Welcome to CryoDRGN’s documentation!

Welcome to ❄️🐉 cryoDRGN’s documentation! This page contains user guides for the cryodrgn open source software package, including in-depth tutorials for training and analyzing cryoDRGN models.

Quick start

CryoDRGN can be installed with pip. We recommend installing cryodrgn in a separate anaconda environment. See cryoDRGN Installation for more details and advanced installation instructions.

$ pip install cryodrgn

The entry point for all cryoDRGN commands are the cryodrgn and cryodrgn_utils executables:

(cryodrgn) $ cryodrgn -h
(cryodrgn) $ cryodrgn_utils -h

Use the -h flag for help, e.g. to display all available subcommands or cryodrgn <command> -h to see the available parameters to each command.

Contents:

• ❄️🐉 cryoDRGN Introduction
  • Background
  • Input data requirements
  • Tutorial overview
  • References
• cryoDRGN Installation
  • 1) Install anaconda
  • 2) Setting up the cryoDRGN environment
  • 3) Install cryoDRGN
  • 4) Testing the Installation
  • Updating cryoDRGN
  • Known Issues
• cryoDRGN EMPIAR-10076 tutorial
  • 1) Obtain the dataset
  • 2) Consensus reconstruction (optional)
  • 3) Preprocess inputs
  • 4) CryoDRGN training
  • 5) cryoDRGN analysis
  • 6) Particle filtering with the cryoDRGN Jupyter notebook
  • 7) CryoDRGN high-resolution training
  • 8) Analysis
• cryoDRGN Conformational Landscape Analysis
  • 1. Quickstart
  • 2. Assigning 3D conformational states (”classes”)
  • 3. Masking (optional)
  • 4. Visualizing conformational landscapes
  • 5. Visualizing the full conformational landscape
• cryoDRGN preprocess for large datasets
  • Numbers
  • Technicalities
  • Still too large
• CryoDRGN2 quickstart
  • Setup
  • Example usage
  • Note on training settings
  • Questions and contact
  • Reference
• FAQ / Troubleshooting
• API
  • commands.train_nn
  • commands.train_vae
  • commands.abinit_homo
  • commands.abinit_het

Indices and tables

• Index

• Module Index

• Search Page