scipion-em-cryoassess 3.0.1

Plugin to use cryoassess within the Scipion framework

This plugin provide a wrapper around Cryoassess software tools for automatic micrograph and 2D classes assessment.

prod:

devel:

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-cryoassess

2. Developer’s version

   • download repository

   git clone https://github.com/scipion-em/scipion-em-cryoassess.git

   • install

   scipion installp -p path_to_scipion-em-cryoassess --devel

Cryoassess software will be installed automatically with the plugin but you can also use an existing installation by providing CRYOASSESS_ENV_ACTIVATION (see below). You also have to download training models separately (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)”

CRYOASSESS_ENV_ACTIVATION (default = conda activate cryoassess-0.1.0): Command to activate the cryoassess environment.

The deep-learning models can be downloaded from authors’ website and set with the following variables:

CRYOASSESS_MODEL_MIC (default = software/em/cryoassess-models/micassess_051419.h5)

CRYOASSESS_MODEL_2D (default = software/em/cryoassess-models/2dassess_062119.h5)

Verifying

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

scipion test cryoassess.tests.test_protocols_cryoassess.TestCryoassess

Supported versions

0.1.0

Protocols

• assess micrographs

• assess 2D classes

References

1. High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Yilai Li, Jennifer N.Cash, John J.G. Tesmer, Michael A.Cianfrocco. Structure 2020, Volume 28 (7), Pages 858-869.e3