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

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)