EMPIAR-11064 - Cryo-EM structure of hnRNPDL amyloid fibrils [multiple data sets in TIFF format]

EMPIAR-11064

Cryo-EM structure of hnRNPDL amyloid fibrils [multiple data sets in TIFF format]

Publication:

Cryo-EM structure of hnRNPDL amyloid fibrils

Chaves-Sanjuan A, Garcia-Pardo J, Bartolome-Nafria A, Gil-Garcia M, Visentin C, Bolognesi M, Ricagno S, Ventura S

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Related EMDB entry:

EMD-14738

Deposited:

2022-04-08

Released:

2023-01-03

Last modified:

2023-01-12

Imageset size:

880.13 GB

Imageset DOI:

10.6019/EMPIAR-11064

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Unaligned multi-frame movies of hnRNPDL amyloid fibrils

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

1114

Image size:

(4096, 4096)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(0.889, 0.889)

Details:

---

Files:

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2. Mototion corrected and dose weighted single frame micrographs of hnRNPDL amyloid fibrils

Category:

micrographs - single frame

Image format:

TIFF

No. of images or tilt series:

1114

Image size:

(4096, 4096)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.889, 0.889)

Details:

Particle position correspond to start-to-end fibril coordinates

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

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)