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Plugin to use TomoTwin within the Scipion framework

Project description

This plugin provides a wrapper for TomoTwin software: Particle picking in Tomograms using triplet networks and metric learning

PyPI release License Supported Python versions SonarCloud quality gate Downloads

Installation

You will need to use 3.0+ version of Scipion to be able to run these protocols. To install the plugin, you have two options:

  1. Stable version

scipion installp -p scipion-em-tomotwin
  1. Developer’s version

    • download repository

    git clone -b devel https://github.com/scipion-em/scipion-em-tomotwin.git
    • install

    scipion installp -p /path/to/scipion-em-tomotwin --devel

TomoTwin software will be installed automatically with the plugin but you can also use an existing installation by providing TOMOTWIN_ENV_ACTIVATION (see below).

Important: you need to have conda (miniconda3 or anaconda3) pre-installed to use this program.

Configuration variables

CONDA_ACTIVATION_CMD: If undefined, it will rely on conda command being in the PATH (not recommended), which can lead to execution problems mixing scipion python with conda ones. One example of this could can be seen below but depending on your conda version and shell you will need something different: CONDA_ACTIVATION_CMD = eval “$(/extra/miniconda3/bin/conda shell.bash hook)”

TOMOTWIN_ENV_ACTIVATION (default = conda activate tomotwin-0.5.1): Command to activate the TomoTwin environment.

TOMOTWIN_MODEL (default = software/em/tomotwin_model-052022/tomotwin_model_p120_052022_loss.pth): Path to the pre-trained model.

NAPARI_ENV_ACTIVATION (default = conda activate napari-0.4.4): Command to activate the Napari viewer environment.

Verifying

To check the installation, simply run the following Scipion tests:

  • scipion tests tomotwin.tests.test_protocols_tomotwin.TestTomoTwinRefBased

  • scipion tests tomotwin.tests.test_protocols_tomotwin.TestTomoTwinClusterBased

Supported versions

0.3.0, 0.5.1

Protocols

  • reference-based picking

  • clustering-based picking (step 1)

  • clustering-based picking (step 2)

  • create tomo masks

References

  1. TomoTwin: Generalized 3D Localization of Macromolecules in Cryo-electron Tomograms with Structural Data Mining. Gavin Rice, Thorsten Wagner, Markus Stabrin, Stefan Raunser. https://www.biorxiv.org/content/10.1101/2022.06.24.497279v1

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