Jan Byška is an Assistant Professor at Masaryk University in Brno, Czech Republic, and an Adjunct Associate Professor at the University of Bergen. He is a member of Visitlab and VisGroup research laboratories where his work focuses mostly on various challenges in the field of visualization of molecular and time-dependent data.
Publications
2020
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[Bibtex] @ARTICLE{Palenik-2019-Splatting,
author={J. P\'{a}lenik and J. By\v{s}ka and S. Bruckner and H. Hauser},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics},
year={2020},
volume={26},
number={1},
pages={643--653},
keywords={Data visualization;Computational modeling;Time series analysis;Atmospheric measurements;Particle measurements;Analytical models;Kernel;Scale space;time-series;scientific simulation;multi-scale analysis;space-time cube;molecular dynamics},
doi={10.1109/TVCG.2019.2934258},
ISSN={1077-2626},
month={},
pdf = "pdfs/scale-space-splatting.pdf",
images = "images/scale-space-teaser.png",
thumbnails = "images/scale-space-teaser-thumb.png",
abstract = "Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.",
}2019
[Bibtex] @article{Byska-2019-LongMolecularDynamicsSimulations,
author = {Byška, J. and Trautner, T. and Marques, S.M. and Damborský, J. and Kozlíková, B. and Waldner, M.},
title = {Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization},
journal = {Computer Graphics Forum},
volume = {38},
number = {3},
pages = {441-453},
keywords = {CCS Concepts, Human-centered computing -- Scientific visualization; User centered design},
doi = {10.1111/cgf.13701},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13701},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13701},
abstract = {Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.},
year = {2019},
pdf = "pdfs/AnalysisOfLongMolecularDynamicsSimulationsUsingInteractiveFocusAndContextVisualization_Trautner.pdf",
images = "images/Byska-2019-LongMolecularDynamicsSimulations.png",
thumbnails = "images/Byska-2019-LongMolecularDynamicsSimulations.png"
}2018
[Bibtex]
@ARTICLE {Jurcik2018Caver,
author = "Adam Jur\v{c}\'{i}k and David Bedn\'{a}\v{r} and Jan By\v{s}ka and Sergio M. Marques and Katar\'{i}na Furmanov\'{a} and Luk\'{a}\v{s} Daniel and Piia Kokkonen and Jan Brezovsk\'{y} and Ond\v{r}ej Strnad and Jan \v{s}\v{t}oura\v{c} and Anton\'{i}n Pavelka and Martin Ma\v{n}\'{a}k and Ji\v{r}\'{i} Damborsk\'{y} and Barbora Kozl\'{i}kov\'{a}",
title = "CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories",
journal = "Bioinformatics",
year = "2018",
abstract = "MOTIVATION:Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications.RESULTS:CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow efficient analysis and data reduction in large protein structures and molecular dynamics simulations.",
images = "images/analyst.jpg",
thumbnails = "images/analyst.jpg"
} [Bibtex]
@ARTICLE {Furmanova2018COZOID,
author = "Furmanov{\'a}, Katar{\'\i}na and By{\v{s}}ka, Jan and Gr{\"o}ller, Eduard M and Viola, Ivan and Pale{\v{c}}ek, Jan J and Kozl{\'i}kov{\'a}, Barbora",
title = "COZOID: contact zone identifier for visual analysis of protein-protein interactions",
journal = "BMC bioinformatics",
year = "2018",
abstract = "BackgroundStudying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge.ResultsIn this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. It helps proteomic experts to select the most relevant configurations and explore their contact zones at different levels of detail. The system integrates a set of methods that follow and support the workflow of proteomics experts. The first visual abstraction method, the Matrix view, is based on customized interactive heat maps and provides the users with an overview of all possible residue-residue contacts in all PPI configurations and their interactive filtering. In this step, the user can traverse all input PPI configurations and obtain an overview of their interacting amino acids. Then, the models containing a particular pair of interacting amino acids can be selectively picked and traversed. Detailed information on the individual amino acids in the contact zones and their properties is presented in the Contact-Zone list-view. The list-view provides a comparative tool to rank the best models based on the similarity of their contacts to the template-structure contacts. All these techniques are interactively linked with other proposed methods, the Exploded view and the Open-Book view, which represent individual configurations in three-dimensional space. These representations solve the high overlap problem associated with many configurations. Using these views, the structural alignment of the best models can also be visually confirmed.ConclusionsWe developed a system for the exploration of large sets of protein-protein complexes in a fast and intuitive way. The usefulness of our system has been tested and verified on several docking structures covering the three major types of PPIs, including coiled-coil, pocket-string, and surface-surface interactions. Our case studies prove that our tool helps to analyse and filter protein-protein complexes in a fraction of the time compared to using previously available techniques.",
