# 1. 从GitHub下载安装CIRI-deep环境 ``` git clone https://github.com/gyjames/CIRIdeep.git ``` # 安装环境 ``` conda create -n CIRIdeep python=3.7 source activate CIRIdeep pip install tensorflow-1.15.1+git06e2e8aa.dtk2404-cp37-cp37m-linux_x86_64.whl pip install -r requirements.txt pip install 'h5py<3.0.0' pip install protobuf==3.20.1 -i https://pypi.tuna.tsinghua.edu.cn/simple ``` 环境安装包如下: ``` absl-py 2.1.0 astor 0.8.1 certifi 2022.12.7 cycler 0.11.0 gast 0.2.2 google-pasta 0.2.0 grpcio 1.62.3 h5py 2.10.0 importlib-metadata 6.7.0 joblib 1.3.2 Keras 2.3.1 Keras-Applications 1.0.8 Keras-Preprocessing 1.1.2 kiwisolver 1.4.5 Markdown 3.4.4 MarkupSafe 2.1.5 matplotlib 3.3.4 numpy 1.19.2 opt-einsum 3.3.0 pandas 1.1.5 Pillow 9.5.0 pip 22.3.1 protobuf 3.20.1 pyparsing 3.1.2 python-dateutil 2.9.0.post0 pytz 2024.1 PyYAML 6.0.1 scikit-learn 0.24.2 scipy 1.7.3 setuptools 65.6.3 six 1.16.0 tensorboard 1.15.0 tensorflow 1.15.1+git06e2e8aa.dtk2404 tensorflow-estimator 1.15.1 termcolor 2.3.0 threadpoolctl 3.1.0 typing_extensions 4.7.1 Werkzeug 2.2.3 wheel 0.38.4 wrapt 1.16.0 zipp 3.15.0 ``` # 测试 ## 用CIRI-deep进行预测 ``` python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_totalRNA.tsv -seqFeature ./demo/cisfeature.tsv -splicing_max ./demo/splicingamount_max.tsv -predict_list ./demo/predict_list.txt -model_path ./models/CIRIdeep.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total -splicing_dir ./demo/splicingamount ``` ## 用CIRI-deepA进行预测 ``` python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_polyA.tsv -seqFeature ./demo/cisfeature.tsv -predict_list ./demo/predict_list.txt -model_path ./models/CIRIdeepA.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_polyA --CIRIdeepA ``` # 参考文档 ``` https://github.com/gyjames/CIRIdeep.git ```