Graph Reference Sequence Alignment With Branching-Aware Traversal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for aligning biological sequences to graph references are computationally expensive and inefficient, leading to inaccurate results due to the exponential computational complexity of aligning against each path through the graph reference.
Innovation Solution
A new class of techniques that involve traversing the graph underlying the graph reference using a linear alignment algorithm augmented to handle branching and merging, generating state data for each node position, and using methods like bit-parallel automaton (BPA) alignment to efficiently align biological sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques align biological sequences to graph references by examining each path through the graph, then alignment accuracy is maintained, but computational complexity becomes exponential and alignment time becomes prohibitively long
Solution Approach 1:
The patent segments the graph reference into nodes and edges, allowing the alignment algorithm to traverse and process the graph structure systematically rather than enumerating all possible paths. This segmentation enables dynamic programming to compute alignments by breaking down the complex exponential search space into manageable subproblems at each node, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent employs preliminary actions by pre-processing the graph reference into a traversable structure with nodes representing sequences and edges representing transitions. This preparation allows the alignment algorithm to efficiently navigate the graph without repeatedly examining all paths, thereby reducing alignment time while preserving the ability to accurately identify variants.
2Ease of manufacture
If conventional techniques align sequences to graph references using standard linear alignment algorithms, then implementation is simple, but the algorithm cannot handle branching and merging in the graph reference
Solution Approach 1:
The patent creates a universal alignment framework that extends standard linear alignment algorithms to handle graph reference structures. The augmented algorithm performs multiple functions: it traverses the graph, handles branching at nodes with multiple outgoing edges, manages merging of paths, and maintains alignment state across complex graph topologies. This multi-functional approach retains the simplicity of linear alignment concepts while adding the versatility needed for graph references.
Solution Approach 2:
The patent introduces dynamic elements to the alignment algorithm by making it adaptive to the graph reference structure. The algorithm dynamically adjusts its behavior based on whether it is encountering branching points, merging points, or linear sequences in the graph. This dynamic capability allows the same algorithm to handle diverse graph configurations without requiring separate specialized algorithms for each case.
3Loss of information
If the alignment algorithm traverses all paths through the graph reference, then complete alignment information is obtained, but computational complexity increases exponentially
Solution Approach 1:
The patent merges the alignment computation across multiple paths by using dynamic programming to combine results from different graph traversals. Instead of independently processing each path and then merging results (which would be exponential), the algorithm merges computations at nodes where paths converge, building up alignment information incrementally. This merging approach obtains complete alignment information while keeping computational complexity manageable through shared subproblem solutions.
Solution Approach 2:
The patent changes the fundamental parameter of how alignment is computed from path-based enumeration to node-based dynamic programming. By shifting from counting paths to tracking alignment states at nodes, the algorithm transforms the computational complexity from exponential (number of paths) to linear or polynomial (number of nodes and edges), while preserving complete alignment information through systematic state tracking.
Data Source
AI summary
Techniques for aligning a biological sequence to a graph reference construct. The graph reference construct includes first, second, and third nodes. The techniques may include: accessing first state data indicating an extent to which each of multiple subsequences of the biological sequence matches the construct when aligned so as to end at a last position of a sequence represented by the first node; accessing second state data indicating an extent to which each of the multiple subsequences matches the construct when aligned so as to end at a last position of a sequence represented by the second node; and generating third state data using the first state data and the second state data, the third state data indicating an extent to which each of the multiple subsequences matches the construct when aligned so as to end at a first position of a sequence represented by the third node.