DeepGlyco

DeepGlyco is a framework for predicting fragment mass spectra of intact glycopeptides.

Dependency

For model training, NVIDIA graphics cards with CUDA are recommended.

The following software and packages are required:

• Python (version 3.9.16, Anaconda distribution is recommended)
• PyTorch (version 1.12.1)
• DGL (version 1.0.1)
• numpy (version 1.23.5)
• pandas (version 1.5.2)
• scipy (version 1.10.1)
• scikit-learn (version 1.2.1)
• statsmodels (version 0.13.2)
• h5py (version 3.7.0)
• pymzml (version 2.5.2)

The following packages are optional for visualization:

• matplotlib (version 3.6.2)
• networkx (version 3.0)
• tensorboard (version 2.10.0)

Later versions may be compatible, but have not been tested.

Tutorial

Tutorials are avaliable in the docs folder.

Model Training for MS/MS Prediction

DeepGlyco Tutorial: Training New Models for MS/MS describes the analysis workflow for training MS/MS models.

Model Finetuning with New Data

DeepGlyco Tutorial: Model Finetuning with New Data describes the analysis workflow for finetuning MS/MS models using users' data.

Model Training for iRT Prediction

DeepGlyco Tutorial: Training New Models for iRT describes the analysis workflow for training iRT models.

DIA Analysis Using Predicted Libraries

DeepGlyco Tutorial: DIA Analysis Using Predicted Libraries describes the DIA analysis workflow using GproDIA and spectral libraries predicted by DeepGlyco.

Rescoring Glycopeptide Spectral Matches

DeepGlyco Tutorial: Rescoring Glycopeptide Structures describes the analysis workflow for rescoring glycopeptide spectral matches reported by other tools using spectral libraries predicted by DeepGlyco.

License

DeepGlyco is distributed under a BSD license. See the LICENSE file for details.