Skip to content

EMReady is a three-dimensional nested U-net-based framework for improving the interpretability of cryo-EM maps using similarity and correlation-guided deep learning.

License

scipion-em/scipion-em-emready

Repository files navigation

EMReady plugin

EMReady: Improvement of cryo-EM maps by simultaneous local and non-local deep 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-emready

or through the plugin manager by launching Scipion and following Configuration >> Plugins

  1. Developer's version

    • download repository
    git clone -b devel https://github.com/scipion-em/scipion-em-emready.git
    • install
    scipion installp -p /path/to/scipion-em-emready --devel

EMReady software will be installed automatically with the plugin but you can also use an existing installation by providing EMREADY_ENV_ACTIVATION and EMREADY_HOME (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)"

EMREADY_ENV_ACTIVATION (default = conda activate emready-1.0): Command to activate the EMReady environment.

EMREADY_HOME (default = software/em/emready-1.0): Path with EMReady source code.

Verifying

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

scipion test emready.tests.test_protocol_sharpening.TestEMReadySharpening

Supported versions

1.0

Protocols

  • sharpening

References

  1. He J, Li T, Huang S-Y. Improvement of cryo-EM maps by simultaneous local and non-local deep learning. Nature Communications, 2023; 14:3217.

About

EMReady is a three-dimensional nested U-net-based framework for improving the interpretability of cryo-EM maps using similarity and correlation-guided deep learning.

Topics

Resources

License

Stars

Watchers

Forks

Contributors 4

  •  
  •  
  •  
  •  

Languages