EMPIAR-10234 - Single particle cryoEM of mouse protocadherin gamma B6 [multiple data sets in MRC format]

EMPIAR-10234

Single particle cryoEM of mouse protocadherin gamma B6 [multiple data sets in MRC format]

Publication:

Visualization of clustered protocadherin neuronal self-recognition complexes

Brasch J, Goodman KM, Potter CS, Carragher B, Honig B, Shapiro L, Noble AJ, Mannepalli S, Bahna F, Rapp M, Dandey VP, Bepler T, Berger B, Maniatis T

Nature 569 (2019) 280-283

PMID: 30971825

DOI: 10.1038/s41586-019-1089-3

Related EMDB entry:

EMD-9197 EMD-9199 EMD-9198 EMD-9200

Deposited:

2018-10-04

Released:

2019-04-18

Last modified:

2022-01-31

Imageset size:

438.78 GB

Imageset DOI:

10.6019/EMPIAR-10234

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Micrograph frames

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

1138

Image size:

(3710, 3838)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(1.061, 1.061)

Details:

Frames and references.

Files:

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

2. Micrographs along with all other magnification images from the collection

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

1138

Image size:

(3710, 3838)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.061, 1.061)

Details:

Frame aligned with MotionCor2, with and without dose weighting. Also includes tilt-series from the same collection session.

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)