The evolution of SPHIRE-crYOLO particle picking and its application in automated cryo-EM processing workflows
Thorsten Wagner, Stefan Raunser
Abstract
Particle selection is a crucial step when processing electron cryo microscopy data. Several automated particle picking procedures were developed in the past but most struggle with non-ideal data sets. In our recent Communications Biology article, we presented crYOLO, a deep learning based particle picking program. It enables fast, automated particle picking at human levels of accuracy with low effort. A general model allows the use of crYOLO for selecting particles in previously unseen data sets without further training. Here we describe how crYOLO has evolved since its initial release. We have introduced filament picking, a new denoising technique, and a new graphical user interface. Moreover, we outline its usage in automated processing pipelines, which is an important advancement on the horizon of the field. Wagner and Raunser recently presented a deep learning based particle picking program for Cryo-EM, crYOLO. Here they discuss recent improvements to the program, a graphical user interface and share their thoughts on desired future developments.