Deepfold

A tool for RNA secondary structure prediction with deep neural networks. DeepFold learn the underlying RNA folding mechanism directly from structure-known sequences without any energy assumption or parameters, and can also predict pseudoknots. It provides predicting scripts and models for input RNA ct file.

For updates, please refer to https://github.com/lulab/deepfold

License

DeepFold is free for non-commercial research. For commercial use, please contact the authors.

1. Installation

Pre-requisite:

1. Linux
2. Python (>=2.7)
3. Python packages keras

2. Usage and Examples

    Run subset model:
python /path/to/folder/deepfold.py -S /path/to/your/input_folder /path/to/your/output_folder/;
    Run fullset model:
python /path/to/folder/deepfold.py -F /path/to/your/input_folder /path/to/your/output_folder/;

3. Contact

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Boqin Hu huboqin_cn@163.com Yang Wu wuyang@ict.ac.cn