images = "images/cozoid.jpg",
thumbnails = "images/cozoid.jpg"
}2017
[Bibtex] @ARTICLE {Furmanova2017Ligand,
author = "Furmanov{\'a}, Katar{\'\i}na and Jare{\v{s}}ov{\'a}, Miroslava and By{\v{s}}ka, Jan and Jur{\v{c}}{\'i}k, Adam and Parulek, J{\'u}lius and Hauser, Helwig and Kozl{\'i}kov{\'a}, Barbora",
title = "Interactive exploration of ligand transportation through protein tunnels",
journal = "BMC Bioinformatics",
year = "2017",
volume = "18(Suppl 2)",
number = "22",
month = "feb",
abstract = "Background: Protein structures and their interaction with ligands have been in the focus of biochemistry andstructural biology research for decades. The transportation of ligand into the protein active site is often complexprocess, driven by geometric and physico-chemical properties, which renders the ligand path full of jitter andimpasses. This prevents understanding of the ligand transportation and reasoning behind its behavior along the path.Results: To address the needs of the domain experts we design an explorative visualization solution based on amulti-scale simplification model. It helps to navigate the user to the most interesting parts of the ligand trajectory byexploring different attributes of the ligand and its movement, such as its distance to the active site, changes of aminoacids lining the ligand, or ligand ââ¬Åstucknessââ¬?. The process is supported by three linked views ââ¬â 3D representation of thesimplified trajectory, scatterplot matrix, and bar charts with line representation of ligand-lining amino acids.Conclusions: The usage of our tool is demonstrated on molecular dynamics simulations provided by the domainexperts. The tool was tested by the domain experts from protein engineering and the results confirm that it helps tonavigate the user to the most interesting parts of the ligand trajectory and to understand the ligand behavior",
pdf = "pdfs/Furmanova2017.pdf",
images = "images/Furmanova2016Interactive.png",
thumbnails = "images/Furmanova2016Interactive.png",
note = "https://doi.org/10.1186/s12859-016-1448-0"
}@ARTICLE {Kocincova2017SS,
author = "Kocincov{\'a}, Lucia and Jare{\v{s}}ov{\'a}, Miroslava and By{\v{s}}ka, Jan and Parulek, J{\'u}lius and Hauser, Helwig and Kozl{\'i}kov{\'a}, Barbora",
title = "Comparative visualization of protein secondary structures",
journal = "BMC Bioinformatics",
year = "2017",
volume = "18(Suppl 2)",
number = "23",
month = "feb",
abstract = "Background: Protein function is determined by many factors, namely by its constitution, spatial arrangement, anddynamic behavior. Studying these factors helps the biochemists and biologists to better understand the proteinbehavior and to design proteins with modified properties. One of the most common approaches to these studies is tocompare the protein structure with other molecules and to reveal similarities and differences in their polypeptidechains.Results: We support the comparison process by proposing a new visualization technique that bridges the gapbetween traditionally used 1D and 3D representations. By introducing the information about mutual positions ofprotein chains into the 1D sequential representation the users are able to observe the spatial differences between theproteins without any occlusion commonly present in 3D view. Our representation is designed to serve namely forcomparison of multiple proteins or a set of time steps of molecular dynamics simulation.Conclusions: The novel representation is demonstrated on two usage scenarios. The first scenario aims to compare aset of proteins from the family of cytochromes P450 where the position of the secondary structures has a significantimpact on the substrate channeling. The second scenario focuses on the protein flexibility when by comparing a setof time steps our representation helps to reveal the most dynamically changing parts of the protein chain.",
pdf = "pdfs/Kocincova2017.pdf",
images = "images/Lucia2016Comparative.png",
