Genome Graph Data Structure for Reference Bias Reduction
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Solution Overview
Problem
The current linear reference genome model is insufficient for representing allelic diversity in complex genomic regions, leading to reference bias and inaccurate analysis, particularly in mammalian genomes, as it fails to capture genomic variations effectively.
Innovation Solution
A genome graph data structure and coordinate system are proposed, where genomic variations are represented as edges with unique identifiers, positions, and nucleotide sequences, allowing for the efficient identification and extraction of sequence paths, and extending existing genomic data file formats to support graph-based genome analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a linear reference genome model is used, then computational analysis is simpler and easier to reference, but it fails to represent allelic diversity in complex genomic regions, leading to reference bias and inaccurate analysis
Solution Approach 1:
The patent segments the genome representation from a single linear reference into multiple paths through a graph structure. The genome graph divides the genomic space into multiple alternative loci paths that represent different haplotypes and variations, allowing the system to segment the reference into manageable, alternative routes that can be selectively traversed based on the query sequence characteristics.
Solution Approach 2:
The patent transitions from a one-dimensional linear reference genome to a multi-dimensional graph structure where nodes represent genomic positions and edges represent alternative sequences. This dimensional change allows the system to represent multiple allelic variants simultaneously in a structured space, enabling accurate mapping by navigating through multiple dimensions of genomic variation rather than forcing all sequences onto a single linear path.
2Device complexity
If a single tiling path is used to produce a consensus representation, then the reference genome is simpler to reference, but genomic variations are excluded, introducing pervasive reference bias
Solution Approach 1:
The patent merges multiple alternative loci and haplotype representations into a unified genome graph structure. By combining multiple reference paths that represent different genomic variations into a single integrated graph, the system preserves all variant information while maintaining a cohesive reference framework that can be efficiently navigated.
Solution Approach 2:
The genome graph serves multiple functions simultaneously: it acts as a comprehensive reference that includes all known variations, a mapping target that can accommodate diverse query sequences, and a structure that enables accurate variant detection. This multi-functional reference system eliminates the need to choose between simplicity and completeness.
3Adaptability or versatility
If alternate loci scaffolds are included in regions with known alternate haplotypes, then representation of allelic diversity is improved, but the complexity of the reference genome increases
Solution Approach 1:
The patent implements a dynamic reference system where the genome graph structure allows flexible navigation between alternative paths based on the query sequence. The system dynamically selects the most appropriate path through the graph during mapping, adapting to the specific characteristics of each query sequence rather than requiring a static, overly complex reference structure.
Solution Approach 2:
The genome graph acts as an intermediary structure between the simple linear reference and the complex reality of genomic diversity. It mediates by providing a structured framework that incorporates variations in a systematic way, using nodes and edges to represent genomic positions and alternatives, thereby managing complexity through structured organization rather than unstructured inclusion of all variants.
Data Source
AI summary
A method for storing, by a processor, a genomic graph representing a plurality of individual genomes, including: storing a linear representation of a reference genome in a data storage; receiving a first genome; identifying variations in the first genome from the reference genome; generating graph edges for each variation in the first genome from the reference genome; generating for each generated graph edge: an edge identifier that uniquely identifies the current edge in the genome graph; a start edge identifier that identifies the edge from which the current edge branches out; a start position that indicates the position on the start edge that serves as an anchoring point for the current edge; an end edge identifier that identifies the edge into which the current edge joins in; an end position that indicates the position on the end edge that serves as an anchoring point for the current edge; and a sequence indicating the nucleotide sequence of the current edge; and storing the edge identifier, start edge identifier, start position, end edge identifier, end edge position, and sequence for each generated graph edge in the data storage. Based on this genome graph data structure, we further propose a scheme for specifying a path, which may traverse one or more edges, and the ways to extend existing genomic data formats such as SAM, VCF and MPEG-G to support the use of genome graph reference using our proposed coordinate system.