Background: The development of high throughput sequencing techniques provides us with the possibilities to\nobtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because\nof the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need\nfor bioinformatics tools that address this problem.\nResults: Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in\nbiological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a forcedirected\ngraph network style, involving multiple network analysis methods such as degree threshold, but more\nimportantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure.\nValuable biological information can be highlighted on the nodes in the network, by the integration of various\ndatabases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, diseaseto-\ngene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol,\ngene synonyms and gene type from the NCBI database.\nConclusions: DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can\nvisualize the dynamic perturbations in biological networks and allows the user to investigate the changes over\ntime. The integrated data from various online databases makes it easy to identify the biological relevance of nodes\nin the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to\nvisualize a network.
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