EMPIAR-12488 - Cryo-EM structure of wheat ribosome reveals unique features of the plant ribosomes [8323 multi-frame micrographs composed of 30 frames each in MRC format]

EMPIAR-12488

Cryo-EM structure of wheat ribosome reveals unique features of the plant ribosomes [8323 multi-frame micrographs composed of 30 frames each in MRC format]

Publication:

Cryo-EM structure of wheat ribosome reveals unique features of the plant ribosomes

Mishra RK, Sharma P, Khaja FT, Uday AB, Hussain T

Structure (London, England : 1993) 32 (2024) 562-574.e3

PMID: 38458197

DOI: 10.1016/j.str.2024.02.006

Related EMDB entry:

EMD-36331

Deposited:

2024-10-05

Released:

2025-06-13

Last modified:

2025-06-13

Imageset size:

4.60 TB

Imageset DOI:

10.6019/EMPIAR-12488

Experimental metadata:

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Compressed by PDBj:

Image formats and pixel types shown below are those of the original, non-compressed files.

MRC movies (data/Movies/FoilHole_*_Fractions.mrc) in this entry were compressed by bzip2 (pbzip2)
by EMPIAR Japan to save space and bandwidth.

Some programs (e.g. RELION >= 4.0.1 and CryoSPARC) can process compressed movies without explicit
decompression. For other programs, movies have to be decompressed beforehand. In this case,
you might want to convert decompressed MRC movies into TIFF to save your local storage.

Original size:   8,181,850,251 kilobytes
Compressed size: 4,937,792,183 kilobytes

Contains:

micrographs - multiframe

Image Sets

1. wheat ribosome 60S subunit micrographs

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

8323

Image size:

(None, None)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(None, None)

Details:

---

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)