ReviewComputational and structural biotechnology journal2020
T cell receptor sequence clustering and antigen specificity.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- T-cell receptor clonotypic diversity and specialization in digestive system cancers.NPJ precision oncology · 2026Article
- Benchmarking unsupervised methods for inferring TCR specificity.NAR genomics and bioinformatics · 2025Review
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- A fingerprint approach to pioneer structure-based T cell receptor repertoire analysis and specificity prediction.Frontiers in immunology · 2025Article
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Predictability of antigen binding based on short motifs in the antibody CDRH3.Briefings in bioinformatics · 2024Article
- BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.Briefings in bioinformatics · 2024Article
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
- Antigen-specific and cross-reactive T cells in protection and disease.Immunological reviews · 2023Review
- B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants.Frontiers in microbiology · 2022Article
- The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.Nature machine intelligence · 2021Article
- Using the T Cell Receptor as a Biomarker in Type 1 Diabetes.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.
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Registered trials
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.