NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
Bibliographic record
Abstract
Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. Nature Communications. Accepted (August 2019) Abstract: Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using in vitro co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.019 |
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".