Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches
- A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled ”DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography” paper.
Author: | Noushin Hajarolasvadi, Daniel Baum |
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Document Type: | Research data |
Date of first Publication: | 2024/07/09 |
Date data created: | 2024-07-08 |
Download Url: | https://www.zib.de/ext-data/PolNet_Medium_Size_Dataset_4v4r_and_3j9i.zip |
DOI: | https://doi.org/10.12752/9686 |
Licence (German): | ![]() |