MolVa: Workshop on Molecular Graphics and Visual Analysis of Molecular Data 2018

Permanent URI for this collection

https://diglib7.eg.org/handle/10.2312/2632405
Co-located with EuroVis 2018, June 4, 2018, Brno, Czech Republic
Table of Contents
Computational Analysis of Dynamic Molecular Data
An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data
[full paper Image] [meta data Image]
Salaheddin Alakkari and John Dingliana
Atomic Accessibility Radii for Molecular Dynamics Analysis
[full paper Image] [meta data Image]
Norbert Lindow, Daniel Baum, and Hans-Christian Hege
Frameworks for Molecular Data Visualization
VIA-MD: Visual Interactive Analysis of Molecular Dynamics
[full paper Image] [meta data Image]
Robin Skånberg, Mathieu Linares, Carolin Konig, Patrick Norman, Daniel Jönsson, Ingrid Hotz, and Anders Ynnerman
Mol*: Towards a Common Library and Tools for Web Molecular Graphics
[full paper Image] [meta data Image]
David Sehnal, Alexander Rose, Jaroslav Koca, Stephen Burley, and Sameer Velankar

BibTeX (MolVa: Workshop on Molecular Graphics and Visual Analysis of Molecular Data 2018)
@inproceedings{
10.2312:molva.20181100,

booktitle = {
Workshop on Molecular Graphics and Visual Analysis of Molecular Data},

editor = {
Jan Byska and Michael Krone and Björn Sommer
},
title = {{
An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data}},

author = {
Alakkari, Salaheddin
 and 
Dingliana, John
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-061-1},

DOI = {
10.2312/molva.20181100}

}
@inproceedings{
10.2312:molva.20181101,

booktitle = {
Workshop on Molecular Graphics and Visual Analysis of Molecular Data},

editor = {
Jan Byska and Michael Krone and Björn Sommer
},
title = {{
Atomic Accessibility Radii for Molecular Dynamics Analysis}},

author = {
Lindow, Norbert
 and 
Baum, Daniel
 and 
Hege, Hans-Christian
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-061-1},

DOI = {
10.2312/molva.20181101}

}
@inproceedings{
10.2312:molva.20181102,

booktitle = {
Workshop on Molecular Graphics and Visual Analysis of Molecular Data},

editor = {
Jan Byska and Michael Krone and Björn Sommer
},
title = {{
VIA-MD: Visual Interactive Analysis of Molecular Dynamics}},

author = {
Skånberg, Robin
 and 
Linares, Mathieu
 and 
König, Carolin
 and 
Norman, Patrick
 and 
Jönsson, Daniel
 and 
Hotz, Ingrid
 and 
Ynnerman, Anders
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-061-1},

DOI = {
10.2312/molva.20181102}

}
@inproceedings{
10.2312:molva.20181103,

booktitle = {
Workshop on Molecular Graphics and Visual Analysis of Molecular Data},

editor = {
Jan Byska and Michael Krone and Björn Sommer
},
title = {{
Mol*: Towards a Common Library and Tools for Web Molecular Graphics}},

author = {
Sehnal, David
 and 
Rose, Alexander
 and 
Koca, Jaroslav
 and 
Burley, Stephen
 and 
Velankar, Sameer
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-061-1},

