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Dagstuhl Seminar 20171

Visualization of Biological Data – From Analysis to Communication Postponed

( Apr 19 – Apr 24, 2020 )

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Dagstuhl Seminar 21401: Visualization of Biological Data - From Analysis to Communication (2021-10-03 - 2021-10-08) (Details)




The rapidly expanding data generated by advanced methods in biology is creating enormous challenges at the interface between humans and biological data that can be represented and analyzed visually. To meet these challenges, a blend of methodology from the visualization, bioinformatics, and biology domains is required. In this Dagstuhl Seminar we aim to bring together researchers from the multiple domains to discuss how to continue the biovis interdisciplinary dialogue, to foster the development of an international community, to discuss the state-of-the-art and advance areas of research that might benefit from joint efforts of all groups involved. The seminar will be structured around 4 main topics of interest to both the bioinformatics and visualization communities, including teaching biology visualization.

Topic 1: Data abstraction to support building custom visual tools of biological data

In biology, there are precise semantic relationships between data entities. Can we give domain experts the possibility to build custom visualization dashboards via data abstraction? We will make an inventory of data types in various subfields of biological research, and describe their properties and semantic relationships (e.g., a "chromosome" is a coordinate system). These properties and relationships could be described following the standards provided by existing knowledge organization systems.

Topic 2: Interactive analysis for biological data exploration

Exploratory data analysis requires interactive visualizations and construction of workflows that combine visual displays with components for data management, preprocessing, modeling, and statistical analysis. There are many toolboxes available that support such analysis, but there is little consensus on their systematic design to support biologists in data integration, replication of results, storytelling, and big data analysis. How do we best combine interactive visualization with other tasks such as data normalization, machine learning, embedding, and network inference?

Topic 3: Collaboration and communication through new tools

We will investigate how established visualizations can be combined in a consistent manner that is intuitively understandable by domain experts, and how insights can be communicated at different levels of granularity. Further research questions are, how the dynamics of the underlying processes can be visualized in such a setting, how comparison between different conditions, organisms, or groups can be efficiently supported, and, given the experimental nature of data in biology, how different levels of data uncertainty and inconsistency can be intuitively represented.

Topic 4: A curriculum for teaching visualization in bioinformatics

Despite the increasing importance of visualization for bioinformatics, there is currently a general lack of integration into the bioinformatics education, and a useful and appropriate curriculum has not yet been developed. In this topic the following questions will be addressed: What should a modern and seminal curriculum for visualization in bioinformatics look like? What are the essential topics, and how can comprehensive training be achieved?

Copyright Karsten Klein, Georgeta Elisabeta Marai, Kay Katja Nieselt, and Blaz Zupan

  • Karsten Klein (Universität Konstanz, DE) [dblp]
  • Georgeta Elisabeta Marai (University of Illinois - Chicago, US) [dblp]
  • Kay Katja Nieselt (Universität Tübingen, DE) [dblp]
  • Blaz Zupan (University of Ljubljana, SI) [dblp]

  • bioinformatics
  • computer graphics / computer vision
  • modelling / simulation

  • Interdisciplinarity
  • Informatics
  • Biology
  • Computational biology
  • Visualization