In Silico HLA Matching for Accurate Rare Allele Detection
Solution Overview
Problem
Existing HLA typing methods are time-consuming, expensive, and often inaccurate, especially when dealing with large populations, and struggle to identify minor and rare alleles, limiting the effectiveness of donor-recipient matching in transplant procedures.
Innovation Solution
A bioinformatics database utilizing omics data, such as exome and whole genome sequencing, combined with a De Bruijn graph-based algorithm, to determine HLA types accurately and efficiently across a large population, enabling rapid and comprehensive matching of donors and recipients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wet chemistry or PCR-based methods are used for HLA typing, then relatively accurate results are obtained, but significant time and high cost are required especially for large populations
Solution Approach 1:
The patent creates a digital copy of HLA typing by mapping sequencing reads to reference HLA sequences in silico, eliminating the need for physical wet chemistry or PCR reactions. This computational approach replicates the functionality of conventional methods while dramatically reducing time and cost for large population analysis
Solution Approach 2:
The patent replaces mechanical/chemical HLA typing systems (wet chemistry, PCR, hybridization) with a computational bioinformatics system that uses sequence alignment algorithms. This substitution enables rapid processing of large numbers of samples without the time-consuming laboratory procedures of conventional methods
2Productivity
If nested/tandem PCR or hybridization methods are used for high throughput HLA determination, then processing speed is improved, but accuracy deteriorates because hybridization differences among HLA alleles are very minor
Solution Approach 1:
The patent segments the HLA typing process into distinct computational steps: quality control filtering, alignment to reference sequences, variant calling, and genotype determination. This segmentation allows each step to be optimized independently, maintaining high throughput while achieving high accuracy through systematic processing of sequencing data
Solution Approach 2:
The patent performs preliminary actions by conducting quality control filtering and alignment to reference sequences before final genotype determination. These preliminary computational steps prepare the data in advance, enabling accurate differentiation of minor HLA allele differences while maintaining high processing throughput
3Adaptability or versatility
If conventional HLA typing methods are used, then standard HLA types can be determined, but minor and rare alleles cannot be effectively identified
Solution Approach 1:
The patent creates a universal HLA typing system that can identify all HLA alleles including common, minor, and rare variants through a single sequencing and bioinformatics pipeline. The system is not limited to predefined panels but can detect any HLA allele present in the sequencing data by comparing against comprehensive reference sequences
Solution Approach 2:
The patent changes the detection parameters by using next-generation sequencing technology that provides deep coverage of HLA regions, enabling detection of low-frequency alleles. The bioinformatics pipeline adjusts parameters such as alignment thresholds and variant frequency cutoffs to optimize detection of rare HLA alleles while maintaining accuracy
Data Source
AI summary
Contemplated systems and methods use high-accuracy in silico HLA analysis to so establish a transplant match database suitable for transplantation, and particularly stem cell and solid organ transplant.