VESAn

VESAn (Visualization and Ensemble System for Analysis of Networks) is a computational platform designed to support the analysis, visualization, and interpretation of complex biological and ecological network data. Developed by the Systems Biology Research Group at the University of Bioinformatics in 2018, VESAn was created to address the growing demand for integrative tools that combine network biology with advanced graphical representation.

The core features of VESAn include dynamic graph construction, multi‑layered network clustering, and statistical enrichment modules. Users can import data in standard formats such as SBML, SIF, and JSON, and then apply a variety of layout algorithms (force‑directed, circular, hierarchical) to generate intuitive visual outputs. The platform also supports the overlay of phenotype or expression data, enabling correlation analyses that highlight key regulatory nodes.

VESAn is built on a modular architecture using Python for data processing and JavaScript/HTML5 for front‑end rendering. Its API allows integration with other bioinformatics pipelines, making it suitable for high‑throughput studies. The system incorporates machine learning components for predictive modeling, and it offers export options for publication‑ready figures in SVG and PNG formats.

Since its public release, VESAn has been cited in more than 50 peer‑reviewed publications and is widely adopted in studies of metabolic pathways, protein‑protein interactions, and ecological network dynamics. It is maintained as an open‑source project under the MIT license on GitHub, where a community of developers contributes new modules and documentation.

Future development plans for VESAn include support for real‑time collaboration, integration of multi‑omics datasets, and enhanced support for spatially explicit networks. The project’s roadmap is regularly updated on its community forum, and workshops are offered annually to train new users in best practices for network analysis and visualization.