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Deep learning-based prediction of the selection factors for quantifying selection in immune receptor repertoires

  • Rocha, B. & von Boehmer, H. Peripheral selection of the T cell repertoire. Science 251, 1225–1228 (1991).

    Google Scholar 

  • Egerton, M., Scollay, R. & Shortman, K. Kinetics of mature T-cell development in the thymus. Proc. Natl Acad. Sci. USA 87, 2579–2582 (1990).

    Google Scholar 

  • Janeway, C. et al. Immunobiology: The Immune System in Health and Disease Vol. 2 (Garland, 2001).

  • Roth, D. B. V(D)J recombination: mechanism, errors, and fidelity. In Mobile DNA III 311–324 (American Society for Microbiology, 2015).

  • Sethna, Z. et al. Population variability in the generation and selection of T-cell repertoires. PLOS Comput. Biol. 16, e1008394 (2020).

    Google Scholar 

  • Isacchini, G., Walczak, A. M., Mora, T. & Nourmohammad, A. Deep generative selection models of T and B cell receptor repertoires with SONIA. Proc. Natl Acad. Sci. USA 118, e2024364118 (2021).

  • Pai, J. A. & Satpathy, A. T. High-throughput and single-cell T cell receptor sequencing technologies. Nat. Methods 18, 881–892 (2021).

    Google Scholar 

  • Murugan, A., Mora, T., Walczak, A. M. & Callan Jr, C. G. Statistical inference of the generation probability of T-cell receptors from sequence repertoires. Proc. Natl Acad. Sci. USA 109, 16161–16166 (2012).

    Google Scholar 

  • Marcou, Q., Mora, T. & Walczak, A. M. High-throughput immune repertoire analysis with IGoR. Nat. Commun. 9, 561 (2018).

    Google Scholar 

  • Sethna, Z., Elhanati, Y., Callan Jr, C. G., Walczak, A. M. & Mora, T. OLGA: fast computation of generation probabilities of B- and T-cell receptor amino acid sequences and motifs. Bioinformatics 35, 2974–2981 (2019).

    Google Scholar 

  • Elhanati, Y., Murugan, A., Callan Jr, C. G., Mora, T. & Walczak, A. M. Quantifying selection in immune receptor repertoires. Proc. Natl Acad. Sci. USA 111, 9875–9880 (2014).

    Google Scholar 

  • Jiang, Y. & Li, S. C. Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires. Brief. Bioinform. 24, bbad038 (2023).

    Google Scholar 

  • Davidsen, K. et al. Deep generative models for T cell receptor protein sequences. eLife 8, e46935 (2019).

    Google Scholar 

  • Zhou, Z.-H. A brief introduction to weakly supervised learning. Natl Sci. Rev. 5, 44–53 (2018).

    Google Scholar 

  • Li, Y.-F., Guo, L.-Z. & Zhou, Z.-H. Towards safe weakly supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 43, 334–346 (2019).

    Google Scholar 

  • Hou, X. et al. Analysis of the repertoire features of TCR beta chain CDR3 in human by high-throughput sequencing. Cell. Physiol. Biochem. 39, 651–667 (2016).

    Google Scholar 

  • Das, J. & Jayaprakash, C. Systems Immunology: An Introduction to Modeling Methods for Scientists (CRC, 2018).

  • Rao, R. et al. Evaluating protein transfer learning with TAPE. Adv. Neural. Inf. Process. Syst. 32, 9689–9701 (2019).

    Google Scholar 

  • Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Vol. 1 (eds Burstein, J. et al.) 4171–4186 (Association for Computational Linguistics, 2019).

  • Huang, H., Wang, C., Rubelt, F., Scriba, T. J. & Davis, M. M. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat. Biotechnol. 38, 1194–1202 (2020).

    Google Scholar 

  • Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).

    Google Scholar 

  • Chen, S.-Y., Yue, T., Lei, Q. & Guo, A.-Y. TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function. Nucleic Acids Res. 49, D468–D474 (2021).

    Google Scholar 

  • Emerson, R. O. et al. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nat. Genet. 49, 659–665 (2017).

    Google Scholar 

  • Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. Preprint at https://arxiv.org/abs/1301.3781 (2013).

  • Elhanati, Y., Sethna, Z., Callan Jr, C. G., Mora, T. & Walczak, A. M. Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination. Immunol. Rev. 284, 167–179 (2018).

    Google Scholar 

  • Ruiz Ortega, M., Spisak, N., Mora, T. & Walczak, A. M. Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals. PLoS Genet. 19, e1010652 (2023).

    Google Scholar 

  • Nolan, S. et al. A large-scale database of T-cell receptor beta sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Front. Immunol 16, 1488851 (2025).

    Google Scholar 

  • Weyand, C. M. & Goronzy, J. Aging of the immune system. Mechanisms and therapeutic targets. Ann. Am. Thorac. Soc. 13, S422–S428 (2016).

    Google Scholar 

  • Egorov, E. S. et al. The changing landscape of naive T cell receptor repertoire with human aging. Front. Immunol. 9, 1618 (2018).

    Google Scholar 

  • Britanova, O. V. et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. J. Immunol. 192, 2689–2698 (2014).

    Google Scholar 

  • Palmer, D. B. The effect of age on thymic function. Front. Immunol. 4, 316 (2013).

    Google Scholar 

  • Krishna, C., Chowell, D., Gönen, M., Elhanati, Y. & Chan, T. A. Genetic and environmental determinants of human TCR repertoire diversity. Immun. Ageing 17, 26 (2020).

