IMGT/HighV QUEST paradigm for T cell receptor IMGT clonotype diversity and next generation repertoire immunoprofiling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it