SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM

TLDR

Selecting particles from digital micrographs is essential in cryo‑EM, yet manual selection is tedious and time‑consuming; many automatic pickers exist, but non‑ideal datasets still pose a challenge. The authors present crYOLO, a deep‑learning particle picker based on YOLO, including a general network that can automatically pick from unseen datasets for on‑the‑fly preprocessing. crYOLO employs the YOLO framework, is trained on 200–2500 particles per dataset, and is distributed as a standalone program and within the SPHIRE workflow. After training, the network achieves high recall and precision while processing up to five micrographs per second.

Abstract

Abstract Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets—typically comprising thousands of particles—is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200–2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.