METHODS AND SYSTEMS FOR GENOMIC ANALYSIS

• US 20150066824A1
• Filed: 08/29/2014
• Published: 03/05/2015
• Est. Priority Date: 08/30/2013
• Status: Active Grant

• Current Assignee: Personalis, Inc.
• Original Assignee: Personalis, Inc.
• Inventors: Pratt, Mark R., West, John, Chen, Richard, Li, Ming, Harris, Jason
• Granted Patent: US 9,183,496 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: C12Q 1/68 involving nucleic acids
• CPC Class Codes: C12Q 1/6869 Methods for sequencing
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• CPC Class Codes: G16H 50/00 ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics

Abstract

A computer-implemented method for processing and/or analyzing nucleic acid sequencing data comprises receiving a first data input and a second data input. The first data input comprises untargeted sequencing data generated from a first nucleic acid sample obtained from a subject. The second data input comprises target-specific sequencing data generated from a second nucleic acid sample obtained from the subject. Next, with the aid of a computer processor, the first data input and the second data input are combined to produce a combined data set. Next, an output derived from the combined data set is generated. The output is indicative of the presence or absence of one or more polymorphisms of the first nucleic acid sample and/or the second nucleic acid sample.

Claims

1. A computer-implemented method, comprising:

a. receiving a first data input, wherein said first data input comprises untargeted sequencing data generated from a first nucleic acid sample of a subject;

b. receiving a second data input, wherein said second data input comprises target-specific sequencing data generated from a second nucleic acid sample of said subject;

c. combining, with the aid of a computer processor, said first data input and said second data input to produce a combined data set; and

d. generating, with the aid of a computer processor, an output derived from said combined data set, wherein said output is indicative of the presence or absence of one or more polymorphisms of said first nucleic acid sample and/or said second nucleic acid sample.

2. The method of claim 1, wherein said output comprises a first alignment, wherein said first alignment is generated by mapping said first data input onto a first reference sequence.

3. The method of claim 2, wherein said output further comprises a second alignment, wherein said second alignment is generated by mapping said second data input onto a second reference sequence, and optionally wherein said first reference sequence and said second reference sequence are the same sequence.

4. The method of claim 1, wherein said output comprises a uniform alignment, wherein said uniform alignment is generated by mapping said combined data set onto a reference sequence.

5. The method of claim 1, wherein said target-specific sequencing data is based on targeted sequencing of exomes, specific genes, genomic regions, or a combination thereof.

6. The method of claim 5, wherein said genomic regions comprise one or more polymorphisms, sets of genes, sets of regulatory elements, micro-deletions, homopolymers, simple tandem repeats, regions of high GC content, regions of low GC content, paralogous regions, or a combination thereof.

7. The method of claim 6, wherein said one or more polymorphisms comprise one or more single nucleotide variations, copy number variations, insertions, deletions, structural variant junctions, variable length tandem repeats, or a combination thereof.

8. The method of claim 1, wherein said target-specific sequencing data is generated using one or more non-random primers or hybridization probes.

9. The method of claim 8, wherein said non-random primers comprise primers targeting one or more genes, exons, untranslated regions, or a combination thereof.

10. The method of claim 1, wherein said untargeted sequencing data is generated from the use of one or more random primers or hybridization probes.

11. The method of claim 1, wherein said untargeted sequencing data is whole genome sequencing data, off-target data arising from a targeted assay, or a combination thereof.

12. The method of claim 11, wherein said whole genome sequencing data comprises single reads, paired-end reads, or mate-pair reads.

13. The method of claim 1, wherein said first nucleic acid sample and said second nucleic acid sample are the same sample.

14. The method of claim 1, wherein said target-specific sequencing data comprises a specific portion and a non-specific portion, and wherein at least a portion of said untargeted sequencing data is the non-specific portion of the target-specific sequencing data.

15. The method of claim 14, wherein said untargeted sequencing data is said non-specific portion of said target-specific sequencing data.

16. The method of claim 15, wherein said targeted-specific sequencing data is whole exome sequencing data.

17. The method of claim 1, wherein each of said first data input and said second data input comprises variant data, and wherein said combining comprises combining said variant data from said first data input and said second data input into said combined data set.

18. The method of claim 17, wherein said first data input comprises copy number and/or structural variant data, and wherein said second data input comprises single nucleotide variations (SNV) and/or insertion deletion polymorphism (indel) data.

19. The method of claim 18, wherein said copy number and/or structural variant data is generated from a hidden Markov model.

20. The method of claim 1, further comprising performing untargeted sequencing on said first nucleic acid sample to generate said first data input.

21. The method of claim 20, further comprising performing target-specific sequencing on said second nucleic acid sample to generate said second data input.

22. A system for genomic sequencing, comprising:

a. at least one memory location comprising a first data input and second data input, wherein said first data input comprises untargeted sequencing data and said second data input comprises target-specific sequencing data of or related to a genome of a subject or a portion of said genome, wherein said first data input is obtained from a first nucleic acid sample of said subject and said second data input is obtained from a second nucleic acid sample of said subject;

b. a computer processor operably coupled to said at least one memory location, wherein said computer processor is programmed to (i) combine said first data input and said second data input to produce a combined data set and (ii) generate an output from at least a portion of said combined data set, wherein said output is indicative of the presence or absence of one or more polymorphisms in said genome of said subject or a portion of said genome; and

c. an electronic display coupled to said computer processor, wherein said electronic display provides said output for display to a user.

23. The system of claim 22, wherein each of said first data input and said second data input comprises variant data, and wherein said computer processor combines said variant data from said first data input and said second data input into said combined data set.

24. The system of claim 23, wherein said first data input comprises copy number and/or structural variant data, and wherein said second data input comprises single nucleotide variations (SNV) and/or insertion deletion polymorphism (indel) data.

25. The system of claim 24, wherein said copy number and/or structural variant data is generated from a hidden Markov model.

26. The system of claim 22, wherein said computer processor is programmed to map said first data input onto a first reference sequence in memory to generate a first alignment.

27. The system of claim 26, wherein said computer processor is programmed to map said second data input onto a second reference sequence in memory to generate a second alignment.

28. A system for genomic sequencing, comprising:

a. at least one memory location comprising a first data input and second data input, wherein said first data input comprises untargeted sequencing data and said second data input comprises target-specific sequencing data of or related to a genome of a subject or a portion of said genome, wherein said first data input is obtained from a first nucleic acid sample of said subject and said second data input is obtained from a second nucleic acid sample of said subject;

b. a computer processor coupled to said at least one memory location, wherein said computer processor is programmed to (i) combine said first data input and said second data input to produce a combined data set, and (ii) generate an output from at least a portion of said combined data set, wherein said output is indicative of the presence or absence of one or more polymorphisms in said genome of said subject or a portion of said genome; and

c. an electronic data storage unit coupled to said computer processor, wherein said electronic data storage unit comprises said combined data set and/or said output.

29. The system of claim 28, wherein each of said first data input and said second data input comprises variant data, and wherein said computer processor combines said variant data from said first data input and said second data input into said combined data set.

30. The method of claim 29, wherein said first data input comprises copy number and/or structural variant data, and wherein said second data input comprises single nucleotide variations (SNV) and/or insertion deletion polymorphism (indel) data.