Haplotyping System Using Regression Analysis for Allele Selection
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Solution Overview
Problem
Current haplotyping methods face challenges in achieving accurate results due to high polymorphism and sequence similarity in human genes, particularly in regions like HLA genes, leading to inefficiencies and decreased analysis speed when using short sequence reads.
Innovation Solution
A system that calculates a relationship index using multiple regression analysis based on population and clinical information, combined with a unique read algorithm for allele determination, to enhance the accuracy and efficiency of haplotyping by filtering and scoring aligned sequence reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If short sequence reads are used for haplotyping, then sequencing speed and efficiency are improved, but accuracy decreases due to high polymorphism and sequence similarity
Solution Approach 1:
The patent applies preliminary action by performing alignment of short sequence reads to reference alleles before allele determination. The system pre-aligns reads to multiple reference alleles in a database, calculates alignment scores, and uses these preliminary alignment results to guide subsequent allele selection and haplotype assembly, thereby maintaining accuracy despite using short reads
Solution Approach 2:
The patent uses an intermediary approach by introducing alignment scores and probability calculations as intermediate steps between raw sequence reads and final haplotype determination. The system calculates alignment scores between reads and reference alleles, then uses these scores to determine allele probabilities, which subsequently guide haplotype assembly. This intermediary processing layer resolves the contradiction by transforming short read data into reliable haplotype information
2Speed
If traditional haplotyping methods are used with short reads, then analysis speed is maintained, but accuracy decreases due to inability to effectively filter false alleles
Solution Approach 1:
The patent implements feedback by using alignment scores to iteratively refine allele determination. The system calculates initial alignment scores between reads and reference alleles, uses these scores to identify candidate alleles, then refines the selection by comparing scores across multiple alleles and applying probability thresholds. This feedback loop ensures accurate allele determination while maintaining analysis speed through efficient scoring mechanisms
Solution Approach 2:
The patent applies parameter changes by transforming raw sequence data into alignment scores, then into probability values, and finally into allele calls. The system changes the parameter representation at each processing stage: from nucleotide sequences to numerical scores, from scores to probabilities, and from probabilities to discrete allele determinations. These parameter transformations enable accurate allele identification from short reads while maintaining computational efficiency
Data Source
AI summary
Disclosed is a human haplotyping method. The method includes: collecting a sequence of a gene to be analyzed; matching and aligning reads of the collected sequence to a reference stored in a database; electing candidate alleles from among alleles of the reference; and selecting a final allele from among the candidate alleles.