CASSPER

Cryo-EM Automatic Semantic Segmentation based Particle pickER

A Semantic Segmentation based Particle Picking Algorithm for Single Particle Cryo-Electron Microscopy

https://zenodo.org/badge/latestdoi/231048773

This repository contains the following:

1. CASSPER Labelling Tool

The Labelling Tool is used to generate segmented labels for fresh training of CASSPER. The tool and sample mrc files are given in the sub folder. All the labels used for the study is also provided. The instructions and Demo video for operating the labelling tool is also given in the subfolder.

2. CASSPER Training and Prediction code

The mrc files and segmented labels are needed for fresh training. The detailed description is given in the subfolder-Train_and_Predict

3. CASSPER Pre-trained weights

The TSaved folder containing the trained weights for different proteins obtained from CASSPER can be found here.
If prediction without training is to be done, the folder TSaved, containing the saved weights, should be added into the respective protein folder in Train_and_Predict folder.

Instructions on using CASSPER

The detailed instructions about labelling, training and prediction are mentioned in the README files of the respective subfolders. Instructions about labelling tool: https://github.com/airis4d/CASSPER/tree/master/Labelling_Tool and Training and prediction:https://github.com/airis4d/CASSPER/tree/master/Train_and_Predict

Setting up CASSPER

In Linux, just run setup.sh to set up the virtual environment for CASSPER. In that case, you may skip the following procedure.

CASSPER runs on Python 3.6+. We recommend running it from within a virtual environment.

Creating a virtual environment for CASSPER

Set up a virtual environment using pip and Virtualenv

If you are familiar with virtualenv, you can use it to create a virtual environment.

For Python 3.6, create a new environment with your preferred virtualenv wrapper, for example:

• virtualenvwrapper (Bourne-shells)
• virtualfish (fish-shell)

Either follow instructions here or install via pip.

$ pip install virtualenv

Then, create a virtualenv environment by creating a new directory for a Python 3.6 virtualenv environment

$ virtualenv --python=python3.6 cassper

where python3.6 is a valid reference to a Python 3.6 executable.

Activate the environment

$ source cassper/bin/activate

Install the required python packages

Note: make sure that the environment is activated throughout the installation process. When you are done, deactivate it using source deactivate, or deactivate depending on your version.

In the project root directory, run the following to install the required packages. Note that this commands installs the packages within the activated virtual environment.

$ pip install -r requirements.txt

Running CASSPER

Please remember to activate this virtual environment each time you run the codes and run the codes from respective sub-directories itself.

Publication

This folder contains Particle stacks ( in .star format) obtained using crYOLO, CASSPER and Gautomatch for four proteins discussed in the paper https://www.nature.com/articles/s42003-021-01721-1. The 2D images used for 3D reconstruction is also marked and shown for all cases in the folder Publication/2D_images