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Physioillustration: Interactive molecular illustration

 

Focuses on the illustrative visualization of physiological processes in the molecular scale to provide intuitive visual representation, which the user can observe and interact with.

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Institution: University of Bergen

The focus of the Physioillustration project is is to develop novel graphics data representations, visual representations, occlusion handling, visual guidance and storytel-ling, zooming, interaction and integration of physiological models and medical imaging. The visualization technology will be developed and evaluated on multiple scale levels, from molecular machines, up to the organ level. This work has been carried out within the PhysioIllustration research project (# 218023), which is funded by the Norwegian Research Council. Additionally, we would like to thank Helwig Hauser and Visualization group in Bergen for useful ideas and feedback.

enRoute

enRoute is a new approach for interactively exploring experimental data along paths that are dynamically extracted from pathways. By showing an extracted path side-by-side with experimental data, enRoute can present large amounts of data for every pathway node. It can visualize hundreds of samples, dozens of experimental conditions, and even multiple datasets capturing different aspects of a node at the same time. Another important property of this approach is its conceptual compatibility with arbitrary forms of pathways. Most notably, enRoute works well with pathways that are manually created, as they are available in large, public pathway databases.

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Institution: Graz University of Technology, Johannes Kepler University Linz, Medical University of Graz

This work is supported by the following grants: Caleydoplex (P22902, FWF), Tumorheterogeneity (GZ:A3–22.M-5/2012–21, state of Styria), IM-GuS (Austria Wirtschaftsservice), and inGeneious (385567, FFG).

iVUN

This visual analytics system supports an uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks (BRNs). These are often described by mathematical models, such as ordinary differential equations (ODEs), which enable the integration of a multitude of different data and data types using parameter estimation. Due to the limited amount of data, parameter estimation does not necessarily yield a single point in parameter space and many attributes of the model remain uncertain. Our system visualizes the model as a graph, where the statistics of the attributes are mapped to the color of edges and vertices. The graph view is combined with several linked views such as lineplots, scatterplots, and correlation matrices, to support the identification of uncertainties and the analysis of their mutual dependencies as well as their time dependencies.

See the project page for further details.

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Institution: VISUS – Visualization Research Center, University of Stuttgart, Germany, ST – Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany

Dynamic Biomolecular Path Viz

 This visualization represents a static view of a dynamic process. This skin surface approach offers an easy and fast way to render such a dynamic path. To do so, all spheres of all path components belonging to the dynamic path are used as input to the skin surface computation. The result is a smooth surface representing the extension surface of all components of the dynamic molecular path.To add a dynamic component to this static view, the skin surface can be color-coded by the time of penetration along the path.

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Institution: Dept. of Visualization and Data Analysis, Zuse Institute Berlin (ZIB), Theoretical Molecular Biophysics, Dept. of Physics, FU Berlin, Dept. of Physiology and Biophysics, University of California, Irvine

Ana-Nicoleta Bondar acknowledges financial support from the Marie Curie International Reintegration Award IRG-276920. A set of preliminary molecular dynamics simulations were performed using the University of California, Irvine High-Performance Beowulf Cluster (grant acknowledgment: National Institutes of General Medical Sciences GM-74637 and GM-086685).

CompuCell3D

CompuCell3D (CC3D; compucell3d.org) is a software application that simulates the behaviors of generalized cells. The visualization of those cells also illustrates their connectivity with each other. CompuCell3D is primarily used to develop models for multi-cellular biology, however, it is also used for non-biological models.

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Institution: Biocomplexity Institute, Indiana University, Instituto de Fisica Universidade Federal do Rio Grande do Sul, Department of Physics, Biocomplexity Institute Indiana University, CREST, Pervasive Technology Institute Indiana University

This project received support from the National Institutes of Health, National Institute of General Medical Sciences grants R01 GM077138 and R01 GM076692, the Environmental Protection Agency grant R835001, the Lilly Endowment Inc. and the Biocomplexity Institute at Indiana University. Indiana Universitys Research Technologies (RT/UITS), provided time and assistance with BigRed and Quarry clusters for simulations. The Advanced Visualization Lab in RT helped generate the tumor model on their 3D printer. Early versions of CompuCell3D were developed at the University of Notre Dame by JAG, Dr. Mark Alber, Dr. Jesus Izaguirre, Joseph Coffland, and collaborators with the support of National Science Foundation, Division of Integrative Biology, grant IBN-00836563. Other developers (current and past) from Indiana University include Mitja Hmeljak, Chris Mueller and Alex Dementsov. The following source software communities have provided a foundation for CC3D: Python, numpy, IPython, VTK, NetworkX and matplotlib.

Epithelial Volume Viz

Thi is a set of techniques that may be used to produce detailed 3D models of the individual cells in biological epithelial tissues. The inputs to the techniques are a 3D volumetric model of the tissue and a mesh model of the cell faces lying on it s atipical surface. We have applied these techniques to construct the individual epithelial cells of the wing imaginal disc of Drosophila melanogaster. To date, 3D epithelial cell models have been created, allowing for the calculation and visualization of cell parameters. The results show position-dependent patterns of cell shape in the wing imaginal disc. Our procedures should offer a general data processing pipeline for the construction of detailed 3D models of a wide variety of epithelial tissues.

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Institution: Dresden University of Technology, Max Planck Institute for the Physics of Complex Systems Dresden, Drexel University, Centro de Genomica e Investigacion Oncologica

This research was founded by the National Science Foundation grants, ACI-0083287 and IIS-084541 (DB), a Drexel Synergy Grant (DB), German Research Foundation Grant DA586/8-1 (CD), and Max Planck Institute Visiting Scientist Fellowship (DB and LB).

