bio-clinical-databases-hla-typing

## 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 ```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. ```python 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