DAQ Score

DAQ Score is a computational tool using deep learning that can estimate the residue-wise local quality for protein models from cryo-Electron Microscopy (EM) maps.
Tutorial Video from DAQ & DeepMainmast Workshop is made available at our lab channel.

Free Server (Recommended)

https://em.kiharalab.org/algorithm/daqscore

Google Colab Version


Please use DAQ Colab.

Docker Container


To use the DAQ Docker image, you can either build the image on your own machine, or download the pre-built image.
To build your own image, please use the tutorial available at the DAQ Github repository.
The pre-built image can be downloaded from here. Please follow the rest of this guide to learn how to use it.

Tech Specs


CPU: >=4 cores
Memory: >=10Gb
GPU: any GPU supports CUDA with more than 12GB memory.

Docker Requirements and Installation Guides

Linux:

(1) Nvidia driver available at

https://www.nvidia.com/download/index.aspx

(2) Docker available at

https://www.docker.com/

(3) Nvidia container toolkit available at

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html

Windows:

(1) Nvidia driver available at

https://www.nvidia.com/download/index.aspx

(2) Docker available at

https://www.docker.com/

(3) WSL 2: Instructions can be found at

https://docs.microsoft.com/en-us/windows/wsl/install

(4) CUDA: Please follow the instructions at

https://docs.nvidia.com/cuda/wsl-user-guide/index.html, under Section 3. Cuda Support for WSL 2

(5) Open the Docker Desktop app and Enable WSL 2 Integration under Settings. Instructions available at

https://docs.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers

Usage guide

After installing the requirements, add the container to your Docker installation by issuing the following command:
sudo docker load --input /path/to/daq.tar

Now, you are ready to run the DAQ container. Place all your input files(map and structure file) in one directory; for example, if the directory is named “inputs”, it’ll contain the .pdb and .mrc files.
To run DAQ, you can issue the following command in the terminal:

sudo docker run --gpus=all -v /fullpath/inputs/:/DAQ-main/inputs daq --mode=0 --gpu=0 -F "2566_3J6B_9.mrc" -P "3J6B_9.pdb" --window 9 --stride 2

  • /fullpath/inputs/: Should be replaced with the full path to the inputs directory containing the input files.
  • --gpu: You can specify which GPU to use
  • -F: Map name
  • -P: Structure file
  • --window: half_window_size
  • --stride: stride size
After execution, your outputs will be saved in a new directory that will automatically be created inside the inputs directory.

License

© 2021 Genki Terashi, Xiao Wang, Sai Raghavendra Maddhuri Venkata Subramaniya, John J. G. Tesmer, and Daisuke Kihara, and Purdue University

GPL v3. (If you are interested in a different license, for example, for commercial use, please contact us.)


Reference

Citation of the following reference should be included in any publication that uses data or results generated by DAQ program.

Genki Terashi, Xiao Wang, Sai Raghavendra Maddhuri Venkata Subramaniya, John J. G. Tesmer, & Daisuke Kihara. Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods, 19(9):1116-1125, (2022).