Signal enhancement for two-dimensional cryo-EM data processing

TL;DR

This paper presents an efficient algorithm for enhancing cryo-EM images, improving downstream analysis tasks and enabling the construction of initial models at near-atomic resolution.

Contribution

The authors introduce a novel, computationally efficient signal enhancement algorithm with built-in quality measures for cryo-EM data processing.

Findings

Enhanced images enable ab initio modeling at ~10 Å resolution

Algorithm improves data quality for various downstream tasks

Code is publicly available and easy to use

Abstract

Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as 2-D classification, removing uninformative images, constructing {ab initio} models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to assess its performance and alleviate the risk of model bias. We demonstrate the effectiveness of the proposed algorithm on several experimental data sets. In particular, we show that the quality of the resulting images is high enough to produce ab initio models of $\sim 10$ \AA resolution. The algorithm is accompanied by a publicly available, documented and easy-to-use code.