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>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto 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:
OptiTypefor HLA class I typing from DNA/RNA reads - CLI:
arcasHLA extract→arcasHLA genotypefor 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
bash# 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
bash# RNA mode OptiTypePipeline.py \ -i rna_R1.fq rna_R2.fq \ -r \ -o optitype_rna_output \ -c config.ini
OptiType Config
ini# 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.
bash# 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.
bash# 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
bash# 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.
python# 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'] }) return 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.
pythonimport 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
