Sequence Graph Genotyping for Short Tandem Repeat Expansions
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Solution Overview
Problem
Existing methods struggle to accurately identify medically relevant repeat expansions using short reads that do not fully traverse the repeat sequence, due to challenges in aligning and mapping these reads to reference sequences.
Innovation Solution
The use of sequence graphs with self-loops representing repeat sequences, combined with probabilistic modeling and alignment techniques, allows for the genotyping of repeat sequences, even with incomplete or short reads, by aligning reads to directed graphs and determining genotypes using paired end reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If short reads are used for sequencing, then sequencing cost and time are reduced, but the ability to fully traverse and accurately identify repeat sequences is worsened
Solution Approach 1:
The patent introduces sequence graphs as an intermediary data structure between short reads and repeat sequence identification. The sequence graph captures all possible repeat expansions at a locus, allowing short reads to be aligned against multiple potential repeat configurations simultaneously, thus enabling accurate repeat detection despite read length limitations
Solution Approach 2:
The patent transforms the one-dimensional linear reference sequence into a multi-dimensional sequence graph structure where nodes represent genomic positions and edges represent possible sequences including repeat expansions. This dimensional transformation allows short reads to map to multiple possible repeat configurations, resolving the contradiction between read length and detection accuracy
2Ease of operation
If traditional linear reference sequences are used, then alignment is simple and fast, but the ability to represent and detect repeat expansions is worsened
Solution Approach 1:
The patent segments the reference genome into locus-specific sequence graphs that separately model known repeat regions. This segmentation allows traditional linear alignment to be used for non-repeat regions while dedicated sequence graphs handle repeat expansions, maintaining overall system simplicity while improving detection reliability
Solution Approach 2:
The sequence graph structure serves multiple functions: it represents the canonical reference sequence, models all possible repeat expansions, and provides a unified alignment framework. This multi-functionality allows a single data structure to maintain alignment simplicity while enabling comprehensive repeat detection
3Productivity
If reads shorter than repeat sequences are used, then sequencing efficiency is improved, but complete traversal of repeat sequences for accurate genotyping is worsened
Solution Approach 1:
The patent implements a feedback mechanism where the sequence graph is iteratively refined based on alignment results. Reads that partially traverse repeat regions provide feedback that helps infer the full repeat expansion size by comparing against all possible repeat configurations in the sequence graph, enabling accurate genotyping despite incomplete read traversal
Solution Approach 2:
The patent performs preliminary construction of sequence graphs that encode all possible repeat expansions before alignment. This preliminary action prepares the alignment system to interpret partial reads correctly by providing pre-computed expectations of what reads should look like at each possible repeat size, thereby enabling accurate genotyping from incomplete reads
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.