Generation and Implementation of a Structural Polymorphic Graph Genome

Abstract

The present disclosure describes a method, a non-transitory computer-readable medium, and a system capable of generating a structural polymorphic graph genome having alternative contiguous sequences representing structural variant haplotypes. For example, the disclosed system can identify candidate structural variants that meet an occurrence threshold within a genomic sample database. From among the candidate structural variants, the system selects a structural variant haplotype based on one or both of the structural variant haplotype meeting a relative haplotype frequency and finding adjacent variants adjacent to a particular structural variant haplotype. The system can similarly select a reference haplotype corresponding to a structural variant haplotype selected from a reference genome. Based on the selected haplotypes, the disclosed system generates a structural polymorphic graph genome that includes both an alternative contiguous sequence representing the structural variant haplotype and a reference sequence representing the reference haplotype.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the benefit and priority of U.S. Provisional Application No. 63 / 367,075, filed on June 27, 2022, entitled "GENERATING AND IMPLEMENTING A STRUCTURAL VARIATION GRAPH GENOME". The above application is hereby incorporated by reference in its entirety.

Background Art

[0002] In recent years, biotechnology companies and research institutions have improved the hardware and software for sequencing nucleotides and determining nucleotide calls for genomic samples. For example, some existing sequencing machines and sequencing data analysis software (collectively referred to as "existing sequencing systems") predict individual nucleotides within a sequence by using conventional Sanger sequencing or Sequencing - By - Synthesis (SBS) methods. When using SBS, existing sequencing systems can monitor thousands of oligonucleotides being synthesized in parallel from a template to predict nucleotide calls for increasing nucleotide reads. Cameras in many existing sequencing systems capture images of the irradiated fluorescent tags incorporated into the oligonucleotides. After capturing such images, some existing sequencing systems determine nucleotide calls for the nucleotide reads corresponding to the oligonucleotides and transmit the base call data to a computing device equipped with sequencing data analysis software that aligns the nucleotide reads with a reference genome. Based on the differences between the aligned nucleotide reads and the reference genome, existing systems can further utilize a variant caller to identify variants of the genomic sample, such as Single Nucleotide Polymorphisms (SNPs), insertions or deletions (indels), or other variants.

[0003] Despite these recent advances, existing sequencing systems often generate or use reference genomes that misrepresent a particular population, resulting in inaccurate read alignment and incorrect variant calling. For example, some existing sequencing systems use a linear reference genome that purports to represent a consensus or example of an organism's genes and other nucleotide sequences. However, approximately 93% of the primary assembly for the most common linear human reference genome (GRCh38 from the Genome Reference Consortium) is based on libraries from only 11 individuals, and 70% of the linear human reference genome is from a single individual. Thus, existing systems often use a linear human reference genome that does not represent a particular population or common variants. In fact, many linear human reference genomes cannot represent larger deletions or insertions (e.g., indels spanning 50 base pairs), translocations, inversions, copy number variations (CNVs), or other structural variants.

[0004] To address this lack of genetic representation in linear reference genomes, some existing sequencing systems generate or use a reference graph genome. For example, some reference graph genomes include both a linear reference genome and graph enhancements, or alternative contiguous sequences that represent SNPs or small indels (e.g., base pairs 10 or fewer, base pairs 50 or fewer). Such reference graph genomes better represent the genetics of some populations, but in the expanded representation of existing reference graph genomes, larger indels, translocations, inversions, or other structural variants that genomic samples frequently have are omitted, having the same drawbacks as existing linear reference genomes.

[0005] Because existing linear and graph reference genomes cannot represent structural variants, existing sequencing systems often misalign nucleotide reads of more diverse genomic samples with the reference genome, generating inaccurate variants or other nucleotide calls based on such misalignments. In fact, in some cases, existing linear or graph reference genomes lack graph-enhanced or alternative contiguous sequences that represent structural variants to which nucleotide reads can be accurately aligned. Since existing reference genomes often cannot represent structural variants, existing sequencing systems also often cannot accurately determine when different segments of nucleotide reads best align with different parts of the existing reference genome in a split alignment. As a result of such split alignments or other complex alignments with structural variants, existing sequencing systems frequently generate incorrect variant calls that either incorrectly identify the presence or absence of a structural variant or do not provide information about the associated structural variant.

[0006] To compensate for the inability of some existing reference genomes to represent structural variants, some existing sequencing systems perform both whole genome sequencing (WGS) using the existing reference genome and SBS (or other technologies) and microarrays using genotyping probes that target specific structural variants. In fact, microarrays are specifically designed to target structural variants that are difficult to detect using existing sequencing equipment. By performing both WGS and multiple microarrays, sometimes using different specialized sequencing and microarray equipment, existing sequencing systems increase computer processing and time to determine accurate variant calls for both (i) SNPs and smaller indels and (ii) structural variants.

[0007] Some existing sequencing systems attempt to solve problems of alignment accuracy and base calling accuracy with respect to a graph reference genome, but existing graph reference genomes often include excessive enhancements for alleles that are sufficiently similar (or irrelevant) to alleles shown by many genomic samples. For example, some existing sequencing systems utilize a common graph genome that includes a number of graph enhancements for alleles that are both common and non-common across different populations (e.g., common and non-common SNPs and small indels). Such graph enhancements may be similar to, but not identical to, alleles of many sample genomes, so the general-purpose graph genome frequently causes existing sequencing systems to misalign or miss variants for many samples. Thus, by utilizing a general-purpose graph reference genome that includes excessive graph enhancements, existing sequencing systems can increase the likelihood of a mismatched alignment with nucleotide reads from a genomic sample.

[0008] In addition to problems of alignment accuracy and nucleobase accuracy, existing graph reference genomes are often bulky and consume significant memory and computational resources. In fact, some existing graph reference genomes may include numerous graph enhancements for SNPs or small indels that are irrelevant to a given genomic sample. These numerous alternative paths can potentially consume unnecessary memory. In addition to wasting memory, general-purpose graph reference genomes often increase the computer processing time of existing sequencing systems to determine whether to include or exclude a match to a graph enhancement when performing variant calling.

