Hla Typing

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/clinical-databases/hla-typingThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Calls HLA class I and class II alleles at 2/4/6/8-field resolution from WGS/WES/RNA-seq/long-read data using OptiType, HLA-LA, T1K, Polysolver, HLA-HD, arcasHLA, StarPhase, or HIBAG imputation. Use when typing for HSCT,…

---
name: bio-clinical-databases-hla-typing
description: Calls HLA class I and class II alleles at 2/4/6/8-field resolution from WGS/WES/RNA-seq/long-read data using OptiType, HLA-LA, T1K, Polysolver, HLA-HD, arcasHLA, StarPhase, or HIBAG imputation. Use when typing for HSCT, solid-organ transplant, neoantigen prediction, PGx screening (B*57:01, B*15:02, etc.), or disease-association studies, with reconciliation across tools and IPD-IMGT/HLA version mismatch handling.
tool_type: cli
primary_tool: T1K
---

## Version Compatibility

Reference examples tested with: OptiType 1.3.5, HLA-LA 1.0.4, T1K 1.0.6 (Song 2023), Polysolver 4.0, HLA-HD 1.7.1, arcasHLA 0.6.0, StarPhase 1.0+ (PacBio), HIBAG 1.40+, samtools 1.19+, bwa-mem 0.7.17+. IPD-IMGT/HLA database release frequency is quarterly; tools must be re-bundled with the current release to capture new alleles (~38,000 alleles at Jan 2024; ~43,000+ by Jul 2025).

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Tool reference-bundle vintage matters more than algorithm choice for non-European cohorts; a 2022-bundled HLA-LA will silently miss thousands of post-2022 alleles dominant in African and South Asian ancestry.

# HLA Typing for Clinical Applications

**'Determine HLA genotype for HSCT / neoantigen prediction / PGx screening'** -> Call HLA class I (A, B, C) and class II (DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1) alleles at the resolution required by the downstream application.

- CLI (general-purpose all-rounder): `t1k --preset hla -1 R1.fq -2 R2.fq -f hla_reference.fa`
- CLI (class I gold standard from WES/WGS): `OptiTypePipeline.py -i R1.fq R2.fq -d`
- CLI (class I + II with PRG): `HLA-LA.pl --BAM input.bam --graph PRG_MHC_GRCh38_withIMGT`
- CLI (RNA-seq): `arcasHLA extract sample.bam -o out && arcasHLA genotype out/sample.extracted.fq.gz`
- CLI (long-read transplant-grade): PacBio HiFi StarPhase
- R (imputation from SNP arrays): `HIBAG::predict()` with ancestry-stratified reference panel

## Resolution Levels and What Each Application Requires

HLA nomenclature: **HLA-A\*02:01:01:01** = family : protein-changing : synonymous : intronic/UTR. **Expression suffixes:** `N` (null; DNA present, no protein expressed); `L` (low expression); `S` (secreted); `Q` (questionable); `A` (aberrant). A serologically apparent DR4-positive donor carrying `DRB4*01:03:01:02N` is functionally DR53-negative; a classic HSCT donor-selection failure.

| Application | Min resolution | Why |
|-------------|---------------|-----|
| **HSCT (unrelated donor)** | 6-field (12/12 match) | Null alleles + permissive DPB1 + Bw4/Bw6 + TCE3 core/non-core |
| **Solid organ transplant** | 4-field (2-digit:2-digit) | Eplet-level epitope match (HLAMatchmaker, PIRCHE-II) |
| **ICI neoantigen prediction** | 4-field class I + II | NetMHCpan-4.1 minimum |
| **HLA-disease association** | 4-field | Standard for GWAS HLA fine-mapping |
| **HLA-B\*57:01 abacavir screen** | 4-field, specific | Other \*57 alleles (\*57:03) do NOT cause HSS |
| **HLA-B\*15:02 carbamazepine** | 4-field, specific | \*15:02 only; \*15:01 (NFE-common) is not the risk allele |

## G-Groups vs P-Groups: Routinely Confused

- **G-groups** collapse alleles with identical DNA sequence across the antigen-recognition exons (class I exons 2-3; class II exon 2). Use for sequence-level lab QC.
- **P-groups** collapse alleles encoding identical mature protein across class I positions 1-90 (or class II beta1 domain positions 1-94). Use for epitope-based matching and neoantigen prediction.

## DRB1 + DRB3/4/5 Linkage: The Mandatory Sanity Check

DR haplotype linkage is fixed and is the canonical sanity check on any DR typing:

| DRB1 allele family | Linked DRB3/4/5 |
|---------------------|-----------------|
| DR1 (\*01), DR8 (\*08), DR10 (\*10) | None |
| DR3 (\*03), DR11 (\*11), DR12 (\*12), DR13 (\*13), DR14 (\*14) | DRB3 |
| DR4 (\*04), DR7 (\*07), DR9 (\*09) | DRB4 |
| DR15 (\*15), DR16 (\*16) | DRB5 |

Any caller reporting DRB4 with `DRB1*15:01` is broken or has a chimera. Use this as a routine QC check on automated pipelines.

## Algorithmic Taxonomy: Short-Read Tools

| Tool | Class I | Class II | KIR | Resolution | Approach | Fails when |
|------|---------|----------|-----|-----------|----------|-----------|
| **OptiType** (Szolek 2014 *Bioinformatics* 30:3310) | Yes (~97% 4-digit) | No | No | 4-field | ILP on exons 2-3 | Class II needed; very deep contamination |
| **Polysolver** (Shukla 2015 *Nat Biotechnol* 33:1152) | Yes (~95% 4-digit) | No | No | 4-field | Allele-specific ref alignment | Class II; non-European ancestry under-typing |
| **HLA-LA** (Dilthey 2019 *Bioinformatics* 35:4394) | Yes (~94% class I) | Yes (strong class II) | No | 4-field | Graph-based PRG | High RAM/disk (~30-100 GB scratch) |
| **T1K** (Song 2023 *Genome Res*) | Yes (~99% 4-digit) | Yes (~99%) | Yes (KIR + KIR3DL2 ligand) | 4-field | EM on consensus reference | Newer; less benchmarking on edge cases |
| **HLA-HD** (Kawaguchi 2017 *Hum Mutat* 38:788) | Yes (~98%) | Yes (~95%) | No | 4-field | Bowtie2 against IPD-IMGT | License required for commercial use |
| **arcasHLA** (Orenbuch 2020 *Bioinformatics* 36:33) | Yes (~100% 2-field) | Yes (>99% 2-field) | No | 4-field from RNA-seq | EM on STAR alignment | DNA-seq; population prior bias in non-EUR |
| **PHLAT, HLAforest, HLAminer, seq2HLA, HLAreporter** | Yes | Some | No | Mostly 2-4 field | Various | Older; superseded |

**Operational benchmark consensus:** in the Claeys 2023 *BMC Genomics* 13-tool benchmark (Matey-Hernandez 2018), HLA-HD was the top class-II caller and OptiType (WES) / arcasHLA (RNA) the class-I anchors. T1K (Song 2023, not in that benchmark)

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install arcas-hla

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Hla Typing

• github.com/GPTomics/bioSkills/tree/main/clinical-databases/hla-t — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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