheumatoid Arthritis Co-Expression Network Analysis of Metabolic Disorders |
About the Network Biology and Hub-Gene Discovery Using STRING and Cytoscape Course
Attribute
Detail
Format
Online, Live + LMS
Level
Beginner to Intermediate
Recommended Duration
4–6 Weeks
Certification
e-Certification + e-Marksheet
Category
Bioinformatics and Network Biology Internship
Tools
STRING, Cytoscape, cytoHubba, MCODE, ClueGO, GeneMANIA, Enrichr, Python, NetworkX, and Google Colab
The Network Biology and Hub-Gene Discovery Using STRING and Cytoscape Internship is a project-based programme designed to introduce participants to the construction, visualisation, and analysis of biological interaction networks.
Participants will collect disease-associated genes, construct protein–protein interaction networks, calculate network-centrality measures, identify hub genes, detect densely connected modules, and interpret module-specific biological pathways.
The internship also introduces regulatory-network integration using miRNAs and transcription factors, allowing participants to prioritise candidate biomarkers and therapeutic targets using both biological evidence and network topology.
Aim
To construct and analyse biological networks for identifying hub genes, functional modules, disease regulators, candidate biomarkers, and potential therapeutic targets.
Program Highlights
• Disease-gene collection and identifier standardisation
• STRING-based protein-interaction network construction
• Cytoscape network visualisation and topology analysis
• Hub-gene discovery using cytoHubba
• Functional-module identification using MCODE
• Regulatory-network integration and target prioritisation
Course Curriculum
- Protein–protein interaction and co-expression networks
- Gene-regulatory and transcription factor–gene networks
- miRNA–gene regulatory networks
- Gene–disease and pathway-interaction networks
- Drug–target and therapeutic-interaction networks
- Integrated multi-layer biological networks
Week 1: Gene Collection and Data Standardisation
- Select a disease or biological condition.
- Retrieve disease-associated gene lists.
- Convert and standardise gene identifiers.
- Remove duplicates and unsupported genes.
- Prepare node attributes and metadata.
Week 2: STRING Network Development
- Generate a protein–protein interaction network.
- Select suitable interaction-confidence thresholds.
- Examine experimental and predicted interaction evidence.
- Export node and edge interaction tables.
- Interpret network-interaction enrichment.
Week 3: Cytoscape Network Analysis
- Import STRING network files into Cytoscape.
- Apply appropriate network layouts.
- Run NetworkAnalyzer and calculate topology scores.
- Identify highly connected and influential nodes.
- Customise node, edge, label, and visual styles.
Week 4: Hub Genes and Functional Modules
- Run cytoHubba for hub-gene identification.
- Compare multiple hub-gene ranking methods.
- Detect densely connected modules using MCODE.
- Perform module-specific enrichment analysis.
- Select candidate biomarkers and therapeutic targets.
Week 5: Regulatory Network Integration
- Identify miRNAs regulating candidate genes.
- Identify transcription factors and target genes.
- Construct multi-layer regulatory networks.
- Compare regulators across functional modules.
- Prioritise major regulatory genes and interactions.
Week 6: Validation and Reporting
- Validate candidate genes through scientific literature.
- Prepare final network and module figures.
- Organise analysis files and the GitHub repository.
- Complete the research-style technical report.
- Present the final findings and biological interpretation.
Tools, Techniques, or Platforms Covered
- Collect, clean, and standardise disease-associated gene lists.
- Construct and export protein-interaction networks using STRING.
- Import networks into Cytoscape and calculate centrality measures.
- Identify hub genes and densely connected functional modules.
- Develop miRNA–gene or transcription factor–gene networks.
- Prepare publication-quality network figures and biological interpretations.
STRING Cytoscap NetworkAnalyze cytoHubba MCODE ClueGO and CluePedia GeneMANIA Enrichr miRTarBase-Compatible Resources TRRUST-Compatible Resources
- Curated disease-gene database and standardised gene list
- STRING interaction network and exported interaction tables
- Cytoscape project file and centrality-analysis table
- Hub-gene ranking and MCODE module analysis
- Integrated miRNA or transcription factor regulatory network
- Publication-quality figures, GitHub repository, and final technical report
Network Biology-Based Identification of Hub Genes in Colorectal Cancer
Protein Interaction Network Analysis of Alzheimer’s Disease
miRNA–Gene Regulatory Network in Breast Cancer
Network Analysis of Antimicrobial Resistance Genes
Hub-Gene and Pathway Analysis of Rheumatoid Arthritis
Co-Expression Network Analysis of Metabolic Disorders
Real-World Applications
- Identification of disease-associated hub genes and biomarkers
- Discovery of functional modules and pathway interactions
- Prioritisation of potential therapeutic targets
- Analysis of miRNA and transcription-factor regulation
- Investigation of drug resistance and disease progression
- Preparation of network figures for research reports and publications
Who Should Attend & Prerequisites
- Biotechnology, bioinformatics, and computational biology students
- Genetics, molecular biology, and biomedical science learners
- Undergraduate and postgraduate students
- PhD scholars and early-career researchers
- Faculty members and research professionals
- Learners interested in network biology and disease-gene analysis
Outcomes
- Import and organise node, edge, and attribute tables.
- Apply suitable layouts and customise network visualisation.
- Map node size, colour, and shape to biological attributes.
- Calculate degree, betweenness, and closeness centrality.
- Identify hub genes using cytoHubba and modules using MCODE.
- Group pathways with ClueGO and export high-quality figures.
Certification
