BamBam: parallel comparative analysis of high-throughput sequencing data

• US 9,721,062 B2
• Filed: 05/27/2016
• Issued: 08/01/2017
• 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): Smith, Paulinho E
• Application Number:
  US15/167,530
• Publication Number:
  US 20160275257A1
• Time in Patent Office: 431 Days
• Field of Search: None
• 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-based sequence analysis system comprising:

a computer readable memory configured to store at least a first and a second genomic sequence datasets, the sequence datasets comprising genomic reads associated with respective first and second tissues; and

a sequence analysis engine having a processor coupled with the computer readable memory and configured to;

determine a common genomic location in the first and second genomic sequence datasets;

generate at least a pair of pileups by;

reading a first set of pileups that includes genomic reads from the first genomic sequence dataset and that overlap the common genomic location; and

reading a second set of pileups that includes genomic reads from the second genomic sequence dataset and that also overlap the common genomic location;

infer at least a pair of genotypes for the common genomic location based on the at least the pair of pileups, the at least the pair of genotypes including a first genotype associated with the first tissue and a second genotype associated with the second tissue;

identify a genomic difference between the first genotype and the second genotype in the at least the pair of genotypes;

filter false positives based on a skewing from a random distribution; and

store the genomic difference in a device memory.

2. The system of claim 1, wherein the sequence analysis engine is further configured to infer the at least the pair of genotypes based on a joint probability derived based on the at least the pair of pileups.

3. The system of claim 2, wherein the sequence analysis engine is further configured to select the first and second genotype based on maximizing the joint probability.

4. The system of claim 1, wherein the sequence analysis engine is further configured infer the at least the pair of genotypes based on reads in the pair of pileups exceeding mapping quality thresholds.

5. The system of claim 1, wherein the sequence analysis engine is further configured infer the at least the pair of genotypes based on reads in the pair of pileups exceeding a user-defined base.

6. The system of claim 1, wherein the genomic difference is selected from the group consisting of:

a somatic mutation, a copy number alteration, an allele-specific copy number, a sequence variant, and a sequence loss of heterozygosity.

7. The system of claim 1, wherein the first tissue and the second tissue are from the same patient.

8. The system of claim 1, wherein the first tissue comprises a tumor tissue and the second tissue comprises a matched normal tissue.

9. The system of claim 1, wherein at least one of the first and the second genomic sequence datasets comprises data associated with at least one of the following:

DNA, RNA, mRNA, tRNA, rRNA, miRNA, and asRNA.

10. The system of claim 1, wherein the sequence analysis engine is further configured to keep the first and the second sequence datasets synchronized with respect to a genome.

11. The system of claim 1, wherein the sequence analysis engine is further configured to read the at least the pair of pileups at the same time.

12. The system of claim 1, wherein the at least one of the first set of pileups and the second set of pileups include short reads.

13. The system of claim 1, wherein the at least a pair of pileups includes at least three sets of pileups that include a third set of pileups representing a third genome.

14. The system of claim 13, wherein the third set of pileups represent a relapsed sequence.

15. The system of claim 1, wherein the at least one of the first and the second genomic sequence datasets comprises at least one of a BAM file and a SAM file.

16. The system of claim 1, wherein the common genomic location is relative to a reference genome.

17. The system of claim 1, wherein the sequence analysis engine is further configured to determine the common genomic location by incrementally moving to a next position in the reference genome.

18. The system of claim 17, where in the common genomic location comprises a next common genomic location within the reference genome.