pedroseber / o-glcnacylation_prediction Goto Github PK

These are the datasets and model files associated with the publication Recurrent Neural Network-based Prediction of O-GlcNAcylation Sites in Mammalian Proteins. This work uses primarily RNN models to predict the presence of O-GlcNAcylation sites. The models were trained on multiple sources of literature data on protein O-GlcNAcylation based on human-selected descriptors (v1 of the datasets) or protein sequences (v3 and v5 of the datasets).

The models can be recreated by downloading the datasets and running the ANN_train.py file with the appropriate flags (run python ANN_train.py --help for details).

The plots can be recreated by running the make_plot.py file with the appropriate data version as an input (python make_plot.py v1 for Fig. 1, python make_plot.py v5 for Fig. 2, python make_plot.py shap_heatmap for Fig. 3, python make_plot.py v3 for Fig. S1, python make_plot.py v5_nested for Fig. S2, and python make_plot.py v5_shap for Fig. S3).

The Conda environment defining the specific packages and version numbers used in this work is available as ANN_environment.yaml. To use our trained model, run the Predict.py file as python Predict.py <sequence> -t <threshold> -bs <batch_size>.

Alternatively, create an (N+1)x1 .csv with the first row as a header (such as "Sequences") and all other N rows as the actual amino acid sequences, then run the Predict.py file as python ANN_predict.py <path/to/file.csv> -t <threshold> -bs <batch_size>. Results will be saved as a new .csv file.

To run Shapley value predictions in addition to the model predictions, run the Predict.py file with also the -shap flag. Whatever other flags should be included as needed.