MC-Bayes: A Python-based wrapper for MotionCor3 processing of EER files compatible with Bayesian polishing

Cryo-electron microscopy (cryo-EM) often uses EER (electron event representation) recordings, and Bayesian polishing (reference-based motion correction) is a critical step for achieving high-resolution structures. Until now, performing Bayesian polishing on EER data required the CPU-based RELIONCor implementation of MotionCor2, which is sub-optimal for GPU-heavy processing systems with limited CPU cores or RAM.

MC-Bayes is a Python-based wrapper that processes EER movies using MotionCor3 on one or more GPUs and automatically generates the .star files RELION needs to run Bayesian polishing. The authors report that MC-Bayes completes motion correction two or more times faster than the RELION CPU implementation in typical GPU-equipped environments, except when dozens or hundreds of CPU cores and large amounts of system RAM are available.

This tool is designed for cryo-EM facilities and users who have powerful GPUs but limited CPU resources. By leveraging MotionCor3, MC-Bayes offers a user-friendly, faster pathway to Bayesian polishing of EER data, potentially streamlining high-resolution structural biology workflows.