FNV
FNV renders vector graphic visualizations of small- to moderate-sized biological networks and pathways representing genes, proteins, mRNAs, microRNAs, metabolites, regulatory DNA elements, diseases, viruses, and drugs.
Key Features:
- XML input format: Accepts biological network data formatted in Extensible Markup Language (XML) to represent relationships among genes, proteins, mRNAs, microRNAs, metabolites, regulatory DNA elements, diseases, viruses, and drugs.
- Vector graphics rendering (Adobe ActionScript 3.0): Renders network diagrams as scalable vector graphics using Adobe ActionScript 3.0 for web embedding.
- Flexible layout options: Provides flexible layout options for presenting network diagrams.
- Integration with web-based systems: Has been integrated as a component in Genes2Networks, Lists2Networks, KEA (KEGG Enrichment Analysis), ChEA (ChIP-Enrichment Analysis), and PathwayGenerator.
- PDF embedding: Embeds pathway visualizations directly into PDF files.
Scientific Applications:
- Gene–protein interaction analysis: Visualizes and supports analysis of gene–protein interaction networks and their implications in disease contexts.
- Regulatory mechanism exploration: Enables exploration of regulatory mechanisms involving microRNAs and other regulatory DNA elements.
- Drug–gene interaction investigation: Facilitates investigation of drug–gene interactions and potential therapeutic targets.
- Pathway presentation for publications: Produces publication-ready pathway diagrams embeddable in PDF documents for communication of network-based findings.
Methodology:
Biological data are converted into XML, which the viewer processes and renders as vector graphics via Adobe ActionScript 3.0.
Topics
Details
- Tool Type:
- desktop application, web application
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- JavaScript, Java
- Added:
- 8/3/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Dannenfelser R, Lachmann A, Szenk M, Ma'ayan A. FNV: light-weight flash-based network and pathway viewer. Bioinformatics. 2011;27(8):1181-1182. doi:10.1093/bioinformatics/btr098. PMID:21349871. PMCID:PMC3072557.