Genotyping of human neutrophil antigens (HNA) from whole genome sequencing data
Bibliographic record
Abstract
BACKGROUND: Neutrophil antigens are involved in a variety of clinical conditions including transfusion-related acute lung injury (TRALI) and other transfusion-related diseases. Recently, there are five characterized groups of human neutrophil antigen (HNA) systems, the HNA1 to 5. Characterization of all neutrophil antigens from whole genome sequencing (WGS) data may be accomplished for revealing complete genotyping formats of neutrophil antigens collectively at genome level with molecular variations which may respectively be revealed with available genotyping techniques for neutrophil antigens conventionally. RESULTS: We developed a computing method for the genotyping of human neutrophil antigens. Six samples from two families, available from the 1000 Genomes projects, were used for a HNA typing test. There are 500 ~ 3000 reads per sample filtered from the adopted human WGS datasets in order for identifying single nucleotide polymorphisms (SNPs) of neutrophil antigens. The visualization of read alignment shows that the yield reads from WGS dataset are enough to cover all of the SNP loci for the antigen system: HNA1, HNA3, HNA4 and HNA5. Consequently, our implemented Bioinformatics tool successfully revealed HNA types on all of the six samples including sequence-based typing (SBT) as well as PCR sequence-specific oligonucleotide probes (SSOP), PCR sequence-specific primers (SSP) and PCR restriction fragment length polymorphism (RFLP) along with parentage possibility. CONCLUSIONS: The next-generation sequencing technology strives to deliver affordable and non-biased sequencing results, hence the complete genotyping formats of HNA may be reported collectively from mining the output data of WGS. The study shows the feasibility of HNA genotyping through new WGS technologies. Our proposed algorithmic methodology is implemented in a HNATyping software package with user's guide available to the public at http://sourceforge.net/projects/hnatyping/.
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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 | 0.001 | 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".