PolNet

Python package for generating synthetic datasets of the cellular context for Cryo-Electron Tomography.

Installation

Here is how to get it installed:

git clone anmartinezs/polnet
cd polnet
conda create --name polnet pip
conda activate polnet
pip install -e .

You can check all requirements in the requirements.txt file (JAX is optional).
Install IMOD, PolNet may call to some of its standalone commands: https://bio3d.colorado.edu/imod/doc/guide.html
EMAN2 is required to generate macromolecular templates as the script e2pdb2mrc.py is used.

Installation notes

mrcfile: python package installation with conda may need adding the channel conda-forge, see instructions in: https://pypi.org/project/mrcfile/
jax: python package installation with GPU support is required for faster data generation: https://github.com/google/jax#installation

Content

polnet: python package with the functionality for generating the synthetic data.
scripts: python scripts for generating different types of synthetic datasets. Subfolder deprecated contains some scripts for evaluations carried out during the software development, they are not prepared for external users because some hardcoded paths need to be modified.
tests: unit tests for functionalities in polnet. The script tests/test_transformations.py requires to generate at least 1 output tomo with the script scripts/all_features.py and modified the hardcoded input paths, that is because the size of the input data avoid to upload them to the repository.
docs: contains a PDF with the suplementary material for [1] with the next tables:
- Glossary of acronyms by order of appearance in the main text.
- Glossary mathematical symbols defined in the main text organized by scope
- Table Variables used by the input files to model the generators.
- Table with the structures used to simulate the cellular context.

Generating a generic synthetic dataset

If you want to generate a generic synthetic dataset for cryo-ET without any specific requirement, you must run the script scripts/data_gen/all_features.py:

cd scripts
python data_gen/all_features.py

It is set up to generate a dataset with all different types of structures including membranes, microtubules, actin networks, 18 different cytosolic and 8 membrane-bound macromolecules. Input configuration files for geometry and transformation generators, as well as the macromolecular models are available under data folder.

A PDF with a text description of the setting used in the scripts scripts/data_gen/all_features.py is in docs/default_settings.pdf. The mathematical symbols and acronyms correspond with the definitions in the main publication document.

Main publication (Citation)

[1] Martinez-Sanchez A.*, Jasnin M., Phelippeau H. and Lamm L. "Simulating the cellular context in synthetic datasets for cryo-electron tomography" bioRxiv

anmartinezs / polnet