Overview

Relion (Regularized Likelihood Optimization for Electron Microscopy) is a stand-alone computer program that employs an empirical Bayesian approach to refinement of (multiple) 3D reconstructions or 2D class averages in electron microscopy. This page provides a guide on how to run Relion on the ACCRE cluster at Vanderbilt University.

Prerequisites

Before running Relion on ACCRE, ensure you have:

An active ACCRE account (See Instructions) Access to CSB GPUs (Check by typing $id in the ACCRE command prompt) Familiarity with Linux commands and ACCRE’s batch job submission system, SLURM Electron microscopy data for processing

Setting up the Environment

Log into your ACCRE account. Source the sbgrid environment to access Relion:

source /programs/sbgrid.shrc

Verify the availability of Relion using:

which relion

Instructions

If you do not have an account, you can apply for one at ACCRE
Instructions for requesting a new CSB/Mchaourab account (New Users):
- Open a web browser to: New CSB User using login: sbrequest password: welcomevsb
- Fill out the form completely. Request a password that is NOT the same as your Vanderbilt University e-password. Leave the shell as tcsh.
- Under the Email section fill in item #1 with your Vanderbilt email address, leave #2 blank.
- For the associated lab, choose ‘Mchaourab’ in the drop down box. If you have an office address and phone # please put that information into the fields. The home phone # is optional.
- Click ‘Continue’ at bottom.
- Check your information and once verified, click ‘Submit’ to send this.

Prepare your data as per the requirements of the specific Relion process you intend to run. Refer to the Relion Single Particle Tutorial for guidance on data preparation. Use the SBATCH script template provided below, modifying it according to your specific needs (like job name, number of GPUs, memory requirements, etc.). Save the script to a file on the ACCRE cluster. Submit the job to SLURM using the command:

sbatch <your_script_name>

Monitor the job status with:

squeue -u <your_username>

Sample SBATCH Script for Relion

#!/bin/bash

#SBATCH --partition=pascal # GPU partition
#SBATCH --account=csb_gpu_acc # GPU group
#SBATCH --gres=gpu:4 # Number of GPUs per node
#SBATCH --nodes=4 # Number of nodes
#SBATCH --ntasks=5 # Task allocation

#SBATCH --time=0-80:00:00
#SBATCH --mem=200G
#SBATCH -J [Job Name]
#SBATCH --error=[Error File]
#SBATCH --output=[Output File]

#SBATCH --no-requeue # Prevents restart after node failure

echo "Starting at `date`"
echo "Job name: $SLURM_JOB_NAME JobID: $SLURM_JOB_ID"
echo "Running on hosts: $SLURM_NODELIST"
echo "Running on $SLURM_NNODES nodes."
echo "Running on $SLURM_NPROCS processors."
echo "Current working directory is `pwd`"

# Your commands go here
mpirun [Your Relion Command]

echo "Program finished with exit code $? at: `date`"

Using Relion for Specific Processes

Follow the specific guidelines provided in the Relion documentation for different types of analyses such as 3D classification, refinement, etc. Ensure that the SBATCH script is appropriately modified to accommodate the specific requirements of these processes.

Monitoring the Job

Check the status of your job using the command:

squeue -u [your_username]

Conclusion

This guide provides a basic framework for running Relion on the ACCRE cluster. For more detailed instructions or troubleshooting, refer to the Relion documentation or contact the ACCRE support team.

Additional Resources

Relion Official Documentation

Relion Version History

Relion 1.0 - Initial release. Introduced a new approach to EM data processing based on a Bayesian inference framework, allowing for accurate 3D reconstruction and classification of images.

Relion 2.0 - Significant improvements in speed and efficiency. Introduced new algorithms for faster convergence in 3D classification and auto-refinement, as well as support for multi-body refinement allowing for better handling of flexible structures.

Relion 3.0 - Major update with GPU acceleration, drastically reducing computation times. Introduced new features like multi-class 3D refinement and expanded Bayesian polishing for improved particle motion correction.

Relion 3.1 - Enhanced GPU support for improved performance. Added new functionalities such as direct detector frame alignment and CTF refinement, providing more accurate reconstructions.

Relion 4.0 - Latest major release. Features include improvements in helical processing, support for multi-scale 3D classification, and enhanced user interface for better workflow management. Also, introduces new algorithms for more efficient handling of very large datasets.

Various minor updates and bug fixes in between these major releases have continuously improved the software's stability, usability, and processing capabilities.

This document has been prepared to assist users with running Relion on the ACCRE cluster at Vanderbilt University.

RELION on ACCRE

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