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3D Network exploration and visualisation for lifespan data

BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing facto...

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Autores principales: Hühne, Rolf, Kessler, Viktor, Fürstberger, Axel, Kühlwein, Silke, Platzer, Matthias, Sühnel, Jürgen, Lausser, Ludwig, Kestler, Hans A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199797/
https://www.ncbi.nlm.nih.gov/pubmed/30352578
http://dx.doi.org/10.1186/s12859-018-2393-x
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author Hühne, Rolf
Kessler, Viktor
Fürstberger, Axel
Kühlwein, Silke
Platzer, Matthias
Sühnel, Jürgen
Lausser, Ludwig
Kestler, Hans A.
author_facet Hühne, Rolf
Kessler, Viktor
Fürstberger, Axel
Kühlwein, Silke
Platzer, Matthias
Sühnel, Jürgen
Lausser, Ludwig
Kestler, Hans A.
author_sort Hühne, Rolf
collection PubMed
description BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. RESULTS: Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. CONCLUSION: Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2393-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-61997972018-10-31 3D Network exploration and visualisation for lifespan data Hühne, Rolf Kessler, Viktor Fürstberger, Axel Kühlwein, Silke Platzer, Matthias Sühnel, Jürgen Lausser, Ludwig Kestler, Hans A. BMC Bioinformatics Methodology Article BACKGROUND: The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. RESULTS: Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. CONCLUSION: Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2393-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-23 /pmc/articles/PMC6199797/ /pubmed/30352578 http://dx.doi.org/10.1186/s12859-018-2393-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Hühne, Rolf
Kessler, Viktor
Fürstberger, Axel
Kühlwein, Silke
Platzer, Matthias
Sühnel, Jürgen
Lausser, Ludwig
Kestler, Hans A.
3D Network exploration and visualisation for lifespan data
title 3D Network exploration and visualisation for lifespan data
title_full 3D Network exploration and visualisation for lifespan data
title_fullStr 3D Network exploration and visualisation for lifespan data
title_full_unstemmed 3D Network exploration and visualisation for lifespan data
title_short 3D Network exploration and visualisation for lifespan data
title_sort 3d network exploration and visualisation for lifespan data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199797/
https://www.ncbi.nlm.nih.gov/pubmed/30352578
http://dx.doi.org/10.1186/s12859-018-2393-x
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