Graph Reference Sequence Alignment via K-mer Segmentation
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Solution Overview
Problem
Conventional techniques for aligning sequence reads to graph references are computationally expensive and inefficient due to the complexity of the underlying graphs, leading to inaccurate results and high computational complexity, especially in regions with a large number of variants.
Innovation Solution
A method that reduces the number of paths considered during graph traversal by excluding previously evaluated regions and aligning short subsequences (k-mers) of the sequence read, using techniques such as depth-first search and string matching algorithms to efficiently identify the best-fit position on the graph reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional graph traversal techniques are used to align sequence reads to graph references, then alignment accuracy is improved by accounting for genomic variations, but computational complexity increases exponentially due to the large number of paths that must be evaluated
Solution Approach 1:
The patent segments the sequence read into multiple k-mers (short subsequences) and processes each k-mer independently through the graph. This divides the complex alignment problem into smaller, manageable sub-problems, reducing the computational burden while maintaining alignment accuracy by allowing the read to span across variant regions in the graph reference.
Solution Approach 2:
Instead of evaluating all possible paths through the graph reference, the patent uses partial action by limiting the search to only those paths that contain matching k-mers from the sequence read. This selective approach evaluates a subset of relevant paths rather than exhaustively searching the entire graph space, dramatically reducing computational complexity while preserving alignment accuracy.
2Reliability
If the number of variants in the graph reference is increased to improve sensitivity, then more genomic variations are accounted for, but the number of paths through the graph increases leading to combinatorial explosion
Solution Approach 1:
By segmenting the sequence read into k-mers, the patent can efficiently search through graphs with many variants without being overwhelmed by the combinatorial explosion of paths. Each k-mer independently identifies relevant regions in the graph, and the combination of matching k-mers reconstructs the full alignment, maintaining sensitivity while improving alignment speed.
Solution Approach 2:
The patent performs preliminary action by pre-processing the sequence read into k-mers and using these short subsequences to quickly identify candidate paths in the graph reference before performing full alignment. This preliminary segmentation and matching step filters out irrelevant paths early, enabling the algorithm to handle graphs with many variants efficiently.
3Productivity
If short k-mers are used for alignment to reduce computational burden, then alignment speed is improved, but sensitivity to detect variations may be reduced
Solution Approach 1:
The patent merges the results from multiple independent k-mer alignments to reconstruct the full sequence read alignment. By combining the positional information from several k-mers, the method achieves both speed (through efficient k-mer matching) and sensitivity (through cumulative evidence from multiple segments), resolving the contradiction between alignment speed and detection sensitivity.
Data Source
AI summary
Various embodiments of the disclosure relate to systems and methods for aligning a sequence read to a graph reference. In one embodiment, the method comprises selecting a first node from a graph reference, the graph reference comprising a plurality of nodes connected by a plurality of directed edges, at least one node of the plurality of nodes having a nucleotide sequence. The method further comprises traversing the graph reference according to a depth-first search, and comparing a sequence read to nucleotide sequences generated from the traversal of the graph reference. The traversal of the graph is then modified in response to a determination that each and every node associated with a given nucleotide sequence was previously evaluated.