ArticleProceedings of the National Academy of Sciences of the United States of America2014
Quantifying selection in immune receptor repertoires.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 164 citations in OpenAlex.
- T Cell Thoughts.Immunological reviews · 2026Review
- Integrated B and T Cell Repertoire Analysis Reveals Organized but Heterogeneous Adaptive Immunity in Chronic Experimental Stroke.Research square · 2026Article
- Thymic selection of the T cell receptor repertoire is biased toward autoimmunity in females.eLife · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- T-cell receptor alpha to beta chains binding prediction.Briefings in bioinformatics · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Comprehensive αβ T-Cell Receptor Repertoire Analysis Reveals a Unique CD8+ TCR Landscape in DOCK8-Deficient Patients.Allergy · 2025Article
- Comprehensive analysis of αβT-cell receptor repertoires reveals signatures of thymic selection.Frontiers in immunology · 2025Article
- Combining mutation and recombination statistics to infer clonal families in antibody repertoires.eLife · 2024Article
- Review
- How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires.Frontiers in immunology · 2024Article
- Aberrant adaptive immune response underlies genetic susceptibility to tuberculosis.Frontiers in immunology · 2024Article
- Mathematical Characterization of Private and Public Immune Receptor Sequences.Bulletin of mathematical biology · 2023Article
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- Prospects of Cytomegalovirus-Specific T-Cell Receptors in Clinical Diagnosis and Therapy.Viruses · 2023Review
- Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals.PLoS genetics · 2023Article
- Article
- T Cell Repertoire Abnormality in Immunodeficiency Patients with DNA Repair and Methylation Defects.Journal of clinical immunology · 2022Article
- Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA).Nature biotechnology · 2022Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
The efficient recognition of pathogens by the adaptive immune system relies on the diversity of receptors displayed at the surface of immune cells. T-cell receptor diversity results from an initial random DNA editing process, called VDJ recombination, followed by functional selection of cells according to the interaction of their surface receptors with self and foreign antigenic peptides. Using high-throughput sequence data from the β-chain of human T-cell receptors, we infer factors that quantify the overall effect of selection on the elements of receptor sequence composition: the V and J gene choice and the length and amino acid composition of the variable region. We find a significant correlation between biases induced by VDJ recombination and our inferred selection factors together with a reduction of diversity during selection. Both effects suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution. The inferred selection factors differ little between donors or between naive and memory repertoires. The number of sequences shared between donors is well-predicted by our model, indicating a stochastic origin of such public sequences. Our approach is based on a probabilistic maximum likelihood method, which is necessary to disentangle the effects of selection from biases inherent in the recombination process.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.