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# Deep cryo-EM Map Enhancer (DeepEMhancer)
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**DeepEMhancer** is a python package designed to perform post-processing of
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cryo-EM maps as described in "<a href=https://doi.org/10.1101/2020.06.12.148296 >DeepEMhancer: a deep learning solution for cryo-EM volume post-processing</a>", by Sanchez-Garcia et al, 2020.<br>
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cryo-EM maps as described in "<a href=https://www.nature.com/articles/s42003-021-02399-1 >DeepEMhancer: a deep learning solution for cryo-EM volume post-processing</a>", by Sanchez-Garcia et al, 2021.<br>
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DeepEMhancer is a deep learning model trained on pairs of experimental volumes and atomic model-corrected volumes that is able to obtain post-processed maps using as input raw volumes, preferably half maps. Please notice that post-translational modifications and ligands were not included in the traning set and consequently, results for these features could be inaccurate.<br>
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Simply speaking, DeepEMhancer performs a non-linear post-processing of cryo-EM maps that produces two main effects:
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1) Local sharpening-like post-processing.
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2) Automatic masking/denoising of cryo-EM maps.
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## Table of Contents
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- [INSTALLATION](#installation)
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- [USAGE GUIDE](#usage-guide)
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DeepEMhancer processes input maps by chunking them into smaller cubes that are sent to GPUs. Batch size parameter represent
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the number of smaller cubes that are simultaneously processed by the GPUs. A typical value for an 8 GB GPU could be<br>
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```--batch_size 6```. If OUT OF MEMORY error happens, try to lower batch_size, and if low GPU usage is observed (via nvidia-smi), try
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to increase it.
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to increase it. Setting the environmental variable `TF_FORCE_GPU_ALLOW_GROWTH='true'` prior execution could also help to fix some GPU memory errors. When using multiple GPUs, for certain box sizes, there might happen a reported bug affecting the batch_size, please see [TROUBLESHOOTING](#Troubleshooting) error 3.
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