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Crystal fingerprint space - A novel paradigm for studying crystal-structure sets

Acta Crystallographica Section A: Foundations of Crystallography 2010年第5期66卷 507-517页

作者： Valle, Mario Oganov, Artem R. Data Analysis and Visualization Group Swiss National Supercomputing Centre (CSCS) 6928 Manno via Cantonale Galleria 2 Switzerland Department of Geosciences New York Center for Computational Sciences State University of New York Stony Brook NY 11794-2100 United States Geology Department Moscow State University 119992 Moscow Russian Federation

The initial aim of the crystal fingerprint project was to solve a very specific problem: to classify and remove duplicate crystal structures from the results generated by the evolutionary crystal-structure predictor USPEX. These duplications decrease the genetic diversity of the population used by the evolutionary algorithm, potentially leading to stagnation and, after a certain time, reducing the likelihood of predicting essentially new structures. After solving the initial problem, the approach led to unexpected discoveries: unforeseen correlations, useful derived quantities and insight into the structure of the overall set of results. All of these were facilitated by the projects underlying idea: to transform the structure sets from the physical configuration space to an abstract, high-dimensional space called the fingerprint space. Here every structure is represented as a point whose coordinates (fingerprint) are computed from the crystal structure. Then the spaces distance measure, interpreted as structure closeness, enables grouping of structures into similarity classes. This model provides much flexibility and facilitates access to knowledge and algorithms from fields outside crystallography, e.g. pattern recognition and data mining. The current usage of the fingerprint-space model is revealing interesting properties that relate to chemical and crystallographic attributes of a structure set. For this reason, the mapping of structure sets to fingerprint space could become a new paradigm for studying crystal-structure ensembles and global chemical features of the energy landscape. © 2010 International Union of Crystallography Printed in Singapore - all rights reserved.

关键词： crystal fingerprints fingerprint space structure classification USPEX

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Quantitative visualization of ChIP-chip data by using linked views

Quantitative visualization of ChIP-chip data by using linked...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Min-Yu Huang Gunther H. Weber Xiao-Yong Li Mark D. Biggin Bernd Hamann Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California슠Davis Davis CA USA Visualization Group Lawrence Berkeley National Laboratory Berkeley CA USA Genomics Divisions Lawrence Berkeley National Laboratory Berkeley CA USA Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis Davis CA USA

Most analyses of ChIP-chip in vivo DNA binding have focused on qualitative descriptions of whether genomic regions are bound or not. There is increasing evidence, however, that factors bind in a highly overlapping manner to the same genomic regions and that it is quantitative differences in occupancy on these commonly bound regions that are the critical determinants of the different biological specificity of factors. As a result, it is critical to have a tool to facilitate the quantitative visualization of differences between transcription factors and the genomic regions they bind to understand each factor's unique roles in the network. We have developed a framework which combines several visualizations via brushing-and-linking to allow the user to interactively analyze and explore in vivo DNA binding data of multiple transcription factors. We describe these visualization types and also provide a discussion of biological examples in this paper.

关键词： Correlation Bioinformatics data visualization Genomics Image color analysis Browsers

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visualization as a bridge between chemical and nuclear engineering simulations

Visualization as a bridge between chemical and nuclear engin...

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作者： Fogal, Thomas Xiao, Shanjie Yang, Xue Krüger, Jens Jevremovic, Tatjana Scientific Computing and Imaging Institute University of Utah United States Radiation Protection SLAC National Accelerator Laboratory United States Utah Nuclear Engineering Program University of Utah United States Interactive Visualization and Data Analysis Group DFKI Saarbrücken Germany Civil and Environmental Engineering University of Utah United States

ISBN: (纸本)9780816910656

Collaborating across disciplines can be a difficult task for a variety of reasons. One of which is the lack of common methodologies and tools for visualization and analysis. Tools developed in one group can rarely be applied directly to data from another group, due to differences in data formats, analysis methodologies, and learning curves associated with the advanced visualization systems necessary for modern scientific data. Despite these difficulties, multi-physics collaboration is key to developing and understanding the next generation of computational models, which couple the dynamic processes and scales of multiple disciplines. As a step towards integrative models, we propose a visualization and analysis system to serve as a common interconnect between chemical and nuclear engineering. This choice allows multiple organizations to fruitfully exchange data and example visualizations in a reproducible manner, providing a foundation for productive discussion. In this work, we demonstrate the utility of a visualization system as it applies to bridging simulation domains and providing for multidisciplinary research in nuclear and chemical engineering.

