This plugin provides a wrapper for cryoDRGN software: Deep Reconstructing Generative Networks for cryo-EM heterogeneous reconstruction.
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:
Stable version
scipion installp -p scipion-em-cryodrgnDeveloper’s version
download repository
git clone -b devel https://github.com/scipion-em/scipion-em-cryodrgn.gitinstall
scipion installp -p /path/to/scipion-em-cryodrgn --devel
cryoDRGN software will be installed automatically with the plugin but you can also use an existing installation by providing CRYODRGN_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)”
CRYODRGN_ENV_ACTIVATION (default = conda activate cryodrgn-3.4.0): Command to activate the cryoDRGN environment.
Verifying
To check the installation, simply run the following Scipion test:
scipion test cryodrgn.tests.test_protocols_cryodrgn.TestWorkflowCryoDrgn
Supported versions
3.1.0-beta, 3.3.2, 3.4.0
Protocols
analyze results
preprocess particles
training VAE
training ab initio
References
Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN. Laurel Kinman, Barrett Powell, Ellen Zhong, Bonnie Berger, Joey Davis. https://www.biorxiv.org/content/10.1101/2022.08.09.503342v1
CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks. Ellen D. Zhong, Tristan Bepler, Bonnie Berger, Joseph H. Davis. Nature Methods 18(2), 2021, 176-182. DOI 10.1038/s41592-020-01049-4
Reconstructing continuous distributions of 3D protein structure from cryo-EM images. Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger. ICLR 2020, https://arxiv.org/abs/1909.05215
CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures From Real Cryo-EM Images. Ellen D. Zhong, Adam Lerer, Joseph H. Davis, Bonnie Berger; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4066-4075. https://openaccess.thecvf.com/content/ICCV2021/html/Zhong_CryoDRGN2_Ab_Initio_Neural_Reconstruction_of_3D_Protein_Structures_From_ICCV_2021_paper.html
1 maintainer
Metadata
1 maintainer
Release files for scipion-em-cryodrgn 3.13.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scipion_em_cryodrgn-3.13.1.tar.gz | 63.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scipion_em_cryodrgn-3.13.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 134.5 kB
Release files / scipion_em_cryodrgn-3.13.1.tar.gz
| Download URL | scipion_em_cryodrgn-3.13.1.tar.gz |
|---|---|
| Size | 63.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/6.1.0 CPython/3.8.18
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Release files / scipion_em_cryodrgn-3.13.1-py3-none-any.whl
| Download URL | scipion_em_cryodrgn-3.13.1-py3-none-any.whl |
|---|---|
| Size | 71.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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