Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (10.2%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: ma-seefelder
  • License: agpl-3.0
  • Language: Julia
  • Default Branch: main
  • Size: 68.2 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation Codeowners

README.md

ProteinCoLoc

Description

ProteinCoLoc is a scientific software designed for Bayesian analysis of co-localization in microscopic images. Utilizing cutting-edge statistical methods, ProteinCoLoc provides accurate and detailed insights for molecular biology researchers. A graphical user interface is available that facilitates the use of the software. Additionally, a compiled version of ProteinCoLoc is available at Zenodo that does not require the installation of Julia.

Features

The key features of ProteinCoLoc are:

  • Bayesian Modelling: Robust statistical framework for co-localization analysis.
  • Automatic Background Detection: Streamlines the analysis process with Otsu's thresholding.
  • Localization Analysis: Detailed insights into specific cellular regions.

For more details on this tool's methodology and potential applications, please refer to our published research article.

License

ProteinCoLoc is licensed under the GNU Affero General Public License v3.0

Citation

If you use ProteinCoLoc in your research, please cite our article in Scientific Reports:

Seefelder, M., Kochanek, S. & Klein, F.A.C. ProteinCoLoc streamlines Bayesian analysis of colocalization in microscopic images. Sci Rep 14, 13277 (2024). https://doi.org/10.1038/s41598-024-63884-1

Contact

For support or queries, please get in touch with us at:

Email: manuel.seefelder@uni-ulm.de
Project Maintainer: Dr. Manuel Seefelder, Department of Gene Therapy, Ulm University.

Owner

  • Name: Manuel Seefelder
  • Login: ma-seefelder
  • Kind: user
  • Location: Ulm
  • Company: Ulm University

Citation (CITATION.bib)

@misc{PackageName.jl-2022-a,
  title = {{ProteinCoLoc.jl}: A Julia package for co-localization analysis},
  author = {Manuel Seefelder},
  year = {2023},
  howpublished = {\url{https://github.com/ma-seefelder/ProteinCoLoc}}
}

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