Adaptive Local Alignment for Graph Genomes
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Solution Overview
Problem
Conventional methods for sequence alignment using directed acyclic graphs (DAGs) are computationally expensive and cost-prohibitive due to the combinatorial explosion of calculations, especially when dealing with complex genomic variations and large data sets.
Innovation Solution
The system employs a method to automatically analyze sequence data by identifying candidate mapping positions within a DAG, determining whether an advanced or basic alignment is required, and selecting appropriate algorithms such as pattern matching or graph-aware algorithms based on alignment difficulty, thereby optimizing the alignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear alignment tools are used with DAGs to perform sequence alignment, then alignment accuracy is improved by incorporating genomic variations, but computational cost and processing time increase exponentially due to combinatorial explosion
Solution Approach 1:
The patent segments the DAG into multiple linear paths and processes each path separately using conventional linear alignment tools. This segmentation approach allows the system to maintain high alignment accuracy by incorporating genomic variations while avoiding the combinatorial explosion that would result from processing the entire DAG as a single complex structure. The segmentation divides the computational problem into manageable pieces that can be solved efficiently.
Solution Approach 2:
The patent performs alignment on a subset of linear paths extracted from the DAG rather than exhaustively processing all possible paths. This partial action approach balances alignment accuracy with computational feasibility by selecting representative paths that capture the essential genomic variations without requiring complete enumeration of all DAG paths, thereby reducing computational complexity while maintaining sufficient alignment precision.
2Reliability
If DAGs are used to represent reference genomes with multiple variations, then alignment quality is improved by accounting for SNPs, indels, and structural variants, but processing time and computational resources increase significantly
Solution Approach 1:
The patent creates simplified copies of the DAG in the form of linear paths that can be processed efficiently by conventional alignment tools. These linear path copies retain the essential variation information from the original DAG, allowing the system to maintain high alignment quality while dramatically reducing processing time. The copying approach enables parallel processing of multiple linear representations without the computational burden of the full DAG structure.
3Manufacturing precision
If exact alignment algorithms are applied to complex DAG structures, then mapping precision is improved, but computational expense becomes prohibitive for large data sets
Solution Approach 1:
The patent segments the complex DAG into multiple linear paths, allowing exact alignment algorithms to be applied to each segment independently. This segmentation maintains mapping precision by preserving the variation information in each linear path while improving processing efficiency by breaking down the computationally prohibitive task of aligning to the entire DAG into multiple manageable sub-tasks that can be executed faster and potentially in parallel.
Data Source
AI summary
Systems and methods for analyzing genomic information can include obtaining a sequence read including genetic information; identifying, within a graph representing a reference genome, a plurality of candidate mapping positions that relate to the genetic information, the graph comprising nodes representing genetic sequences and edges connecting pairs of nodes; determining, by means of a computer system, whether an alignment with the graph surrounding each of the plurality of candidate mapping positions is advanced or basic; and performing for each candidate mapping position, by means of the computer system, a local alignment based on whether the local alignment is advanced or basic. The advanced local alignment can include a first-local-alignment algorithm, and the basic local alignment includes a second-local-alignment algorithm. Based on the local alignments, the mapped position of the sequence read can be identified within the genome.