Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms-Reference-Cited by-同舟云学术

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Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms

Published:2021-10-21 Issue:11 Volume:18 Page:1386-1394
ISSN:1548-7091
Container-title:Nature Methods
language:en
Short-container-title:Nat Methods

Author:

Moebel Emmanuel,Martinez-Sanchez Antonio^ORCID,Lamm Lorenz,Righetto Ricardo D.,Wietrzynski Wojciech,Albert Sahradha,Larivière Damien,Fourmentin Eric^ORCID,Pfeffer Stefan^ORCID,Ortiz Julio,Baumeister Wolfgang^ORCID,Peng Tingying,Engel Benjamin D.^ORCID,Kervrann Charles^ORCID

Funder

Agence Nationale de la Recherche

Fourmentin-Guilbert Foundation Région Bretagne

Fourmentin-Guilbert Foundation Region Bretagne

Deutsche Forschungsgemeinschaft

German Research Foundation (DFG) The Munich School for Data Science (MUDS) Helmholtz Association

Publisher

Springer Science and Business Media LLC

Subject

Cell Biology,Molecular Biology,Biochemistry,Biotechnology

Link

https://www.nature.com/articles/s41592-021-01275-4.pdf

Reference62 articles.

1. Schaffer, M. et al. Optimized cryo-focused ion beam sample preparation aimed at in situ structural studies of membrane proteins. J. Struct. Biol. 197, 73–82 (2017).

2. Frank, J. Approaches to large-scale structures. Curr. Opin. Struct. Biol. 5, 194–201 (1995).

3. McEwen, B., Renken, C., Marko, M. & Mannella, C. Principles and practice in electron tomography. Methods Cell Biol. 89, 129–168 (2008).

4. McIntosh, R., Nicastro, D. & Mastronarde, D. New views of cells in 3D: an introduction to electron tomography. Trends Cell Biol. 15, 43–51 (2005).

5. Nicastro, D., Frangakis, A., Typke, D. & Baumeister, W. Cryo-electron tomography of neurospora mitochondria. J. Struct. Biol. 129, 48–56 (2000).

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