DOI = {
10.2312/molva.20181103}

}

Browse

Recent Submissions

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    MolVA 2018: Frontmatter
    (The Eurographics Association, 2018) Byška, Jan; Krone, Michael; Sommer, Björn; Jan Byska and Michael Krone and Björn Sommer
  • Thumbnail Image
    Item
    An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data
    (The Eurographics Association, 2018) Alakkari, Salaheddin; Dingliana, John; Jan Byska and Michael Krone and Björn Sommer
    In this paper, we discuss the problem of decomposing complex and large Molecular Dynamics trajectory data into simple low-resolution representation using Principal Component Analysis (PCA). Since applying standard PCA for such large data is expensive in terms of space and time complexity, we propose a novel online PCA algorithm with O(1) complexity per new timestep. Our approach is able to approximate the full dimensional eigenspace per new time-step of MD simulation. Experimental results indicate that our technique provides an effective approximation to the original eigenspace computed using standard PCA in batch mode.
  • Thumbnail Image
    Item
    Atomic Accessibility Radii for Molecular Dynamics Analysis
    (The Eurographics Association, 2018) Lindow, Norbert; Baum, Daniel; Hege, Hans-Christian; Jan Byska and Michael Krone and Björn Sommer
    In molecular structure analysis and visualization, the molecule's atoms are often modeled as hard spheres parametrized by their positions and radii. While the atom positions result from experiments or molecular simulations, for the radii typically values are taken from literature. Most often, van der Waals (vdW) radii are used, for which diverse values exist. As a consequence, different visualization and analysis tools use different atomic radii, and the analyses are less objective than often believed. Furthermore, for the geometric accessibility analysis of molecular structures, vdW radii are not well suited. The reason is that during the molecular dynamics simulation, depending on the force field and the kinetic energy in the system, non-bonded atoms can come so close to each other that their vdW spheres intersect. In this paper, we introduce a new kind of atomic radius, called 'atomic accessibility radius', that better characterizes the accessibility of an atom in a given molecular trajectory. The new radii reflect the movement possibilities of atoms in the simulated physical system. They are computed by solving a linear program that maximizes the radii of the atoms under the constraint that non-bonded spheres do not intersect in the considered molecular trajectory. Using this data-driven approach, the actual accessibility of atoms can be visualized more precisely.
  • Thumbnail Image
    Item
    VIA-MD: Visual Interactive Analysis of Molecular Dynamics
    (The Eurographics Association, 2018) Skånberg, Robin; Linares, Mathieu; König, Carolin; Norman, Patrick; Jönsson, Daniel; Hotz, Ingrid; Ynnerman, Anders; Jan Byska and Michael Krone and Björn Sommer
    We present a visual exploration environment tailored for large-scale spatio-temporal molecular dynamics simulation data. The environment is referred to as VIA-MD (visual interactive analysis of molecular dynamics) and has been developed in a participatory design process with domain experts on molecular dynamics simulations of complex molecular systems. A key feature of our approach is the support for linked interactive 3D exploration of geometry and statistical analysis using dynamic temporal windowing and animation. Based on semantic level descriptions and hierarchical aggregation of molecular properties we enable interactive filtering, which enables the user to effectively find spatial, temporal and statistical patterns. The VIA-MD environment provides an unprecedented tool for analysis of complex microscopic interactions hidden in large data volumes. We demonstrate the utility of the VIA-MD environment with four use cases. The first two deal with simulation of amyloid plaque associated with development of Alzheimer's, and we study an aqueous solution of 100 probes and an amyloid fibril. The identification of interaction "hotspots" is achieved with the use of combined filter parameters connected with probe molecular planarity and probe-fibril interaction energetics. The third and fourth examples show the wide applicability of the environment by applying it to analysis of molecular properties in material design.
  • Thumbnail Image
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    Mol*: Towards a Common Library and Tools for Web Molecular Graphics
    (The Eurographics Association, 2018) Sehnal, David; Rose, Alexander; Koca, Jaroslav; Burley, Stephen; Velankar, Sameer; Jan Byska and Michael Krone and Björn Sommer
    Advances in experimental techniques are providing access to structures of ever more complex and larger macromolecular systems. Web-browser based visualization and analysis of macromolecular structures and associated data represents a crucial step in gaining knowledge from these data. A common library and a set of tools for working with such macromolecular data sets would streamline this step. We present a project called Mol* (/'mol-star/) whose goal is to provide a common library and a set of tools for macromolecular data visualization and analysis. The project includes modules for data storage, in-memory representation, query language, UI state management, and visualization; and tools for efficient data access.