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].
Other folders (training_data, models, results) and their contents exist for demonstrative purposes and they are utilized by Example.ipynb.
[1] Emmi Jokinen, Markus Heinonen, Jani Huuhtanen, Satu Mustjoki and Harri Lähdesmäki. (2018). TCRGP: Determining epitope specificity of T cell receptors. (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.