Genomic Pattern Analysis Using Reference Graphs and K-mer Hashing
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Solution Overview
Problem
Current methods for analyzing genetic sequences, particularly using next-generation sequencing technologies, face challenges in efficiently mapping short sequence reads to reference genomes due to the large amount of genetic variation and computational complexity, leading to inefficiencies and cost-prohibitive resource requirements.
Innovation Solution
The method involves creating a reference graph that represents a genomic sequence and its variations as a directed acyclic graph, using k-mers to index positions within the graph, and employing a hash table for rapid lookups to identify candidate mapping regions, allowing for efficient alignment of sequence reads and detection of mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive alignment is performed against all known genomes, then mapping accuracy is improved, but computational complexity and resource requirements become intractable
Solution Approach 1:
The reference genome is segmented into k-mers (short subsequences of length k), and the query sequence is divided into overlapping segments. Each segment is independently mapped to the reference using hash table lookups of its k-mers, avoiding the need for exhaustive alignment of the entire sequence against all reference genomes. This segmentation enables parallel processing and dramatically reduces computational complexity while maintaining mapping accuracy.
Solution Approach 2:
Hash tables are pre-computed and stored for the reference genome, containing all k-mers and their corresponding positions. This preliminary action allows query sequences to be rapidly mapped through simple hash lookups rather than performing computationally intensive alignments during the actual mapping process, significantly reducing resource requirements while preserving accuracy.
2Measurement precision
If all known genetic variation is stored and analyzed, then detection precision is improved, but resource requirements become cost-prohibitive
Solution Approach 1:
The method extracts only the essential information needed for mapping by computing hash values of k-mers from the reference genome and storing them in compact hash tables. This extraction process eliminates redundant data while preserving all necessary information for accurate mapping, reducing storage and computational resource requirements while maintaining detection precision for genetic variations.
Solution Approach 2:
The patent uses hash functions to transform k-mer sequences into fixed-size integer keys, changing the parameter representation from variable-length DNA sequences to compact numerical values. This parameter transformation enables efficient storage and rapid comparison of genetic variations without requiring proportional resources, allowing comprehensive variation analysis with reduced resource requirements.
3Reliability
If O(n^2) analysis is performed for paired-end reads, then mapping completeness is improved, but processing speed deteriorates
Solution Approach 1:
Paired-end reads are processed by segmenting the search space into independent hash table lookups for each read. Instead of performing O(n^2) comparisons between all possible mate pairs, each read is independently mapped using hash table lookups, and valid pairs are identified by checking positional constraints. This segmentation reduces the complexity from quadratic to linear while maintaining mapping completeness through constraint validation.
Solution Approach 2:
Hash tables serve as an intermediary data structure that enables rapid lookup of k-mer positions in the reference genome. By using hash tables as a mediator between the query sequences and reference genome, the system avoids direct O(n^2) comparisons and achieves linear-time mapping while preserving all valid mappings through subsequent filtering by pairing constraints.
Data Source
AI summary
The invention provides methods for analyzing sequence data in which a large amount and variety of reference data are efficiently modeled as a reference graph, such as a directed acyclic graph (DAG). The method includes determining positions of k-mers within a reference graph that represents a genomic sequence and known variation, storing the positions of each k-mer in a table entry indexed by a hash of that k-mer, and identifying a region within the reference graph that includes a threshold number of the k-mers by reading from the table entries indexed by hashes of substrings of a subject sequence. The subject sequence may subsequently be mapped to the candidate region.