DeepGP is a deep learning framework for the prediction of MS/MS spectra and retention time of glycopeptides.
For detailed step-by-step instructions on how to get started with DeepGP, please refer to User_guide.md available in the main folder.
Package Requirements: A list of all required packages and software.
Package Installation: Step-by-step guide to installing necessary packages.
Demo Data: Information and access to demo data sets.
Demo Data Description: Detailed description of the demo data.
Step-by-Step Instructions: Comprehensive guide to help you run and understand DeepGP.
The model is available at the Google Drive.
Here are the trained DeepGP models. These models are organized into five files, each denoted by the following names: DeepFLR, human, mouse, human&mouse and mouse_rt.
DeepFLR: This is the base model.
mouse: This is the DeepGP model for spectra prediction trained with mouse datasets, built on top of the DeepFLR base model.
human: This is the DeepGP model for spectra prediction trained with human datasets, built on top of the DeepFLR base model.
human&mouse: This is the DeepGP model for spectra prediction trained with both human and mouse datasets, built on top of the DeepFLR base model.
mouse_rt: The is the DeepGP model for retention time prediction trained with mouse datasets, built on top of the DeepFLR base model.
For further details, please refer to the User Guide.
The demo data for iRT prediction is available at the Google Drive.
This demo data is for clear and comprehensive presentation of iRT pre-processing, calibration, model training, and prediction. It includes three relatively large mouse datasets.
For further details, please refer to the User Guide.
We also present the post-analysis code for the re-identification in the main folder/Post_analysis. For detailed step-by-step instructions on how to perform post analysis, please refer to User_guide_post_analysis.docx available in the main main folder/Post_analysis.
The demo data for post analysis is available at the Google Drive.