A Computational Pipeline for Image-Based Statistical Analysis of Biomolecular Condensates Dynamics Using Morphological Descriptors
Authors/Creators
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1.
Brazilian Biosciences National Laboratory
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2.
Brazilian Center for Research in Energy and Materials
- 3. Laboratório Nacional de Luz Síncrotron
Description
Source Code Description
Here we share the source code for a customized, interactive JupyterLab–Python processing pipeline designed for the automated computational analysis of morphological descriptors of biomolecular condensates, including data handling and statistical analysis.
This pipeline supports image-based quantitative analysis of condensate dynamics using reproducible and extensible workflows.
Related Dataset
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Dataset version: 0.3.2
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Zenodo DOI: https://doi.org/10.5281/zenodo.14387077
Related Publication
Rosa e Silva, I., Gurian Dariani, G., Benevenutti, F. Z., et al.
A computational pipeline for image-based statistical analysis of biomolecular condensates dynamics using morphological descriptors.
Scientific Reports 15, 27560 (2025).
https://doi.org/10.1038/s41598-025-09148-y
Full text: https://rdcu.be/eyk2r
Files
2024_llps_analysis-source_code.zip
Files
(46.2 kB)
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Additional details
Funding
- National Council for Scientific and Technological Development
- Desenvolvimento de um método automatizado de triagem in vitro de moduladores da separação de fase líquido-líquido (LLPS) de proteínas associadas a transtornos neurológicos 407904/2023-9
- National Council for Scientific and Technological Development
- Investigação da neurofisiologia celular na deficiência da enzima conjugadora de ubiquitina UBE2A em modelo humano com células de pluripotência induzidas (hiPSC) 404617/2023-9
Software
- Repository URL
- https://gitlab.com/murilo.carvalho/2024_llps_analysis.git
- Programming language
- Python , Jupyter Notebook
- Development Status
- Active