DeepEMhancer
DeepEMhancer is a deep learning approach for automatic post-processing of cryo-EM maps, performing masking and sharpening in a single step to improve interpretability.
Overview
Cryo-EM maps often require post-processing to improve interpretability due to loss of contrast at high frequencies. DeepEMhancer is a deep learning tool designed to automatically post-process these maps. Trained on pairs of experimental maps and maps sharpened with their corresponding atomic models, DeepEMhancer has learned to perform both masking-like and sharpening-like operations in a single step, reducing noise and revealing more detailed features in the experimental maps without requiring an atomic model.
Run DeepEMhancer on Neurosnap
The DeepEMhancer online webserver allows anybody with a Neurosnap account to run and access DeepEMhancer, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Performs automatic post-processing of cryo-EM maps using a deep learning approach.
- Combines masking-like and sharpening-like operations in a single step.
- Reduces noise levels and enhances map details for improved interpretability.
- Trained on experimental maps and atomic model-sharpened targets to mimic high-quality results.
- Does not require an atomic model for post-processing.
- Improves map similarity to the final atomic model, measured by FSC.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access DeepEMhancer using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using DeepEMhancer in publications or research outputs.
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Sanchez-Garcia, R., Gomez-Blanco, J., Cuervo, A. et al. DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun Biol 4, 874 (2021). https://doi.org/10.1038/s42003-021-02399-1 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |