Systems and methods for mitochondrial analysis

• US 10,584,380 B2
• Filed: 02/03/2016
• Issued: 03/10/2020
• Est. Priority Date: 09/01/2015
• Status: Active Grant

• Current Assignee: Seven Bridges Genomics, Inc.
• Original Assignee: Seven Bridges Genomics, Inc.
• Inventors: Locke, Devin, Szamel, Piotr
• Primary Examiner(s): Skibinsky, Anna
• Application Number:
  US15/014,483
• Publication Number:
  US 20170058341A1
• Time in Patent Office: 1,497 Days
• Field of Search: None
• CPC Class Codes: C12Q 1/6874 involving nucleic acid arrays, e.g. sequencing by hybridisation
• CPC Class Codes: C12Q 1/6888 for detection or identification of organisms
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search

Abstract

The invention provides methods of analyzing an individual'"'"'s mtDNA by transforming available reference sequences into a directed graph that compactly represents all the information without duplication and comparing sequence reads from the mtDNA to the graph to identify the individual or describe their mtDNA. A directed graph can represent all of the genetic variation found among the mitochondrial genomes across all of a number of reference organisms while providing a single article to which sequence reads can be aligned or compared. Thus any sequence read or other sequence fragment can be compared, in a single operation, to the article that represents all of the reference mitochondrial sequences.

Claims

1. A method for analyzing a mitochondrial genome from an organism, the method comprising using at least one hardware processor connected to a tangible memory subsystem to perform:

creating, in the tangible memory subsystem, a mitochondrial DNA (mtDNA) reference graph representing a plurality of mitochondrial sequences, the mtDNA reference graph comprising a directed acyclic graph (DAG) comprising a plurality of vertices stored as objects in the tangible memory subsystem, wherein sequence strings of the plurality of mitochondrial sequences that match each other when aligned are each represented by a single common object and sequence strings that vary are represented as alternate objects, wherein at least one sequence string comprises a plurality of symbols, and wherein each object is stored in the tangible memory subsystem as a sequence string and a list of one or more pointers to adjacent objects, wherein each pointer identifies a physical location in the tangible memory subsystem at which an adjacent object is stored, such that the objects are linked to represent each of the mitochondrial sequences as a path through the mtDNA reference graph;

obtaining a plurality of sequence reads from a biological sample previously obtained from a subject;

aligning the plurality of sequence reads to paths through the mtDNA reference graph, wherein the aligning comprises calculating match scores between sequence reads in the plurality of sequence reads and sequence strings associated with vertices in the plurality of vertices, and looking backwards at each vertex, having one or more predecessor vertices, to the predecessor vertices if and only if a symbol comprises the first symbol of the sequence string associated with its vertex to select a path based on its score; and

providing a report that identifies one or more of the mitochondrial sequences that aligned to the plurality of sequence reads.

2. The method of claim 1, wherein the report identifies mitochondrial heteroplasmy in the subject.

3. The method of claim 1, wherein the subject is an unknown subject and the report provides the identity of the subject.

4. The method of claim 1, wherein the plurality of mitochondrial sequences are obtained from relatives of the subject.

5. The method of claim 1, wherein the vertex objects are connected by edge objects, the directed graph further comprising an adjacency list for each vertex object and edge object, wherein the adjacency list for a vertex object or edge object lists the edge objects or vertex objects to which that vertex object or edge object is adjacent.

6. The method of claim 5, wherein each entry in an adjacency list is a pointer to the adjacent vertex object or edge object.

7. The method of claim 6, wherein each pointer identifies a physical location in the memory subsystem at which the adjacent object is stored.

8. The method of claim 1, wherein the report describes a mutation in the mitochondrial genome of the subject.

9. The method of claim 1, wherein the sequence reads correspond to at least a portion of a D-loop of the mitochondria of the subject.

10. The method of claim 1, wherein finding alignments between the sequence reads and paths through the directed graph comprises a multi-dimensional look-back operation to find a trace through a multi-dimensional matrix based on a score for the trace.

11. The method of claim 1, wherein creating the mtDNA reference graph further comprises:

obtaining each of the plurality of mitochondrial sequences;

using the processor to find the portions of the sequences that match each other when aligned;

creating, using the processor, the objects to represent the portions; and

storing each of the objects in the tangible memory device.

