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SketchBio

 

SketchBio aims to provide a rapid-to-use and easy-to-learn 3D modeling tool for biologists to enable effective interactive 3D “what if” scenario exploration for the exploration of subcellular structures.

It includes three novel features: crystal by example, pose-mode physics, and spring-based layout that accelerate operations common in the formation of molecular models. 

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Institution: University of North Carolina

This work was supported bythe NIH 5-P41-EB002025. Molecular graphics and analyses were performed with the UCSF Chimera package. Chimera is developed by the Resource for Biocomputing, Visualization and Informatics at the University of California, San Francisco (supported by the NIGMS P41-GM103311). 3D rendering was performed using Blender (blender.org). Blender is an open source project supported by the blender Foundation and the online community. Early versions of SketchBio used PyMOL (pumol.org) to import dtata from PDB. PyMOL is an open source project maintained and distributed by Schrodinger. 

RuleBender

RuleBender is a novel visualization system for the integrated visualization, modeling and simulation of rule-based intra-cellular biochemistry. Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks.

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Institution: University of Pittsburgh

Work supported by NSF-IIS-0952720, NSF-CCF-0829788, NIH-GM-076570, NIH-UL1-RR024153. We thank the Pitt Visualization Lab, the Faeder Lab and the Emonet Lab for their helpful feedback, and the reviewers for the exciting future work suggestions.

GenAMap

This is a novel visual analytics system called GenAMap. GenAMap replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information. We present the results of a preliminary evaluation that validated our basic approach.

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Institution: Carnegie Mellon University

This work is supported by DARPA [Z931302]; NSF [DBI-0640543, IIS-0713379]; NIH [1R01GM087694, 1R01GM093156, 0015699]; and an Alfred P. Sloan Research Fellowship to EPX.

LayerCake

LayerCake is a tool designed to assist in the exploration of the genetic variability of the population of viruses at multiple time points and in multiple individuals, a task that necessitates considering large amounts of sequence data and the quality issues inherent in obtaining such data in a practical manner. This design affords the examination of the amount of variability and mutation at each position in the genome for many populations of viruses. This design contains novel visualization techniques that support this specific class of analysis while addressing the issues of data aggregation, confidence visualization, and interaction support that arise when making use of large amounts of sequence data with variable uncertainty. These techniques generalize to a wide class of visualization problems where confidence is not known a priori, and aggregation in multiple directions is necessary.

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Institution: University of Wisconsin, Madison

This work was supported by NSF awards IIS-0946598 and CMMI-0941013. Related virology research was supported by NIH R01 AI084787.

EVEVis

This is a multi-scale visualization method, including a novel tree layout that both shows population status over time and can easily scale to very large populations. From this layout, the user can navigate to visualizations for moments in time or for individual entities. We demonstrate the effectiveness of the visualization on an existing evolutionary simulation called EVE: Evolution in Variable Environments.

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Institution: University of California, Davis

This research was supported in part by the U.S. National Science Foundation through grants CCF-0808896, CNS-0716691, CCF 0811422, CCF 0938114, CCF-1025269, and OCI-0941360, and the UC Davis opportunity fund.

HiTSEE

We present HiTSEE (High-Throughput Screening Exploration Environment) a visualization tool for the analysis of large chemical screens for the analysis of biochemical processes. The tool supports the analysis of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach based on the projection of one or few molecules of interest and the expansion around their neighborhood allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies.

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Institution: University of Konstanz

This work was partially supported by the DFG Research Training Group GK-1042 “Explorative Analysis and Visualization of Large Information Spaces”, the Konstanz Research School Chemical Biology (KoRS-CB), and the “Interdisciplinary Center for interactive Data Analysis, Modeling and Visual Exploration” (INCIDE).

iHAT

The iHAT prototype tool supports the visualization of multiple sequence alignments, associated metadata, and hierarchical clusterings. Moreover, data-type dependent colormaps and aggregation strategies as well as different filtering options support the user in finding correlations between sequences and metadata. Similar to other visualizations such as parallel coordinates or heatmaps, iHAT is aimed at exploiting the human pattern-recognition ability for spotting patterns that might indicate correlation or anticorrelation. Together with its interactive features and a database backend for fast data retrieval, iHAT is a prototype for a visual analytics system for genome-wide association studies.

See the project page for further details.

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Institution: VISUS – Visualization Research Center, University of Stuttgart, Germany; University of Tuebingen, Germany

Galaxy Track Browser

The proliferation of next-generation sequencing (NGS) technologies and analysis tools present new challenges to genome browsers. These challenges include supporting very large datasets, integrating analysis tools with data visualization to help reason about and improve analyses, and sharing or publishing fully interactive visualizations. The Galaxy Track Browser (GTB) is a Web-based genome browser integrated into the Galaxy platform that addresses these challenges. GTB is the first Web-based genome browser to provide a full multi-resolution data model; this model supports efficient data retrieval from very large datasets. GTB leverages the Galaxy platform to combine data visualization and data analysis; users can specify parameter values and run tools to produce new data, all within GTB. GTB also provides interactive filters that dynamically show and hide data and can be used to identify data for further investigation. GTB is available on every Galaxy server, and visualizations can be created for both standard and custom genome builds. Fully interactive GTB visualizations can be shared with colleagues and published on the Web using a simple graphical user interface.

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Institution: Emory University

OpenWalnut

This is a novel and effective method for visualizing probabilistic tractograms within their anatomical context. This illustrative rendering technique, called fiber stippling, is inspired by visualization standards as found in anatomical textbooks. These illustrations typically show slice-based projections of fiber pathways and are typically hand-drawn. Applying the automatized technique to diffusion tractography, it is possible to demonstrate its expressiveness and intuitive usability as well as a more objective way to present white-matter structure in the human brain.

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Institution: Zuse Institute Berlin, Max Planck Institute for Neurological Research Cologne, University of Leipzig

This work was supported by the German Federal Ministry of Education and Research as part of the VisPME research collaboration (01IH08009F) as well as by the AiF (ZIM grant KF 2034701SS8).

Quick2Insight

Provides automatic visualization of features inside biological image volumes in 3D. The software provides a simple and interactive visualization for the exploration of biological datasets through dataset-specific transfer functions and direct volume rendering.

The method employs a K-Means++ clustering algorithm to classify a two-dimensional histogram created from the input volume. The classification process utilizes spatial and data properties from the volume. Then using properties derived from the classified clusters the software automatically generates color and opacity transfer functions and presents the user with a high quality initial rendering of the volume data. User input can be incorporated through the simple yet intuitive interface for transfer function manipulation included in our framework. Our new interface helps users focus on feature space exploration instead of the usual effort intensive, low-level widget manipulation.

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Institution: SAIC-Frederick Inc, KnowledgeVis LLC

This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. 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.

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