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

Wagner T, Merino F, Stabrin M, Moriya T, Antoni C, Apelbaum A, Hagel P, Sitsel O, Raisch T, Prumbaum D, Quentin D, Roderer D, Tacke S, Siebolds B, Schubert E, Shaikh TR, Lill P, Gatsogiannis C, Raunser S

Forschungsartikel (Zeitschrift) | Peer reviewed

Zusammenfassung

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.

Details zur Publikation

Jahrgang / Bandnr. / Volume2
StatusVeröffentlicht
Veröffentlichungsjahr2019
Sprache, in der die Publikation verfasst istEnglisch
DOI10.1038/s42003-019-0437-z
StichwörterCryoelectron Microscopy/methods; Datasets as Topic; Deep Learning; Image Processing; Computer-Assisted/methods; Neural Networks; Computer; Software

Autor*innen der Universität Münster

Gatsogiannis, Christos
Institut für Medizinische Physik und Biophysik
Center for Soft Nanoscience (SoN)