DynOVis: a web tool to study dynamic perturbations for capturing dose-over-time effects in biological networks. 2019

T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands. tim.kuijpers@maastrichtuniversity.nl.

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.

UI MeSH Term Description Entries
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000082 Acetaminophen Analgesic antipyretic derivative of acetanilide. It has weak anti-inflammatory properties and is used as a common analgesic, but may cause liver, blood cell, and kidney damage. Acetamidophenol,Hydroxyacetanilide,Paracetamol,APAP,Acamol,Acephen,Acetaco,Acetominophen,Algotropyl,Anacin-3,Datril,N-(4-Hydroxyphenyl)acetanilide,N-Acetyl-p-aminophenol,Panadol,Tylenol,p-Acetamidophenol,p-Hydroxyacetanilide,Anacin 3,Anacin3
D014584 User-Computer Interface The portion of an interactive computer program that issues messages to and receives commands from a user. Interface, User Computer,Virtual Systems,User Computer Interface,Interface, User-Computer,Interfaces, User Computer,Interfaces, User-Computer,System, Virtual,Systems, Virtual,User Computer Interfaces,User-Computer Interfaces,Virtual System
D015398 Signal Transduction The intracellular transfer of information (biological activation/inhibition) through a signal pathway. In each signal transduction system, an activation/inhibition signal from a biologically active molecule (hormone, neurotransmitter) is mediated via the coupling of a receptor/enzyme to a second messenger system or to an ion channel. Signal transduction plays an important role in activating cellular functions, cell differentiation, and cell proliferation. Examples of signal transduction systems are the GAMMA-AMINOBUTYRIC ACID-postsynaptic receptor-calcium ion channel system, the receptor-mediated T-cell activation pathway, and the receptor-mediated activation of phospholipases. Those coupled to membrane depolarization or intracellular release of calcium include the receptor-mediated activation of cytotoxic functions in granulocytes and the synaptic potentiation of protein kinase activation. Some signal transduction pathways may be part of larger signal transduction pathways; for example, protein kinase activation is part of the platelet activation signal pathway. Cell Signaling,Receptor-Mediated Signal Transduction,Signal Pathways,Receptor Mediated Signal Transduction,Signal Transduction Pathways,Signal Transduction Systems,Pathway, Signal,Pathway, Signal Transduction,Pathways, Signal,Pathways, Signal Transduction,Receptor-Mediated Signal Transductions,Signal Pathway,Signal Transduction Pathway,Signal Transduction System,Signal Transduction, Receptor-Mediated,Signal Transductions,Signal Transductions, Receptor-Mediated,System, Signal Transduction,Systems, Signal Transduction,Transduction, Signal,Transductions, Signal
D016208 Databases, Factual Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references. Databanks, Factual,Data Banks, Factual,Data Bases, Factual,Data Bank, Factual,Data Base, Factual,Databank, Factual,Database, Factual,Factual Data Bank,Factual Data Banks,Factual Data Base,Factual Data Bases,Factual Databank,Factual Databanks,Factual Database,Factual Databases
D016328 NF-kappa B Ubiquitous, inducible, nuclear transcriptional activator that binds to enhancer elements in many different cell types and is activated by pathogenic stimuli. The NF-kappa B complex is a heterodimer composed of two DNA-binding subunits: NF-kappa B1 and relA. Immunoglobulin Enhancer-Binding Protein,NF-kappa B Complex,Nuclear Factor kappa B,Transcription Factor NF-kB,kappa B Enhancer Binding Protein,Ig-EBP-1,NF-kB,NF-kappaB,Nuclear Factor-Kappab,Complex, NF-kappa B,Enhancer-Binding Protein, Immunoglobulin,Factor NF-kB, Transcription,Factor-Kappab, Nuclear,Ig EBP 1,Immunoglobulin Enhancer Binding Protein,NF kB,NF kappa B Complex,NF kappaB,NF-kB, Transcription Factor,Nuclear Factor Kappab,Transcription Factor NF kB
D053263 Gene Regulatory Networks Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations. Gene Circuits,Gene Modules,Gene Networks,Transcriptional Networks,Gene Module,Circuit, Gene,Circuits, Gene,Gene Circuit,Gene Network,Gene Regulatory Network,Module, Gene,Modules, Gene,Network, Gene,Network, Gene Regulatory,Network, Transcriptional,Networks, Gene,Networks, Gene Regulatory,Networks, Transcriptional,Regulatory Network, Gene,Regulatory Networks, Gene,Transcriptional Network
D019295 Computational Biology A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets. Bioinformatics,Molecular Biology, Computational,Bio-Informatics,Biology, Computational,Computational Molecular Biology,Bio Informatics,Bio-Informatic,Bioinformatic,Biologies, Computational Molecular,Biology, Computational Molecular,Computational Molecular Biologies,Molecular Biologies, Computational

Related Publications

T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
May 2007, BMC bioinformatics,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
December 2014, EURASIP journal on bioinformatics & systems biology,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
April 2022, Bioinformatics (Oxford, England),
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
July 2018, Nucleic acids research,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
January 2018, IEEE/ACM transactions on computational biology and bioinformatics,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
October 2005, American journal of public health,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
August 2021, Database : the journal of biological databases and curation,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
January 2016, Journal of pest science,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
September 2017, BMC bioinformatics,
T J M Kuijpers, and J E J Wolters, and J C S Kleinjans, and D G J Jennen
January 2019, Frontiers in genetics,
Copied contents to your clipboard!