Abstract
BackgroundThe 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.ResultsDifferent 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.ConclusionInteractive 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.
Highlights
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
After the basic description of the visualisation techniques and network types given in the “Methods” section, we first present concrete examples for some of the visualisation techniques introduced in the “Methods” section followed by use cases how these techniques were applied with Jmol AgeFactDB network-viewer (JANet) to solve specific tasks
Lifespan observation (LO) network visualisation examples We show examples for the application to lifespan data for S. cerevisiae and C. elegans
Summary
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. These ageing factors (AFs) can be genes, chemical compounds or other factors like dietary restriction They are examined under different experimental conditions in model organisms like the worm (Caenorhabditis elegans), yeast (Saccharomyces cerevisiae), fruit fly (Drosophila melanogaster), mouse (Mus musculus), and many others. The results of these experiments may be extracted from the scientific literature in the form of lifespan observations (LOs). The effects on the lifespan of the organism may differ drastically
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