EMD-14426
Subtomogram average of well-aligned 80S ribosomes from tomograms acquired on cryo-FIB-lamellae of S. pombe
EMD-14426
Subtomogram averaging9.4 Å
![EMD-14426](https://www.ebi.ac.uk/emdb/images/entry/EMD-14426/400_14426.gif)
Map released: 16/11/2022
Last modified: 13/12/2023
Sample Organism:
Schizosaccharomyces pombe
Sample: 80S ribosome inside S. pombe cells
Raw data: EMPIAR-10988
Deposition Authors: Mahamid J
,
Goetz SK
Sample: 80S ribosome inside S. pombe cells
Raw data: EMPIAR-10988
Deposition Authors: Mahamid J
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Convolutional networks for supervised mining of molecular patterns within cellular context.
de Teresa-Trueba I
,
Goetz SK
,
Mattausch A
,
Stojanovska F
,
Zimmerli CE
,
Toro-Nahuelpan M
,
Cheng DWC
,
Tollervey F,
Pape C
,
Beck M
,
Diz-Munoz A
,
Kreshuk A
,
Mahamid J
,
Zaugg JB
(2023) Nat Methods , 20 , 284 - 294
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(2023) Nat Methods , 20 , 284 - 294
Abstract:
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.