Genetic Sequence DAG Alignment for Structural Variant Detection
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Solution Overview
Problem
Current DNA sequencing technologies produce complex data that is difficult to analyze due to heterozygosity, somatic mutations, and structural variants, often requiring simplifications that discard information and ignore heterogeneity when comparing genomic sequences.
Innovation Solution
The method involves aligning directed acyclic graphs (DAGs) to compare genetic sequences without sacrificing information, using weighted scores for matches, mis-matches, and gaps to find the best-scoring alignment, and representing nucleic acids as DAGs to identify chromosomal structural variants and haplotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If genomic sequences are simplified to consensus sequences for comparison, then the comparison process becomes easier and faster, but information about heterogeneity and individual variations is lost
Solution Approach 1:
The patent transitions from one-dimensional linear sequence comparison to two-dimensional DAG graph comparison. By representing sequences as DAGs where nodes can branch to represent multiple variants, the system maintains heterogeneity information while enabling systematic comparison through graph alignment algorithms. This dimensional transformation allows simultaneous preservation of consensus information and individual variant information.
Solution Approach 2:
The patent embeds multiple sequence variants within a hierarchical DAG structure where parent nodes represent consensus sequences and child nodes represent specific variants. This nesting allows the consensus sequence to be contained within the broader DAG structure, enabling comparison at multiple levels of detail simultaneously - both the simplified consensus level and the detailed variant level.
2Loss of information
If complex sequence data with multiple variants is analyzed in detail, then comprehensive genetic information is obtained, but the analysis complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex sequence data into discrete DAG nodes and edges, where each node represents a specific genetic position and its possible variants. This segmentation transforms the continuous complex data into discrete, manageable units that can be processed systematically through graph alignment algorithms, reducing analytical complexity while preserving all variant information.
Solution Approach 2:
The patent introduces DAGs as an intermediary data structure between raw sequence data and final comparison results. The DAG serves as a mediator that organizes complex variant information into a structured format with defined nodes and edges, enabling the use of standardized graph alignment algorithms to handle the complexity systematically rather than dealing with raw complex data directly.
3Device complexity
If traditional linear alignment methods are used for sequence comparison, then the computational process is simple, but the methods cannot adequately represent heterozygosity and structural variants
Solution Approach 1:
The patent extends traditional linear alignment by transforming sequences into DAG structures, adding a graphical dimension to the alignment process. This allows the representation of branching paths for heterozygous positions and structural variants while maintaining alignment algorithms that generalize from traditional linear methods to graph-based methods, preserving simplicity where possible while enhancing representational accuracy.
Data Source
AI summary
The invention provides methods for comparing one set of genetic sequences to another without discarding any information within either set. A set of genetic sequences is represented using a directed acyclic graph (DAG) avoiding any unwarranted reduction to a linear data structure. The invention provides a way to align one sequence DAG to another to produce an alignment that can itself be stored as a DAG. DAG-to-DAG alignment is a natural choice wherever a set of genomic information consisting of more than one string needs to be compared to any non-linear reference. For example, a subpoptilation DAG could be compared to a population DAG in order to compare the genetic features of that subpopulation to those of the population.