Tools for Analysis of Large-Scale Cryo-EM and Cryo-ET Data

Abstract

Cryo-electron microscopy (cryo-EM) is a powerful technique for visualizing the structure and dynamics of macromolecules in near-physiological conditions. Owing to cryo-EM's ability to image biomolecules in varying conformations, methods are increasingly being developed to reconstruct heterogeneous ensembles of 3D biomolecular structures from 2D cryo-EM image datasets. However, the field lacks standardized benchmarks for quantitative evaluation of these methods. We present CryoBench, a set of challenging synthetic cryo-EM datasets and novel metrics for the evaluation of heterogeneous reconstruction methods, along with benchmarking analysis of existing state-of-the-art methods. Next, we explore the semantic segmentation task for cryo-electron tomography (cryo-ET), a related biophysical technique for visualizing macromolecules within their cellular environments. We quantitatively benchmark prevailing supervised training approaches on two datasets, while exploring work towards more generalizable segmentation of large-scale cryo-ET data given only sparse manual annotations.