NORMA 3.0: aesthetic and group-aware visualization and analysis of annotated biological networks
Interpreting a biological network often rests on the relationship between its connectivity and the functional groups annotated on it, such as pathways, complexes, or enriched terms. For example, whether a group forms a cohesive module, how groups overlap, and which proteins connect them. These relationships are often hard to assess visually when groups are numerous or overlap, and most…
Interpreting biological networks often requires understanding the relationships between a network's connectivity and the functional groups annotated on it, such as pathways, complexes, or enriched terms. These relationships can be challenging to assess visually when there are numerous groups or significant overlaps, with most tools only representing membership through node color.
NORMA 3.0 is a comprehensive client-side rewrite of its predecessors that addresses these challenges by delineating and laying out groups using various visualizations, including convex hulls, bubble sets, and fog clouds. Multi-group nodes are rendered as pies, and directed lines are used to represent connections. Parallel edges retain their evidence channels, enhancing the visualization's clarity.
The software offers 18 2D and 9 3D layouts, each scored by silhouette width, outline intrusion, and overlap, providing a comprehensive evaluation of group separation and run time. In addition to these visual enhancements, NORMA 3.0 incorporates edge bundling, network importers for six databases, 21 topological measures, and group-level and enrichment statistics.
Users can compare up to ten networks simultaneously and export high-resolution images for further analysis or presentation. The software also includes an Application Programming Interface (API), allowing for integration with other tools and systems.
NORMA 3.0 is freely available at https://normaviz.org, and its source code is accessible at https://github.com/PavlopoulosLab/NORMA, making it an accessible and powerful resource for researchers in the field of biological network analysis.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.