thumbnails = "images/Lucia2016Comparative.png",
note = "https://doi.org/10.1186/s12859-016-1449-z"
}@INPROCEEDINGS {vad_viktor-2017-WVE,
author = "Viktor Vad and Jan By\v{s}ka and Adam Jur\v{c}\'{i}k and Ivan Viola and Meister Eduard Gr{\"o}ller and Helwig Hauser and Sergio M. Margues and Ji\v{r}\'{i} Damborsk\'{y} and Barbora Kozl\'{i}kov\'{a}",
title = "Watergate: Visual Exploration of Water Trajectories in Protein Dynamics",
booktitle = "Eurographics Workshop on Visual Computing for Biology and Medicine 2017",
year = "2017",
pages = "33--42",
abstract = "The function of proteins is tightly related to their interactions with other molecules. The study of such interactions often requires to track the molecules that enter or exit specific regions of the proteins. This is investigated with molecular dynamics simulations, producing the trajectories of thousands of water molecules during hundreds of thousands of time steps. To ease the exploration of such rich spatio-temporal data, we propose a novel workflow for the analysis and visualization of large sets of water-molecule trajectories. Our solution consists of a set of visualization techniques, which help biochemists to classify, cluster, and filter the trajectories and to explore the properties and behavior of selected subsets in detail. Initially, we use an interactive histogram and a time-line visualization to give an overview of all water trajectories and select the interesting ones for further investigation. Further, we depict clusters of trajectories in a novel 2D representation illustrating the flows of water molecules. These views are interactively linked with a 3D representation where we show individual paths, including their simplification, as well as extracted statistical information displayed by isosurfaces. The proposed solution has been designed in tight collaboration with experts to support specific tasks in their scientific workflows. They also conducted several case studies to evaluate the usability and effectiveness of our new solution with respect to their research scenarios. These confirmed that our proposed solution helps in analyzing water trajectories and in extracting the essential information out of the large amount of input data.",
pdf = "pdfs/Vad_Victor2017.pdf",
images = "images/Watergate.png",
thumbnails = "images/Watergate.png",
proceedings = "In Proceedings of Eurographics Workshop on Visual Computing for Biology and Medicine",
location = "September, 2017 Bremen, Germany",
url = "https://www.cg.tuwien.ac.at/research/publications/2017/vad_viktor-2017-WVE/"
}2016
[Bibtex]
@INPROCEEDINGS {Kolesar2016VCBM,
author = "Ivan Kolesar and Jan By\v{s}ka and Julius Parulek and Helwig Hauser and Barbora Kozl\'{i}kov\'{a}",
title = "Unfolding and Interactive Exploration of Protein Tunnels andtheir Dynamics",
booktitle = "Eurographics Workshop on Visual Computing for Biology and Medicine",
year = "2016",
pages = "1--10",
month = "sep",
abstract = "The presence of tunnels in protein structures substantially influences their reactivity with other molecules. Therefore, studying their properties and changes over time has been in the scope of biochemists for decades. In this paper we introduce a novel approach for comparative visualization and exploration of ensembles of tunnels. Our goal is to overcome occlusion problems present in traditional tunnel representations while providing users a quick way to navigate through the input dataset to identify potentially interesting tunnels. First, we unfold the input tunnels to a 2D representation enabling to observe the mutual position of amino acids forming the tunnel surface and the amount of surface they influence. These 2D images are subsequently described by image moments commonly used in image processing. This way we are able to detect similarities and outliers in the dataset, which are visualized as clusters in a scatterplot graph. The same coloring scheme is used in the linked bar chart enabling to detect the position of the cluster members over time. These views provide a way to select a subset of potentially interesting tunnels that can be further explored in detail using the 2D unfolded view and also traditional 3D representation. The usability of our approach is demonstrated on case studies conducted by the domain experts.",
images = "images/Kolesar-2016-VCBM.png",
thumbnails = "images/Kolesar-2016-VCBM-thumbnail.jpg",
proceedings = "Proceedings of Eurographics Workshop on Visual Computing in Biology and Medicine",
keywords = "unfolding, storytelling, game visualization",
location = "Bergen, Norway",
project = "physioillustration"
}