🌀 Fractal topology is not disorder. 🧬 In CENTRA, fractal-rich networks are often the most functionally intriguing. 🌐 Complexity emerges from structure, not in spite of it.

🧬 ATM, RAD51, and MDM2 show strikingly high local fractal dimension — not in the 🧬 DNA damage response network as you'd expect, but in 🩸 hematopoiesis. 🧭 How central is your gene of interest?

We’d love to hear your thoughts. Tried CENTRA yet? Feedback, bugs, ideas, or a module that completely surprised you? Tell us, we’re already shaping the next updates. 💬 DMs are open, or visit ngs-info.medizin.uni-halle.de/shiny/CENTRA/ for more contact options.

Thinking about using CENTRA for functional exploration? The browser comes with built-in functions to: 📚 parse gene metrics 🔍 generate ready-to-publish graphs 🧠 explore module functions across all networks Everything is fully accessible without any account or login.

🔀 CENTRA assesses the variance of betweenness to see how stable a gene’s bridging role really is. 📉 By randomly rewiring our networks we control for underrepresented genes in the dataset. 🛡️ What remains are genuine, robust information brokers or hidden functional master regulators.

🧠 CENTRA uses Latent Dirichlet Allocation (LDA) to cluster gene sets by their semantic content. 🗂️ Each cluster becomes a distinct biological topic. 🕸️ This transforms unstructured text into functional gene networks.

Each gene in CENTRA comes with its own radar plot visualizing key topological properties: 📡 Centrality 🌿 Local fractality 🛡️ Robustness Perfect for comparing genes across different biomedical topics.

CENTRA’s topics span a wide biological landscape, including: 🧠 Neurodevelopment 🔬 DNA damage response 🦠 Immune regulation 💥 Apoptosis 🧫 Cancer metabolism …besides many others. Each topic forms its own network with distinct functional hubs.

🧩 CENTRA identifies modules using Louvain clustering. 🔍 Each module is tested for functional coherence via Overrepresentation Analysis. 📊 The resulting modules often align with coordinated biological processes and pathways.

🌿 Some genes reveal structure not by connections alone, but by how complex their local neighborhood is. CENTRA uses Local Fractal Dimension to capture this, a measure of how dense the surroundings of a gene become in the network. ✨ High LFD can hint at elevated information density.

⭐ Some genes stand out because of who they’re connected to. CENTRA uses eigenvector centrality to identify these influence hubs, genes linked to other highly connected players across the network. ✨ High eigenvector values can hint at strong regulatory relevance.

🔀 Some genes quietly sit at the crossroads of biological information flow. CENTRA uses betweenness centrality to find these hidden mediators, genes that bridge modules and shape how information moves through a network. ✨ High betweenness can hint at regulatory leverage.

How do we define a key gene in a network? CENTRA uses multiple centrality measures: 🔹 Betweenness 🔹 Eigenvector 🔹 Local Fractal Dimension These combine to reveal functional importance across contexts.

CENTRA is available as interactive browser app: 🧭 Explore topic-specific networks 🔍 Search genes or terms 🌐 Visualize modules, metrics, enrichments

🎨 What if you could explore gene networks by topic, not just predefined categories? CENTRA clusters gene sets from MSigDB into 27 topics using Latent Dirichlet Allocation, a topic modeling method designed for text. 🕸️ Each topic becomes its own biological network.

🚨 Our CENTRA paper is out in NAR Genomics and Bioinformatics! CENTRA shows how centrality and fractal geometry uncover functional master regulators across biological networks. You can explore everything instantly, completely open access. doi.org/10.1093/narg...