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DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

Nguyen P. Nguyen, Jacob Gotberg, Ilker Ersoy, Filiz Bunyak, Tommi White
doi: https://doi.org/10.1101/616169
Nguyen P. Nguyen
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA, ,
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  • For correspondence: npntz3@mail.missouri.edu bunyak@missouri.edu
Jacob Gotberg
2Research Computing Support Services, University of Missouri, Columbia, MO, USA,
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  • For correspondence: gotbergj@missouri.edu
Ilker Ersoy
3MU Informatics Institute, University of Missouri, Columbia, MO, USA,
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  • For correspondence: ersoyi@health.missouri.edu
Filiz Bunyak
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA, ,
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  • For correspondence: npntz3@mail.missouri.edu bunyak@missouri.edu
Tommi White
4Department of Biochemistry and Electron Microscopy Core, University of Missouri, Columbia, MO, USA
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  • For correspondence: whiteto@missouri.edu
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Abstract

Selection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnet’s first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated “deep regression” system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on β-galactosidase (EMPIAR-10017) [2] and β-galactosidase (EMPIAR-10061) [3], was then compared to RELION’s interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

Footnotes

  • Change color maps from Jet to Parula, and change some minor details.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 06, 2019.
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DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression
Nguyen P. Nguyen, Jacob Gotberg, Ilker Ersoy, Filiz Bunyak, Tommi White
bioRxiv 616169; doi: https://doi.org/10.1101/616169
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DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression
Nguyen P. Nguyen, Jacob Gotberg, Ilker Ersoy, Filiz Bunyak, Tommi White
bioRxiv 616169; doi: https://doi.org/10.1101/616169

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