tools-only HLA Typing - Usage Guide
Overview
Call HLA alleles from NGS data (WGS, WES, or RNA-seq) for transplant matching, neoantigen prediction, and pharmacogenomic screening.
Prerequisites
# OptiType (HLA Class I)
conda install -c bioconda optitype
# HLA-HD
# Download from https://www.genome.med.kyoto-u.ac.jp/HLA-HD/
# arcasHLA (RNA-seq)
pip install arcas-hla
arcasHLA reference --update
Quick Start
Tell your AI agent:
- "Run HLA typing on my WES data"
- "Type HLA from my RNA-seq BAM"
- "Check if this patient has HLA-B*57:01 for abacavir"
- "Get 4-field HLA resolution from my sample"
Example Prompts
Basic Typing
"Run OptiType on my WES BAM to get HLA-A, B, C"
"Type HLA from my tumor RNA-seq"
Pharmacogenomics
"Check HLA alleles for carbamazepine contraindication"
"Screen for HLA-B*57:01 before abacavir"
Transplant
"Get full HLA typing (Class I and II) for transplant matching"
What the Agent Will Do
- Extract reads mapping to HLA region (chr6:28-34Mb)
- Run appropriate HLA typing tool based on data type
- Parse results to standard nomenclature
- Report alleles at requested resolution (2-field or 4-field)
- Check against pharmacogenomic risk alleles if requested
Tool Selection
| Data Type | Recommended Tool | Classes |
|---|---|---|
| WES/WGS | OptiType, HLA-HD | I (OptiType) or I+II (HLA-HD) |
| RNA-seq | arcasHLA | I and II |
| Targeted panel | HLA-HD | I and II |
Tips
- OptiType is fastest but only types Class I (HLA-A, B, C)
- 4-field resolution (A*02:01) is clinical standard
- Extract HLA region from BAM before typing to speed up
- RNA-seq typing works well with arcasHLA from STAR-aligned BAMs
- HLA-B*57:01 screening is required before abacavir prescription
- Population-specific alleles affect pharmacogenomic risk