Bambam: parallel comparative analysis of high-throughput sequencing data

• US 10,249,384 B2
• Filed: 05/27/2016
• Issued: 04/02/2019
• Est. Priority Date: 05/25/2010
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Sanborn, John Zachary, Haussler, David
• Primary Examiner(s): Levy, Amy M
• Application Number:
  US15/167,528
• Publication Number:
  US 20160275241A1
• Time in Patent Office: 1,040 Days
• CPC Class Codes: C12Q 1/6886 for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: C12Q 2600/118 Prognosis of disease development
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: G06F 2203/04806 Zoom, i.e. interaction techniques or interactors for controlling the zooming operation
• CPC Class Codes: G06F 3/04845 for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• 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: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 70/20 relating to practices or guidelines
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y02A 90/30 Assessment of water resources

Abstract

The present invention relates to methods for evaluating and/or predicting the outcome of a clinical condition, such as cancer, metastasis, AIDS, autism, Alzheimer'"'"'s, and/or Parkinson'"'"'s disorder. The methods can also be used to monitor and track changes in a patient'"'"'s DNA and/or RNA during and following a clinical treatment regime. The methods may also be used to evaluate protein and/or metabolite levels that correlate with such clinical conditions. The methods are also of use to ascertain the probability outcome for a patient'"'"'s particular prognosis.

Claims

1. A computer implemented method of displaying genomic variants between a tumor tissue and a matched normal tissue comprising:

receiving a reference genome or portion thereof, a first genetic sequence string object including first sub-strings from the tumor tissue and aligned to the reference genome or portion thereof, and a second genetic sequence string object including second sub-strings from the matched normal tissue and aligned to the reference genome or portion thereof;

generating at least one differential sequence object, each generated through incremental synchronized reading of the first sub-strings from the first genetic sequence string object and the second sub-strings from the second genetic sequence string object using a known position of at least one of a plurality of sub-strings corresponding to the known position to produce local alignment between the first sub-strings and the second sub-strings, the known position determined based on the reference genome or portion thereof, the incremental synchronized reading keeping sequence data read from the first genetic sequence string object and the second genetic sequence string object at common genomic locations across the reference genome or portion thereof during the generating;

instantiating, via a browser computer, the at least one differential sequence object stored in a computer memory, the at least one differential sequence object representing a difference between a localized alignment of multiple sequence reads of a tumor genome sequence of the tumor tissue and a matched normal genome sequence of the matched normal tissues;

identifying, via the browser computer, at least one genomic variant between the tumor tissue and the matched normal tissue based on the at least one differential sequence object at a genomic position corresponding to the localized alignment;

generating, via the browser computer, a browser image including a representation of the at least one genomic variant with respect to a reference genome sequence;

displaying, via the browser computer, on a display, the browser image; and

allowing, via the browser computer, displaying of genomic regions associated with the at least one genomic variant relative to the reference genome sequence.

2. The method of claim 1, wherein the browser image includes a change in copy number associated with the at least one genomic variant.

3. The method of claim 1, wherein the at least one genomic variant comprises a variant selected from the group consisting of:

an inter-chromosomal rearrangement, an intra-chromosomal rearrangement, a deletion-type rearrangement, and a mutation.

4. The method of claim 1, wherein the at least one genomic variant is associated with a gene annotation.

5. The method of claim 1, wherein the at least one genomic variant comprises a breakpoint.

6. The method of claim 1, wherein the at least one genomic variant comprises at least one of an insertion and a deletion.

7. The method of claim 6, wherein the at least one of the insertion and the deletion includes a small indel.

8. The method of claim 1, wherein the browser image includes a representation of copy number.

9. The method of claim 8, wherein the representation of copy number comprises a representation of an overall copy number.

10. The method of claim 8, wherein the representation of copy number comprises a representation of an allele specific copy number.

11. The method of claim 1, wherein the browser image comprises a linear plot representing the at least one genomic variant.

12. The method of claim 1, wherein the browser image comprises a circular plot representing the at least one genomic variant.

13. The method of claim 1, wherein the at least one differential sequence object comprises a whole genome difference between the tumor genome sequence and the matched normal genome sequence.

14. The method of claim 1, further comprising enabling, via the browser computer, zooming into or out of the genomic regions.

15. The method of claim 14, further comprising enabling, via the browser computer, zooming out to a full genome view that includes the at least one genomic variant.

16. The method of claim 14, further comprising enabling, via the browser computer, zooming into to a single base pair associated with the at least one genomic variant.

17. The method of claim 1, further comprising:

after generating the at least one differential sequence object, storing the at least one differential sequence object in at least one BAM file; and

reading the at least one differential sequence object from the at least one BAM file as part of instantiating the at least one differential sequence object stored in the computer memory.

18. The method of claim 17, further comprising reading the at least one differential sequence object from multiple BAM files, wherein the at least one differential sequence object is stored in the multiple BAM files.

19. The method of claim 1, wherein generating the at least one differential sequence object includes:

selecting a tumor genotype and a matched normal genotype that maximize a joint probability as a function of the first sub-strings and the second sub-strings aligned to a first known position; and

determining a difference between the tumor genotype and the matched normal genotype at the first known position.