Browse ORBilu by Open Access
- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?
ORBilu is a project developed by
   Visualizing time-related data in biology, a review; Schneider, Reinhard  in Briefings in Bioinformatics (2013) Time is of the essence, also in biology. Monitoring disease progression or timing developmental defects are key aspects in the process of drug discovery and therapy trial. Furthermore, before deciphering ... [more ▼] Time is of the essence, also in biology. Monitoring disease progression or timing developmental defects are key aspects in the process of drug discovery and therapy trial. Furthermore, before deciphering the course of evolution of these complex processes, we need an understanding of the basic dynamics of biological phenomena that are often strictly time-regulated (e.g. circadian rhythms). With the advances in technologies able to measure timing effects and dynamics of regulatory aspects, visualization and analysis tools try to keep up the pace with the new challenge. Beyond the classical timeline plots, notable attempts at more involved temporal interpretation have been made in the recent years, but awareness of the available resources is still limited within the scientific community. Here we review some of the advances in biological visualization of time-driven processes and look at how they allow analyzing data now and in the future. [less ▲] Detailed reference viewed: 229 (4 UL)PhenoTimer: Software for the Visual Mapping of Time-Resolved Phenotypic Landscapes; Schneider, Reinhard in PLoS ONE (2013), 8(8), 72361 Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this ... [more ▼] Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse. [less ▲] Detailed reference viewed: 162 (4 UL)Arena3D: visualizing time-driven phenotypic differences in biological systems; ; et al in BMC Bioinformatics (2012), (13), 45 Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the ... [more ▼] Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the time factor. In this context of data deluge and heterogeneous information, visualization plays an essential role in interpreting complex and dynamic topologies. Thus, software that is able to bring the network, phenotypic and temporal information together is needed. Arena3D has been previously introduced as a tool that facilitates link discovery between processes. It uses a layered display to separate different levels of information while emphasizing the connections between them. We present novel developments of the tool for the visualization and analysis of dynamic genotype-phenotype landscapes. [less ▲] Detailed reference viewed: 143 (2 UL)Using graph theory to analyze biological networks; ; et al in BioData Mining (2011), 4(10), 1-27 Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can ... [more ▼] Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system. [less ▲] Detailed reference viewed: 146 (2 UL) |
||
