EMPIAR-10129 - CryoET of hemagglutinin single particle with spot-to-plunge time of 800ms [multiple data sets in MRC format]

EMPIAR-10129

CryoET of hemagglutinin single particle with spot-to-plunge time of 800ms [multiple data sets in MRC format]

Publication:

Routine single particle CryoEM sample and grid characterization by tomography

Scapin G, Rapp M, Eng ET

eLife 7 (2018)

PMID: 29809143

DOI: 10.7554/eLife.34257

Related EMDB entry:

EMD-7135

Deposited:

2017-12-05

Released:

2017-12-18

Last modified:

2022-07-14

Imageset size:

33.98 GB

Imageset DOI:

10.6019/EMPIAR-10129

Experimental metadata:

Download xml json

Version history:

2018-04-04

REL

Updated the entry title with additional information.

Contains:

tilt series

Image Sets

1. SerialEM tilt-series stacks (no frame alignment) and mdocs

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(3712, 4984)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(2.34, 2.34)

Details:

---

Files:

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2. Appion-Protomo tilt-series alignments

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(3712, 4984)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(2.34, 2.34)

Details:

Includes a full tilt-series alignment run for each tilt-series. To view properly, navigate to the protomo_alignments folder in Appion-Protomo.

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

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)

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)

Ultrathin liquid cells for microsecond time-resolved cryo-EM.

Curtis WA, Wenz J, Krüger CR, Barrass SV, Drabbels M, Lorenz UJ. (2026)

GTpick: A deep neural network for Cryo-EM particle detection.

Ni S, Yang C, Liu Y, Zhang Y, Shi Y, Qian A, Kong R, Chang S. (2025)

Species-specific structural adaptation of the potyviral coat protein in virions and virus-like particles.

Koritnik N, Kežar A, Kavčič L, Žnidarič MT, Leonardi A, De S, Pollari M, Mäkinen K, Podobnik M. (2026)

High-resolution ab initio reconstruction enables cryo-EM structure determination of small particles

Kim K, Li H, Clarke OB. (2025)

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.

Krentzel D, Elphick M, Domart MC, Peddie CJ, Laine RF, Shand C, Henriques R, Collinson LM, Jones ML. (2025)

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.

Arora S, Pan SH, Kumar S, Singh SK, Chauhan U, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Chui KT, Gupta BB. (2025)

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