🐯 cryoTIGER ❄️

Tilt Interpolation Generator for Enhanced Reconstruction in Cryo-Electron Tomography

📄 Published in Communications Biology

⚙️ Installation

• Get cryoTIGER source codes

git clone https://github.com/turonova/cryoTIGER
cd cryoTIGER

💡 Code Acknowledgments

This project makes use of code from FILM: Frame Interpolation for Large Motion, authored by Google Research. Their work provided a basis for running an interpolator in this project and we use their code in the eval folder as well as in the example_train_config folder.

Using environment.yml

• Create cryoTIGER conda environment using .yml file

conda env create -f environment.yml

Note: Depending on your system, this can take some time due to the installation of large libraries like Tensorflow.

Using pip

• The following dependencies should be installed:

  • tensorflow>=2.6.2
  • tensorflow-datasets>=4.4.0
  • tensorflow-addons>=0.15.0
  • gin-config>=0.5.0
  • parameterized>=0.8.1
  • mediapy>=1.0.3
  • scikit-image>=0.19.1
  • apache-beam>=2.34.0
  • google-cloud-bigquery-storage>=1.1.0 # Suppresses a harmless error from beam
  • natsort>=8.1.0
  • gdown>=4.5.4
  • tqdm>=4.64.1
  • cryocat>=0.2.0
  • ipykernel>=6.29.5

• Install cryoTIGER using requirements.txt

pip3 install -r requirements.txt

• Note: To enable GPU support, add the Conda environment's library path to the LD_LIBRARY_PATH environment variable using the following command: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

🧠 Pre-trained Models

• Create a directory with pre-trained models.

mkdir -p <models>

• Download pre-trained models from owncloud and put them into <models>.

The downloaded folder should have the following structure:

<models>/<cryoTIGER_model>/
<models>/<pre-trained_Vimeo-90K_data_model>/

🚀 Running cryoTIGER

• To demonstrate the cryoTIGER tilt interpolation framework, we provide a Jupyter Notebook
• Note: If you move this notebook to a different folder, make sure that the eval directory is still accessible (e.g., by updating the Python path or using relative imports correctly). For best results, keep the notebook within the original project structure.

cryoTIGER_interpolation.ipynb

• Input: dose-filtered tilt series in .mrc format

<tutorial_data>/<experimental_TS>

• Output: interpolated dose-filtered tilt series in .mrc format

<tutorial_data>/<interpolated_TS>

• To obtain the aligned tilt series for input to cryoTIGER, we recommend using the IMOD newstack command:

newstack -bin 2 -InputFile /path/to/input/{TOMO_ID}_dose-filt.st \
            -xform /path/to/input/alignment_files/{TOMO_ID}.xf -origin -nearest \
            -output /path/to/output/{TOMO_ID}_dose-filt_ali_bin2.mrc

📜 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

📚 Citation

APA format

Majtner, T., et al. (2025). cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography. Communications Biology, 8, 1443. Nature Publishing Group. https://doi.org/10.1038/s42003-025-08961-5

BibTeX entry

@article{Majtner2025cryoTIGER,
  author = {Tomáš Majtner and Jan Philipp Kreysing and Maarten W. Tuijtel and Sergio Cruz-León and Jiasui Liu and Gerhard Hummer and Martin Beck and Beata Turoňová},
  title = {{cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography}},
  journal = {{Communications Biology}},
  volume = {8},
  article = {1443},
  year = {2025},
  doi = {10.1038/s42003-025-08961-5},
  url = {https://www.nature.com/articles/s42003-025-08961-5},
  publisher = {Nature Publishing Group}
}