    Google Scholar 

  • Wang, G. C., Dash, P., McCullers, J. A., Doherty, P. C. & Thomas, P. G. T cell receptor αβ diversity inversely correlates with pathogen-specific antibody levels in human cytomegalovirus infection. Sci. Transl. Med. 4, 128ra42 (2012).

    Google Scholar 

  • Jergović, M., Contreras, N. A. & Nikolich-Žugich, J. Impact of CMV upon immune aging: facts and fiction. Med. Microbiol. Immunol. 208, 263–269 (2019).

    Google Scholar 

  • Chu, N. D. et al. Longitudinal immunosequencing in healthy people reveals persistent T cell receptors rich in highly public receptors. BMC Immunol. 20, 19 (2019).

    Google Scholar 

  • Britanova, O. V. et al. Dynamics of individual T cell repertoires: from cord blood to centenarians. J. Immunol. 196, 5005–5013 (2016).

    Google Scholar 

  • Bensouda Koraichi, M., Ferri, S., Walczak, A. M. & Mora, T. Inferring the T cell repertoire dynamics of healthy individuals. Proc. Natl Acad. Sci. USA 120, e2207516120 (2023).

    Google Scholar 

  • Pogorelyy, M. V. et al. Method for identification of condition-associated public antigen receptor sequences. eLife 7, e33050 (2018).

    Google Scholar 

  • Widrich, M. et al. Modern Hopfield networks and attention for immune repertoire classification. Adv. Neural Inf. Process. Syst. 33, 18832–18845 (2020).

    Google Scholar 

  • Akerman, O., Isakov, H., Levi, R., Psevkin, V. & Louzoun, Y. Counting is almost all you need. Front. Immunol. 13, 1031011 (2023).

    Google Scholar 

  • Shugay, M. et al. VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res. 46, D419–D427 (2018).

    Google Scholar 

  • Joglekar, A. V. & Li, G. T cell antigen discovery. Nat. Methods 18, 873–880 (2021).

    Google Scholar 

  • Hudson, D., Fernandes, R. A., Basham, M., Ogg, G. & Koohy, H. Can we predict T cell specificity with digital biology and machine learning? Nat. Rev. Immunol. 23, 511–521 (2023).

  • Gomez-Tourino, I., Kamra, Y., Baptista, R., Lorenc, A. & Peakman, M. T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes. Nat. Commun. 8, 1792 (2017).

    Google Scholar 

  • Savola, P. et al. Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis. Nat. Commun. 8, 15869 (2017).

    Google Scholar 

  • Huth, A., Liang, X., Krebs, S., Blum, H. & Moosmann, A. Antigen-specific TCR signatures of cytomegalovirus infection. J. Immunol. 202, 979–990 (2019).

    Google Scholar 

  • Faham, M. et al. Discovery of T cell receptor β motifs specific to HLA–B27–positive ankylosing spondylitis by deep repertoire sequence analysis. Arthritis Rheumatol. 69, 774–784 (2017).

    Google Scholar 

  • Zhao, Y. et al. Preferential use of public TCR during autoimmune encephalomyelitis. J. Immunol. 196, 4905–4914 (2016).

    Google Scholar 

  • Lu, C. et al. Clinical significance of T cell receptor repertoire in primary Sjogren’s syndrome. EBioMedicine 84, 104252 (2022).

  • Seder, R. A., Darrah, P. A. & Roederer, M. T-cell quality in memory and protection: implications for vaccine design. Nat. Rev. Immunol. 8, 247–258 (2008).

    Google Scholar 

  • Feng, X. et al. A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions. Brief. Bioinform. 26, bbaf030 (2025).

    Google Scholar 

  • Kobyzev, I., Prince, SimonJ. D. & Brubaker, M. A. Normalizing flows: an introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3964–3979 (2020).

    Google Scholar 

  • Zhang, W., Gou, Y., Jiang, Y. & Zhang, Y. Adversarial VAE with normalizing flows for multi-dimensional classification. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) 205–219 (Springer, 2022).

  • Tino, P., Leonardis, Y., Leonardis, A. & Tang, K. A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5, 726–742 (2021).

    Google Scholar 

  • Jiang, Y., Huo, M., Zhang, P., Zou, Y. & Li, S. TCR2vec: a deep representation learning framework of T-cell receptor sequence and function. Preprint at bioRxiv https://doi.org/10.1101/2023.03.31.535142 (2023).

  • Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017).

  • Jiang, Y., Rensi, S., Wang, S. & Altman, R. B. DrugOrchestra: jointly predicting drug response, targets, and side effects via deep multi-task learning. Preprint at biorxiv https://doi.org/10.1101/2020.11.17.385757 (2020).

  • Dai, Z. et al. BEV-Net: assessing social distancing compliance by joint people localization and geometric reasoning. In Proc. IEEE/CVF International Conference on Computer Vision 5401–5411 (IEEE, 2021).

  • Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H. & Sugiyama, M. Relative density-ratio estimation for robust distribution comparison. Neural Comput. 25, 1324–1370 (2013).

    MathSciNet 

    Google Scholar 

  • Kingma, D. & Ba, J. Adam: a method for stochastic optimization. In Proc. International Conference on Learning Representations (ICLR, 2015)

  • Slabodkin, A. et al. Individualized VDJ recombination predisposes the available IG sequence space. Genome Res. 31, 2209–2224 (2021).

    Google Scholar 

  • Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet 

    Google Scholar 

  • Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Google Scholar 

  • Jiang, Y. Tcrsep. Zenodo https://doi.org/10.5281/zenodo.15691314 (2025).TS: In panel a, please change the x axis labels 0.5, 0.6 and 0.7 to 0.50, 0.60 and 0.70, respectively.

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    2025-08-11 00:00:00

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