Clean

Description

clean website  

CLEAN, Contrastive Learning enabled Enzyme ANnotation, is a machine learning algorithm to assign Enzyme Commission (EC) number with better accuracy, reliability, and sensitivity than all existing computational tools.

Environment Modules

Run module spider clean to find out what environment modules are available for this application.

System Variables

• HPC_CLEAN_DIR - installation directory
• HPC_CLEAN_BIN - executable directory

Additional Information

When loading the module CLEAN, the environment is loaded with necessary dependencies to run CLEAN_infer_fasta.py. Users should still clone the CLEAN repo locally from https://github.com/tttianhao/CLEAN to their work directory. Once cloned, the user should also run git clone https://github.com/facebookresearch/esm.git; mkdir data/esm_data inside the CLEAN repo directory before running the command python CLEAN_infer_fasta.py --fasta_data price for the first time.

To work with FASTA files, download the provided files from the repo and move them to data/pretrained.

Citation

If you publish research that uses clean you have to cite it as follows:

Tianhao Yu and Haiyang Cui and Jianan Canal Li and Yunan Luo and Guangde Jiang and Huimin Zhao. Enzyme function prediction using contrastive learning. Science. 379. 6639. 1358-1363. 2023. 10.1126/science.adf2465. https://www.science.org/doi/abs/10.1126/science.adf2465.