BackgroundExperimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets.ResultsHere we present Vanted (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow.ConclusionsVanted is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org.

BackgroundThe quantification of metabolic fluxes is gaining increasing importance in the analysis of the metabolic behavior of biological systems such as organisms, tissues or cells. Various methodologies (wetlab or drylab) result in sets of fluxes which require an appropriate visualization for interpretation by scientists. The visualization of flux distributions is a necessary prerequisite for intuitive flux data exploration in the context of metabolic networks.ResultsWe present FluxMap, a tool for the advanced visualization and exploration of flux data in the context of metabolic networks. The template-based flux data import assigns flux values and optional quality parameters (e. g. the confidence interval) to biochemical reactions. It supports the discrimination between mass and substance fluxes, such as C- or N-fluxes. After import, flux data mapping and network-based visualization allow the interactive exploration of the dataset. Various visualization options enable the user to adapt layout and network representation according to individual purposes.ConclusionsThe Vanted add-on FluxMap comprises a comprehensive set of functionalities for visualization and advanced visual exploration of flux distributions in biological networks. It is available as a Java open source tool from http://www.vanted.org/fluxmap.

Motivation: Modern data acquisition methods in biology allow the procurement of different types of data in increasing quantity, facilitating a comprehensive view of biological systems. As data are usually gathered and interpreted by separate domain scientists, it is hard to grasp multidomain properties and structures. Consequently, there is a need for the integration of biological data from different sources and of different types in one application, providing various visualization approaches.Results: In this article, methods for the integration and visualization of multimodal biological data are presented. This is achieved based on two graphs representing the meta-relations between biological data and the measurement combinations, respectively. Both graphs are linked and serve as different views of the integrated data with navigation and exploration possibilities. Data can be combined and visualized multifariously, resulting in views of the integrated biological data.Availability: http://vanted.ipk-gatersleben.de/hive/.Contact: rohn@ipk-gatersleben.de

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Biological data is measured in increasing quantity and quality, resulting in data describing biological systems from different perspectives. Based on data integration methods, visual data mining and visual analytics can be used to promote the understanding of combined biological data and facilitate the exploration process. In this paper a number of view types are presented and integrated into a comprehensive software tool, in order to support researchers in visualizing flexible combinations of multimodal biological data and to create integrated views on comprehensive datasets spanning multiple "omics" areas. A number of interaction techniques accompany these views, enabling the efficient exploration of the data.

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