TCRGP is a novel Gaussian process method that can predict if TCRs recognize certain epitopes. This method can utilize different CDR sequences from both TCRα and TCRβ chains from single-cell data and learn which CDRs are important in recognizing the different epitopes. TCRGP has been developed at Aalto University.
- For a comprehensive description of TCRGP see [1]
- For examples of usage, see Examples.ipynb
To use TCRGP, you will need to have
- TensorFlow (We have used version 1.8.0)
- GPflow (We have used version 1.1.1)
- And some other Python packages, which are imported at the beginning of tcrgp.py
The data in folder data has been obtained from [2], [3] and [4].
Folder training_data/paper contains training data files used for the paper. Folder training_data/examples can be utilized with the example.ipynb Folder models contains pretrained models for different epitopes. Folder results can be used to store result files.
TCRGP has been updated in August 20th, 2019, and is not fully compatible with the older version.
Software and data for the single-cell RNA-sequencing analysis of HCC-patients from [1] are available at https://github.com/janihuuh/tcrgp_manu_hcc.
[1] Emmi Jokinen, Jani Huuhtanen, Satu Mustjoki, Markus Heinonen, and Harri Lähdesmäki. (2019). Determining epitope specificity of T cell receptors with TCRGP. (submitted)
[2] Shugay, M. et al. (2017). VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic acids research, 46(D1), D419-D427
[3] Dash, P. et al. (2017). Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature, 547(7661), 89
[4] Kawashima S. et al. (2007). AAindex: amino acid index database, progress report 2008. Nucleic Acids Res., 36, D202–D205.