Sequence Variant Detection Using DAG-Based Read Alignment
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Solution Overview
Problem
State-of-the-art sequence alignment methods struggle to align shorter reads with structural variations, leading to the rejection of meaningful variants, particularly rare mutations near structural variations, due to low alignment scores and computational inefficiencies.
Innovation Solution
Implementing multi-dimensional alignment algorithms using directed acyclic graphs (DAGs) to account for genetic variability, allowing sequence reads to align through multiple pathways, thereby enhancing accuracy and capturing rare variants near structural variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If linear, local sequence alignment methods are used, then alignment speed is improved, but alignment accuracy deteriorates for reads containing structural variations
Solution Approach 1:
The patent transforms the traditional linear, one-dimensional sequence alignment process into a multi-dimensional alignment framework using directed acyclic graphs (DAGs). The DAG structure introduces additional dimensions by representing multiple alternative sequences and structural variations simultaneously, allowing reads to be aligned against numerous possible reference paths in parallel. This dimensional expansion enables the algorithm to capture rare variants near structural variations while maintaining computational efficiency through graph-based optimization.
2Use of energy by moving object
If reads are aligned against a single reference sequence, then computational resources are reduced, but rare variants near structural variations are lost
Solution Approach 1:
The directed acyclic graph (DAG) reference sequence construct serves multiple functions simultaneously: it represents the primary reference sequence, incorporates known structural variations, and accounts for rare variants near structural variations. This multi-functional reference structure allows the alignment algorithm to perform multiple alignment tasks against a single unified reference, eliminating the need for separate alignment processes while preserving genetic variation information that would otherwise be lost.
3Measurement precision
If multiple sequences are accounted for at certain positions in the reference sequence construct, then alignment accuracy is improved, but device complexity increases
Solution Approach 1:
The directed acyclic graph (DAG) structure serves as an intermediary data structure that organizes and manages multiple alternative sequences at varying positions. Rather than directly handling the complexity of multiple sequences, the DAG provides a structured framework where nodes represent sequence positions and edges represent possible transitions between alternative sequences. This intermediary structure simplifies the alignment process by pre-organizing the complexity into a traversable graph format.
Data Source
AI summary
The invention provides methods for identifying rare variants near a structural variation in a genetic sequence, for example, in a nucleic acid sample taken from a subject. The invention additionally includes methods for aligning reads (e.g., nucleic acid reads) to a reference sequence construct accounting for the structural variation, methods for building a reference sequence construct accounting for the structural variation or the structural variation and the rare variant, and systems that use the alignment methods to identify rare variants. The method is scalable, and can be used to align millions of reads to a construct thousands of bases long, or longer.