关键词： visualization

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Large data visualization on Distributed Memory Multi-GPU Clusters 10

Large Data Visualization on Distributed Memory Multi-GPU Clu...

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Conference on High-performance Graphics

作者： Thomas Fogal Hank Childs Siddharth Shankar Jens Kriiger R. Daniel Bergeron Philip Hatcher Scientific Computing & Imaging Institute University of UtahDepartment of Computer Science University of New Hampshire Lawrence Berkeley National Laboratory Scientific Computing & Imaging Institute University of Utah Interactive Visualization and Data Analysis Group DFKI Germany Department of Computer Science University of New Hampshire

ISBN: (纸本)9781617827242

data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.

关键词： distributed memory data visualisation visualization dataset supercomputer Representation of data performance characteristics

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Generation of accurate integral surfaces in time-dependent vector fields

Generation of accurate integral surfaces in time-dependent v...

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作者： Garth, Christoph Krishnan, Hari Tricoche, Xavier Bobach, Tom Joy, Kenneth I. Institute of Data Analysis and Visualization University of California Davis United States Computer Science Dept. Purdue University Geometric Algorithms Group University of Kaiserslautern

We present a novel approach for the direct computation of integral surfaces in time-dependent vector fields. As opposed to previous work, which we analyze in detail, our approach is based on a separation of integral surface computation into two stages: surface approximation and generation of a graphical representation. This allows us to overcome several limitations of existing techniques. We first describe an algorithm for surface integration that approximates a series of time lines using iterative refinement and computes a skeleton of the integral surface. In a second step, we generate a well-conditioned triangulation. Our approach allows a highly accurate treatment of very large time-varying vector fields in an efficient, streaming fashion. We examine the properties of the presented methods on several example datasets and perform a numerical study of its correctness and accuracy. Finally, we investigate some visualization aspects of integral surfaces. © 2008 IEEE.

关键词： Numerical methods

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PointCloudXplore 2: Visual exploration of 3D gene expression

PointCloudXplore 2: Visual exploration of 3D gene expression

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2nd Workshop of the DFG's International Research Training group on visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering, VLUDS 2007

作者： Rübel, O. Weber, G.H. Huang, M.-Y. Bethel, E.W. Keränen, S.V.E. Fowlkes, C.C. Hendriks, C.L. Luengo DePace, Angela H. Simirenko, L. Eisen, M.B. Biggin, M.D. Hagen, H. Malik, J. Knowles, D.W. Hamann, B. International Research Training Group University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California Davis United States Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Genomics Division Lawrence Berkeley National Laboratory Berkeley United States Computer Science Department University of California Irvine United States Computer Science Division University of California Berkeley United States Lawrence Berkeley National Laboratory Life Sciences Division Berkeley United States Department of Molecular and Cellular Biology Center for Integrative Genomics University of California Berkeley United States

ISBN: (纸本)9783885794417

To better understand how developmental regulatory networks are defined in the genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP ) has developed a suite of methods to describe 3D gene expression data, i.e., the output of the network at cellular resolution for multiple time points. To allow researchers to explore these novel data sets we have developed PointCloudXplore (PCX). In PCX we have linked physical and information visualization views via the concept of brushing (cell selection). For each view dedicated operations for performing selection of cells are available. In PCX, all cell selections are stored in a central management system. Cells selected in one view can in this way be highlighted in any view allowing further cell subset properties to be determined. Complex cell queries can be defined by combining different cell selections using logical operations such as AND, OR, and NOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. PCX2 has shown to be an effective tool for visual exploration of 3D gene expression data. We discuss (i) all views available in PCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture of PCX2., and (iv) illustrate the usefulness of PCX2 using selected examples.

