BAMBAM: PARALLEL COMPARATIVE ANALYSIS OF HIGH-THROUGHPUT SEQUENCING DATA
A differential sequence object is constructed on the basis of alignment of sub-strings via incremental synchronization of sequence strings using known positions of the sub-strings relative to a reference genome sequence. An output file is then gencrated that comprises only relevant changes with respect to the reference genome.
1 . A computer-based method of variant calling, comprising:
storing simultaneously, in at least one memory of a sequence analysis system, a first subset of first reads corresponding to nucleic acids of a tumor sample and a second subset of second reads corresponding to nucleic acids from a matched normal sample, the first subset of first reads and the second subset of second reads overlapping a common genomic location;
aligning, via at least one processor of the sequence analysis system, the first subset of reads and the second subset of reads using the common genomic location;
comparing, via the at least one processor, the first subset of first reads to the second subset of second reads to determine genetic differences between the tumor sample and the matched normal sample;
generating, via the at least one processor, at least one differential genetic sequence object in the at least one memory representing the genetic differences; and
storing, in the at least one memory via the at least one processor, one or more variants in the tumor sample that are identified from the at least one differential sequence object.
2 . The method of claim 1 , wherein the genetic differences comprise somatic mutations.
3 . The method of claim 1 , wherein the genetic differences comprise structural rearrangements.
4 . The method of claim 1 , wherein the genetic differences comprise copy number variations.
5 . The method of claim 1 , wherein the genetic differences comprise loss of heterozygosity.
6 . The method of claim 1 , further comprising determining allele-specific copy numbers.
7 . The method of claim 1 , wherein generating the at least one differential genetic sequence object comprises maximizing a likelihood of a genotype.
8 . The method of claim 1 , wherein the first subset of reads and the second subset of reads are aligned relative to a reference genome.
9 . The method of claim 1 , wherein the first reads and second reads are in BAM format or SAM format.
10 . The method of claim 1 , wherein the at least one differential genetic sequence object is stored in a SAM/BAM file format.
11 . The method of claim 1 , further comprising generating a patient-specific instruction based on the one or more variants.
12 . The method of claim 11 , wherein the patient-specific instruction comprises at least one of: a diagnosis, a prognosis, a prediction of treatment outcome, a recommendation for a treatment strategy, or a prescription.
13 . The method of claim 1 , wherein the tumor sample and the matched normal sample are from the same patient.
14 . The method of claim 1 , further comprising processing the first subset of first reads of the tumor sample and the second subset of second reads of the matched normal sample at the same time.
15 . The method of claim 1 , wherein aligning the first subset of reads and the second subset of reads using the common genomic location comprises synchronizing alignment based on a window around the common genomic location.
16 . The method of claim 15 , wherein the window has a length less than a read from the first subset of reads and the second subset of reads.
17 . The method of claim 1 , wherein the at least one differential genetic sequence object represents differences across the entire genome.
18 . The method of claim 1 , wherein the at least one differential genetic sequence object includes metadata describing the genetic differences.
19 . The method of claim 1 . further comprising storing the differential genetic sequence object in a medical records database.
20 . The method of claim 1 . further comprising incrementally processing subsets of reads for the tumor sample and the matched normal sample for a series of common genomic locations. thereby reducing memory usage.