rsnaped 0.1.0

Python module for single-molecule image processing.

rSNAPed

rSNAPed : RNA Sequence to NAscent Protein Experiment Designer.

Authors: Luis U. Aguilera, William Raymond, Brooke Silagy, Brian Munsky.

:warning: This software is in a very early and experimental stage: at this point, it is intended to be used for testing and debugging purposes!

Description

This library is intended to quantify single-molecule gene expression experiments. Specifically, the code uses Cellpose to segment the cell in the image. Then, it uses Trackpy to detect spots inside the mask. Finally, it uses the spot position to quantify the spot intensity. The code also generates simulated data using rSNAPsim. If you use rSNAPed, please make sure you properly cite cellpose, trackpy and rSNAPsim.

Usage

• Tracking for single-molecule translation (RNA + nascent protein) spots.
• Tracking for single-molecule RNA spots.
• Simulating the single-molecule translation for any gene.
• Design of single-molecule gene expression experiments.

Simulating translation

The code is intended to simulated single-molecule translation. A video with the simulated cell and a data frame containing spot and intensity positions are generated. This simulation can be used to train new algorithms or for teaching new students.

Local installation using PIP

• 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

Local installation from the Github repository

• 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). Only 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). Only for Mac users check the specific version for your computer on this link :

    conda install pytorch -c pytorch

• To include the rest of 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

References for main dependencies

• rSNAPsim: Aguilera, Luis U., et al. "Computational design and interpretation of single-RNA translation experiments." PLoS computational biology 15.10 (2019): e1007425.

• 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

• Cellpose: Stringer, Carsen, et al. "Cellpose: a generalist algorithm for cellular segmentation." Nature Methods 18.1 (2021): 100-106.

Licenses for dependencies

For a complete list containing the complete licenses for the dependencies, check file: Licenses_Dependencies.txt.

• 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