Rolf Hühne1,2, Viktor Kessler1,3, Axel Fürstberger1, Silke Kühlwein1, Matthias Platzer2, Jürgen Sühnel2, Ludwig Lausser1, Hans A Kestler4,5. 1. Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany. 2. Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstr. 11, Jena, 07745, Germany. 3. Institute of Neural Information Processing - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany. 4. Institute of Medical Systems Biology - Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany. hans.kestler@uni-ulm.de. 5. Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstr. 11, Jena, 07745, Germany. hans.kestler@uni-ulm.de.
Abstract
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.
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.
Authors: Jordan D Ward; Brendan Mullaney; Benjamin J Schiller; Le D He; Sarah E Petnic; Carole Couillault; Nathalie Pujol; Teresita U Bernal; Marc R Van Gilst; Kaveh Ashrafi; Jonathan J Ewbank; Keith R Yamamoto Journal: PLoS One Date: 2014-03-20 Impact factor: 3.240
Authors: Lewis Y Geer; Aron Marchler-Bauer; Renata C Geer; Lianyi Han; Jane He; Siqian He; Chunlei Liu; Wenyao Shi; Stephen H Bryant Journal: Nucleic Acids Res Date: 2009-10-23 Impact factor: 16.971
Authors: Johannes Schobel; Madeleine Volz; Katharina Hörner; Peter Kuhn; Franz Jobst; Julian D Schwab; Nensi Ikonomi; Silke D Werle; Axel Fürstberger; Klaus Hoenig; Hans A Kestler Journal: Int J Environ Res Public Health Date: 2021-05-11 Impact factor: 3.390