Systems and methods for analyzing viral nucleic acids
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Publication Number
US-11702708-B2
Publication Date
2023-07-18
Expiration Date
Abstract
The invention provides systems and methods for analyzing viruses by representing viral genetic diversity with a directed acyclic graph (DAG), which allows genetic sequencing technology to detect rare variations and represent otherwise difficult-to-document diversity within a sample. Additionally, a host-specific sequence DAG can be used to effectively segregate viral nucleic acid sequence reads from host sequence reads when a sample from a host is subject to sequencing. Known viral genomes can be represented using a viral reference DAG and the viral sequence reads from the sample can be compared to viral DAG to identify viral species or strains from which the reads were derived. Where the viral sequence reads indicate great genetic diversity in the virus that was infecting the host, those reads can be assembled into a DAG that itself properly represents that diversity.
Core Innovation
The invention analyzes a virus having a viral genome comprising a plurality of viral sequences by using a viral DNA reference directed acyclic graph (DAG). The viral DNA reference DAG represents at least some of the plurality of viral sequences and comprises nodes and edges connecting the nodes, with nodes stored as objects in non-transitory memory and comprising symbol strings representing viral sequences.
The method obtains a plurality of sequence reads from a sample containing viral nucleic acid and aligns one or more sequence reads to the viral DNA reference DAG. The aligning is performed at least in part by determining alignment scores between the sequence reads and symbol strings associated with nodes of the viral DNA reference DAG, and an alignment score for the second node is determined based on an alignment score associated with the first node.
Based on the aligned one or more sequence reads, the method determines an identity of a virus in the sample and provides a report that includes the identity of the virus in the sample. In the described system form, the same workflow is executed by at least one computer hardware processor with processor-executable instructions stored in non-transitory memory.
Claims Coverage
The independent claims are clm-00001, clm-00016, and clm-00020, and they cover methods or systems that align sequence reads to a viral DNA reference DAG to determine a virus identity and provide a report. Across the independent claims, there are 3 main inventive feature themes.
Viral DNA reference directed acyclic graph with symbol-string objects and pointer connections
Accessing a viral DNA reference directed acyclic graph (DAG) from non-transitory memory, where the viral DNA reference DAG represents at least some of the plurality of viral sequences and comprises nodes and edges, the nodes include a first node and a second node stored as objects in non-transitory memory with symbol strings, and the first object includes a list of pointers to objects including a pointer to the second object.
Alignment scores between reads and DAG node symbol strings with node-to-node score dependence
Aligning one or more sequence reads to the viral DNA reference DAG at least in part by determining alignment scores between the sequence reads and symbol strings associated with nodes of the viral DNA reference DAG, where determining an alignment score for the second node includes determining the alignment score for the second node based on an alignment score associated with the first node.
Virus identity determination from aligned reads and report generation
Determining, based on the aligned one or more sequence reads, an identity of a virus in the sample and providing a report that includes the identity of the virus in the sample.
Across clm-00001, clm-00016, and clm-00020, the claims converge on aligning sample sequence reads to a viral DNA reference DAG represented with node/edge objects and pointer-connected symbol strings, using alignment scores that depend on relationships between nodes, and determining a virus identity from the aligned reads to generate a report.
Stated Advantages
Documented Applications
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