EMPIAR-10753 - Tilt-series taken from SARS-CoV-2 infected Vero cells. [multiple data sets in MRC format]

EMPIAR-10753

Tilt-series taken from SARS-CoV-2 infected Vero cells. [multiple data sets in MRC format]

Publication:

Correlative multi-scale cryo-imaging unveils SARS-CoV-2 assembly and egress

Mendonça L, Howe A, Gilchrist JB, Sheng Y, Sun D, Knight ML, Zanetti-Domingues LC, Bateman B, Krebs AS, Chen L, Radecke J, Li VD, Ni T, Kounatidis I, Koronfel MA, Szynkiewicz M, Harkiolaki M, Martin-Fernandez ML, James W, Zhang P

Nature communications 12 (2021)

PMID: 34330917

DOI: 10.1038/s41467-021-24887-y

Related EMDB entry:

EMD-12991 EMD-12992

Deposited:

2021-07-07

Released:

2021-07-23

Last modified:

2022-02-25

Imageset size:

52.66 GB

Imageset DOI:

10.6019/EMPIAR-10753

Experimental metadata:

Download xml json

Contains:

tilt series

Image Sets

1. Tilt-series taken on the periphery of SARS-CoV-2 infected Vero cells

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(4091, 5759)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.63, 1.63)

Details:

Tilt-series aligned in IMOD from motion-corrected sums (MotionCor2). Corresponding angles are provided (.tlt files).

Files:

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2. Tilt-series taken on cryolamella of SARS-CoV-2 infected Vero cells

Category:

tilt series

Image format:

MRC

No. of images or tilt series:

Image size:

(5759, 4091)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(2.13, 2.13)

Details:

Tilt-series aligned in IMOD from motion-corrected sums (MotionCor2). Corresponding angles are provided (.tlt files).

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)

Browse All Files

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Citations

AlignPCA-2D: PCA-reduced Euclidean vector alignment for 2D classification in cryo-EM.

Ramírez-Aportela E, Zarrabeitia OL, Fonseca YC, Ceska T, Subramaniam S, Carazo JM, Sorzano COS. (2026)

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Yu C, Xu Z, Zeng Q, Wan X, El-Messiry H, Zhang F, Han R. (2026)

Helical reconstruction of amyloids in cryoSPARC.

Schaefer JH, O'Neill RT, Donnelly JP, Powers ET, Kelly JW, Lander GC. (2026)

An unattended image-processing pipeline for on-the-fly quality assessment and 3D exploration in cryo-EM.

Marchán Torres D, Conesa P, Garcia A, Iceta M, Broche L, Gragera M, Linares R, Kwong HS, Chichón FJ, Svensson O, Arranz R, Effantin G, Kandiah E, Carazo JM, Sorzano COS. (2026)

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Poudel B, Gyawali R, Dhakal A, Cheng J, Xu D. (2026)

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)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (2026)

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

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

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