Cryo-electron tomography: Challenges and computational strategies for particle picking

Abstract

Cryo-electron tomography (cryo-ET) and subtomogram averaging have emerged as powerful techniques for investigating cellular structures and their spatial organization. However, the exact localization of proteins in the crowded and noisy environment of cellular tomograms is challenging. This review provides a comprehensive overview of existing deep learning-based particle-picking procedures, which were proposed to overcome these challenges. We evaluate both annotation-based and annotation-free methods, highlighting their respective strengths, weaknesses, and ideal use cases. Furthermore, we assess these methodologies based on various criteria, such as the effort required to generate the necessary input data, inference runtime, and filament support. Additionally, we consider practical factors such as the availability of documentation and tutorials to guide researchers in selecting the most appropriate approach for their needs.