Browsing by Author "Byška, Jan"

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    Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Byška, Jan; Trautner, Thomas; Marques, Sérgio M.; Damborský, Jiří; Kozlíková, Barbora; Waldner, Manuela; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    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.
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    DockVis: Visual Analysis of Molecular Docking Data
    (The Eurographics Association, 2019) Furmanová, Katarína; Kozlíková, Barbora; Vonásek, Vojtěch; Byška, Jan; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    Molecular docking is one of the key mechanisms for predicting possible interactions between ligands and proteins. This highly complex task can be simulated by several software tools, providing the biochemists with possible ligand trajectories, which have to be subsequently explored and evaluated for their biochemical relevance. This paper focuses on aiding this exploration process by introducing DockVis visual analysis tool. DockVis operates primarily with the multivariate output data from one of the latest available tools for molecular docking, CaverDock. CaverDock output consists of several parameters and properties, which have to be subsequently studied and understood. DockVis was designed in tight collaboration with protein engineers using the CaverDock tool. However, we believe that the concept of DockVis can be extended to any other molecular docking tool providing the users with corresponding computation results.
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    DockVis: Visual Analysis of Molecular Docking Trajectories
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Furmanová, Katarína; Vávra, Ondřej; Kozlíková, Barbora; Damborský, Jiří; Vonásek, Vojtěch; Bednář, David; Byška, Jan; Benes, Bedrich and Hauser, Helwig
    Computation of trajectories for ligand binding and unbinding via protein tunnels and channels is important for predicting possible protein–ligand interactions. These highly complex processes can be simulated by several software tools, which provide biochemists with valuable information for drug design or protein engineering applications. This paper focuses on aiding this exploration process by introducing the DockVis visual analysis tool. DockVis operates with the multivariate output data from one of the latest available tools for the prediction of ligand transport, CaverDock. DockVis provides the users with several linked views, combining the 2D abstracted depictions of ligands and their surroundings and properties with the 3D view. In this way, we enable the users to perceive the spatial configurations of ligand passing through the protein tunnel. The users are initially visually directed to the most relevant parts of ligand trajectories, which can be then explored in higher detail by the follow‐up analyses. DockVis was designed in tight collaboration with protein engineers developing the CaverDock tool. However, the concept of DockVis can be extended to any other tool predicting ligand pathways by the molecular docking. DockVis will be made available to the wide user community as part of the Caver Analyst 3.0 software package ().
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    EuroVis 2020 Posters: Frontmatter
    (The Eurographics Association, 2020) Byška, Jan; Jänicke, Stefan; Byška, Jan and Jänicke, Stefan
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    EuroVis 2021 Posters: Frontmatter
    (The Eurographics Association, 2021) Byška, Jan; Jänicke, Stefan; Schmidt, Johanna; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
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    MolVa 2020: Frontmatter
    (The Eurographics Association, 2020) Byška, Jan; Krone, Michael; Sommer, Björn; Byška, Jan and Krone, Michael and Sommer, Björn
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    MolVa 2021: Frontmatter
    (The Eurographics Association, 2021) Byška, Jan; Krone, Michael; Sommer, Björn; Byška, Jan and Krone, Michael and Sommer, Björn
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    MolVa 2023: Frontmatter
    (The Eurographics Association, 2023) Byška, Jan; Krone, Michael; Sommer, Björn; Byška, Jan; Krone, Michael; Sommer, Björn
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    State of the Art of Molecular Visualization in Immersive Virtual Environments
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Kuťák, David; Vázquez, Pere‐Pau; Isenberg, Tobias; Krone, Michael; Baaden, Marc; Byška, Jan; Kozlíková, Barbora; Miao, Haichao; Hauser, Helwig and Alliez, Pierre
    Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.
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    xOpat: eXplainable Open Pathology Analysis Tool
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Horák, Jirí; Furmanová, Katarína; Kozlíková, Barbora; Brázdil, Tomáš; Holub, Petr; Kacenga, Martin; Gallo, Matej; Nenutil, Rudolf; Byška, Jan; Rusnak, Vit; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open-source, browserbased WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists' annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol-agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.