IMGT/HighV QUEST paradigm for T cell receptor IMGT clonotype diversity and next generation repertoire immunoprofiling
Bibliographic record
Abstract
T cell repertoire diversity and clonotype follow-up in vaccination, cancer, infectious and immune diseases represent a major challenge owing to the enormous complexity of the data generated. Here we describe a next generation methodology, which combines 5′RACE PCR, 454 sequencing and, for analysis, IMGT, the international ImMunoGeneTics information system (IMGT), IMGT/HighV-QUEST web portal and IMGT-ONTOLOGY concepts. The approach is validated in a human case study of T cell receptor beta (TRB) repertoire, by chronologically tracking the effects of influenza vaccination on conventional and regulatory T cell subpopulations. The IMGT/HighV-QUEST paradigm defines standards for genotype/haplotype analysis and characterization of IMGT clonotypes for clonal diversity and expression and achieves a degree of resolution for next generation sequencing verifiable by the user at the sequence level, while providing a normalized reference immunoprofile for human TRB. Dynamic changes in T cell repertoire underlie immune responses during infection, allergy, autoimmunity and cancer. Here, Li et al. present a workflow for high throughput sequencing and analysis of T cell receptor sequences, and use it to monitor the T cell response to influenza vaccination in a human patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".