Generation and use of simulated genomic data

Inventors

Foryciarz, Agata • Dean, II, Dennis A.

Assignees

Seven Bridges Genomics Inc

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.

Core Innovation

The invention simulates genomic data for a population exhibiting a variant frequency from input data representing nucleic-acid sequences obtained by chemical analysis of biological samples. The input data are computationally represented as a directed acyclic graph (DAG) data structure including a plurality of nodes and edges connecting the nodes, where the nodes include an origin node and a terminus node and each node corresponds to a genomic position and a variant type.

Each edge of the DAG has weights corresponding to the occurrences of nodes connected by edges in the input data. Simulated genomic data are created by repeatedly traversing a sequence of nodes starting from the origin node and terminating with the terminus node while maintaining variant frequency of the population for the input data, and the traversal is performed probabilistically in accordance with the edge weights.

The simulated genomic data are stored and converted into a standard file format, and the DAG data structure is updated based on determination whether new variants or update counts of variants already present. The update uses a hash value assigned to each node by a hash function, where the hash value is a unique value determined based on the genomic position and the variant type associated with each node.

Claims Coverage

The document includes two independent claims. The claims cover weighted DAG representation of variant-frequency input data, probabilistic traversal to generate simulated genomic data while maintaining variant frequency, and hash-based updating of the DAG with storage and conversion to a standard file format.

Overall, the independent claims focus on weighted DAG representation, probabilistic traversal for simulated genomic data while maintaining variant frequency, and hash-based updating with storage and conversion to a standard file format.

Stated Advantages

Documented Applications