[0009] These exist in existing sequencing systems, along with further problems and challenges. SUMMARY OF THE INVENTION

[0010] This disclosure describes one or more embodiments of a system, method, and non-transitory computer-readable storage medium that solve one or more of the above problems or provide other advantages over the art. In particular, the disclosed system can generate or implement a structural polymorphic graph genome having alternative contiguous sequences representing structural variant haplotypes. For example, the disclosed system can identify candidate structural variants that meet an occurrence threshold in a genomic sample database. From among the candidate structural variants, the system selects a structural variant haplotype based on one or both of the structural variant haplotype meeting a relative haplotype frequency and finding adjacent variants adjacent to a particular structural variant haplotype. The system can similarly select a reference haplotype corresponding to the structural variant haplotype selected from the reference genome. Based on the selected haplotypes, the system generates a structural polymorphic graph genome that includes both an alternative contiguous sequence representing the structural variant haplotype and a reference sequence representing the reference haplotype. Based on comparing nucleotide reads of a genomic sample to the alternative contiguous sequence representing the structural variant haplotype, the disclosed system can determine a nucleotide call (e.g., a structural variant call) for the genomic sample.

[0011] Additional features and advantages of one or more embodiments of this disclosure are described in the following description, some of which will be apparent from the description, or can be learned by practice of such exemplary embodiments.

Brief Description of the Drawings

[0012] The "Detailed Description of the Invention" refers to the drawings briefly described below.

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[0013] The present disclosure describes one or more embodiments of a structural variant recognition sequencing system capable of generating a structural polymorphic graph genome having an alternative contiguous array representing a structural variant haplotype selected from candidate structural variants. For example, the structural variant recognition sequencing system can identify candidate structural variants in a genomic sample database that meet a threshold frequency (or otherwise another occurrence threshold). Such candidate structural variants can include deletions or insertions, duplications, inversions, translocations, copy number variations (CNVs), or other structural variants that exceed a threshold number of base pairs (e.g., 50). From among the candidate structural variants, the structural variant recognition sequencing system selects a structural variant haplotype based on one or both of meeting another occurrence threshold and finding flanking variants adjacent to a particular structural variant haplotype. The system can similarly select a reference haplotype for a genomic region corresponding to a structural variant haplotype selected from a reference genome. Based on the selected haplotypes, the system generates a structural polymorphic graph genome that includes both an alternative contiguous array representing the structural variant haplotype and a reference array representing the reference haplotype.

[0014] As suggested above, the structural variant recognition array determination system can identify candidate structural variants from a genomic sample database based on an occurrence threshold. For example, the structural variant recognition array determination system can identify candidate structural variants that meet a specific variant frequency or minimum count in the genomic sample database. Such a genomic sample database can include a digital catalog of nucleotide reads, whole genomes, exomes, exons, or other nucleotide sequences from a diverse set of genomic samples. When identifying candidate structural variants, the structural variant recognition array determination system can identify deletions or insertions that exceed a threshold number of base pairs (e.g., >50 base pairs) or various other structural variants in various genomic regions across a linear reference genome. From within the genomic sample database, the structural variant recognition array determination system can identify such candidate structural variants from long nucleotide reads or other continuous sequences.

[0015] In certain implementations, the structural variant recognition array determination system selects a structural variant haplotype from among the identified candidate structural variants. For example, in some cases, the structural variant recognition array determination system selects a structural variant haplotype that meets a threshold frequency or threshold number in the target genomic region corresponding to the candidate structural variant. Additionally or alternatively, the structural variant recognition array determination system selects a structural variant haplotype that is in phase with adjacent variants within a continuous sequence of the genomic sample database. Such adjacent variants can include SNPs or indels of less than a threshold number of base pairs (e.g., <50 base pairs).

[0016] After selecting a structural variant haplotype, in some embodiments, the structural variant recognition array determination system integrates the structural variant haplotype and a reference haplotype from a linear reference genome into a data organization structure. For example, in a particular implementation, the structural variant recognition array determination system maps a reference haplotype, SNPs, and a structural variant haplotype from a linear reference genome to genomic coordinates within the linear reference genome. The structural variant recognition array determination system can further associate nucleotide identifiers (e.g., characters for A, T, C, G, U) for the mapped reference haplotype, SNPs, and structural variant haplotype with values representing genomic coordinates in an organization structure (e.g., a hash table, a matrix).

[0017] In addition to, or instead of, generating a structural polymorphism graph genome, in some embodiments, the structural variant recognition array determination system determines a nucleotide call for a genomic sample based on comparing nucleotide reads of the genomic sample to the structural polymorphism graph genome. For example, in some embodiments, the structural variant recognition array determination system identifies nucleotide reads from the genomic sample. The structural variant recognition array determination system further aligns a subset of the nucleotide reads to an alternative contiguous sequence representing a structural variant haplotype within the structural polymorphism graph genome. Based on the aligned subset of nucleotide reads, the structural variant recognition array determination system generates a nucleotide call (e.g., a variant call) for the genomic sample.

[0018] In addition to generating nucleobase calls, in some embodiments, the structural variant recognition sequencing system reports various data corresponding to the nucleobase calls corresponding to the structural variant haplotypes. For example, in some cases, the structural variant recognition sequencing system generates an alignment file or variant call file that includes annotations indicating the structural variant haplotype, the frequency of the structural variant haplotype, or the genomic coordinates for the structural variant haplotype corresponding to the nucleobase call.

[0019] Beyond reporting variant calls corresponding to structural variants, the structural variant recognition sequencing system can better align and generate variant calls for split read alignments. As suggested above, the structural variant recognition sequencing system can determine when a nucleotide read aligns with a structural variant haplotype. For example, in certain cases, the structural variant recognition sequencing system determines that a subset of nucleotide reads overlaps with a breakpoint of an alternative contiguous sequence representing a structural variant haplotype in a structural polymorphic graph genome. Based on the detection of such overlaps, the structural variant recognition sequencing system generates an alignment file or variant call file having annotations indicating an alignment reflecting the structural variant haplotype within the genomic sample.

[0020] As described above, the structural variant recognition sequencing system provides several technical advantages compared to existing sequencing systems by improving read alignment and base calling accuracy, computational efficiency, and memory consumption. For example, the structural variant recognition sequencing system improves the accuracy of read alignment and nucleotide calling by generating or utilizing a structural polymorphic graph genome that describes the structural variants. Unlike existing linear reference genomes or existing graph reference genomes that cannot accurately or appropriately represent structural variants, the structural variant recognition sequencing system can generate or implement a structural polymorphic graph genome that includes alternative contiguous sequences representing structural variant haplotypes. By selecting a structural variant haplotype that is in phase with each of the adjacent variants within the contiguous sequence, in some cases, the structural variant recognition sequencing system can (i) incorporate the structural variant haplotype into an alternative contiguous sequence that facilitates a better alignment between such adjacent variants and nucleotide reads that reflect the structural variant, and (ii) the intelligently selected alternative contiguous sequence of the structural polymorphic graph genome. By further or alternatively selecting a structural variant haplotype that meets an occurrence threshold in the target genomic region, in some cases, the structural variant recognition sequencing system can efficiently facilitate a better alignment between nucleotide reads that reflect a more general structural variant haplotype and the selected alternative contiguous sequence of the structural polymorphic graph genome by incorporating the structural variant haplotype into the alternative contiguous sequence.

