easymode

pretrained neural networks for cellular cryoET

Easymode is a collection of general pretrained neural networks for cellular cryo-electron tomography (cryoET). It provides single command line interface functions to handle feature detection: segmentation of cellular features, coordinate extraction, and data preprocessing. Inspired by, built on, and built to work with: Warp/M, RELION, MemBrain-seg, Ais, and Pom.

Preprint | Documentation | Model library | Tutorials

installation

conda create -n easymode python=3.10 cudatoolkit=11.2 cudnn=8.1 git -c conda-forge
conda activate easymode
pip install tensorflow==2.11.0
pip install git+https://github.com/mgflast/easymode.git

For full installation instructions including CUDA setup, Ais, and Pom, see the installation guide.

quick start

# List available models
easymode list

# Segment features
easymode segment ribosome microtubule --data warp_tiltseries/reconstruction

# Extract particle coordinates
easymode pick ribosome --data segmented/ --output coordinates/ribosome --size 2000000 --spacing 300

# Reconstruct tomograms (requires WarpTools and AreTomo3)
easymode reconstruct --frames frames/ --mdocs mdocs/ --apix 1.56 --dose 4.6

# Denoise tomograms
easymode denoise --method n2n --mode direct --data warp_tiltseries/reconstruction --output denoised

Pretrained networks are hosted on HuggingFace and downloaded automatically on first use. See the user guide for detailed usage.

training data

All networks were trained on a dataset of over 4000 tilt series from 50+ sources, covering many prokaryotic, archaeal, and eukaryotic species, different sample preparation techniques, hardware configurations, pixel sizes, electron doses, and defocus values. Tomograms were reconstructed in multiple flavours using WarpTools, AreTomo3, cryoCARE, and DeepDeWedge at 10.0 A voxel size. See the preprint for more details.

references

1. Tegunov et al., 'Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 A in cells', Nature Methods (2021): https://doi.org/10.1038/s41592-020-01054-7
2. Tegunov & Cramer, 'Real-time cryo-electron microscopy data preprocessing with Warp', Nature Methods (2019): https://doi.org/10.1038/s41592-019-0580-y
3. Peck et al., 'AreTomoLive: Automated reconstruction of comprehensively-corrected and denoised cryo-electron tomograms in real-time and at high throughput', bioRxiv (2025): https://doi.org/10.1101/2025.03.11.642690
4. Lamm et al., 'MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography', bioRxiv (2024): https://doi.org/10.1101/2024.01.05.574336
5. Burt et al., 'An image processing pipeline for electron cryo-tomography in RELION-5', FEBS Open Bio (2024): https://doi.org/10.1002/2211-5463.13873
6. Buchholz et al., 'Content-aware image restoration for electron microscopy', IEEE (2019): https://doi.org/10.1109/ISBI.2019.8759519
7. Wiedemann & Heckel, 'A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography', Nature Communications (2024): https://doi.org/10.1038/s41467-024-51438-y
8. Last et al., 'Streamlining segmentation of cryo-electron tomography datasets with Ais', eLife (2024): https://doi.org/10.7554/eLife.98552.3