EMPIAR-12116 - Cas12k-sgRNA-dsDNA-S15-TniQ-TnsC transposon recruitment complex [multiple data sets in TIFF and DM4 formats]

EMPIAR-12116

Cas12k-sgRNA-dsDNA-S15-TniQ-TnsC transposon recruitment complex [multiple data sets in TIFF and DM4 formats]

Publication:

Structural basis for the assembly of the type V CRISPR-associated transposon complex

Schmitz M, Querques I, Oberli S, Chanez C, Jinek M

Cell 185 (2022) 4999-5010.e17

PMID: 36435179

DOI: 10.1016/j.cell.2022.11.009

Related EMDB entry:

EMD-15975 EMD-15976

Deposited:

2024-02-05

Released:

2025-02-04

Last modified:

2025-02-04

Imageset size:

4.45 TB

Imageset DOI:

10.6019/EMPIAR-12116

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Unaligned multi-frame micrographs of the Cas12k-sgRNA-dsDNA-S15-TniQ-TnsC transposon recruitment complex

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

16166

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.325, 0.325)

Details:

The micrographs are the 36 raw movie frames (accumulating 67.68 electrons per squared Angstrom in a 1.0 second exposure).

Files:

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2. Gain reference

Category:

micrographs - single frame

Image format:

DM4

No. of images or tilt series:

Image size:

(11520, 8184)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.325, 0.325)

Details:

Gain reference

Files:

This entry cannot be downloaded from PDBj.

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

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)

CryoWriter: a robotic solution for improved Cryo-EM grid preparation.

Kv C, Ekundayo B, Di Fabrizio M, Mohammed I, Radecke J, Stahlberg H, Kube M. (2026)