[0021] Regardless of the disclosed selection approach, alternative contiguous arrays of the structural variant recognition sequencing system facilitate improved alignment with nucleotide reads that exhibit larger indels, translocations, inversions, CNVs, or other structural variants. By improving alignment with the structural polymorphic graph genome, the structural variant recognition sequencing system can also determine more accurate nucleotide calls with a higher confidence as to whether such calls match or differ from the reference bases of the reference genome. In fact, the disclosed structural polymorphic graph genome facilitates variant calls or other nucleotide calls that existing reference genomes do not facilitate (or cannot facilitate) with the same quality (e.g., Q-score) or mapping quality (e.g., MAPQ).

[0022] In addition to improving alignment and base-calling accuracy, the structural variant recognition sequencing system improves the computational speed and memory of a sequencing system that uses a graph reference genome. In contrast to a general graph reference genome that includes graph enhancements for irrelevant or redundant alleles and that represents an indefinite number of SNPs and / or small indels (e.g., base pairs of 10 or less) indiscriminately, the structural variant recognition sequencing system reduces the memory required to store a relatively small structural polymorphism graph genome as compared to an indefinite number of graph-enhanced gene graph reference genomes. Instead of inefficiently using computing resources such as processing and memory storage in determining between an excessive number of possible read alignment matches with an indiscriminate alternative contiguous sequence for SNPs, small indels, or structural variants in a virtual universal graph reference genome, the structural variant recognition sequencing system saves computer processing and other resources by using the structural polymorphism graph genome. To save such computing resources, in some embodiments, the structural polymorphism graph genome includes (i) fewer (but more relevant) alternative contiguous sequences that represent selected adjacent variants and corresponding structural variant haplotypes for comparing genomic regions of a sample, and (ii) more efficient mapping resulting from fewer candidate alternative contiguous sequence matches as compared to a virtual universal graph reference genome that includes an indiscriminate number of alternative contiguous sequences that include SNPs, small indels, or structural variants.

[0023] Beyond improving the computational efficiency of a structural polymorphic graph genome having a target alternative contiguous array, in some embodiments, a structural variant recognition sequencing system improves computational efficiency by reducing the number of sequencing assays and the computing devices used to determine variant calls about structural variants. As noted above, some existing sequencing systems consume significant computer processing and time by performing both (i) whole-genome sequencing (WGS) on a special sequencing device to generate nucleotide reads about a genomic sample, and (ii) multiple genotyping microarrays on a microarray device. By comparing the nucleotide reads to a reference genome for WGS and analyzing the optical signals from DNA probes in the microarray, existing sequencing systems can determine accurate variant calls, on the one hand, for both SNPs and smaller indels based on the reference genome, and on the other hand, for targeted structural variants from DNA probes. In contrast to such existing sequencing systems, in some embodiments, a structural variant recognition sequencing system facilitates a more computationally efficient approach by using a specialized sequencing device to determine nucleotide reads without, or with fewer, genotyping microarrays for targeted structural variants to determine variant calls corresponding to the structural variants. Thus, a structural variant recognition sequencing system can obviate some or all of the genotyping microarrays for structural variants by generating or utilizing a structural polymorphic graph genome having an alternative contiguous array representing the structural variant haplotype.

[0024] As shown by the foregoing discussion, the present disclosure utilizes various terms to describe the features and advantages of a structural variant recognition sequencing system. As used herein, for example, the term "structural variant" refers to a polymorphism in the structure of a biological chromosome (e.g., deletion, insertion, translocation, inversion), or a polymorphism with respect to the nucleotide sequence of a biological chromosome. In some cases, a structural variant includes a polymorphism with respect to a threshold number of base pairs (e.g., >50 base pairs) within a biological chromosome. Thus, in certain implementations, a structural variant includes an insertion or deletion exceeding the threshold number of base pairs, a duplication exceeding the threshold number of base pairs, an inversion, a translocation, or a copy number variation (CNV). The present disclosure describes several examples of 50 base pairs as the threshold number of base pairs, but in some embodiments, the threshold number of base pairs for a structural variant may be different, such as 35, 45, 100, or 1,000 base pairs.

[0025] In related terms, the term "candidate structural variant" refers to a structural variant selected from a genomic sample database. In some cases, a candidate structural variant includes a structural variant that meets an occurrence threshold amount within the genomic sample database. For example, a candidate structural variant can include a structural variant from a genomic sample database that meets a threshold frequency or threshold number in a target genomic region (e.g., a gene or promoter region) for a nucleotide sequence within the genomic sample database.

[0026] As shown immediately above, the structural variant recognition array determination system can select candidate structural variants from a genomic sample database. As used herein, the term "genomic sample database" refers to a database of digitally represented nucleotide sequences derived from genomic samples that includes an organization, index, or search function for identifying variants, reference alleles, or reference haplotypes. For example, a genomic sample database can include (i) digitally represented nucleotide reads, whole genomes, exomes, exons, or other nucleotide sequences from a diverse set of genomic samples, and (ii) an organization or index for genomic coordinates or regions capable of identifying nucleotide sequences digitally represented for variants or reference alleles or haplotypes. By way of illustration, in some embodiments, the genomic sample database includes one or more of the International Genome Sample Resource (IGSR) from the 1000 Genomes Project, the Genome Aggregation Database (gnomAD), the Database of Genomic Variants (DGV), or other databases containing nucleotide sequences representing structural variants such as databases containing nucleotide reads spanning 300 base pairs. In some cases, the genomic sample database represents a subset of nucleotide sequences selected from one or more of the aforementioned databases or other databases.

[0027] As described above, in some embodiments, the structural variant recognition array determination system selects a structural variant haplotype from among candidate structural variants in a genomic sample database. As used herein, the term "structural variant haplotype" refers to a structural variant that exists in an organism (or organisms from a population) and is inherited from one or more ancestors as part of a grouping of nucleotide sequences. In particular, a structural variant haplotype can include (or represent) a group of alleles that includes one or more structural variants that tend to be inherited together by organisms in a population from a single parent. Thus, a structural variant haplotype can include structural variants and other variants as part of a group of alleles and can correspond to a particular gene.

[0028] In contrast, the term "reference haplotype" refers to a group of nucleotide sequences represented by a reference genome that is inherited from one or more ancestors as part of a grouping of nucleotide sequences. In particular, a reference haplotype can include a group of alleles from a linear reference genome that tend to be inherited together by such organisms from a single parent. In some cases, a reference haplotype includes a group of alleles corresponding to a gene.

