Streaming Long‐Read Sequence Alignments for HLA Predictions Using HLAminer
Bibliographic record
Abstract
Long-read sequencing platforms such as the Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) platforms now offer sufficient read lengths, throughput, and accuracy at competitive costs to analyze polymorphic regions of the human genome, including the highly complex human leukocyte antigen (HLA) gene cluster-a cornerstone of human immunity. Here, we present a streamlined protocol for predicting HLA signatures from whole-genome shotgun (WGS) long-read sequencing data by directly streaming sequence alignments into HLAminer. This method is as simple as running minimap2, scales efficiently with the number of sequences, and works with any read aligner compatible with the SAM file format-eliminating the need to store bulky alignment files on disk. We provide a step-by-step guide for predicting HLA class I and class II alleles from third-generation long-read sequencing data and demonstrate the robustness of predictions even with older, less accurate WGS nanopore datasets and relatively low (10×) sequencing coverage. Code availability: HLAminer is available under the BC Cancer software license agreement (academic use) at https://github.com/bcgsc/HLAminer. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: HLA prediction from streamed ONT or PacBio long-read alignments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".