Contaminant detection: improving template matching based particle selection for cryoelectron microscopy

Cryoelectron microscopy (cryoEM) has emerged as a powerful technique for structure determination of macromolecular assemblies (particles) in cell biology and is in great need of automation because it is both labor-intensive and time-consuming. One of the key steps in the automation is the selection of large numbers of individual particles in the cryoEM-acquired images. Due to the fact that images of this type usually have a very low signal-to-noise ratio, the current prevailing methods for particle selection are based on template matching, namely searching for peaks in the space formed by cross-correlating one or multiple reference templates with an entire image. Such methods generally require a second stage of human screening to remove false positives due to contaminants. This paper presents advancement in eliminating the human screening through automatic contaminant detection. Results of experimenting with both ribosome and GroEL particles demonstrate that on average the false positive rates can be reduced by about 6% using the proposed approach.