The data come from a wide range of sources including individual studies and large databases such as BIOGRID (Breitkreutz et al., 2008), GEO (Barrett et al., 2009), I2D (Brown and Jurisica, 2005) and Pathway Commons ( ). The networks are grouped into six categories: co-expression, co-localization, genetic interaction, physical interaction, predicted and shared protein domain. The plugin uses a large dataset of functional association networks, which includes over 800 networks for six organisms: Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae. A set of known DNA repair genes were provided as a query (gray nodes) and a number of additional DNA repair genes were predicted to be related (white nodes). The GeneMANIA Cytoscape plugin analysis view showing an example prediction. The resulting predicted network of functional relationships among query and predicted genes is then available as an annotated Cytoscape network for further analysis ( Fig. The plugin extends the Cytoscape network visualization and analysis platform (Shannon et al., 2003) and the functionality of the GeneMANIA gene function prediction website (Warde-Farley et al., 2010) to enable computational biologists and biologists to conduct queries using any number of genes and networks as long as their machine has enough memory. GeneMANIA has been shown to be as good or better in speed and accuracy compared with other gene function prediction algorithms in a competition based on mouse functional association network data (Pena-Castillo et al., 2008). The plugin implements the GeneMANIA algorithm (Mostafavi et al., 2008), which uses a guilt-by-association approach to derive predictions from a combination of potentially heterogeneous data sources. Expand the package to support another very useful use case, where tapping a node expands the network to show the node’s neighborhood.The GeneMANIA Cytoscape plugin is a standalone tool for making fast and efficient gene function predictions.Create a dashboard example where the bidirectional communication is utilized.If you find bugs, please report it on the issues page.įor the next steps (help is very much appreciated), I would like to: But for simple networks that I tested, it worked. Don’t expect it to work without bugs as I didn’t test it extensively and I have close to 0 experience in this field haha. I published the component as a package in pipy test repository here and the GitHub repository has use instructions. To be honest, I really wanted to do a react component and collaborate with but the react framework seemed quite complex. Despite being more verbose, I can directly use the original Cytoscape.js library, which has a ton of useful functions. Since my knowledge in web development is very weak and my time is limited, instead of using a react template I chose to go for the reactless one. More precisely, the component returns the element (node or edge) id tapped by the user, so that now we can trigger other visualizations based on the interaction with the network. I’ve been working on improving the concept of a Cytoscape.js component for Streamlit, and I just finished an initial version (0.0.1) of a bidirectional component.
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