Automated cryoEM data acquisition and analysis of 284742 particles of GroEL.

One of the goals in developing our automated electron microscopy data acquisition system, Leginon, was to improve both the ease of use and the throughput of the process of acquiring low dose images of macromolecular specimens embedded in vitreous ice. In this article, we demonstrate the potential of the Leginon system for high-throughput data acquisition by describing an experiment in which we acquired images of more than 280,000 particles of GroEL in a single 25 h session at the microscope. We also demonstrate the potential for an automated pipeline for molecular microscopy by showing that these particles can be subjected to completely automated procedures to reconstruct a three-dimensional (3D) density map to a resolution better than 8 A.

ACE: automated CTF estimation.

We present a completely automated algorithm for estimating the parameters of the contrast transfer function (CTF) of a transmission electron microscope. The primary contribution of this paper is the determination of the astigmatism prior to the estimation of the CTF parameters. The CTF parameter estimation is then reduced to a 1D problem using elliptical averaging.

Detecting particles in cryo-EM micrographs using learned features.

A new learning-based approach is presented for particle detection in cryo-electron micrographs using the Adaboost learning algorithm. The approach builds directly on the successful detectors developed for the domain of face detection. It is a discriminative algorithm which learns important features of the particle's appearance using a set of training examples of the particles and a set of images that do not contain particles.

Automatic particle selection: results of a comparative study.

Manual selection of single particles in images acquired using cryo-electron microscopy (cryoEM) will become a significant bottleneck when datasets of a hundred thousand or even a million particles are required for structure determination at near atomic resolution. Algorithm development of fully automated particle selection is thus an important research objective in the cryoEM field. A number of research groups are making promising new advances in this area.