Big Data Visual Analytics in Life Sciences

Current Projects

PROLINT – Visual Analysis of Protein-Ligand Interactions

Project partners:
Prof. Dr. Timo Ropinski, Forschungsgruppe Visual Computing, University of Ulm
Prof. Dr. Barbora Kozlíková, Department of Computer Graphics and Design, Masaryk University, Czech Republic

Projekt duration:
10/2018 – 09/2021

Funding:
Deutsche Forschungsgemeinschaft e.V. (DFG) (DFG-GACR Cooperation: Joint German-Czech Research Projects)

Abstract:
The visual analysis of protein structures has been researched in several projects within the past few years. While molecular structures are relevant, it is necessary to focus on both interaction partners and to also take into account their physico-chemical properties in order to understand protein interactions. Thus, within this research project, we plan to enable the visual analysis of protein-ligand interactions as captured in state-of-the-art simulations by focusing on these properties. The main goal is to make these time-dependent data sets better accessible for protein designers, and help them to develop adaptions that enable a more efficient interaction. In particular, we will develop novel visualization techniques which convey the relevant properties by means of abstract representations as well as structural embeddings. We will enhance these techniques for a visual comparison of different interactions, which eventually enable us to develop domain-centered immersive visual analytics approaches. We will evaluate our methods together with domain experts in order to ensure their effectiveness for the visual analysis of protein-ligand interactions.

Projektbeschreibung in FIT
Projektbeschreibung in DFG GEPRIS

IVM – Illustrative Visualization of Uncertainty in Dynamic Molecular Structures

Project partner:
Prof. Dr.-Ing. habil. Kai Lawonn, Visualization and Explorative Data Analysis Group, Universität Jena

Project duration:
01/2021 – 12/2022

Funding:
Deutsche Forschungsgemeinschaft e.V. (DFG)

Abstract:
Ziel dieses Forschungsvorhabens ist es, neue Visualisierungstechniken für Unsicherheiten in biomolekularen Daten (beispielsweise Proteinstrukturen, DNA oder RNA) zu entwickeln, welche eine verlässliche und zugleich effiziente, umfassende visuelle Analyse der Daten ermöglichen. Hierzu soll unter anderem der Einsatz illustrativer Visualisierungstechniken zur Darstellung der Unsicherheit untersucht werden. Für die visuelle Analyse und Quantifizierung der Unsicherheiten sollen sowohl dreidimensionalen Struktur-Daten als auch Sequenz-Daten in die Visualisierung einbezogen und kombiniert werden. Neben den in den Daten enthaltenen Unsicherheiten sollen zusätzlich die durch Analyseverfahren induzierten Unsicherheitsfaktoren quantifiziert werden.