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. 2016 Sep 22;2:16020.
doi: 10.1038/npjsba.2016.20. eCollection 2016.

MINERVA-a Platform for Visualization and Curation of Molecular Interaction Networks

Free PMC article

MINERVA-a Platform for Visualization and Curation of Molecular Interaction Networks

Piotr Gawron et al. NPJ Syst Biol Appl. .
Free PMC article


Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.

Conflict of interest statement

The authors declare no conflict of interest.


Figure 1
Figure 1
MINERVA interface and functionalities. (a) Main interface, displaying drug target search results for terms ‘levodopa’ and ‘carbidopa’. Information fetched from DrugBank and ChEMBL are displayed in the left panel, while targets of queried drugs are shown in the display area as markers. (b) Export of the selected content. A portion of the diagram is selected and exported as a model (CellDesigner or SBGN formats). (c) Display of experimental data and content commenting. Publicly available datasets (left panel, ‘General overlays’) or user-provided datasets (left panel, ‘User-provided overlays’) can be visualized on top of the displayed content. Users can pin comments to elements or interactions of the displayed network directly from the display area.
Figure 2
Figure 2
A schematic diagram describing the MINERVA architecture. The back-end—webserver—handles data upload of SBGN-compliant model(s) to the database (‘Structured model’), its annotation (‘Annotated model’) and integration with other provided source files, like pre-defined overlays, overview images and data mining results. The front-end displays the models visualized with the Google Maps API, including overlays based on custom, user-provided datasets. Moreover, the webserver handles search queries, either to the model or to external databases, and visualizes the query results. Finally, a selected part of the model can be exported either as a SGBN-compliant model (.xml file) or as an image (.png or .pdf). A separate view allows exporting network contents or structure.

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    1. Hofmann-Apitius, M. et al. Bioinformatics mining and modeling methods for the identification of disease mechanisms in neurodegenerative disorders. Int. J. Mol. Sci. 16, 29179–29206 (2015). - PMC - PubMed
    1. Le Novère, N. et al. The systems biology graphical notation. Nat. Biotechnol. 27, 735–741 (2009). - PubMed
    1. Kutmon, M. et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 44, D488–D494 (2016). - PMC - PubMed
    1. Fujita, K. A. et al. Integrating pathways of Parkinson’s disease in a molecular interaction map. Mol. Neurobiol. 49, 88–102 (2014). - PMC - PubMed
    1. Aranda, B. et al. PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods. 8, 528–529 (2011). - PMC - PubMed