[0029] As used herein, the term "reference genome" refers to a digital nucleic acid sequence assembled as a representative example (or examples) of the genes and other genetic sequences of an organism. Regardless of sequence length, in some cases, the reference genome represents a set of nucleic acid sequences in an exemplary gene set or digital nucleic acid sequence determined to be representative of the organism. For example, the linear human reference genome can be GRCh38 (or other version of the reference genome) from the Genome Reference Consortium. GRCh38 can include alternative contigs that represent alternative haplotypes such as SNPs and small indels (e.g., up to 10 base pairs, up to 50 base pairs), but GRCh38 includes alternative haplotypes with a limited representation of population structure variants. In fact, the structural variants represented by GRCh38 include only those represented by the 11 individuals from which the GRCh38 library was constructed.

[0030] Additionally, as used herein, the term "graph reference genome" refers to a reference genome that includes both a linear reference genome and alternative contigs (or graph extensions) that represent haplotypes or other alternative nucleic acid sequences. For example, a graph reference genome can include a linear reference genome and alternative contigs corresponding to one or more population haplotype sequences identified from a genomic sample database. By way of example, a graph reference genome can include the Illumina DRAGEN Graph Reference Genome hg19.

[0031] As disclosed herein, the term "structural polymorphic graph genome" refers to a graph reference genome that includes an alternative contiguous array representing a structural variant haplotype and a reference array representing a reference haplotype. For example, in some embodiments, the structural polymorphic graph genome includes a linear reference genome complemented with an alternative contiguous array representing a structural variant haplotype. In addition to such alternative contiguous arrays, in some embodiments, the structural polymorphic graph genome includes alternative nucleotides or additional alternative contiguous arrays representing alternative haplotypes, such as SNPs and / or indels of less than a threshold number of base pairs (e.g., <50 base pairs). Although the present disclosure uses the term "structural polymorphic graph genome", a structural variant recognition sequencing system can represent and use the structural polymorphic graph genome in the form of a graph hash table or other digital organizational structure.

[0032] As further used herein, the term "contiguous sequence" (or simply "contig") refers to a consensus nucleotide sequence for a genomic region of a genomic sample (or multiple genomic samples of a species) based on a set of overlapping nucleotide segments corresponding to the genomic region. In particular, a contiguous sequence includes a consensus nucleotide sequence for a genomic region of one or more genomic samples based on nucleotide reads for one or more genomic samples that cover (or overlap) the genomic region.

[0033] In this context, the term "alternate contig" (or simply "alt contig") refers to a contiguous sequence that represents a population haplotype added (e.g., lifted over) to a linear reference genome (or other reference genome) at a particular genomic coordinate or genomic coordinates. In some implementations, a structural variant graph genome may include alternate contigs mapped to genomic coordinates of a primary assembly for a linear reference genome. For example, an alternate contig may represent a population haplotype that includes a structural variant having a lift over to two or more genomic coordinates in a linear reference genome corresponding to two or more sides of a structural variant break end. In some cases, a hash table of a structural variant graph genome includes identifiers that associate an alternate contig representing a structural variant haplotype with genomic coordinates representing a reference haplotype from a primary assembly of a linear reference genome.

[0034] Furthermore, as used herein, the term "genomic coordinate" refers to a specific location or position of a nucleotide base within a genome (e.g., the genome of an organism or a reference genome). In some cases, a genomic coordinate includes an identifier for a particular chromosome of the genome and an identifier for the position of a nucleotide base within the particular chromosome. For example, a genomic coordinate (singular or plural) may include a number, name, or other identifier of a chromosome (e.g., chr1 or chrX), and a specific position (singular or plural) such as a numbered position following the identifier of the chromosome (e.g., chr1:1234570 or chr1:1234570~1234870). Further, in a particular implementation, a genomic coordinate refers to a source of a reference genome (e.g., mt for a mitochondrial DNA reference genome, or SARS-CoV-2 for a reference genome for the SARS-CoV-2 virus), and a position of a nucleotide base within the source for the reference genome (e.g., mt:16568 or SARS-CoV-2:29001). In contrast, in a particular case, a genomic coordinate refers to the position of a nucleotide base within a reference genome without reference to a chromosome or source (e.g., 29727).

[0035] Furthermore, as used herein, "genomic region" refers to a range of genomic coordinates. Similar to genomic coordinates, in certain embodiments, a genomic region can be identified by an identifier for a chromosome and a specific position(s), e.g., a numbered position following an identifier for a chromosome (e.g., chr1:1234570~1234870). In various implementations, genomic coordinates include positions within a reference genome. In some cases, genomic coordinates are specific to a particular reference genome.

[0036] As further used herein, the term "reference sequence" refers to a nucleotide sequence derived from a reference genome. For example, a reference sequence includes a sequence of nucleobases digitally represented by a primary assembly of a linear reference genome. As suggested above, in some embodiments, a reference sequence digitally represents a reference haplotype from a primary assembly of a linear reference genome.

[0037] As further used herein, the term "adjacent variant" refers to a variant nucleobase or variant nucleobases that do not align or differ from one or more corresponding nucleobases of the reference genome and are adjacent to a structural variant haplotype (or a portion thereof) within a nucleotide sequence. For example, an adjacent variant includes a variant nucleobase or variant nucleobases that do not align or differ from a reference nucleobase or reference nucleobases and match a structural variant haplotype within a nucleotide sequence (e.g., a contiguous sequence) from a genomic sample database. As suggested above, an adjacent variant can include an SNP, a deletion of fewer than a threshold number of base pairs, or an insertion of fewer than a threshold number of base pairs. In some cases, an adjacent variant can be a structural variant.

[0038] As further used herein, the term "nucleobase call" (or simply "base call") refers to the determination or prediction of a particular nucleobase (or nucleobase pair) for an oligonucleotide (e.g., a nucleotide read) during a sequencing cycle or for a genomic locus of a sample genome. In particular, a nucleobase call can refer to (i) the determination or prediction of the type of nucleobase incorporated within an oligonucleotide on a nucleotide-sample slide (e.g., a read-based nucleobase call), or (ii) the determination or prediction of the type of nucleobase present at a genomic locus or region within a genome, including a variant call or a non-variant call in a digital output file. In some cases, for a nucleotide read, a nucleobase call can include the determination or prediction of a nucleobase based on intensity values obtained from fluorescently tagged nucleotides added to the oligonucleotides of a nucleotide-sample slide (e.g., within a cluster of a flow cell). Alternatively, a nucleobase call can include the determination or prediction of a nucleobase from a chromatogram peak or a current change resulting from a nucleotide passing through a nanopore of a nucleotide-sample slide. In contrast, a nucleobase call can also include the final prediction of a nucleobase at a genomic locus of a sample genome for a variant call file (VCF) or another base call output file based on a nucleotide read corresponding to the genomic locus. Thus, a nucleobase call can include a base call corresponding to a genomic locus and a reference genome, e.g., an indication of a variant or non-variant at a particular position corresponding to the reference genome. Indeed, a nucleobase call can refer to a single nucleotide variant (SNV), a variant call including but not limited to an insertion or deletion (indel), or a base call that is part of a structural variant. As suggested above, a single nucleobase call can be an adenine (A) call, a cytosine (C) call, a guanine (G) call, a thymine (T) call, or a uracil (U) call.

