Graph-Based Haplotype Inference via Community Detection
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Solution Overview
Problem
Current DNA sequencing methods, such as next-generation sequencing, provide genotype information but fail to accurately assign alleles to haplotypes, leading to inefficiencies and inaccuracies in genetic disease diagnosis due to reliance on extrinsic keys and binary encoding, which limits comparison and reduces the informative value of genetic data.
Innovation Solution
A method that transforms alleles into vertices in a graph with edges connecting alleles from sequence fragments, using community detection to infer haplotypes without the need for extrinsic decoding, allowing for direct representation and comparison of haplotype data within the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard sequencing methods are used to collect genotype information, then sequencing speed and cost are improved, but the ability to assign alleles to haplotypes is lost
Solution Approach 1:
The method segments the genotype data by creating separate binary variables for each allele (e.g., A1 presence, A2 presence) rather than treating them as a single categorical value. This segmentation preserves the individual allele information needed for haplotype assignment while maintaining the efficiency of binary data processing in standard sequencing pipelines.
Solution Approach 2:
The invention transforms the traditional single-dimension genotype data into a multi-dimensional representation where each allele is represented as a separate binary dimension. This dimensional expansion allows the system to capture haplotype information (phase relationships between alleles) without requiring new sequencing technologies, as the additional dimensions are derived computationally from existing genotype calls.
2Device complexity
If SNP alleles are encoded as binary 0 or 1, then data processing is simplified, but the ability to compare datasets and maintain medical significance is reduced
Solution Approach 1:
The method changes the parameter representation from categorical allele codes (A, C, G, T) to binary presence/absence indicators for each allele. This parameter transformation simplifies computational operations while preserving the biological meaning through the use of reference allele designation, allowing datasets to be compared by aligning their binary structures rather than their categorical codes.
Solution Approach 2:
The binary encoding system acts as an intermediary representation that bridges the gap between complex categorical allele data and simplified computational processing. By using binary variables that indicate the presence or absence of each allele relative to a reference, the system maintains the ability to compare datasets while reducing processing complexity.
3Measurement precision
If reference-based haplotype calling is used, then haplotype inference is achieved, but the process becomes time consuming due to extrinsic key lookup
Solution Approach 1:
The invention extracts the haplotype determination process from the traditional reference-based lookup approach. Instead of comparing against external reference haplotypes and requiring key lookups, the method directly computes haplotype probabilities from the binary allele data and observed genotype frequencies, eliminating the time-consuming extrinsic key lookup step while maintaining inference accuracy.
4Measurement precision
If graph-based haplotype assembly is used, then haplotype reconstruction is achieved, but the matrix construction requires artificial binary reduction that adds complexity
Solution Approach 1:
The invention inverts the traditional approach by starting with binary allele representations and directly constructing haplotype probabilities, rather than first creating complex SNP-fragment matrices and then reducing them to binary form. This reversal eliminates the artificial binary reduction step and the associated matrix construction complexity, as the binary data is used directly in the probabilistic model.
Data Source
AI summary
The invention relates to methods for determining a haplotype for an organism by using a system for transforming SNP alleles found in sequence fragments into vertices in a graph with edges connecting vertices for alleles that appear together in a sequence fragment. A community detection operation can be used to infer the haplotype from the graph. The system may produce a report that includes the haplotype of the SNPs found in the genome of that organism.