Speaker
Description
The growth of cryo-EM into a mainstream structural biology tool has led to its widespread adoption for users across a range of expertise, where experts represents a small fraction of cryo-EM users. Considering the manual and subjective decisions involved in solving a structure, such as the programs, parameters and determination of good micrographs and good 2D class averages, cryo-EM frustrates many users. To make cryo-EM data processing more user-friendly, we have developed an automated pipeline for cryo-EM data preprocessing and assessment using a combination of deep learning and image analysis tools to help streamline the process of cryo-EM structure determination. Specifically, we have built a deep-learning based generic micrograph classifier that can assess the quality of a micrograph with an accuracy of 96% allowing bad micrographs to be removed without user decision. We have also built a 2D class average classifier that can identify the good 2D class averages from RELION and help to find the optimal parameters in 2D classification. We have verified the performance of our pipeline on multiple datasets including both EMPIAR and real-world datasets. We propose that our automatic pipeline will make cryo-EM preprocessing more convenient for cryo-EM users from a range of backgrounds.
