https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Jin_Size 2026-05-24T21:07:06Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2024Jin_Size&diff=4676&oldid=prev 2024-08-13T06:23:03Z

<p>Created page with &quot;== Citation == Jin, Weisheng / Zhou, Ye / Bartesaghi, Alberto. Accurate size-based protein localization from cryo-ET tomograms. 2024. J. Structural Biology X, Vol. 10, p. 100104 == Abstract == Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an essential step in the cryo-ET/STA image analysis pipeline t...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Jin, Weisheng / Zhou, Ye / Bartesaghi, Alberto. Accurate size-based protein localization from cryo-ET tomograms. 2024. J. Structural Biology X, Vol. 10, p. 100104<br /> <br /> == Abstract ==<br /> <br /> Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of<br /> protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an<br /> essential step in the cryo-ET/STA image analysis pipeline that consists in locating the position of proteins within<br /> crowded cellular tomograms so that they can be aligned and averaged in 3D to improve resolution. While<br /> extensive work in 2D particle picking has been done in the context of single-particle cryo-EM, comparatively<br /> fewer strategies have been proposed to pick particles from 3D tomograms, in part due to the challenges associated<br /> with working with noisy 3D volumes affected by the missing wedge. While strategies based on 3D<br /> template-matching and deep learning are commonly used, these methods are computationally expensive and<br /> require either an external template or manual labelling which can bias the results and limit their applicability.<br /> Here, we propose a size-based method to pick particles from tomograms that is fast, accurate, and does not<br /> require external templates or user provided labels. We compare the performance of our approach against a<br /> commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection<br /> accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on nonspecialized<br /> CPU hardware. We demonstrate the effectiveness of our approach by automatically detecting particles<br /> from tomograms representing different types of samples and using these particles to determine the highresolution<br /> structures of ribosomes imaged in vitro and in situ.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S2590152424000096<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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