tomoDRGN documentation

Example ribosomal heterogeneity learned with tomoDRGN on dataset EMPIAR-10499

[CryoDRGN](zhonge/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.

References

1. GitHub: bpowell122/tomodrgn

2. BioRxiv: https://www.biorxiv.org/content/10.1101/2023.05.31.542975v1

3. Nature Methods: https://www.nature.com/articles/s41592-024-02210-z