Sequence-Graph STR Genotyping for Short-Read Repeat Expansions
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Solution Overview
Problem
Existing methods struggle to accurately identify and determine repeat expansions using short reads due to the difficulty in aligning them to reference sequences, as there is no clear one-to-one mapping and short reads often fail to fully cover medically relevant repeat sequences, which is crucial for diagnosing genetic disorders like Fragile X syndrome and Huntington's disease.
Innovation Solution
The use of sequence graphs with self-loops representing repeat sequences, combined with probabilistic modeling and alignment techniques, allows for the alignment and genotyping of repeat sequences, even with short reads, by aligning them to directed graphs with vertices and edges, and determining genotypes using paired-end reads and anchored reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If short reads are used for sequencing, then sequencing cost and time are reduced, but alignment accuracy to reference sequences deteriorates due to lack of clear one-to-one mapping
Solution Approach 1:
The patent introduces sequence graphs as an intermediary data structure between short reads and reference sequences. Instead of directly aligning short reads to linear reference sequences (which causes ambiguity), the invention uses sequence graphs that explicitly model repeat regions with self-loops and alternative paths. This intermediary representation allows short reads to be accurately mapped to their genomic locations by traversing graph paths, resolving the alignment ambiguity while maintaining the efficiency of short read sequencing.
Solution Approach 2:
The patent transforms the one-dimensional linear reference sequence into a multi-dimensional graph structure. By representing repeat regions as self-loops and alternative alleles as branching paths in the sequence graph, the invention adds dimensional complexity to the reference representation. This allows short reads to be aligned in the context of multiple possible paths simultaneously, improving alignment accuracy without sacrificing sequencing speed.
2Productivity
If short reads are used for sequencing, then sequencing cost and time are reduced, but the ability to fully cover medically relevant repeat sequences deteriorates
Solution Approach 1:
The patent applies partial action by using short reads that only partially cover repeat sequences, combined with probabilistic modeling to infer the complete repeat structure. Instead of requiring reads to fully traverse entire repeat regions (which would be costly and time-consuming), the invention uses multiple overlapping partial reads and statistical methods to reconstruct the full repeat expansion status, maintaining sequencing efficiency while recovering complete information.
Solution Approach 2:
The patent implements feedback through iterative probabilistic modeling that uses alignment results to refine repeat genotype estimates. The system performs multiple rounds of alignment and probability calculation, where each iteration uses information from previous iterations to improve the accuracy of repeat sequence reconstruction from partial reads. This feedback loop enables complete information recovery without requiring complete read coverage.
3Measurement precision
If sequence graphs with self-loops are used to represent repeat sequences, then genotyping accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the genome into distinct regions: simple unique regions and complex repeat regions. Sequence graphs are constructed only for loci containing repeat sequences, while other regions use traditional linear alignment. This segmentation limits the computational overhead of graph processing to only where necessary, maintaining high genotyping accuracy for repeats without unnecessarily increasing complexity for the entire genome.
Solution Approach 2:
The patent applies different data structures to different genomic regions based on their specific requirements. Repeat-containing loci use sequence graphs with self-loops to capture local complexity, while unique regions use simple linear references. This local quality approach ensures that computational complexity is concentrated only where it provides value (in repeat regions), rather than uniformly across the entire genome.
Data Source
AI summary
The disclosed embodiments concern methods, apparatus, systems and computer program products for genotyping repeat sequences such as medically significant short tandem repeats (STRs). The methods involve aligning reads to a repeat sequence represented by a sequence graph, and using the aligned reads to genotype the repeat sequence. The sequence graph is a directed graph each including at least one self-loop representing a repeat sub-sequence. In some implementations, the reads are paired end reads, and both mates of each read pair may be used to genotype the repeat sequences. Some implementations can be used to determine degenerate codon repeats. Some implementations can be used to genotype repeat sequences each including two or more repeat sub-sequences. Some implementations can be used to genotype nucleic acid sequences each including at least one repeat sub-sequence and another genetic variant such as an insertion, deletion, or substitution.