Typing and copy number determination for <scp>HLA‐DRB3</scp>, ‐<scp>DRB4</scp> and ‐<scp>DRB5</scp> from next‐generation sequencing data
Bibliographic record
Abstract
BACKGROUND: HLA-DRB3, DRB4 and DRB5 (DRB3/4/5) are paralogues of HLA-DRB1. They have important roles in transplantation and have been reported to be related to many diseases. HLA typing methods for DRB3/4/5 based on NGS data have many limitations now, such as need of polymerase chain reaction (PCR) or low accuracy. MATERIALS AND METHODS: We present a HLA typing method for DRB3/4/5 based on read mapping and haplotype assembly from NGS data. Also, copy number of DRB3/4/5 is determined by a k-means clustering method according to ratio of sequencing depth between DRB3/4/5 and DRB1. RESULTS: We achieved 100%, 100%, 100% accuracy on simulated data and 95.88%, 98.89%, 99.34% accuracy on MHC capture Illumina sequencing data at 4-digit resolution with 30-fold coverage for DRB3/4/5 separately. We also explored the DRB3/4/5 profiles in five continental populations through low coverage WGS data generated by the 1000 Genome Project. We found that frequency of DRB4 in African were significantly lower than that in all other populations. CONCLUSION: Our method for DRB3/4/5 typing has high accuracy. It is a good supplement to regular HLA typing and could help in disease studies, medical applications and human population diversity studies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".