PT  - JOURNAL ARTICLE
AU  - Thorsten Wagner
AU  - Felipe Merino
AU  - Markus Stabrin
AU  - Toshio Moriya
AU  - Claudia Antoni
AU  - Amir Apelbaum
AU  - Philine Hagel
AU  - Oleg Sitsel
AU  - Tobias Raisch
AU  - Daniel Prumbaum
AU  - Dennis Quentin
AU  - Daniel Roderer
AU  - Sebastian Tacke
AU  - Birte Siebolds
AU  - Evelyn Schubert
AU  - Tanvir R. Shaikh
AU  - Pascal Lill
AU  - Christos Gatsogiannis
AU  - Stefan Raunser
TI  - SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EM
AID  - 10.1101/356584
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 356584
4099  - http://biorxiv.org/content/early/2019/03/13/356584.short
4100  - http://biorxiv.org/content/early/2019/03/13/356584.full
AB  - Selecting particles from digital micrographs is an essential step in single particle electron cryomicroscopy (cryo-EM). Since manual selection of complete datasets typically comprising many thousands of particles is a tedious and time-consuming process, many automatic particle pickers have been developed in the past few decades. However, non-ideal datasets pose a challenge to particle picking. Here, we present a novel automated particle picking software called crYOLO, which is based on the deep learning object detection system “You Only Look Once” (YOLO). After training the network with 500 – 2,500 particles per dataset, it automatically recognizes particles with high recall and precision reaching a speed of up to five micrographs per second. Importantly, we demonstrate a powerful general network trained on more than 40 datasets to select previously unseen datasets, thus paving the way for completely automated “on-the-fly” cryo-EM data pre-processing during data acquisition. CrYOLO is available as a standalone program under http://sphire.mpg.de/ and will be part of the image processing workflow in SPHIRE.