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000596161 1001_ $$0P:(DE-H253)PIP1094649$$aGenthe, Erik$$b0$$udesy
000596161 245__ $$aPickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms
000596161 260__ $$aSan Diego, Calif.$$bElsevier$$c2023
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000596161 520__ $$aParticle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram averaging (STA) pipelines. In this paper, we introduce a deep learning framework called PickYOLO to tackle this problem. PickYOLO is a super-fast, universal particle detector based on the deep-learning real-time object recognition system YOLO (You Only Look Once), and tested on single particles, filamentous structures, and membrane-embedded particles. After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles with high yield and reliability at a rate of 0.24–3.75 s per tomogram. PickYOLO can automatically detect number of particles comparable to those manually selected by experienced microscopists. This makes PickYOLO a valuable tool to substantially reduce the time and manual effort needed to analyse cryoET data for STA, greatly aiding in high-resolution cryoET structure determination.
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000596161 7001_ $$aMiletic, Sean$$b1
000596161 7001_ $$0P:(DE-H253)PIP1099839$$aTekkali, Indira$$b2$$udesy
000596161 7001_ $$0P:(DE-H253)PIP1099073$$aHennell James, Rory$$b3
000596161 7001_ $$0P:(DE-H253)PIP1021412$$aMarlovits, Thomas$$b4$$eCorresponding author
000596161 7001_ $$0P:(DE-H253)PIP1023889$$aHeuser, Philipp$$b5$$eCorresponding author$$udesy
000596161 773__ $$0PERI:(DE-600)1469822-5$$a10.1016/j.jsb.2023.107990$$gVol. 215, no. 3, p. 107990 -$$n3$$p107990$$tJournal of structural biology$$v215$$x1047-8477$$y2023
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