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rSNAPed: RNA Sequence to NAscent Protein Experiment Designer.Authors: Luis U. Aguilera, William Raymond, Tatsuya Morisaki, Brooke Silagy, Timothy J. Stasevich, and Brian Munsky. |
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rSNAPed is a library to simulate single-molecule gene expression experiments to test machine learning and computational pipelines. The code generates simulated intensity translation spots using rSNAPsim. Cell segmentation is performed using Cellpose. Spot detection and tracking is achieved using Trackpy. If you use rSNAPed, please make sure you properly cite cellpose, trackpy and rSNAPsim.
- Simulating the single-molecule translation for any gene.
- Design of single-molecule gene expression experiments.
- Tracking for single-molecule translation (RNA + nascent protein) spots.
- Tracking for single-molecule RNA spots.
You must accept our Content Policy when using this library:
- All simulated images generated with this software are intended to be used to test Machine learning or computational algorithms.
- All images generated with this software should always be labeled with the specific terms "simulated data" or "simulated images".
- All datasets resulting from a simulated image should explicitly be reported with the term "simulated data".
- Under any circumstance, a simulated image or dataset generated with rSNAPed should not be used to misrepresent real data.
- For public or private use, you must disclose that the generated images are simulated data and give proper credit to rSNAPed.
| Description | Link |
|---|---|
| How to simulate your cell! 👉 | |
| Harringtonin experiment 👉 | |
| Manual particle tracking 👉 | |
| 🔥 Automated cell segmentation and particle tracking 🔥 👉 | |
| Multiplexing experiments 👉 | |
The code generates videos with the simulated cell and a data frame containing spot and intensity positions. This simulation can be used to train new algorithms.
- To create a virtual environment using:
conda create -n rsnaped_env python=3.8.5 -y
source activate rsnaped_env- Open the terminal and use pip for the installation:
pip install rsnaped- To create a virtual environment navigate to the location of the requirements file, and use:
conda create -n rsnaped_env python=3.8.5 -y
source activate rsnaped_env- To install GPU for Cellpose (Optional step). For Linux and Windows users check the specific version for your computer on this link :
conda install pytorch cudatoolkit=10.2 -c pytorch -y
- To install CPU for Cellpose (Optional step). For Mac users check the specific version for your computer on this link :
conda install pytorch -c pytorch
- To include the rest of the requirements use:
pip install -r requirements.txt
Additional steps to deactivate or remove the environment from the computer:
- To deactivate the environment use
conda deactivate
- To remove the environment use:
conda env remove -n rsnaped_env
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rSNAPsim: Aguilera, Luis U., et al. "Computational design and interpretation of single-RNA translation experiments." PLoS computational biology 15.10 (2019): e1007425.
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Trackpy: Dan Allan, et al. (2019, October 16). soft-matter/trackpy: Trackpy v0.4.2 (Version v0.4.2). Zenodo. http://doi.org/10.5281/zenodo.3492186
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Cellpose: Stringer, Carsen, et al. "Cellpose: a generalist algorithm for cellular segmentation." Nature Methods 18.1 (2021): 100-106.
For a complete list containing the complete licenses for the dependencies, check file: Licenses_Dependencies.md.
- License for rSNAPsim: MIT. Copyright © 2018 Dr. Luis Aguilera, William Raymond
- License for Trackpy: BSD-3-Clause. Copyright © 2013-2014 trackpy contributors https://github.com/soft-matter/trackpy. All rights reserved.
- License for Cellpose: BSD 3-Clause. Copyright © 2020 Howard Hughes Medical Institute
Luis Aguilera, William Raymond, Tatsuya Morisaki, Brooke Silagy, Timothy J. Stasevich, & Brian Munsky. (2022). rSNAPed. RNA Sequence to NAscent Protein Experiment Designer. (v0.1-beta.2). Zenodo. https://doi.org/10.5281/zenodo.6967555