Multiscale Unfolding: Illustratively Visualizing the Whole Genome at a Glance

Halladjian, Sarkis; Kouril, David; Miao, Haichao; Groeller, Eduard; Viola, Ivan; Isenberg, Tobias

doi:10.1109/tvcg.2021.3065443

Cited by 6 publications

(4 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These works are geared towards accurately conveying the scientific achievements and ensuring usability in traditional media. (Halladjian et al, 2021) introduce "Multiscale Unfolding," an interactive approach to the visual depiction of several hierarchic scales of DNA in a single view. This technique allows viewers to interact with any scale at any time.…”

Section: Visual Abstraction Formalismmentioning

confidence: 99%

See 1 more Smart Citation

From complex data to clear insights: visualizing molecular dynamics trajectories

Belghit,

Spivak,

Dauchez

et al. 2024

Front. Bioinform.

View full text Add to dashboard Cite

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

show abstract

Section: Visual Abstraction Formalismmentioning

confidence: 99%

“…( Halladjian et al, 2021 ) introduce “Multiscale Unfolding,” an interactive approach to the visual depiction of several hierarchic scales of DNA in a single view. This technique allows viewers to interact with any scale at any time.…”

Section: General Considerations On Visualization Of Complex Molecular...mentioning

confidence: 99%

From complex data to clear insights: visualizing molecular dynamics trajectories

Belghit,

Spivak,

Dauchez

et al. 2024

Front. Bioinform.

View full text Add to dashboard Cite

show abstract

“…Over the last few years, multiple different techniques have been developed to visualize complex cellular environments while simultaneously reducing the rendering overload (e.g., multiscale unfolding), in which different system scales are represented with varying levels of detail. One such example is the illustrative visualization of the whole genome from the lab of Ivan Viola (Halladjian et al, 2021). Another technique is the dynamic adjustment of texture and other visual characteristics, such as color.…”

Section: Tetrahedral Building Block Modules and Subdivisionsmentioning

confidence: 99%

A new visual design language for biological structures in a cell

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For visualizing inner cell structures, Mindek et al [MKS∗18] proposed a multipipeline for visualizing the organelle system. Similarly, other work [LLC∗19, HMK∗20, HKM∗22] proposed different systems to interact with diverse scales of cell or genome datasets. In contrast to these approaches, however, we are not interested in the representation of spatial scale but in the temporal development in the form of cell lineage.…”

Section: Related Workmentioning

confidence: 99%

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Hong

Trubuil

Isenberg

2022

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.

show abstract

Multiscale Unfolding: Illustratively Visualizing the Whole Genome at a Glance

Cited by 6 publications

References 65 publications

From complex data to clear insights: visualizing molecular dynamics trajectories

From complex data to clear insights: visualizing molecular dynamics trajectories

A new visual design language for biological structures in a cell

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Contact Info

Product

Resources

About