FluoRender

FluoRender is an interactive tool for neurobiologists to visualize confocal microscopy data in their research. Multiple channels, detailed three-dimensional structures, and time-dependent sequences are the three major features of confocal microscopy data. With these features and usability in mind, we designed and engineered our system, which is now a free package for public download. We present the visualization pipeline and main features of our system for 3D/4D multi-channel confocal data visualization. Our system supports different input formats commonly seen for confocal microscopy. By minimizing pre-processing and optimizing data reading codes, it can read 3D/4D data with minimal latency. It has easy-to-use parameters for volume rendering effects, which are adjusted with real-time speed. It uses several image post-processing methods for detail enhancement, which are applied after volumetric data are rendered, and thus their adjustments are real-time even for 4D sequences. For multi-channel data, our system supports three different blending modes and channel grouping. Users can easily change all the settings and emphasize the most important features.

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Institution: SCI Institute and the School of Computing, Department of Neurobiology and Anatomy, University of Utah

This publication is based on work supported by Award No. KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST), DOE SciDAC:VACET, NSF OCI-0906379, NIH-1R01GM098151-01.

GVF Alignment Viz

By applying gradient vector flow analysis to the MSA data, it is possible to extract and visually emphasize conservations and other patterns that are relevant during the MSA exploration process. In contrast to the traditional visual representation of MSAs, which exploits color-coded tables, the proposed visual metaphor allows us to provide an overview of large MSAs as well as to highlight global patterns, outliers, and data distributions.

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Institution: Linkoping University, Sweden

This work was supported through grants from the Excellence Center at Linkoping and Lund in Information Technology (ELLIIT), the Swedish Research Council (VR, grant 2011–4113), and the Swedish e-Science Research Centre (SeRC). The presented technique has been integrated into the Voreen volume rendering engine (www.voreen.org), which is an open source visualization framework.

rNAV

rNAV (for rna NAVigator) is a tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations.

We now propose an automatic mRNAs extraction from a simple genbank or embl genome file. Moreover, rNAV now features an automatic annotation enrichement plugin powered by DAVID statistical enrichement tool.
rNAV algorithms can be gathered into pipelines which can then be saved and reused over several sessions. To support exploration awareness, rNAV also provides an exploration tree view that allows to navigate through the steps of the analysis but also to select the sub-networks to visualize and compare. These comparisons are facilitated by the integration of multiple and fully linked views.

This framework had been used to analyse various real biological data including several mycoplasma strains.

Some key features :

  • Automatic functional annotation with DAVID[1] webservice (Database for Annotation, Visualization and Integrated Discovery)
  • Annotation clustering : cluster redudant annotations
  • Simultaneous visual analysis of annotations AND positions of several targets with enhanced neighbors tool.
  • Visual analysis of sRNA regulatory networks at genome scale
  • Automatic mRNA extraction from genome files (genbank or embl) with user defined region
  • Integration of two interactions prediction tools : ssearch and IntaRNA[2]
  • Position clustering : cluster targets from their interaction positions
  • Multiple and fully linked view
  • Exploration tree view
  • Labels can display many input information like P-value, Similarity or interaction energy
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Institution: Universite Bordeaux 1, Universite Bordeaux 2, Universite Paris 7, ANR (France)

This work was partially done under the EVIDEN project (ANR 2010 JCJC 0201 01), supported by the ANR (France); and under the MycoRNA project, PEPS CNRS/IdEx Bordeaux, 2013.

Vesper

VESpeR is a suite of web-based visualisation components that enable biologists to investigate the taxonomic, temporal and geographic coverage of DWCA species-referenced data that can be placed directly into the workflow of biologists who use such data. VESpeR allows biologists to perform tasks such as sanity checking of data, view patterns in geographical, taxonomic or temporal aspects in an interrelated context, and accurately view data even when it spans conflicting taxonomic classifications. This can make a significant contribution to the efficiency and usability of online catalogues for both the providers and end-users of the data they hold. Co-ordinated components The components are co-ordinated such that selections and actions in one component will be reflected in the data shown in other components. VESpeR multiple views VESpeR includes

  • a novel cross-taxonomy viewer that allows users to crosswalk different classifications, allowing them to accurately match specimens between data from different sources
  • interactive map to investigate specimen geographic coverage
  • interactive timeline to investigate specimen temporal coverage
  • sanity checker to view data completeness and vocabulary size

Bio-visualisation Visualisation techniques have been recognised as one of the major directions in future research when handling and querying biological data, offering the ability to find patterns and outliers in data which traditional query interfaces cannot match. A case in point is the multitude of species-referenced databases covering data from genomic to biodiversity data linked by taxonomic classifications that hold geographic and temporal-faceted data alongside other data. Many online databases hold collections of such data, often in archive format, but visual querying tools are invariably limited to a map interface of spatial distribution, neglecting the fact that biologists may wish to query or explore other facets of the data such as the classification or temporal distribution. Add onto this the problem of many complementary databases using different taxonomic classifications to reference their specimens and we have a situation where much of the potential utility of this data remains unused. VESpeR is designed to help address this.

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Institution: IIDI

VESpeR is a project at the Institute for Informatics and Digital Innovation. The Institute is part of Edinburgh Napier University based in Scotland, United Kingdom. At the Institute for Informatics & Digital Innovation we are dedicated to helping you and your organisation deal with new digital challenges and opportunities as they arise. We can help you to look ahead and shape our digital future. We work across every field of computer technology from sensors and mobile networks to data intensive applications requiring intelligent processing and filtering. We can help you explore not just the technology but how people interact with the technology and how it impacts on society. www.iidi.napier.ac.uk Tel: +44 (0)131 455 2651 Email: iidi@napier.ac.uk

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