REPIC -- an ensemble learning methodology for cryo-EM particle picking

Abstract

Cryo-EM (cryogenic electron microscopy) particle identification from micrographs (i.e., picking) is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. Moreover, current computational methods ("pickers") identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REPIC, an ensemble learning methodology that uses multiple pickers to find consensus particles. REPIC identifies consensus particles by framing its task as a graph problem and using integer linear programming to select particles. REPIC picks high-quality particles when the best picker is not known a priori and for known difficult-to-pick particles (e.g., TRPV1). Reconstructions using consensus particles achieve resolutions comparable to those from particles picked by experts, without the need for downstream particle filtering. Overall, our results show REPIC requires minimal (often no) manual picking and significantly reduces the burden on cryo-EM users for picker selection and particle picking. Availabilityhttps://github.com/ccameron/REPIC