qbic-pipelines/t2pmhc_benchmark

Introduction

This pipeline was created for benchmarking t2pmhc with state-of-the-art TCR-pMHC binding predictors. The pipeline is NOT INTENDED for users but to showcase the benchmark performed for the t2pmhc publication.

Three test sets were used in the study.

1. public test set (available here at data/test/public_test_set.tsv). See publication for more information.
2. Immrep23-solution (available here)
3. epytope-viral (available on Zenodo as described in the ePytope-TCR manuscript

Usage

You can run the pipeline using:

Example samplesheet.csv:

model_name,dataset_name,model_samplesheet,dataset_graphs
gcn,test1,t2pmhc_samplesheet.tsv,/path/to/gcn_graphs
gat,test1,t2pmhc_samplesheet.tsv,/path/to/gat_graphs
mixtcrpred,test1,t2pmhc_samplesheet.csv,
mixtcrpred-pan,test1,mixtcrpred_samplesheet.csv,
ergo2,test1,ergo2_samplesheet.csv,
tabr-bert,test1,tabr-bert_samplesheet.csv,

The model_samplesheets must be in the format as expected by the individual models.

For t2pmhc models (gcn, gat), pre-computed graphs must be provided via the dataset_graphs column. Graphs can be created using t2pmhc directly.

MixTCRpred is licensed and must be installed by the user as described in their Github. The required location in this pipeline is bin/MixTCRpred. The pretrained models must be stored here: bin/MixTCRpred/pretrained_models
MixTCRpred-pan is not made available by the authors, but must be retrained as described by the authors (here). The model must be stored here: bin/MixTCRpred/pretrained_models/mixtrcpred_pan_epitope.ckpt

nextflow run qbic-pipelines/t2pmhc_benchmark \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

Citations

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

ERGO-II

Springer, I. et al. (2021). Contribution of T Cell Receptor Alpha and Beta CDR3, MHC Typing, V and J Genes to Peptide Binding Prediction. Frontiers in Immunology, 12, 664514. https://doi.org/10.3389/fimmu.2021.664514

MIXTCRpred

Croce, G. et al. (2024). Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Nature Communications, 15, 3211. https://doi.org/10.1038/s41467-024-47461-8

TABR-BERT

Zhang, J. et al. (2024). Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method. Briefings in Bioinformatics, 25(1), bbad436. https://doi.org/10.1093/bib/bbad436