12. The method of claim 1, wherein each of the plurality of mitochondrial sequences represents at least 80% of a mitochondrial genome.

13. A method of detecting mitochondrial heteroplasmy in a subject, the method comprising using at least one hardware processor connected to a tangible memory subsystem to perform:

creating, in the tangible memory subsystem, a mitochondrial DNA (mtDNA) reference graph representing a plurality of known variations in the mitochondrial genome, the mtDNA reference graph comprising a directed acyclic graph (DAG), in which each of the known variations is associated with a path through the DAG, the DAG comprising a plurality of objects, wherein each object is stored in the tangible memory subsystem as a sequence string and a list of one or more pointers to adjacent objects, wherein each sequence string represents mtDNA nucleotide sequence information and at least one sequence string comprises a plurality of symbols, and wherein each pointer identifies a physical location in the tangible memory subsystem at which an adjacent object is stored, such that the objects are linked to represent a plurality of mitochondrial genomes as a path through the mtDNA reference graph;

obtaining a plurality of sequence reads from a biological sample previously obtained from a subject;

aligning the plurality of sequence reads to the mtDNA reference graph, the aligning comprising finding a position on the DAG for a sequence read based on the sequence read and sequence strings associated with objects in the plurality of objects by calculating match scores between the sequence read and the sequence strings, and looking backwards at each object, having one or more predecessor objects, to the predecessor objects if and only if a symbol comprises the first symbol of the sequence string associated with its object to select a path based on its score; and

identifying, based on the aligned sequence reads, at least one position in the mtDNA reference graph in which the aligned plurality of sequence reads align to alternate paths.

14. The method of claim 13, further comprising providing a report identifying mitochondrial heteroplasmy in the subject based on the identified at least one position.

15. The method of claim 13, further comprising identifying a second position in the mtDNA reference graph in which sequence reads align to alternate paths.

16. The method of claim 13, wherein aligning each sequence read to the mtDNA reference graph comprises performing a multi-dimensional look-back operation to find a trace through a multi-dimensional matrix based on a score for the trace.

17. A method of identifying an unknown individual, comprising using at least one hardware processor connected to a tangible memory subsystem to perform:

creating, in the tangible memory subsystem, a mitochondrial DNA (mtDNA) reference graph representing a plurality of known variations in the mitochondrial genome, the mtDNA reference graph comprising a directed acyclic graph (DAG), in which each of the known variations is associated with a path through the DAG, the DAG comprising a plurality of objects, wherein each object is stored in the tangible memory subsystem, as a sequence string and a list of one or more pointers to adjacent objects, wherein each sequence string represents mtDNA nucleotide sequence information and at least one sequence string comprises a plurality of symbols, and wherein each pointer identifies a physical location in the tangible memory subsystem at which an adjacent object is stored, such that the objects are linked to represent a plurality of mitochondrial genomes as a plurality of paths through the mtDNA reference graph;

obtaining a plurality of sequence reads from a biological sample previously obtained from a subject;

aligning the plurality of sequence reads to the mtDNA reference graph, the aligning comprising finding a position on the DAG for a sequence read based on the sequence read and sequence strings associated with objects in the plurality of objects by calculating match scores between the sequence read and the sequence strings, and looking backwards at each object, having one or more predecessor objects, to the predecessor objects if and only if a symbol comprises the first symbol of the sequence string associated with its object to select a path based on its score; and

determining, based on the aligned sequence reads, the identity of the unknown subject.

18. The method of claim 17, wherein at least one of the known variations comprises variations in the mitochondrial genome of a maternal-line individual related to the unknown subject.

19. The method of claim 17, further comprising:

obtaining a plurality of maternal-line sequence reads from a sample from a maternal-line relative of the unknown subject;

aligning each maternal-line sequence read to the mtDNA reference graph, the aligning comprising finding a position on the mtDNA reference graph for the maternal-line sequence read based on the sequence read and the nucleotide sequence information associated with each object; and

comparing the alignment of the maternal-line sequence reads to the alignment of the sequence reads of the unknown subject; and

determining the identity of the unknown subject based on the comparison.

20. The method of claim 17, wherein the known variations comprise a hyper-variable region of the mitochondrial genome.