BamQuery: a proteogenomic tool for the genome-wide exploration of the immunopeptidome
Bibliographic record
Abstract
ABSTRACT MHC-I-associated peptides (MAPs) derive from selective yet highly diverse genomic regions, including allegedly non-protein-coding sequences, such as endogenous retroelements (EREs). Quantifying canonical (exonic) and non-canonical MAPs-encoding RNA expression in malignant and benign cells is critical for identifying tumor antigens (TAs) but represents a challenge for immunologists. We present BamQuery, a computational tool attributing an exhaustive RNA expression to MAPs of any origin (exon, intron, UTR, intergenic) from bulk and single-cell RNA-sequencing data. We show that non-canonical MAPs (including TAs) can derive from multiple different genomic regions (up to 35,343 for EREs), abundantly expressed in normal tissues. We also show that supposedly tumor-specific mutated MAPs, viral MAPs, and MAPs derived from proteasomal splicing can arise from different unmutated non-canonical genomic regions. The genome-wide approach of BamQuery allows comprehensive mapping of all MAPs in healthy and cancer tissues. BamQuery can also help predict MAP immunogenicity and identify safe and actionable TAs.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".