Turn a gene list into biological insight in two intensive hands-on days. Learn PPI networks in STRING, network analysis in Cytoscape, and hub gene identification — building, scoring and interpreting interaction networks on real gene expression datasets.
The live crash course costs nothing — the course fee is ₹0. The certificate and recording are an optional add-on from ₹599, with additional tiers available if you want study materials, practice datasets, project resources, or personalized guidance.
No charges to attend the live crash course.
Proof of completion, plus access to the full session replay.
Everything you need to rebuild the whole workflow on your own gene list.
For learners who want to apply network analysis to a real project of their own.
Each tier includes everything in the one before it. Add-ons are entirely optional — you can attend the full live crash course without paying anything. See what we cover ↓
Start with what a gene-gene interaction network actually represents, then build one in STRING, carry it into Cytoscape, and finish by pulling out the hub genes and enriched pathways that explain your data.
What a network actually represents biologically, why interaction data is the bridge between a differential expression list and a mechanism, and where network analysis is genuinely used in disease research.
Build a network from your own gene list — the evidence channels behind each edge, what a confidence score means and where to set the threshold, and running functional enrichment directly in STRING.
Make the network readable: build it, customise the layout, and interpret what the shape is telling you rather than accepting the default hairball.
Import the STRING network into Cytoscape, apply topology metrics — degree, betweenness, closeness — to find the driver genes, and export a figure at publication resolution.
If you have a differential expression list, or will have one soon, and want to turn it into a network that actually explains something — this crash course is for you.
Undergraduate, postgraduate and PhD scholars in biotechnology, bioinformatics, biochemistry, genetics and microbiology.
Biologists analysing differential gene expression lists and high-throughput NGS data who need the interpretation step, not just the statistics.
R&D scientists in pharmaceuticals, drug discovery and precision medicine looking for therapeutic targets in their own data.
You understand basic biology but want to learn what actually happens between a list of gene symbols and a network figure in a paper.
Complete the crash course to earn a certificate of completion issued by DrOmics Labs, demonstrating your practical understanding of PPI network construction, functional enrichment and hub gene identification.
DrOmics is a molecular diagnostics and bioinformatics lab first. The training exists because we kept meeting researchers stuck on data they couldn't analyze.
DrOmics was founded by a translational bioinformatician who earned her PhD at the International Centre for Genetic Engineering and Biotechnology (ICGEB) and worked as a senior bioinformatics scientist before starting the lab. She built DrOmics around a gap she saw repeatedly: capable life-science researchers sitting on sequencing data with no practical way to analyze it.
That's the gap this masterclass is designed to close. The session itself is run by the DrOmics bioinformatics training team.
"My DEG list used to just sit there. After understanding confidence scores and degree centrality, I can finally say which genes matter instead of just listing all of them."
"The jump from STRING to Cytoscape was explained really well. Importing the network and running cytoHubba on it made the whole workflow finally click."
"I had used STRING for years without really understanding the edges. Having the evidence channels and confidence thresholds explained properly changed how I read every network."
No. Day 1 starts with what a gene-gene interaction network is and why it matters, before touching any tool. A basic understanding of molecular biology — DNA, RNA, proteins and gene expression — is all you need.
None at all. STRING runs in the browser and Cytoscape is a point-and-click desktop application — there is no coding anywhere in this crash course. Basic computer literacy is enough.
The crash course covers the STRING database for building and scoring interaction networks, and Cytoscape for analysing them — including the cytoHubba and MCODE plugins for hub genes and dense clusters.
A basic understanding of molecular biology concepts — DNA, RNA, proteins and gene expression — plus a computer with an internet connection. No prior network analysis or programming experience is expected.
Yes. Both days are hands-on: you will build and score a network in STRING and read its enrichment on Day 1, then import it into Cytoscape, run the topology metrics and pull out hub genes on Day 2.
Yes — the course fee is ₹0 and attending the live crash course costs nothing. The certificate and recording are ₹599, study material and practice datasets are ₹999, and the project template with a 1:1 review is ₹1,699. These add-ons are completely optional and are not required to attend the live session.
Participants who choose the certificate option and complete the required learning activities receive a Certificate of Completion in Gene Network Analysis with STRING & Cytoscape, issued by DrOmics Labs.
Join the free live crash course and learn how a gene list moves from a STRING interaction network to hub genes and enriched pathways in Cytoscape. Understand the tools, interpret what the network is telling you, and leave with a publication-ready figure you produced yourself. Certificate, recording and study materials are optional add-ons.
