Profiling model T-cell metagenomes with short reads
Bibliographic record
Abstract
MOTIVATION: T-cell receptor (TCR) diversity in peripheral blood has not yet been fully profiled with sequence level resolution. Each T-cell clonotype expresses a unique receptor, generated by somatic recombination of TCR genes and the enormous potential for T-cell diversity makes repertoire analysis challenging. We developed a sequencing approach and assembly software (immuno-SSAKE or iSSAKE) for profiling T-cell metagenomes using short reads from the massively parallel sequencing platforms. RESULTS: Models of sequence diversity for the TCR beta-chain CDR3 region were built using empirical data and used to simulate, at random, distinct TCR clonotypes at 1-20 p.p.m. Using simulated TCRbeta (sTCRbeta) sequences, we randomly created 20 million 36 nt reads having 1-2% random error, 20 million 42 or 50 nt reads having 1% random error and 20 million 36 nt reads with 1% error modeled on real short read data. Reads aligning to the end of known TCR variable (V) genes and having consecutive unmatched bases in the adjacent CDR3 were used to seed iSSAKE de novo assemblies of CDR3. With assembled 36 nt reads, we detect over 51% and 63% of rare (1 p.p.m.) clonotypes using a random or modeled error distribution, respectively. We detect over 99% of more abundant clonotypes (6 p.p.m. or higher) using either error distribution. Longer reads improve sensitivity, with assembled 42 and 50 nt reads identifying 82.0% and 94.7% of rare 1 p.p.m. clonotypes, respectively. Our approach illustrates the feasibility of complete profiling of the TCR repertoire using new massively parallel short read sequencing technology. AVAILABILITY: ftp://ftp.bcgsc.ca/supplementary/iSSAKE.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".