usnistgov/UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series

UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. 

All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".

This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.

1. pre-processed training, evaluation and testing data for machine learning;

         UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt

2. Streamlined  input datasets based on the fragmentation dictionary;

        Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt

3. Predictions on the validation and test sets;

       UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt

4. Evaluation by comparison with Prosit;

      a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt

      b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt

5. CSS for Different HCD Fragment Ion Series;

       CS_for_ion_splits.tsv

6. Application 1: PSM rescoring;

      PSM rescoring_zipfiles.7z,  PSM rescoring_readme.txt

7. Application 2: In-silico spectral library search  

      in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt