Graph-Based Genomic Reference Model for Read Alignment Bias
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Solution Overview
Problem
Current genomic analysis methods face challenges such as reference allele bias, inefficient data storage, and computational limitations in processing large-scale genomic data, particularly in representing polymorphisms and structural variations, which hinder accurate and efficient DNA sequence analysis.
Innovation Solution
A graph-based reference genome framework, known as the GNOmics Graph Model (GGM), is introduced, which represents all known polymorphisms, including SNPs, indels, and structural changes, using nodes and edges to enable efficient data compression and parallel computing, allowing for accurate read alignment and reduced storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a linear reference genome is used for read mapping, then the mapping process is simple and straightforward, but reference allele bias occurs and polymorphic variants are not accurately represented
Solution Approach 1:
The linear reference genome is segmented into multiple alternative alleles at polymorphic positions, creating a graph structure where each node represents a possible variant. This segmentation allows reads to be mapped against multiple alternative sequences simultaneously, eliminating reference allele bias while maintaining manageable complexity through localized branching at variant sites.
Solution Approach 2:
The patent transitions from a one-dimensional linear reference genome to a multi-dimensional graph structure where additional dimensions represent alternative alleles and polymorphic variants. This dimensional expansion allows the system to represent genetic diversity without proportionally increasing computational complexity, as the graph structure organizes alternatives in a navigable framework.
2Adaptability or versatility
If multiple alternative reference genomes are created to represent population diversity, then polymorphism representation improves, but data storage requirements and computational complexity increase significantly
Solution Approach 1:
Multiple alternative reference genomes are merged into a single graph-based reference structure where shared sequences are represented once and alternative alleles are connected through graph edges. This merging eliminates redundant storage of identical sequences while maintaining the ability to represent population diversity, reducing both storage requirements and computational complexity compared to maintaining separate reference genomes.
Solution Approach 2:
The graph-based reference genome serves multiple functions simultaneously: it represents the canonical reference sequence, incorporates population-specific variants, and provides alternative alleles for diverse populations. This universal structure eliminates the need for separate reference genomes for different populations, reducing overall system complexity while enhancing adaptability to various genetic backgrounds.
3Reliability
If a graph-based reference genome is used to represent all polymorphisms, then reference allele bias is eliminated and variant detection accuracy improves, but the complexity of the reference structure increases
Solution Approach 1:
The graph-based reference genome is designed to be dynamically adaptable, allowing efficient navigation and traversal algorithms that adjust the effective complexity based on the query sequence. Reads are mapped by dynamically selecting relevant paths through the graph based on sequence similarity, which maintains computational efficiency despite the underlying complex structure. The system adapts to each mapping query rather than requiring exhaustive processing of all possible paths.
Data Source
AI summary
This disclosure provides a computational framework with related methods and systems to enhance the analysis of genomic information. More specifically, the disclosure provides for a graph-based reference genome framework, referred to as a GNOmics Graph Model (GGM), which represents genomic sequence information in edges with nodes representing transitions between edges. The disclosed GGM framework can represent all known polymorphisms simultaneously, including, SNPs, indels, and various rearrangements, in a data-efficient manner. The edges can contain weights to reflect the likelihood of a path within the GGM incorporating any particular edge. The disclosure also provides for systems and methods for using the GGM as a reference model for the rapid assembly of short sequence reads and analysis of DNA sequence variation with enhanced computational efficiency.