DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks

Abstract Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results i...

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Main Authors: T. J. M. Kuijpers, J. E. J. Wolters, J. C. S. Kleinjans, D. G. J. Jennen
Format: Article
Language:English
Published: BMC 2019-08-01
Series:BMC Bioinformatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12859-019-2995-y
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spelling doaj-42f5c033e0d94dc2944e6364afbe102c2020-11-25T03:46:04ZengBMCBMC Bioinformatics1471-21052019-08-0120111010.1186/s12859-019-2995-yDynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networksT. J. M. Kuijpers0J. E. J. Wolters1J. C. S. Kleinjans2D. G. J. Jennen3Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht UniversityDepartment of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht UniversityDepartment of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht UniversityDepartment of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht UniversityAbstract Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.http://link.springer.com/article/10.1186/s12859-019-2995-yDynamic network visualizationBiological knowledge integrationNetwork analysis
collection DOAJ
language English
format Article
sources DOAJ
author T. J. M. Kuijpers
J. E. J. Wolters
J. C. S. Kleinjans
D. G. J. Jennen
spellingShingle T. J. M. Kuijpers
J. E. J. Wolters
J. C. S. Kleinjans
D. G. J. Jennen
DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
BMC Bioinformatics
Dynamic network visualization
Biological knowledge integration
Network analysis
author_facet T. J. M. Kuijpers
J. E. J. Wolters
J. C. S. Kleinjans
D. G. J. Jennen
author_sort T. J. M. Kuijpers
title DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
title_short DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
title_full DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
title_fullStr DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
title_full_unstemmed DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
title_sort dynovis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2019-08-01
description Abstract Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.
topic Dynamic network visualization
Biological knowledge integration
Network analysis
url http://link.springer.com/article/10.1186/s12859-019-2995-y
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