3DScape: three dimensional visualization plug-in for Cytoscape

3DScape is the first plug-in which enables three-
dimensional network visualization in Cytoscape. The extra dimension is useful in accommodating, visualizing, and distinguishing larger networks with multiple crossing connections.
Special features in 3DScape include 3D layout algorithms, mapping onto 3D models and animation effects on a series of expression data. 3DScape is available at http://www.rendware.co

VISUALIZING THE DATA VISUALIZATION NETWORK: THE DVMAP PROJECT

Data visualization is a familiar buzzword. Experts in the humanities, social and natural sciences, as well as technology, along with semi-experts and the general public, reach people everywhere with trends and conclusions drawn from visualized data. Governments, industries, businesses, sciences, marketers, academics, students and others value data visualization methods and tools as critical, applicable tools for understanding the world, which provide rich information analyses for specialists and generalists alike. Unfortunately, no single resource offers a space where people working in the multifaceted field of data visualization can share projects they are working on, tools they created, educational opportunities in the field, nor where they (and their work) are situated geographically. A research group at New Mexico Institute of Mining and Technology seeks to fill this gap with a repository of data visualization resources called ―Data Visualization Map (DVMap).‖ The DVMap is an interactive network data and geographic representation graph that provides a data visualization space for people across the world to share, view and/or collaborate on projects and publications; tools deployed or under development; educational opportunities in data visualization, such as formal programs, summer seminars, conferences; and the geographical locations of the users, projects, tools and educational opportunities. Given the necessity of this repository, this paper outlines the structure, underlying methodology, and anticipated outcomes for the DVMap data visualization network. The paper also accounts for limitations of the project and the potential problems of creating a map that wants to share work – especially work in progress – with everyone

Visualizing metabolic network dynamics through time-series metabolomic data.

BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine. RESULTS: The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. CONCLUSIONS: The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user\u27s guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies