bio-clinical-databases-hla-typing

name	bio-clinical-databases-hla-typing
description	Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.
tool_type	cli
primary_tool	OptiType

Version Compatibility

Reference examples tested with: OptiType 1.3+, STAR 2.7.11+, pandas 2.2+, samtools 1.19+

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.

HLA Typing

"Determine HLA genotype from my sequencing data" → Call HLA alleles from WGS/WES/RNA-seq reads for transplant matching, neoantigen prediction, or pharmacogenomic screening.

• CLI: OptiType for HLA class I typing from DNA/RNA reads
• CLI: arcasHLA extract → arcasHLA genotype for RNA-seq based typing

OptiType (HLA Class I)

Goal: Call HLA Class I alleles (HLA-A, B, C) at 4-field resolution from WGS, WES, or RNA-seq data.

Approach: Extract HLA region reads from BAM, then run OptiType's integer linear programming algorithm to determine optimal allele assignment.

From DNA-seq

# Extract HLA reads from BAM
samtools view -h input.bam chr6:28000000-34000000 | \
    samtools fastq -1 hla_R1.fq -2 hla_R2.fq -

# Run OptiType
OptiTypePipeline.py \
    -i hla_R1.fq hla_R2.fq \
    -d \
    -o optitype_output \
    -c config.ini

# Output: optitype_output/sample_result.tsv
# Contains HLA-A, HLA-B, HLA-C alleles (4-field resolution)

From RNA-seq

# RNA mode
OptiTypePipeline.py \
    -i rna_R1.fq rna_R2.fq \
    -r \
    -o optitype_rna_output \
    -c config.ini

OptiType Config

# config.ini
[mapping]
razers3=/path/to/razers3
threads=4

[ilp]
solver=glpk
threads=4

[behavior]
deletebam=true
unpaired_weight=0
use_discordant=false

HLA-HD (Full Resolution)

Goal: Perform high-resolution HLA typing for both Class I and Class II loci from WGS/WES data.

Approach: Extract HLA-region reads, then run HLA-HD which uses Bowtie2 mapping against the IPD-IMGT/HLA database.

# HLA-HD for high-resolution typing
# Supports Class I and Class II

# Extract HLA reads
samtools view -b input.bam chr6:28000000-34000000 > hla_region.bam
samtools sort -n hla_region.bam -o hla_sorted.bam
samtools fastq -1 hla_R1.fq -2 hla_R2.fq hla_sorted.bam

# Run HLA-HD
hlahd.sh \
    -t 8 \
    -m 100 \
    -f freq_data \
    hla_R1.fq \
    hla_R2.fq \
    gene_split_filt \
    dictionary \
    sample_name \
    output_dir

# Output includes HLA-A, B, C, DRB1, DQB1, DPB1 at 4-field resolution

arcasHLA (RNA-seq)

Goal: Genotype HLA alleles directly from RNA-seq BAM files aligned with STAR.

Approach: Extract HLA-mapped reads with arcasHLA extract, then genotype using an EM algorithm against the IMGT/HLA database.

# Fast HLA typing from RNA-seq
# Extracts and genotypes in one step

# From STAR-aligned BAM
arcasHLA extract sample.bam -o output_dir
arcasHLA genotype output_dir/sample.extracted.fq.gz -o output_dir

# Output: sample.genotype.json
# {
#   "A": ["A*02:01", "A*24:02"],
#   "B": ["B*35:01", "B*44:03"],
#   "C": ["C*04:01", "C*05:01"]
# }

arcasHLA Merge

# Merge multiple samples
arcasHLA merge output_dir/*.genotype.json -o merged_hla.tsv

HLA Nomenclature

HLA-A*02:01:01:01
     |  |  |  |
     |  |  |  +-- Non-coding variation (optional)
     |  |  +----- Synonymous variation (optional)
     |  +-------- Protein sequence (usually reported)
     +----------- Allele group

Resolution levels:
- 2-field: A*02:01 (protein sequence - clinical standard)
- 4-field: A*02:01:01 (includes synonymous changes)
- Full: A*02:01:01:01 (includes non-coding)

HLA and Pharmacogenomics

Goal: Screen patient HLA alleles for known drug hypersensitivity associations.

Approach: Cross-reference called HLA alleles against a curated table of HLA-drug adverse reaction associations.

# Key HLA-drug associations
HLA_DRUG_ASSOCIATIONS = {
    'B*57:01': {
        'drug': 'Abacavir',
        'reaction': 'Hypersensitivity syndrome',
        'screening': 'Required before prescribing'
    },
    'B*15:02': {
        'drug': 'Carbamazepine',
        'reaction': 'SJS/TEN',
        'populations': 'High risk in Han Chinese, Southeast Asian'
    },
    'B*58:01': {
        'drug': 'Allopurinol',
        'reaction': 'SJS/TEN',
        'populations': 'High risk in Han Chinese, Korean, Thai'
    },
    'A*31:01': {
        'drug': 'Carbamazepine',
        'reaction': 'DRESS',
        'populations': 'European, Japanese'
    }
}

def check_hla_drug_risk(hla_alleles, drug):
    '''Check if patient HLA poses drug reaction risk'''
    risks = []
    for allele in hla_alleles:
        if allele in HLA_DRUG_ASSOCIATIONS:
            assoc = HLA_DRUG_ASSOCIATIONS[allele]
            if assoc['drug'].lower() == drug.lower():
                risks.append({
                    'allele': allele,
                    'drug': drug,
                    'reaction': assoc['reaction']
                })
     risks

Parse OptiType Results

Goal: Parse OptiType TSV output into structured HLA calls and format for clinical reporting.

Approach: Read the tab-separated result file and extract allele pairs for each HLA locus.

import pandas as pd

def parse_optitype(result_file):
    '''Parse OptiType TSV output'''
    df = pd.read_csv(result_file, sep='\t')
    # Columns: A1, A2, B1, B2, C1, C2, Reads, Objective
    hla_calls = {
        'HLA-A': [df['A1'].iloc[0], df['A2'].iloc[0]],
        'HLA-B': [df['B1'].iloc[0], df['B2'].iloc[0]],
        'HLA-C': [df['C1'].iloc[0], df['C2'].iloc[0]]
    }
    return hla_calls

def format_hla_report(hla_calls):
    '''Format HLA calls for clinical report'''
    report = []
    for gene, alleles in hla_calls.items():
        allele_str = '/'.join(sorted(set(alleles)))
        report.append(f'{gene}: {allele_str}')
    return '\n'.join(report)

Class I vs Class II

Class	Genes	Function	Typing Priority
Class I	HLA-A, B, C	Present intracellular peptides	Neoantigen, PGx
Class II	HLA-DR, DQ, DP	Present extracellular peptides	Transplant, autoimmune

Tool Comparison

Tool	Input	Classes	Resolution	Speed
OptiType	WGS/WES/RNA	I only	4-field	Fast
HLA-HD	WGS/WES	I and II	4-field	Moderate
arcasHLA	RNA-seq	I and II	4-field	Fast
HLA-LA	WGS	I and II	4-field	Slow

Related Skills

• clinical-databases/pharmacogenomics - HLA-drug interactions
• variant-calling/clinical-interpretation - Clinical reporting
• single-cell/cell-type-annotation - HLA expression