LOH-HLA Analysis Wrapper Script

Description

A comprehensive bioinformatics tool for Loss of Heterozygosity (LOH) analysis in HLA regions, designed for parallel processing of multiple genomic samples.

Module Details

• Authors: Edwin Huang + LLMs
• Categories: HLA analysis
• Source Repository: https://github.com/mskcc/lohhla
• Contact: [email protected]

Input Files

1. BAM List File (Text file)
   • Contains paths to tumor BAM files
2. HLA Type List File (Text file)
   • Contains corresponding HLA type file paths
3. Optional: Normal BAM file

Output Files

1. Coverage plots
2. HLA allele mapping results
3. LOH analysis reports
4. Temporary intermediate files (if clean-up disabled)

Parameters

Parameter	Description	Default Value	Type
Output Directory	Specifies location for analysis results	Current working directory	File path
Normal BAM File	Optional reference normal sample	FALSE	File path
HLA FASTA Location	Reference HLA sequence database	~/lohhla/data/hla_all.fasta	File path
HLA Exon Location	HLA exon boundary information for plotting	~/lohhla/data/hla.dat	File path
Minimum Coverage	Minimum read coverage at mismatch sites	30	Numeric
K-mer Size	Size of genomic fragments for read mapping	50	Numeric
Mismatch Tolerance	Maximum allowed mismatches in read-to-allele mapping	1	Numeric
Mapping Step	Perform mapping to HLA alleles	TRUE	Boolean
Fishing Step	Identify reads matching specific k-mers	TRUE	Boolean
Plotting Step	Generate visualization of analysis results	TRUE	Boolean
Coverage Step	Analyze coverage differences across regions	TRUE	Boolean
Clean Up	Remove temporary files after analysis	TRUE	Boolean
Ignore Warnings	Continue execution despite non-critical warnings	TRUE	Boolean
Parallel Cores	Number of CPU cores for parallel processing	1	Numeric
Novoalign Directory	Path to alignment tool executable	Empty	File path
GATK Directory	Path to Genome Analysis Toolkit executable	Empty	File path

Computational Requirements

• R environment
• Required R libraries: optparse, parallel
• External tools: Novoalign, GATK (optional)
• Sufficient computational resources based on sample complexity and selected cores

Use Cases

• Cancer genomics research
• HLA region variation analysis
• Immunogenomics studies
• Personalized medicine investigations

Limitations

• Requires matched BAM and HLA type files
• Performance dependent on input data quality
• Computational intensity increases with sample complexity

Recommended Workflow

1. Prepare input BAM and HLA type files
2. Configure analysis parameters
3. Select appropriate computational resources
4. Execute wrapper script
5. Review generated results