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  linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

Waschke, J., Hlawitschka, M., Anlas, K., Trivedi, V., Roeder, I., Huisken, J., et al. (2021). linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser. PLoS Computational Biology, 17(11): e1009503. doi:10.1371/journal.pcbi.1009503.

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 Creators:
Waschke, Johannes1, 2, Author              
Hlawitschka, Mario2, Author
Anlas, Kerim3, Author
Trivedi, Vikas3, 4, Author
Roeder, Ingo5, 6, Author
Huisken, Jan7, Author
Scherf, Nico6, 8, 9, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Faculty of Computer Science and Media, University of Applied Sciences, Leipzig, Germany, ou_persistent22              
3European Molecular Biology Laboratory (EMBL), Barcelona, Spain, ou_persistent22              
4European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, ou_persistent22              
5National Center of Tumor Diseases (NCT), Dresden, Germany, ou_persistent22              
6Institute for Medical Informatics and Biometry, University Hospital Carl Gustav Carus, Dresden, Germany, ou_persistent22              
7Morgridge Institute for Research, Madison, WI, USA, ou_persistent22              
8Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, Leipzig, Germany, ou_persistent22              
9Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3282987              

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 Abstract: In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.

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Language(s): eng - English
 Dates: 2021-02-012021-09-302021-11-01
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pcbi.1009503
Other: eCollection 2021
PMID: 34723958
PMC: PMC8584757
 Degree: -

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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 17 (11) Sequence Number: e1009503 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1