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

Thorsten Wagner , Felipe Merino , Markus Stabrin , Thorsten Wagner , Felipe Merino , Markus Stabrin , Toshio Moriya , Claudia Antoni , Amir Apelbaum , Philine Hagel , Oleg Sitsel , Tobias Raisch , Daniel Prumbaum , Dennis Quentin , Daniel Roderer , Sebastian Tacke , Birte Siebolds , Evelyn Schubert , Tanvir R. Shaikh , Pascal Lill , Christos Gatsogiannis , Stefan Raunser
2019 Communications Biology 1,336 citations

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.

Keywords

Computer scienceArtificial intelligencePreprocessorParticle (ecology)SoftwareProcess (computing)WorkflowSingle particle analysisCryo-electron microscopyComputer visionPrecision and recallPattern recognition (psychology)DatabasePhysics

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Year
2019
Type
article
Volume
2
Issue
1
Pages
218-218
Citations
1336
Access
Closed

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Thorsten Wagner, Felipe Merino, Markus Stabrin et al. (2019). SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Communications Biology , 2 (1) , 218-218. https://doi.org/10.1038/s42003-019-0437-z

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DOI
10.1038/s42003-019-0437-z