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DynOVis:一种用于研究动态扰动的网络工具,可捕捉生物网络中随时间变化的剂量效应。

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

机构信息

Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands.

Present Address: School for Mental Health and Neuroscience (MHeNS), University Eye clinic Maastricht, Maastricht University Medical Centre + (MUMC+), P.O. Box 5800, Maastricht, 6229 HX, The Netherlands.

出版信息

BMC Bioinformatics. 2019 Aug 13;20(1):417. doi: 10.1186/s12859-019-2995-y.

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.

摘要

背景

高通量测序技术的发展为我们提供了获取大量数据集的可能性,这些数据集可以捕捉到动态扰动对细胞过程的影响。然而,由于这些过程的动态性质,分析结果具有挑战性。因此,非常需要能够解决这个问题的生物信息学工具。

结果

在这里,我们介绍了 DynOVis,这是一种网络可视化工具,可以捕获生物网络中动态的随时间变化的剂量效应。DynOVis 是一个集成了 R 包和 JavaScript 库的工作框架,提供了一种力导向图网络样式,涉及多种网络分析方法,如度数阈值,但更重要的是,它允许节点表达动画以及动态曝光的逐帧视图。通过在 DynOVis 中整合各种数据库,可以在网络中的节点上突出显示有价值的生物学信息。这些信息包括 ConsensusPathDB 中的途径-基因关联、Comparative Toxicogenomics databases 中的疾病-基因关联,以及来自 NCBI 数据库的 Entrez 基因 ID、基因符号、基因同义词和基因类型。

结论

DynOVis 可以成为分析具有动态性质的生物网络的有用工具。它可以可视化生物网络中的动态扰动,并允许用户研究随时间的变化。来自各种在线数据库的综合数据使得很容易识别网络中节点的生物学相关性。通过 DynOVis,我们提供了一项易于使用的服务,不需要任何生物信息学技能即可可视化网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a56/6693283/e71b59e575db/12859_2019_2995_Fig1_HTML.jpg

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