EMPIAR-10670 - Cryo-ET detects bundled triple helices but not ladders in meiotic budding yeast [multiple data sets in MRC format]

EMPIAR-10670

Cryo-ET detects bundled triple helices but not ladders in meiotic budding yeast [multiple data sets in MRC format]

Publication:

Cryo-ET detects bundled triple helices but not ladders in meiotic budding yeast

Ma OX, Chong WG, Lee JKE, Cai S, Siebert CA, Howe A, Zhang P, Shi J, Surana U, Gan L

PloS one 17 (2022)

PMID: 35421110

DOI: 10.1371/journal.pone.0266035

Related EMDB entry:

EMD-31081

Deposited:

2021-02-22

Released:

2024-01-11

Last modified:

2024-01-11

Imageset size:

291.64 GB

Imageset DOI:

10.6019/EMPIAR-10670

Experimental metadata:

Download xml json

Contains:

tilt series
subtomograms

Image Sets

1. Tilt series of yeast cells

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(4096, 4096)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(None, None)

Details:

---

Files:

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2. Tomograms of yeast cells

Category:

subtomograms

Image format:

MRC

No. of images or tilt series:

Image size:

(None, None)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(None, None)

Details:

---

Files:

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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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)