Generation and use of simulated genomic data
AssigneeSeven Bridges Genomics Inc.
InventorsAgata Foryciarz, Dennis A. Dean, II
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Abstract
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.
Claims21
A method of simulating genomic data using input data representing nucleic-acid sequences obtained by chemical analysis of biological samples obtained from a population of organisms, the population exhibiting a variant frequency, the method comprising the steps of:
- a. computationally representing the input data for the population exhibiting the variant frequency as a directed acyclic graph (DAG) data structure comprising a plurality of nodes and edges connecting the nodes, wherein the nodes include an origin node and a terminus node, each node corresponds to a genomic position and a variant type, and the edges have weights corresponding to the occurrences of nodes connected by edges in the input data;
- and
- b. repeatedly:
- computationally traversing a sequence of nodes of the DAG data structure starting from the origin node and terminating with the terminus node to create simulated genomic data for the population with maintaining variant frequency of the population for the input data, the traversed nodes corresponding to a first variant type representing a single-nucleotide polymorphism (SNP), a second variation type representing a haplotype and a third variation type representing insertion and deletion of a nucleotide, the traversal being performed probabilistically in accordance with the edge weights;
- and
- (ii) storing, in a database, the simulated genomic data corresponding to the traversal and converting the simulated genomic data into a standard file format;
- and
- (iii) updating the DAG data structure based on determination whether new variants or update counts of variants already present using a hash value assigned to each node by a hash function, the hash value being a unique value determined based on the genomic position and the variant type associated with each node.
A system for simulating genomic data using input data representing nucleic-acid sequences obtained by chemical analysis of biological samples obtained from a population of organisms, the population exhibiting a variant frequency, the system comprising:
- a computer memory;
- a graph generator for computationally representing the input data for the population exhibiting the variant frequency as a data structure in the memory encoding a directed acyclic graph (DAG) comprising a plurality of nodes and edges connecting the nodes, wherein the nodes include an origin node and a terminus node, each node specifies a genomic position and a variant type, and the edges have associated weights corresponding to the occurrences of nodes connected by edges in the input data;
- and
- a population simulator for simulating variant datasets by repeatedly:
- (i) traversing a sequence of nodes of the DAG data structure starting from the origin node and terminating with the terminus node to create simulated genomic data for the population with maintaining variant frequency of the population for the input data, the traversed nodes corresponding to a first variant type representing a single-nucleotide polymorphism (SNP), a second variation type representing a haplotype and a third variation type representing insertion and deletion of a nucleotide, the traversal being performed probabilistically in accordance with the edge weights;
- and
- (ii) storing, in the computer memory, the simulated genomic data corresponding to the traversal and converting the simulated genomic data into a standard file format,
- the graph generator is further configured to update the DAG data structure based on determination whether new variants or update counts of variants already present using a hash value assigned to each node by a hash function, the hash value being a unique value determined based on the genomic position and the variant type associated with each node.
Dependencies are inferred from the claim text.