Improvement of cryo-EM maps by simultaneous local and non-local deep learning
Copyright (C) 2023 Jiahua He, Tao Li, Sheng-You Huang and Huazhong University of Science and Technology
Platform: Linux (Mainly tested on CentOS 7).
GPU: A GPU with >10 GB memory is required, advanced GPU like A100 is recommended.
Download EMReady via github
git clone https://github.com/huang-laboratory/EMReady.git
cd EMReady
conda env create -f environment.yml
If conda fails, you could install the packages youself. Basically. you can first create an environment named emready_env by conda create -n emready_env python=3.9, then install the packages listed in environment.yml using conda or pip.
Set "EMReady_home" to the root directory of EMReady, for example, if EMReady is unzipped to "/home/data/EMReady", set EMReady_home="/home/data/EMReady"
Set "active" to path of conda activator, for example
activate="/home/data/anaconda3/bin/activate
set "EMReady_env" to name of the python conda virtual environment that have all the required packages installed. An conda environment named "emready_env" will be created using the quick installation command, so EMReady_env="emready_env". If the environment is built with a different name, users should modify "EMReady_env" accordingly.
Running EMReady is very straight forward with one command like
EMReady.sh in_map.mrc out_map.mrc [Options]
Required arguments:
in_map.mrc: File name of input EM density map in MRC2014 format.
out_map.mrc: File name of the output EMReady-processed density map.
Options:
-g GPU_ID: ID(s) of GPU devices to use. e.g. '0' for GPU #0, and '2,3,6' for GPUs #2, #3, and #6. (default: '0')
-s STRIDE: The step of the sliding window for cutting the input map into overlapping boxes. Its value should be an integer within [12,48]. (default: 12)
-b BATCH_SIZE: Number of boxes input into EMReady in one batch. (default: 10)
-m MASK_MAP: Input mask map in MRC2014 format. (default: None)
-c MASK_MAP_CONTOUR: Set the contour level of the mask. (default: 0.0)
-p MASK_STRUCTURE: Input structure mask files in PDB or CIF format (default: None)
-r MASK_STRUCTURE_RADIUS: Zone radius in angstroms (default: 4.0)
-mo MASK_OUT_PATH: File path of the output binary mask map. (default: None)
--use_cpu: Run EMReady on CPU instead of GPU.
Notes:
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Users can specify a larger STRIDE of sliding window (default=12) to reduce the number of overlapping boxes to calculate. If users run out of memory, they may set it to a larger value. Howerver, since the size of the overlapping boxes is 48×48×48, the value of STRIDE should not exceed 48.
-
By default, EMReady will run on GPU(s). Users can adjust the BATCH_SIZE according to the VRAM of their GPU. Empirically, an NVIDIA A100 with 40 GB VRAM can afford a BATCH_SIZE of 200. Users can run EMReady on CPUs by setting --use_cpu. But this may take very long time for large density maps.
If you find our work useful, please cite our related paper:
@article{EMReady2023,
title = {Improvement of cryo-EM maps by simultaneous local and non-local deep learning},
author = {He J, Li T, Huang SY},
journal = {Nature communications},
year = {2023},
volume = {14},
number = {1},
pages = {3217},
doi = {10.1038/s41467-023-39031-1}
}