Sequence Alignment Using Directed Acyclic Graphs for Repeating Elements
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Solution Overview
Problem
Current sequence alignment methods are inadequate for handling genetic data with many large repeat sequences, as they struggle to distinguish complete or partial alignments to repeat sequences, leading to mis-identification of genotypes and distortions in downstream analysis due to the high frequency and similarity of repetitive elements like Alu elements in human genomes.
Innovation Solution
The use of multi-dimensional alignment algorithms that account for the variability of repeat sequences through directed acyclic graphs (DAGs) and other reference sequence constructs, allowing for more accurate alignment of paired reads by limiting possible co-alignments and reducing mismatches, deletions, and insertions, while enabling efficient parallelization and reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequence alignment methods are used to align reads to a reference genome, then alignment speed and computational efficiency are maintained, but alignment accuracy deteriorates due to inability to distinguish reads mapping to repetitive elements from those mapping to unique regions
Solution Approach 1:
The reference genome is segmented into unique regions and repetitive regions. The alignment algorithm processes reads by first attempting to map to unique regions, and only when that fails does it consider repetitive regions. This segmentation allows the system to maintain high alignment accuracy by eliminating false positives from repetitive elements, while keeping computational complexity manageable by avoiding exhaustive searching of all genomic regions for every read.
2Measurement precision
If alignment algorithms account for all possible repeat sequence variations, then alignment accuracy improves, but computational resources and time increase significantly
Solution Approach 1:
The algorithm performs preliminary actions by first attempting to align reads to unique genomic regions before considering repetitive regions. This preliminary filtering step eliminates the need to process all possible repeat sequence variations for every read, thereby maintaining high alignment accuracy for reads that map to unique regions while significantly improving alignment speed by avoiding unnecessary computational effort on reads that will ultimately map unambiguously.
3Measurement precision
If reads are discarded when they cannot be uniquely aligned, then alignment precision improves, but loss of genetic information increases
Solution Approach 1:
The alignment algorithm dynamically adjusts its behavior based on read characteristics. For reads that can be uniquely aligned to non-repetitive regions, the algorithm maintains strict alignment precision standards. For reads that initially appear to map to repetitive regions, the algorithm dynamically considers multiple factors including paired-end information, local sequence context, and probabilistic models to determine whether the read can be confidently assigned to a specific location, thereby reducing read loss while maintaining precision.
4Reliability
If exhaustive searching of all possible alignment locations is performed, then alignment completeness improves, but computational complexity increases
Solution Approach 1:
The algorithm applies different quality standards and processing approaches to different genomic regions. For unique regions, simple and fast alignment methods are used. For repetitive regions, the algorithm applies more sophisticated local quality assessments that consider paired-end constraints, local sequence context, and statistical models. This local quality approach ensures alignment completeness for reads in repetitive regions while avoiding the computational complexity of exhaustive searching across the entire genome.
Data Source
AI summary
The invention includes methods for aligning reads (e.g., nucleic acid reads) comprising repeating sequences, methods for building reference sequence constructs comprising repeating sequences, and systems that can be used to align reads comprising repeating sequences. The method is scalable, and can be used to align millions of reads to a construct thousands of bases long. The methods and systems can additionally account for variability within a repeating sequence, or near to a repeating sequence, due to genetic mutation.