关键词： visualization

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PointCloudXplore: A visualization Tool for 3D Gene Expression data 1

PointCloudXplore: A Visualization Tool for 3D Gene Expressio...

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1st Workshop of the DFG's International Research Training group "visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering"

作者： Rübel, Oliver Weber, Gunther H. Keränen, Soile V. E. Fowlkes, Charless C. Hendriks, Cris L. Luengo Simirenko, Lisa Shah, Nameeta Y. Eisen, Michael B. Biggin, Mark D. Hagen, Hans Sudar, Damir Malik, Jitendra Knowles, David W. Hamann, Bernd International Research Training Group 'Visualization of Large and Unstructured Data Sets' University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)9783885794387

The Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of threedimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several extensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data. © 2006 Gesellschaft für Informatik, Bonn.

关键词： visualization

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PointCloudXplore: Visual analysis of 3D Gene Expression data Using Physical Views and Parallel Coordinates

PointCloudXplore: Visual Analysis of 3D Gene Expression Data...

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8th Joint Eurographics/IEEE VGTC Symposium on visualization, EuroVis 2006

作者： Rübel, O. Weber, G.H. Keränen, S.V.E. Fowlkes, C.C. Hendriks, C.L. Luengo Simirenko, L. Shah, N.Y. Eisen, M.B. Biggin, M.D. Hagen, H. Sudar, D. Malik, J. Knowles, D.W. Hamann, B. International Research Training Group "Visualization of Large and Unstructured Data Sets" University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory CA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)3905673312

To allow a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos. Here we report the first components of a visualization tool, PointCloudXplore, that allows the relationships between different gene's expression to be analyzed using the BDTNP's datasets. PointCloudXplore uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PointCloudXplore, physical views of the data are linked to parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within the embryo. Parallel coordinates, on the other hand, show only some features of each gene's expression, but allow simultaneous analysis of data for many more genes than would be possible in a physical view. We have developed several extensions to standard parallel coordinates to facilitate brushing the visualization of 3D gene expression data. © The Eurographics Association 2006.

关键词： visualization

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visualization for validation and improvement of three-dimensional segmentation algorithms

Visualization for validation and improvement of three-dimens...

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7th Joint Eurographics - IEEE VGTC Symposium on visualization, VisSym 2005

作者： Weber, G.H. Luengo Hendriks, C.L. Keränen, S.V.E. Dillard, S.E. Ju, D.Y. Sudar, D. Hamann, B. Berkeley Drosophila Transcription Network Project Visualization and Computer Graphics Research Group Institute for Data Analysis and Visualization University of California Davis One Shields Avenue DavisCA95616 United States Genomics and Life Sciences Division Lawrence Berkeley National Laboratory One Cyclotron Road BerkeleyCA94720 United States

ISBN: (纸本)3905673193

The Berkeley Drosophila Transcription Network Project (BDTNP) is developing a suite of methods that will allow a quantitative description and analysis of three dimensional (3D) gene expression patterns in an animal with cellular resolution. An important component of this approach are algorithms that segment 3D images of an organism into individual nuclei and cells and measure relative levels of gene expression. As part of the BDTNP, we are developing tools for interactive visualization, control, and verification of these algorithms. Here we present a volume visualization prototype system that, combined with user interaction tools, supports validation and quantitative determination of the accuracy of nuclear segmentation. visualizations of nuclei are combined with information obtained from a nuclear segmentation mask, supporting the comparison of raw data and its segmentation. It is possible to select individual nuclei interactively in a volume rendered image and identify incorrectly segmented objects. Integration with segmentation algorithms, implemented in MATLAB, makes it possible to modify a segmentation based on visual examination and obtain additional information about incorrectly segmented objects. This work has already led to significant improvements in segmentation accuracy and opens the way to enhanced analysis of images of complex animal morphologies. © The Eurographics Association 2005.

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