[0039] As further used herein, the term "nucleotide read" (or simply "read") refers to the inferred sequence of one or more nucleobases (or nucleobase pairs) from all or part of a sample nucleotide sequence (e.g., a sample genomic sequence, cDNA). In particular, a nucleotide read includes the sequence of nucleotide calls determined or predicted for a nucleotide sequence (or group of monoclonal nucleotide sequences) from a sample library fragment corresponding to a genomic sample. For example, in some cases, a sequencing device determines a nucleotide read by generating nucleotide calls for nucleobases that have passed through nanopores of a nucleotide-sample slide, determined via fluorescent tagging, or determined from clusters within a flow cell.

[0040] As further used herein, the term "alignment score" refers to a numerical score, metric, or other quantitative measurement that assesses the accuracy of an alignment between a nucleotide read (or a fragment of a nucleotide read) and another nucleotide sequence from a reference genome. In particular, an alignment score includes a metric that indicates the degree to which the nucleobases of a nucleotide read match or are similar to a reference sequence or an alternative contiguous sequence from the reference genome. In certain implementations, the alignment score takes the form of a Smith-Waterman score for a local alignment, or a variation or version of the Smith-Waterman score (e.g., the various settings or configurations used by DRAGEN by Illumina, Inc. for Smith-Waterman scoring).

[0041] In this context, the term "alternative contig fragment alignment score" refers to an alignment score for an alignment between one or more read fragments and an alternative contiguous sequence. In particular, the alternative contig fragment alignment score can include an alignment score for an alignment between one or more inner read fragments and one or more outer read fragments of a nucleotide read and an alternative contiguous sequence. As will be described below, the alternative contig fragment alignment score can, in certain circumstances, replace or function as a split group score.

[0042] As further used herein, the term "alignment file" refers to a digital file indicating a relative alignment or mapping of nucleotide reads to a nucleotide sequence of a reference genome or other reference nucleotide sequence. In particular, the alignment file can include data indicating the relative mapping positions of nucleotide reads and nucleotide sequences of the reference genome. In some embodiments, the alignment file includes or consists of a Sequence Alignment / Map (SAM) file, a Binary Alignment Map (BAM) file, a FAST-All (FASTA) file, or a FASTQ file.

[0043] As used herein, for example, the term "configurable processor" refers to a circuit or chip that can be configured or customized to execute a particular application. For example, a configurable processor includes an integrated circuit chip designed to be configured or customized on-site by an end-user computing device to execute a particular application. Configurable processors include, but are not limited to, ASICs, ASSPs, coarse-grained reconfigurable arrays (CGRAs), or FPGAs. In contrast, configurable processors do not include CPUs or GPUs. In some embodiments, the structural variant recognition array determination system uses a configurable processor (e.g., an FPGA) or a processor (e.g., a CPU) to implement the various embodiments described herein.

[0044] The following paragraphs describe a structural variant recognition array determination system with respect to exemplary diagrams depicting exemplary embodiments and implementations. For example, FIG. 1 shows a schematic diagram of a computing system 100 in which a structural variant recognition array determination system 106 operates, according to one or more embodiments. As shown, computing system 100 includes an array determination device 102 connected to a local device 108 (e.g., a local server device), one or more server devices 110, and client device 114. As shown in FIG. 1, array determination device 102, local device 108, server device 110, and client device 114 can communicate with each other via network 118. Network 118 includes any suitable network over which computing devices can communicate. Exemplary networks are considered in more detail below with respect to FIG. 10. Although FIG. 1 shows an embodiment of the structural variant recognition array determination system 106, the present disclosure describes the following alternative embodiments and configurations.

[0045] As shown by FIG. 1, the sequencer 102 includes a computing device and a sequencing device system 104 for sequencing a genomic sample or other nucleic acid polymer. In some embodiments, by using a processor to execute the sequencing device system 104, the sequencer 102 analyzes nucleotide fragments or oligonucleotides extracted from a genomic sample and generates nucleotide reads or other data using computer-implemented methods and systems either directly or indirectly on the sequencer 102. More specifically, the sequencer 102 receives a nucleotide-sample slide (e.g., a flow cell) containing nucleotide fragments extracted from a sample, and further copies and determines the nucleotide sequence of such extracted nucleotide fragments.

[0046] In one or more embodiments, the sequencer 102 uses SBS to sequence nucleotide fragments into nucleotide reads and determine nucleotide base calls for the nucleotide reads. In addition to, or as an alternative to, communicating via the network 118, in some embodiments, the sequencer 102 bypasses the network 118 and communicates directly with the local device 108 or the client device 114. By executing the sequencing device system 104, the sequencer 102 can further store the nucleotide base calls as part of the base call data formatted as a binary base call (BCL) file and transmit the BCL file to the local device 108 and / or the server device 110.

[0047] As further shown by FIG. 1, the local device 108 is located at or near the same physical location as the sequencing device 102. In fact, in some embodiments, the local device 108 and the sequencing device 102 are integrated into the same computing device. The server device 108 can generate, receive, analyze, store, and transmit digital data by, for example, executing a structural variant recognition sequencing system 106 to receive base call data or determine variant calls based on analyzing such base call data. As shown in FIG. 1, the sequencing device 102 can transmit (and the local device 108 can receive) the base call data generated during the execution of the sequencing by the sequencing device 102. By executing software in the form of the structural variant recognition sequencing system 106, the local device 108 can align nucleotide reads with the structural polymorphic graph genome 112 and determine genetic variants based on the aligned nucleotide reads. The local device 108 can also communicate with the client device 114. In particular, the local device 108 can transmit to the client device 114 a variant call file (VCF), or data including nucleotide base calls, sequencing metrics, error data, or other information indicating other metrics.

[0048] As further shown by FIG. 1, the server device 110 is located remotely from the local device 108 and the sequencing device 102. Similar to the local device 108, in some embodiments, the server device(s) 110 includes a version of the structural variant recognition sequencing system 106. Thus, the server device 110 can generate, receive, analyze, store, and transmit digital data, such as by receiving base call data or determining variant calls based on analyzing such base call data. In some cases, the sequencing device 102 may transmit base call data from the sequencing device 102 (and the server device 110 may receive the base call data). The server device 110 can also communicate with the client device 114. In particular, the server device 110 can transmit data including VCF or other sequencing-related information to the client device 114.

