A mediator for shaking hands between graph theory and systems biology.
NAP is a Shiny-based web application designed to profile the topology of medium-scale networks (up to a few thousand edges) and facilitate direct comparison through an intuitive interface. It bridges graph-theoretical metrics with systems biology needs, enabling users to explore, rank, and compare network topology seamlessly.
- Topological Exploration: Compute and visualize metrics such as degree, betweenness, closeness, clustering coefficient and more.
- Node & Edge Ranking: Rank elements by any computed metric and export results as tables or plots.
- Multi-Network Comparison: Load multiple networks simultaneously to compare topological properties side-by-side.
- Feature Distributions: Plot distributions of any topological feature across networks.
- Feature Correlation: Generate scatter plots of one metric against another to uncover relationships.
- Dynamic & Static Layouts: Visualize networks using layouts like Fruchterman–Reingold, Kamada–Kawai, circular, grid, and interactive 3D layers.
git clone https://github.com/PavlopoulosLab/NAP.git
cd NAP
# In R console or RStudio:
source("install_dependencies.R") # install required R packages
shiny::runApp(port = 3838) # launch the app
Access NAP directly at: https://pavlopoulos-lab-services.org/shiny/app/nap
- Upload one or more network edge-list files (tab-delimited).
- Select topological metrics to compute and visualize.
- Compare metric distributions and rankings across uploaded networks.
- Explore interactive network views, adjust layouts, and export publication-ready figures.
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The Network Analysis Profiler (NAP v2.0): A web tool for visual topological comparison between multiple networks Koutrouli M., Theodosiou T., Iliopoulos I., Pavlopoulos G.A. EMBnet.journal, 2021 May;26:e943. doi: 10.14806/ej.26.0.943
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NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks Theodosiou T., Efstathiou G., Papanikolaou N., Kyrpides N.C., Bagos P.G., Iliopoulos I., Pavlopoulos G.A. BMC Research Notes, 2017;10:278. doi: 10.1186/s13104-017-2607-8 PMID: 28705239
This project is released under the MIT License. See the LICENSE file for details.