Multi-dimensional Sequence Alignment via Directed Acyclic Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sequence alignment methods, such as Smith-Waterman, struggle to accurately align shorter reads, especially in the presence of structural variations, leading to the rejection of rare variants located near structural variations, which can be critical for disease detection.
Innovation Solution
The development of multi-dimensional alignment algorithms that transform linear sequence alignment processes into multi-dimensional alignment algorithms, utilizing directed acyclic graphs (DAGs) to account for genetic variability, allowing for better alignment of reads through an entire genome and identification of rare variants near structural variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear sequence alignment methods (e.g., Smith-Waterman) are used, then computational simplicity is maintained, but alignment accuracy deteriorates in the presence of structural variations
Solution Approach 1:
The patent transforms the traditional linear sequence alignment problem into a multi-dimensional alignment problem by representing sequences as paths through a directed acyclic graph (DAG). This dimensional transformation allows the algorithm to simultaneously consider multiple alignment paths and structural variations, thereby improving alignment accuracy without excessive complexity increase.
Solution Approach 2:
The patent segments the alignment process into discrete steps represented as nodes in a DAG, where each node corresponds to a specific alignment state or event (match, mismatch, insertion, deletion, structural variation). This segmentation allows complex alignments to be broken down into manageable computational units while maintaining overall accuracy.
2Reliability
If conventional alignment methods are used, then processing speed is maintained, but detection of rare variants deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-processing sequence data to identify potential structural variations and rare variants before the main alignment process. The DAG structure is constructed in advance to incorporate known structural variations, allowing the alignment algorithm to efficiently focus on detecting rare variants without re-processing the entire sequence data multiple times.
3Measurement precision
If multiple sequencing reads are performed to minimize errors, then sequence accuracy is improved, but computational cost increases
Solution Approach 1:
The patent merges multiple sequencing reads into a unified alignment framework using the DAG structure. By combining multiple reads and their corresponding alignment paths in the DAG, the algorithm can simultaneously process and compare multiple sequences, identifying consensus patterns and rare variants while reducing redundant computational operations that would occur if each read were processed separately.
Data Source
Figure 1(A)
Figure 1(B)
Figure 2A~2C
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.