[0049] In some embodiments, the server device 110 comprises a distributed set of servers and includes several server devices that are distributed across the network 118 and located in the same or different physical locations. Further, the server device 110 may include a content server, an application server, a communication server, a web hosting server, or another type of server.

[0050] As described above, as part of the server device 110 or the local device 108, the structural variant recognition array determination system 106 can generate or implement a structural polymorphic graph genome having an alternative contiguous array representing a structural variant haplotype. For example, the structural variant recognition array determination system 106 can identify candidate structural variants at a threshold frequency (or otherwise meeting another occurrence threshold) within a genomic sample database. From among the candidate structural variants, the structural variant recognition array determination system 106 selects a structural variant haplotype based on one or both of meeting another occurrence threshold and finding adjacent variants adjacent to a particular structural variant haplotype. The structural variant recognition array determination system 106 can similarly select a reference haplotype for a genomic region corresponding to a structural variant haplotype selected from a reference genome. Based on the selected haplotypes, the structural variant recognition array determination system 106 generates a structural polymorphic graph genome including both an alternative contiguous array representing the structural variant haplotype and a reference array representing the reference haplotype. Based on comparing nucleotide reads of a genomic sample to the alternative contiguous array representing the structural variant haplotype, the structural variant recognition array determination system 106 can determine nucleotide calls for the genomic sample.

[0051] As further illustrated in FIG. 1, client device 114 can generate, store, receive, and transmit digital data by executing a sequencing application 116. In particular, client device 114 can receive sequencing data from local device 108 or receive a call file (e.g., BCL) and sequencing metrics from sequencing device 102. Further, client device 114 can communicate with local device 108 or server device 110 to receive a VCF including other metrics such as nucleotide calls and / or base call quality metrics or pass filter metrics. Thus, client device 114 can present or display information regarding variant calls or other nucleotide calls within the graphical user interface of sequencing application 116 to a user associated with client device 114. For example, client device 114 can present structural variant calls and / or sequencing metrics for a sequenced genomic sample within the graphical user interface of sequencing application 116.

[0052] FIG. 1 depicts client device 114 as a desktop or laptop computer, but client device 114 may comprise various types of client devices. For example, in some embodiments, client device 114 includes non-mobile devices such as a desktop computer or server, or other types of client devices. In still other embodiments, client device 114 includes mobile devices such as a laptop, tablet, mobile phone, or smart phone. Further details regarding client device 114 are discussed below with respect to FIG. 10.

[0053] As further illustrated in FIG. 1, client device 114 includes a genotyping application 116. The genotyping application 116 can be a web application or a native application (e.g., a mobile application, a desktop application) stored and executed on the client device 114. The genotyping application 116 can include instructions that, when executed, cause the client device 114 to receive data from the structural variant recognition genotyping system 106 and present data from base calls or VCF for display on the client device 114. Further, the genotyping application 116 can instruct the client device 114 to display an overview of multiple genotyping runs.

[0054] As further shown in FIG. 1, a version of the structural variant recognition genotyping system 106 can be located and implemented (e.g., fully or partially) on the client device 114 or the genotyping device 102. In still other embodiments, the structural variant recognition genotyping system 106 is implemented by one or more other components of the computing system 100, such as the local device 108. In particular, the structural variant recognition genotyping system 106 can be implemented in a variety of different ways across the genotyping device 102, the local device 108, the server device 110, and the client device 114. For example, the structural variant recognition genotyping system 106 can be downloaded from the server device 110 to the structural variant recognition genotyping system 106 and / or the local device 108, and all or part of the functionality of the structural variant recognition genotyping system 106 is implemented on each respective device within the computing system 100.

[0055] As described above, the structural variant recognition array determination system 106 can generate and implement a structural polymorphic graph genome. FIGS. 2A and 2B show an overview of such an embodiment for the structural variant recognition array determination system 106. According to one or more embodiments, FIG. 2A shows an example of a structural variant recognition array determination system 106 that generates a structural polymorphic graph genome 212 including an alternative contiguous array representing a structural variant haplotype and a reference array representing a reference haplotype. According to one or more embodiments, FIG. 2B shows an example in which the structural variant recognition array determination system 106 aligns nucleotide reads of a genomic sample with the structural polymorphic graph genome 212 and determines nucleotide base calls for the genomic sample based on the aligned nucleotide reads.

[0056] As shown in FIG. 2A, the structural variant recognition array determination system 106 identifies candidate structural variants 204a-204n from the genomic sample database 202 based on an occurrence threshold. For example, the structural variant recognition array determination system 106 identifies candidate structural variants 204a-204n that meet the occurrence threshold amount in the genomic sample database 202. In some implementations, the structural variant recognition array determination system 106 selects candidate structural variants 204a-204n from the genomic sample database 202 by selecting structural variants that meet a threshold count (e.g., occurring three or more times) or a threshold frequency (e.g., 10%, 30% variant frequency) at the target genomic coordinates in the genomic sample database 202. As described above, the genomic sample database 202 can include various databases including nucleotide reads from a diverse set of genomic samples such as one or more combinations of the 1000 Genomes Project, gnomAD, or the IGSR from DGV.

[0057] As further shown by FIG. 2A, the structural variant recognition array determination system 106 identifies various structural variant types from among the candidate structural variants 204a-204n. For example, based on meeting a threshold occurrence amount, the structural variant recognition array determination system 106 identifies candidate structural variants 204a and 204c that exhibit deletions exceeding a threshold number of base pairs, candidate structural variants 204b and 204d that exhibit translocations, candidate structural variants 204f and 204g that exhibit insertions exceeding a threshold number of base pairs, and candidate structural variants 204e and 204n that exhibit duplications exceeding a threshold number of base pairs. For purposes of illustration and space constraints, FIG. 2A shows candidate structural variants 204a-204n as merely examples. The structural variant recognition array determination system 106 can identify different types of structural variants (e.g., translocations, CNVs) and additional structural variants not shown in FIG. 2A from the genomic sample database 202.

[0058] From among the candidate structural variants 204a-204n, as further shown by FIG. 2A, the structural variant recognition array determination system 106 selects a structural variant haplotype. In some cases, the structural variant recognition array determination system 106 selects a structural variant haplotype that meets an additional occurrence threshold amount in a particular genomic region as classified in the genomic sample database 202. For example, in a particular implementation, the structural variant recognition array determination system 106 selects a structural variant haplotype that meets a threshold variant frequency (e.g., 15%, 25%) or a threshold count (3, 10) at the target genomic coordinates corresponding to the candidate structural variants 204a-204n.

