Computational Tools

Predictions of TCR-epitope interactions

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Croce G, Bobisse S, Moreno DL, Schmidt J, Guillaume P, Harari A, Gfeller D. (2024) Nature Communications, 5(1):3211.

Repository for the R package EPIC-ATAC, to Estimate the Proportion of Immune and Cancer cells from bulk ATAC-Seq data.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data. Gabriel AA, Racle J, Falquet M, Jandus C, Gfeller D. (2023) BioRxiv, 10.1101/2023.10.11.561826.

Repository for the R package EPIC, to Estimate the Proportion of Immune and Cancer cells from bulk gene expression data.

EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data. Racle, J., & Gfeller, D. (2020). Methods in molecular biology (Clifton, N.J.), 2120, 233–248. https://doi.org/10.1007/978-1-0716-0327-7_17

Coarse-graining of large single-cell RNA-seq data into super-cells.

Metacells untangle large and complex single-cell transcriptome networks. Bilous M, Tran L, Cianciaruso C, Gabriel A, Michel H, Carmona, S, Pittet MJ, Gfeller D., 2022. BMC Bioinformatics, 23, 336.

MixMHCpred2.1 is a predictor of HLA-I ligand displayed at the cell surface.

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. Bassani-Sternberg, M., Chong, C., Guillaume, P., Solleder, M., Pak, H., Gannon, P. O., Kandalaft, L. E., Coukos, G., & Gfeller, D. (2017). PLoS computational biology, 13(8), e1005725. https://doi.org/10.1371/journal.pcbi.1005725

The Length Distribution and Multiple Specificity of Naturally Presented HLA-I Ligands. Gfeller, D., Guillaume, P., Michaux, J., Pak, H. S., Daniel, R. T., Racle, J., Coukos, G., & Bassani-Sternberg, M. (2018). Journal of immunology (Baltimore, Md. : 1950), 201(12), 3705–3716. https://doi.org/10.4049/jimmunol.1800914

PRedictor of IMmunogenic Epitopes

Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. Schmidt, J., Smith, A. R., Magnin, M., Racle, J., Devlin, J. R., Bobisse, S., Cesbron, J., Bonnet, V., Carmona, S. J., Huber, F., Ciriello, G., Speiser, D. E., Bassani-Sternberg, M., Coukos, G., Baker, B. M., Harari, A., & Gfeller, D. (2021). Medicine, 2(2), 100194. https://doi.org/10.1016/j.xcrm.2021.100194

MixMHCpred2.1 is a predictor of HLA-I ligand displayed at the cell surface.

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Racle, J., Michaux, J., Rockinger, G. A., Arnaud, M., Bobisse, S., Chong, C., Guillaume, P., Coukos, G., Harari, A., Jandus, C., Bassani-Sternberg, M., & Gfeller, D. (2019). Nature biotechnology, 37(11), 1283–1286. https://doi.org/10.1038/s41587-019-0289-6

Machine learning predictions of MH C-II specificities reveal alternative binding mode of class II epitopes. Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. (2023) Immunity, 2023, 56, 1-17.

Tool for motif deconvolution in large HLA-I ligand datasets.

Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions. Bassani-Sternberg, M., & Gfeller, D. (2016). Journal of immunology (Baltimore, Md. : 1950), 197(6), 2492–2499. https://doi.org/10.4049/jimmunol.1600808

Tool for Motif Deconvolution in large HLA-II ligand datasets without the need of prior alignment.

PhosMHCpred is a predictor for HLA-I – phosphorylated ligand interactions.

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands. Solleder, M., Guillaume, P., Racle, J., Michaux, J., Pak, H. S., Müller, M., Coukos, G., Bassani-Sternberg, M., & Gfeller, D. (2020). Molecular & cellular proteomics : MCP, 19(2), 390–404. https://doi.org/10.1074/mcp.TIR119.001641d.