Update on Mar.27 2024
Single Particle spIsoNet (spIsoNet) is designed to correct for the preferred orientation problem in cryoEM by self-supervised deep learning, by recovering missing information from well-sampled orientations in Fourier space.
Unlike conventional supervised deep learning methods that need explicit input-output pairs for training, spIsoNet autonomously extracts supervisory signals from the original data, ensuring the reliability of the information used for training.
spIsoNet is designed for single particle analysis and subtomogram averaging. For the correcting missing wedge in cryoET, please refer to IsoNet.
Please find tutorial/spIsoNet_v1.0_Tutorial.md for detailed document.
We maintain an spIsoNet Google group for discussions or news.
To subscribe or visit the group via the web interface please visit https://groups.google.com/u/1/g/spisonet.
To post to the forum you can either use the web interface or email to [email protected]
We suggest using anaconda environment to manage the spIsoNet package. The installation involves 4 steps: 1. Install anaconda and create a conda environment. 2. Install cuda and pytorch. 3. Install spIsoNet and dependencies. 4. For Misalignment correction, setup RELION_EXTERNAL_RECONSTRUCT_EXECUTABLE and CONDA_ENV environment variable
Example commands to install spIsoNet
Option 1:
conda create -n spisonet python=3.10
conda activate spisonet
pip install torch --index-url https://download.pytorch.org/whl/cu118
cd <path to spIsoNet>
pip install .
and then set the following environment variable for Misalignment Correction
export RELION_EXTERNAL_RECONSTRUCT_EXECUTABLE="python <path to spIsoNet>/spIsoNet/bin/relion_wrapper.py"
export CONDA_ENV="spisonet"
Option 2:
conda env create -f setup.yml
conda activate spisonet
and then set the following environment variable for Misalignment Correction
export RELION_EXTERNAL_RECONSTRUCT_EXECUTABLE="python <path to spIsoNet>/spIsoNet/bin/relion_wrapper.py"
export CONDA_ENV="spisonet"
Option 3:
conda create -n spisonet python=3.10
conda activate spisonet
conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
and then set the following environment variable for Misalignment Correction
export RELION_EXTERNAL_RECONSTRUCT_EXECUTABLE="python <path to spIsoNet>/spIsoNet/bin/relion_wrapper.py"
export CONDA_ENV="spisonet"
export PATH=<path to spIsoNet>/spIsoNet/bin:$PATH
export PYTHONPATH=<path to spIsoNet>:$PYTHONPATH
The environment we verified are:
- cuda11.8 cudnn8.5 pytorch2.0.1, pytorch installed with pip.
- cuda11.3 cudnn8.2 pytorch1.13.1, pytorch installed with conda.
