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Tool is being activelty miantained. This tool is considered complete. Bugs will be fixed, but no new features will be added 

eXamine

eXamine accurately conveys small and annotated modules consisting of several dozens of proteins and annotations. We demonstrate that eXamine facilitates the interpretation of integrative network analysis results in a guided case study. This study has resulted in a novel biological insight regarding the virally-encoded G-protein coupled receptor US28.

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Kasper Dinkla is supported by the Netherlands Organisation for Scientific Research (NWO) under project no. 612.001.004.

LEVER

LEVER 3-D is an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. LEVER 3-D automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. This tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

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Institution: Drexel University

Voreen

Voreen is a ray-casting technique for rendering large volumetric multi-channel microscopy data streams on commodity hardware.

The volumetric data is managed at different levels of detail by an octree structure. In contrast to previous octree-based techniques, the octree is built incrementally and therefore supports streamed microscopy data as well as data set sizes exceeding the available main memory.Furthermore, this approach allows the user to interact with the partially rendered data set at all stages of the octree construction.

This rendering technique allows biologists to visualize their scanned specimen on their standard desktop computers without high-end hardware requirements.

The user can interact with the data set during the initial loading to explore the already loaded parts, change rendering parameters like color maps or adjust clipping planes. Streamed data can also be visualized to detect and stop flawed scans early during the scan process.

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Institution: University of Munster

MIMTool

 

Background: To understand protein function, it is important to study protein- protein interaction networks. These networks can be represented in network diagrams called protein interaction maps that can lead to better understanding by visualization. We address the problem of drawing of protein interactions in Kohn's Molecular Interaction Map (MIM) notation. Even though there are some existing tools for graphical visualization of protein interactions in general, there is no tool that can draw protein interactions with MIM notation with full support. Results: MIMTool was developed for drawing protein interaction maps in Kohn's MIM notation. MIMTool was developed using the Qt toolkit libraries and introduces several unique features such as full interactivity, object dragging, ability to export files in MIMML, SBML and line drawing with automatic bending and crossover minimization, which are not available in other diagram editors. MIMTool also has a unique orthogonal edge drawing method that is both easy and more flexible than other orthogonal drawing methods present in other interaction drawing tools. Conclusions: MIMTool facilitates faster drawing, updating and exchanging of MIMs. Among its several features, it also includes a semi-automatic drawing algorithm that makes use of shortest path algorithm for constructing lines with small number of bends and crossings. MIMTool contributes a much needed software tool that was missing and will facilitate wider adoption of Kohn's MIM notation.

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Institution: Bogazici University, Istanbul; Boston University, Boston; National Lab for Cancer Research, National Cancer Inst., NIH, Frederick

This work was supported by the Scientific and  Technological Research Council of Turkey (TUBITAK) under the grant number 107T382 and in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. It was also supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

 

Sunburst view of in situ gene-expression data from EMAGE

The eMouse Atlas of Gene Expression (EMAGE) is an online resource that publishes the results of in situ gene expression experiments on the developmental mouse. The resource provides comprehensive search facilities, but few analytical tools or visual mechanisms for navigating the data set.

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Institution: Heriot-Watt University

Polymerization Illustration

This is a three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. Physical and empirical modeling are integrated, according to which approach suits the different involved levels of detail best, and we additionally enable a simple form of interactive steering while the process is illustrated.

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Institution: Department of Informatics, University of Bergen

Helium

The ability to predict and visualize the inheritance of genes that facilitate resistance to pathogens or any other commercially important characteristic is crucially important to experimental plant genetics and commercial plant breeding programmes. Derivation of this information by traditional molecular techniques is expensive and time consuming, even with developments in high-throughput technologies over the past five years. This is especially true in industrial settings where, due to time constraints relating to growing seasons, many thousands of plant lines may need to be screened quickly, efficiently and economically every year.

There is, however, a cognitive limitation conceptualising large pedigree structures. While it may not be achievable or indeed necessary to understand every mating relationship between related individuals or lines, an overall picture can lead to insight into the data and any patterns it may contain. This can also aid in the identification of problems within datasets when coupled with expert domain knowledge. Tools that provide the ability to pull these data types together, and integrate with pedigree structure, will facilitate breeders and researchers to make more informed decisions relating to suitable plant lines to use in subsequent crossings. It is imperative that we develop more suitable crops to meet both the population imposed mandate for increased yield/production and adaptation to a changing global climate.

Our aim is to make Helium a generic platform in which various data types can be shown in a pedigree context and we hope to have a beta version available soon that users can download and experiment with. We have also modified the Germinate database platform to handle pedigree data in a new format which makes it suitable for inclusion into Helium.

Helium won best paper award at BioVis 2014 in Boston, USA.

 

 

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Institution: Information and Computational Sciences, The James Hutton Institute; School of Computing, Edinburgh Napier University

This project was funded by the Scottish Government’s Rural and Environment Science and Analytical Services (RESAS) division and Edinburgh Napier University.

CRF StickWRLD

StickWRLD is an experimental solution which, while leaving room for improvement, has been successfully applied in several biological research projects.

 
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Institution: Nationwide Children’s Hospital, The Ohio State University, Miami University

This project was funded partially by Nationwide Children’s Hospital, The Ohio State University, and by an NIH-AREA award (1R15GM94732-1 A1 to CL).

iGPSe

The Interactive Genomics Patient Stratification explorer (iGPSe) significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. This system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, it is possible to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction.

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Institution: The Ohio State University

This work is partially supported by National Institutes of Health under Grant R01CA141090.

inPHAP

inPHAP is an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP's ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci.

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Institution: University of Tübingen

Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Tübingen University supported this project.

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