We are excited to include cryoDRGN into the suite of software available on the COSMIC² science gateway. cryoDRGN is a deep-learning-based heterogenous reconstruction algorithm that utilizes variational autoencoders to embed single-particle images into a latent space for decoding into conformations and populations.
What does this mean for your single-particle cryoEM project? cryoDRGN allows you to assess underlying heterogeneity after performing a consensus 3D refinement into a single 3D reconstruction. An example of how this can be used was highlighted in the preprint, where the authors separated distinct conformations of assembling ribosomes based on the latent space-embedding:
As explained by the authors on their Github repo page, this is experimental and requires empirically determining the best way for the variational autoencoder to encode and decode the data into latent space. Let us know if it works for you and if you have additional features or options to help you use cryoDRGN.
To help users navigate outputs from cryoDRGN, be sure to check out our Youtube tutorial on downloading and visualizing the results.