ArticleGenome medicine2015
Annotation of pseudogenic gene segments by massively parallel sequencing of rearranged lymphocyte receptor loci.
Article in Genome medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 64 citations in OpenAlex.
- DTCR: generating realistic, diverse, and epitope-specific T cell receptor sequences via a discrete diffusion model.Briefings in bioinformatics · 2026Article
- Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery.Immunological reviews · 2026Review
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- ProTCR: a protein language model-driven framework for decoding TCR-antigen recognition toward precision immunotherapies.Briefings in bioinformatics · 2026Article
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- A Biologically Informed Vision-Guided Framework for Interpretable T Cell Receptor-Epitope Binding Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine.Science advances · 2025Article
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved generalizability.Chemical science · 2025Article
- Identifying T cell antigen at the atomic level with graph convolutional network.Nature communications · 2025Article
- Article
- Inborn errors of immunity underlie clonal T cell expansions in large granular lymphocyte leukemia.The Journal of clinical investigation · 2025Article
- Masked language modeling pretraining dynamics for downstream peptide: T-cell receptor binding prediction.Bioinformatics advances · 2025Article
- Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.Briefings in bioinformatics · 2024Article
- Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning.Cell genomics · 2024Article
- Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method.Briefings in bioinformatics · 2023Article
- BERTrand-peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing.Bioinformatics (Oxford, England) · 2023Article
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
- epiTCR: a highly sensitive predictor for TCR-peptide binding.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe adaptive immune system generates a remarkable range of antigen-specific T-cell receptors (TCRs), allowing the recognition of a diverse set of antigens. Most of this diversity is encoded in the complementarity determining region 3 (CDR3) of the β chain of the αβ TCR, which is generated by somatic recombination of noncontiguous variable (V), diversity (D), and joining (J) gene segments. Deletion and non-templated insertion of nucleotides at the D-J and V-DJ junctions further increases diversity. Many of these gene segments are annotated as non-functional owing to defects in their primary sequence, the absence of motifs necessary for rearrangement, or chromosomal locations outside the TCR locus.
methodsWe sought to utilize a novel method, based on high-throughput sequencing of rearranged TCR genes in a large cohort of individuals, to evaluate the use of functional and non-functional alleles. We amplified and sequenced genomic DNA from the peripheral blood of 587 healthy volunteers using a multiplexed polymerase chain reaction assay that targets the variable region of the rearranged TCRβ locus, and we determined the presence and the proportion of productive rearrangements for each TCRβ V gene segment in each individual. We then used this information to annotate the functional status of TCRβ V gene segments in this cohort.
resultsFor most TCRβ V gene segments, our method agrees with previously reported functional annotations. However, we identified novel non-functional alleles for several gene segments, some of which were used exclusively in our cohort to the detriment of reported functional alleles. We also saw that some gene segments reported to have both functional and non-functional alleles consistently behaved in our cohort as either functional or non-functional, suggesting that some reported alleles were not present in the population studied.
conclusionsIn this proof-of-principle study, we used high-throughput sequencing of the TCRβ locus of a large cohort of healthy volunteers to evaluate the use of functional and non-functional alleles of individual TCRβ V gene segments. With some modifications, our method has the potential to be extended to gene segments in the α, γ, and δ TCR loci, as well as the genes encoding for B-cell receptor chains.
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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.