Graph-Based Genomic Data Simulation for Subpopulation Fidelity
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Solution Overview
Problem
Current simulation frameworks for genomic data are limited by the types of variants they incorporate, their scalability, accuracy, speed, and/or their support for relatively small subpopulations within a larger group, necessitating techniques that respect variant patterns within a subpopulation and overcome these limitations.
Innovation Solution
A graph-based approach using Directed Acyclic Graphs (DAGs) to simulate genomic datasets from large-scale populations, incorporating individual sample data, variant type, position, and zygosity, enabling probabilistic traversal to generate variant datasets that maintain statistical fidelity to the original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current simulation frameworks are used to generate genomic data, then data generation is possible, but the frameworks are limited by variant types incorporated, scalability, accuracy, speed, and support for small subpopulations
Solution Approach 1:
The patent segments the population into distinct subpopulations, each with its own graph structure representing unique variant patterns. This allows the simulation framework to handle multiple subpopulations with different genetic characteristics simultaneously, improving adaptability while maintaining accuracy through population-specific modeling.
Solution Approach 2:
The patent introduces a graph-based data structure dimension that captures complex variant relationships (including structural variants, insertions, deletions, and haplotypes) beyond traditional linear sequence representation. This graphical dimension enables accurate representation of subpopulation-specific variant patterns while improving scalability through efficient graph traversal algorithms.
2Reliability
If more comprehensive variant types are incorporated into simulation frameworks, then accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing real genomic data to construct graph structures that encode variant patterns, frequencies, and relationships before simulation begins. This upfront preparation includes building graphs that represent haplotypes, structural variants, and population-specific characteristics, thereby reducing computational complexity during the actual simulation phase while maintaining high accuracy.
Solution Approach 2:
The patent creates simplified graph-based representations (copies) of complex genomic variant data that preserve essential statistical properties and variant patterns. These graph copies enable efficient simulation by replacing computationally intensive sequence manipulations with streamlined graph traversal operations, thereby reducing computational complexity while maintaining fidelity to the original data.
3Reliability
If larger datasets are simulated to improve statistical validity, then benchmarking capability improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements self-service through automated graph construction and simulation processes that minimize manual intervention. The system automatically processes real genomic data into graph structures, performs probabilistic sampling to generate simulated datasets, and validates results without requiring extensive manual configuration or processing, thereby improving both statistical validity and simulation speed.
Solution Approach 2:
The patent utilizes parameter changes by adjusting sampling probabilities, graph traversal depths, and dataset generation parameters to optimize simulation speed while maintaining statistical validity. The system dynamically adjusts these parameters based on the complexity of the input data and desired output characteristics, enabling efficient generation of large-scale statistically valid datasets.
Data Source
AI summary
Embodiments of the invention utilize a graph-based approach for simulating genomic datasets from large scale populations. Genomic data may be represented as a directed acyclic graph (DAG) that incorporates individual sample data including variant type, position, and zygosity. A simulator may operate on the DAG to generate variant datasets based on probabilistic traversal of the DAG. This probabilistic traversal reflects genomic variant types associated with the subpopulation used to build the DAG, and as a result, the generated variant datasets maintain statistical fidelity to the original sample data.