[0059] In addition to, or instead of, the appearance threshold, in some embodiments, the structural variant recognition array determination system 106 selects a structural variant haplotype adjacent to an adjacent variant in the contiguous sequence of the genomic sample database 202. In some cases, the adjacent variant is in phase with each structural variant haplotype in the nucleotide sequence of the genomic sample database 202. As shown by FIG. 2A, for example, the structural variant recognition array determination system 106 determines that the candidate structural variant 204c is in phase with the adjacent variant 206a in the contiguous sequence (or other nucleotide sequence) of the genomic sample database 202. Similarly, the structural variant recognition array determination system 106 determines that the candidate structural variant 204d is in phase with the adjacent variant 206b, the candidate structural variant 204g is in phase with the adjacent variants 206c and 206d, and the candidate structural variant 204n is in phase with the adjacent variant 206e, each being in the respective contiguous sequence (or other nucleotide sequence) of the genomic sample database 202. Thus, as shown by the dotted circles in FIG. 2A, in some embodiments, the structural variant recognition array determination system 106 selects the candidate structural variants 204c, 204d, 204g, and 204n as the structural variant haplotypes to include in the structural polymorphism graph genome 212.

[0060] In addition to selecting candidate structural variants 204c, 204d, 204g, and 204n as structural variant haplotypes, as further shown in FIG. 2A, the structural variant recognition array determination system 106 identifies reference haplotypes 210a-210n corresponding to the selected structural variant haplotypes from the linear reference genome 208. For example, in some cases, the structural variant recognition array determination system 106 identifies reference haplotypes 210a-210n at the genomic coordinates of the linear reference genome 208 corresponding to the selected structural variant haplotypes. In fact, the structural variant recognition array determination system 106 can identify the genomic coordinates of reference haplotypes 210a-210n for incorporating the selected variant haplotypes into the structural polymorphism graph genome 212 as a lift-over group.

[0061] As further shown in FIG. 2A, the structural variant recognition array determination system 106 generates a structural polymorphism graph genome 212. As shown, the structural polymorphism graph genome 212 includes alternative contiguous sequences 214a, 214b, 214c, and 214n representing the selected structural variant haplotypes. In some embodiments, one or more of the alternative contiguous sequences also include adjacent variants 206a-206e.

[0062] To organize different structural variant haplotypes for a particular genomic region, in certain cases, the structural variant recognition array determination system 106 generates the structural polymorphism graph genome 212 by ordering different subsets of alternative contiguous sequences corresponding to different genomic regions according to the structural variant frequencies in the genomic sample database 202. Thus, in some cases, the structural variant recognition array determination system 106 generates the structural polymorphism graph genome 212 by ordering (i) a first subset of alternative contiguous sequences corresponding to a first genomic region according to the frequencies in the genomic sample database 202, and (ii) a second subset of alternative contiguous sequences corresponding to a second genomic region according to the frequencies in the genomic sample database 202.

[0063] As further shown in FIG. 2A, the structural polymorphic graph genome 212 includes reference arrays 216a, 216b, 216c, and 216n that represent reference haplotypes corresponding to selected structural variant haplotypes. In fact, in some embodiments, the structural polymorphic graph genome 212 includes a linear reference genome 208 and is backward compatible with the linear reference genome 208. As will be further described below, in some embodiments, the structural variant recognition sequencing system 106 generates the structural polymorphic graph genome 212 by constructing a hash table or other organizational structure.

[0064] In addition to, or as an alternative to, generating the structural polymorphic graph genome 212, in some embodiments, the structural variant recognition sequencing system 106 aligns nucleotide reads of a genomic sample to the structural polymorphic graph genome 212 and determines nucleotide base calls for the genomic sample based on the aligned nucleotide reads. FIG. 2B shows an example of one such implementation of the structural polymorphic graph genome 212. As shown in FIG. 2B, the structural variant recognition sequencing system 106 identifies or receives nucleotide reads 218 for a genomic sample. In some cases, for example, the structural variant recognition sequencing system 106 sequences oligonucleotides extracted from the genomic sample and receives base call data (e.g., a BCL file or a FASTQ file) from a sequencing device that determined individual nucleotide base calls for the nucleotide reads 218 in the base call data. Depending on the type of sequencing performed, in some embodiments, the structural variant recognition sequencing system 106 identifies either single-end reads or paired-end reads, and either short nucleotide reads (e.g., less than 300 base pairs or less than 10,000 base pairs) or long nucleotide reads (e.g., more than 300 base pairs or more than 10,000 base pairs) as the nucleotide reads 218.

[0065] As further shown in FIG. 2B, the structural variant recognition array determination system 106 aligns nucleotide read 218 with different sequences of the structural polymorphism graph genome 212. In particular, the structural variant recognition array determination system 106 aligns a subset of nucleotide reads 220 from nucleotide read 218 with alternative contiguous sequence 214b of the structural polymorphism graph genome 212. As FIG. 2B suggests, some or all of the subset of nucleotide reads 220 overlap with alternative contiguous sequence 214b. In this particular example, the subset of nucleotide reads 220 overlaps with alternative contiguous sequence 214b, which represents candidate structural variant 204f - i.e., an insertion that exceeds a threshold number of bases.

[0066] In addition to alternative contiguous sequence 214b, in some embodiments, the structural variant recognition array determination system 106 aligns different subsets of nucleotide reads for a genomic sample with one or more of alternative contiguous sequences 214a, 214c, or 214n of the structural polymorphism graph genome 212 or reference sequences 216a - 216n. Thus, in certain implementations, the structural variant recognition array determination system 106 aligns a particular nucleotide read with alternative contiguous sequences representing different types of structural variant haplotypes, including but not limited to insertions, deletions, duplications, inversions, translocations, or CNVs. Similarly, in some cases, the structural variant recognition array determination system 106 aligns a particular nucleotide read with a reference sequence representing a reference haplotype from a linear reference genome.

[0067] As further shown in FIG. 2B, the structural variant recognition array determination system 106 determines nucleotide base calls 222 for a genomic sample based on a subset of nucleotide reads 220 that align with an alternative contiguous sequence 214b. For example, the structural variant recognition array determination system 106 generates one or more variant calls corresponding to the structural variant haplotype represented by the alternative contiguous sequence 214b. The structural variant recognition array determination system 106 partially determines such variant calls because the alignment of the subset of nucleotide reads 220 with the alternative contiguous sequence 214b exhibits a better mapping metric, base call quality metric, or other sequencing metric than the alignment of the subset of nucleotide reads 220 with the reference sequence 216b. In some embodiments, the structural variant recognition array determination system 106 generates a variant call file 224 that includes the nucleotide base call 222 along with other nucleotide base calls based on the read alignment.

