EMPIAR-10093 - Structure of an ion channel in nano disc [multiple data sets in MRC and MRCS formats]

EMPIAR-10093

Structure of an ion channel in nano disc [multiple data sets in MRC and MRCS formats]

Publication:

Electron cryo-microscopy structure of the mechanotransduction channel NOMPC

Jin P, Bulkley D, Guo Y, Zhang W, Guo Z, Huynh W, Wu S, Meltzer S, Cheng T, Jan LY, Jan YN, Cheng Y

Nature 547 (2017) 118-122

PMID: 28658211

DOI: 10.1038/nature22981

Related EMDB entry:

EMD-8702

Deposited:

2017-04-27

Released:

2017-06-28

Last modified:

2022-01-14

Imageset size:

203.90 GB

Imageset DOI:

10.6019/EMPIAR-10093

Experimental metadata:

Download xml json

Contains:

micrographs - single frame
picked particles - multiframe - processed

Image Sets

1. Motion corrected dose weighted summed frames

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

1873

Image size:

(3838, 3710)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.2156, 1.2156)

Details:

---

Files:

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2. Motion corrected dose weighted particle images

Category:

picked particles - multiframe - processed

Image format:

MRCS

No. of images or tilt series:

175314

Image size:

(400, 400)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.2156, 1.2156)

Details:

Particle stack, Class12456-merge.mrcs, and associated star file, Class12456-merge_onestack.star

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)