Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 10 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: Leiden-chemical-immunology
- License: mit
- Language: Python
- Default Branch: main
- Size: 29.2 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Introduction
Glyco-PAINT is a research method to study interactions of glycans on lectins on live cells. The Glyco-PAINT Application Processing Pipeline is a software solution to process and analyse experimental results.
Reference1
The software and selected research results are described in: Steuten, K., Bakker, J., Doelman, W. et al. Glyco-PAINT-APP: Subcellular analysis of immune cell-lectin binding enables correlation of glycan binding parameters to receptor biology. doi: https://doi.org/10.1101/2025.01.24.634682
The Pipeline, presented here, is a research application to extract information from the GlycoPaint recordings. The pipeline is a collection of Python and R scripts and depends heavily on the Glyco-PAINT method, Fiji and TrackMate.
Riera, R., Hogervorst, T.P., Doelman, W. et al. Single-molecule imaging of glycan–lectin interactions on cells with Glyco-PAINT. Nat Chem Biol 17, 1281–1288 (2021). https://doi.org/10.1038/s41589-021-00896-2
Ershov, D., Phan, M.-S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles-Orszag, A., … Tinevez, J.-Y. (2022). TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods, 19(7), 829–832. doi:10.1038/s41592-022-01507-1
Software environment
The software has been tested on a MacBook with MacOS 14 (Intel) and 15 (Arm), on a Windows 10 and 11.
System requirements
No special system requirements are identified, other than a minimum memory of 16GB. More memory and a fast processor will reduce runtimes.
Installation
To assist with installing the components of the pipeline, Installation instructions are provided.
License
The software is provided as is under the MIT license.
Functionality
An overview of the functionality of the pipeline, how to use it and a detailed demo case with sample data it is provided in the Paint Pipeline Documentation.
Sample image data
Sample image data is provided on Zenodo: 
. This data can be used to run the demonstration case described in Paint Pipeline Documentation.
Owner
- Name: Leiden-chemical-immunology
- Login: Leiden-chemical-immunology
- Kind: organization
- Repositories: 1
- Profile: https://github.com/Leiden-chemical-immunology
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite the following work."
authors:
- family-names: Steuten
given-names: Kas
- family-names: Bakker
given-names: Johannes
- family-names: Doelman
given-names: Ward
- family-names: Torres-García
given-names: Diana
- family-names: Riera
given-names: Roger
- family-names: Albertazzi
given-names: Lorenzo
- family-names: van Kasteren
given-names: Sander
title: "Glyco-PAINT-APP: Subcellular analysis of immune cell-lectin binding enables correlation of glycan binding parameters to receptor biology"
year: 2025
doi: 10.1101/2025.01.24.634682
url: https://doi.org/10.1101/2025.01.24.634682
type: preprint
publisher: bioRxiv
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