pytom-match-pick

GPU-accelerated template matching for cryo-electron tomography, originally developed in PyTom, as a standalone Python package that is run from the command line.

This software is developed by Marten L. Chaillet (@McHaillet) and Sander Roet (@sroet) in the group of Friedrich Förster at Utrecht University.

Requires

miniconda3
nvidia-cuda-toolkit

Installation

There are 2 options for creating a conda environment. We recommend option (1) which will later allow cupy to build against a system installed cuda-toolkit. Compared to option (2) this can give an almost two-fold speedup:

1. (recommended) Create a new python 3 environment:

   conda create -n pytom_tm python=3
   

2. Create a new environment with a prebuild cupy version and complete CUDA-toolkit. This is more reliable, but takes more disk space and has less optimal performance.

   conda create -n pytom_tm -c conda-forge python=3 cupy cuda-version=11.8
   

Once the environment is created, activate it:

conda activate pytom_tm

Then install the code with pip (building cupy can take a while!):

python -m pip install pytom-match-pick[plotting]

The installation above also adds the optional dependencies [matplotlib, seaborn] which are required to run pytom_estimate_roc.py. They are not essential to the core template matching functionality, so for some systems (such as certain cluster environments) it might be desirable to skip them. In that case remove [plotting] from the pip install command:

python -m pip install pytom-match-pick

Cupy warning

Having issues running the software? If cupy is not correctly installed,

python -c "import pytom_tm"

can show a cupy warning. If this is the case, this probably means cupy is not correctly installed. Alternatively, cupy can sometimes be installed without issue but not detect CUDA correctly. In that case, the following should raise some errors:

python -c "import cupy as cp; a = cp.zeros((100,100))"

To solve cupy installation issues, please check the cupy docs. It might be solved by installing a specific build compatible with the installed cuda toolkit.

Usage

The following scripts are available to run with --help to see parameters:

• create a template from an mrc file containing a density map: pytom_create_template.py --help
• create a mask for template matching: pytom_create_mask.py --help
• run template matching with the mask (.mrc) and template (.mrc) on a tomogram (.mrc): pytom_match_template.py --help
• extract candidates from a job file (.json) created in the template matching output folder: pytom_extract_candidate.py --help
• estimate an ROC curve from a job file (.json): pytom_estimate_roc.py --help
• merge multiple star files to a single starfile: pytom_merge_stars.py --help

Detailed usage instructions and a tutorial are available on our site.

Usage questions, ideas and solutions, engagement, etc

Please use our github discussions for:

• Asking questions about bottlenecks.
• Share ideas and solutions.
• Engage with other community members about processing strategies.
• etc...

Developer install

If you want the most up-to-date version of the code you can get install it from this repository via:

git clone https://github.com/SBC-Utrecht/pytom-match-pick.git
cd pytom-match-pick
python -m pip install '.[all]'

if you don't want the optional plotting dependencies use the following install command instead:

python -m pip install '.[dev]'

For development, please also install pre-commit to check and autostyle the code before you make PRs:

pre-commit install

This uses Ruff to check and format whenever you make commits.

If you update anything in the (documentation) docs/ folder make sure to test build the website locally:

mkdocs serve

Tests

With the developer install also comes the ability to run the unittests, from the git repository run:

cd tests
python -m unittest discover

Contributing

Contributions to the project are very welcome! Feel free to make a pull request or suggest an implementation in the issues. For PR's we will gladly give you feedback on how to integrate the code.

Citation

Chaillet, M. L., van der Schot, G., Gubins, I., Roet, S., Veltkamp, R. C., & Förster, F. (2023). Extensive angular sampling enables the sensitive localization of macromolecules in electron tomograms. International Journal of Molecular Sciences, 24(17), 13375. https://doi.org/10.3390/ijms241713375