CryoDRGN has proven a powerful deep learning method for heterogeneity analysis in single particle cryo-EM. In particular, the method models a continuous distribution over 3D structures by using a Variational Auto-Encoder (VAE) based architecture to generate a reconstruction voxel-by-voxel once given a fixed coordinate from a continuous learned latent space.
TomoDRGN extends the cryoDRGN framework to cryo-ET by learning heterogeneity from datasets in which each particle is sampled by multiple projection images at different stage tilt angles. For cryo-ET samples imaging particles in situ, tomoDRGN therefore enables continuous heterogeneity analysis at a single particle level within the native cellular environment. This new type of input necessitates modification of the cryoDRGN architecture, enables tomography-specific processing opportunities (e.g. dose weighting for loss weighting and efficient voxel subset evaluation during training), and benefits from tomography-specific interactive visualizations.
TomoDRGN documentation, including installation instructions, tutorials, CLI and API references, and more, are now hosted on GitHub Pages.
- Powell, B.M., Davis, J.H. Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN. bioRxiv
- Zhong, E.D., Bepler, T., Berger, B. & Davis, J.H. CryoDRGN: Reconstruction of Heterogeneous cryo-EM Structures Using Neural Networks. Nature Methods, doi:10.1038/s41592-020-01049-4 (2021)
- Kinman, L.F., Powell, B.M., Zhong, E.D. et al. Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN. Nat Protoc 18, 319–339 (2023). https://doi.org/10.1038/s41596-022-00763-x
- Sun, J., Kinman, L., Jahagirdar, D., Ortega, J., Davis. J. KsgA facilitates ribosomal small subunit maturation by proofreading a key structural lesion. bioRxiv, doi:10.1101/2022.07.13.499473 (2022)
Please file bug reports, feature requests, etc on this GitHub repository's Issues page.