Dataset Information

EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing.

ABSTRACT:

Summary

The Electron Microscopy Hole Punch (EMHP) is a streamlined suite of tools for quick assessment, sorting and hole masking of electron micrographs. With recent advances in single-particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of protein structures using a smaller computational footprint, we saw the need for a fast and simple tool for data pre-processing that could run independent of existing high-performance computing (HPC) infrastructures. EMHP provides a data preprocessing platform in a small package that requires minimal python dependencies to function.

Availability and implementation

https://www.bitbucket.org/chazbot/emhp Apache 2.0 License.

Contact

bowman@scripps.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Berndsen Z

PROVIDER: S-EPMC5860320 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing.

Berndsen Zachary Z Bowman Charles C Jang Haerin H Ward Andrew B AB

Bioinformatics (Oxford, England) 20171201 23

<h4>Summary</h4>The Electron Microscopy Hole Punch (EMHP) is a streamlined suite of tools for quick assessment, sorting and hole masking of electron micrographs. With recent advances in single-particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of protein structures using a smaller computational footprint, we saw the need for a fast and simple tool for data pre-processing that could run independent of existing high-performance computing (HPC) infrastr ...[more]

PMID: 28961740

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Project description:BackgroundCryo-EM data generated by electron tomography (ET) contains images for individual protein particles in different orientations and tilted angles. Individual cryo-EM particles can be aligned to reconstruct a 3D density map of a protein structure. However, low contrast and high noise in particle images make it challenging to build 3D density maps at intermediate to high resolution (1-3 Å). To overcome this problem, we propose a fully automated cryo-EM 3D density map reconstruction approach based on deep learning particle picking.ResultsA perfect 2D particle mask is fully automatically generated for every single particle. Then, it uses a computer vision image alignment algorithm (image registration) to fully automatically align the particle masks. It calculates the difference of the particle image orientation angles to align the original particle image. Finally, it reconstructs a localized 3D density map between every two single-particle images that have the largest number of corresponding features. The localized 3D density maps are then averaged to reconstruct a final 3D density map. The constructed 3D density map results illustrate the potential to determine the structures of the molecules using a few samples of good particles. Also, using the localized particle samples (with no background) to generate the localized 3D density maps can improve the process of the resolution evaluation in experimental maps of cryo-EM. Tested on two widely used datasets, Auto3DCryoMap is able to reconstruct good 3D density maps using only a few thousand protein particle images, which is much smaller than hundreds of thousands of particles required by the existing methods.ConclusionsWe design a fully automated approach for cryo-EM 3D density maps reconstruction (Auto3DCryoMap). Instead of increasing the signal-to-noise ratio by using 2D class averaging, our approach uses 2D particle masks to produce locally aligned particle images. Auto3DCryoMap is able to accurately align structural particle shapes. Also, it is able to construct a decent 3D density map from only a few thousand aligned particle images while the existing tools require hundreds of thousands of particle images. Finally, by using the pre-processed particle images, Auto3DCryoMap reconstructs a better 3D density map than using the original particle images.

Dataset Information

EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing.

Summary

Availability and implementation

Contact

Supplementary information

Publications

EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing.

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