[0068] As described above, the structural variant recognition array determination system 106 can select structural variant haplotypes from a genomic sample database for inclusion within a structural polymorphic graph genome. According to one or more embodiments, FIG. 3 shows the structural variant recognition array determination system 106 that selects structural variant haplotypes for a target genomic region of a structural polymorphic graph genome based on one or both of a fading criterion 308 and a region occurrence threshold 310. Although not shown in FIG. 3, in certain implementations, the structural variant recognition array determination system 106 also selects structural variant haplotypes for other target genomic regions for inclusion within a structural polymorphic graph genome consistent with the following disclosure.

[0069] As suggested by FIG. 3, the structural variant recognition sequencing system 106 identifies candidate structural variants from the genomic sample database 300. Consistent with the above disclosure, the genomic sample database 300 can include long nucleotide reads (e.g., reads over 300 base pairs or over 1,000 base pairs) that include various structural variants. In some embodiments, the genomic sample database 300 includes contiguous sequences from a diverse set of genomic samples that include structural variants (e.g., from different geographic regions or countries of the world). Indeed, the genomic sample database 300 can include contiguous sequences organized according to haplotypes that include one or more structural variants. As described below, the structural variant recognition sequencing system 106 can utilize some such contiguous sequences that include adjacent variants in phase with the corresponding structural variant haplotype for improved alignment, mapping, and base calling.

[0070] As shown in FIG. 3, in some embodiments, the structural variant recognition sequencing system 106 identifies candidate structural variants based on a population occurrence threshold 301. The population occurrence threshold 301 provides an example of a threshold amount of occurrence. For example, the structural variant recognition sequencing system 106 identifies candidate structural variants that occur at a threshold frequency or greater within the population represented by the genomic sample database 300. In some cases, the threshold frequency constitutes a particular percentage (e.g., 1%, 5%) of the genomic samples represented by contiguous sequences (or other nucleotide sequences) within the genomic sample database 300. In addition to, or instead of, this, the structural variant recognition sequencing system 106 identifies candidate structural variants that occur at a threshold count or greater within the genomic samples represented by contiguous sequences (or other nucleotide sequences) within the genomic sample database 300. In some cases, the threshold count constitutes a particular number (e.g., 3, 10, 25, 100) of the genomic samples represented by such contiguous sequences or other nucleotide sequences within the genomic sample database 300.

[0071] In addition to generally identifying candidate structural variants from a population, in some embodiments, the structural variant recognition sequencing system 106 determines candidate structural variants corresponding to a particular genomic region. As shown in FIG. 3, for example, the structural variant recognition sequencing system 106 identifies candidate structural variants 302 for the target genomic region 314. In some cases, the target genomic region 314 represents a gene, a promoter region, or other genomic region.

[0072] For a given target genomic region, the structural variant recognition sequencing system 106 may identify different types of candidate structural variants. As shown by FIG. 3, for example, the structural variant recognition sequencing system 106 identifies candidate structural variants 302 for the target genomic region 314. Based on meeting a population occurrence threshold 301, the structural variant recognition sequencing system 106 identifies candidate structural variants 304a and 304b that exhibit deletions exceeding a threshold number of base pairs (e.g., <50, 100, or 1,000 base pairs) for the target genomic region 314, candidate structural variants 304c and 304d that exhibit duplications exceeding a threshold number of base pairs, candidate structural variants 304e and 304f that exhibit insertions exceeding a threshold number of base pairs, candidate structural variants 304g and 304h that exhibit inversions, and candidate structural variants 304i and 304j that exhibit translocations.

[0073] For illustrative purposes and spatial constraints, FIG. 3 shows candidate structural variants 204a - 204n as merely an example of the target genomic region 314. The structural variant recognition sequencing system 106 may identify different types of structural variants (e.g., CNVs) and fewer or more structural variants than shown in FIG. 3 for the target genomic region 314 from the genomic sample database 300. Similarly, in some embodiments, the structural variant recognition sequencing system 106 may identify (or may not identify) different groups of candidate structural variants for different target genomic regions corresponding to the genomic coordinates of the linear reference genome from the genomic sample database 300.

[0074] In addition to identifying candidate structural variants 302, in some embodiments, the structural variant recognition array determination system 106 selects a structural variant haplotype 312 from among the candidate structural variants 302 based on one or both of a fading criterion 308 and a region occurrence threshold 310. For example, in a particular implementation, the structural variant recognition array determination system 106 selects a structural variant haplotype 312 based on the fading criterion 308 by selecting a structural variant haplotype that is in phase with each of the adjacent variants in a contiguous array. As shown in FIG. 3, candidate structural variants 304b, 304d, 304f, 304h, and 304j are in phase with adjacent variants 306a, 306b, 306c, 306d, and 306e adjacent to the candidate structural variants 304b, 304d, 304f, 304h, and 304j, respectively, in their respective contiguous arrays. In contrast, candidate structural variants 304a, 304e, 304e, 304g, and 304i do not match the adjacent variants in their respective contiguous arrays. Thus, in some embodiments, the structural variant recognition array determination system 106 selects candidate structural variants 304b, 304d, 304f, 304h, and 304j as the structural variant haplotype 312 of the target genomic region 314. In some such embodiments, the structural variant recognition array determination system 106 removes or excludes candidate structural variants 304a, 304e, 304e, 304g, and 304i from consideration.

[0075] By selecting structural variant haplotypes that are in phase with adjacent variants in a continuous or other nucleotide sequence respectively, the structural variant recognition array determination system 106 can select a structural variant haplotype that facilitates better mapping and alignment with nucleotide reads in the structural polymorphism graph genome than other structural variant haplotypes lacking such in-phase adjacent variants. When the structural polymorphism graph genome includes a structural variant haplotype having such phased adjacent variants, and when the nucleotide reads also include adjacent variants that are also represented by alternative continuous sequences of the structural polymorphism graph genome, the structural variant recognition array determination system 106 is more likely to align the nucleotide reads of the genomic sample that include some or all of the corresponding structural variants. When mapped to an alternative continuous sequence having adjacent variants of the structural polymorphism graph genome, the structural variant recognition array determination system 106 is also likely to determine a relatively high mapping quality metric (e.g., MAPQ) and local alignment score (e.g., Smith-Waterman score) of the nucleotide reads to the alternative continuous sequence compared to a reference sequence (or other alternative continuous sequence) lacking such adjacent variants.

[0076] In addition to, or instead of, the fading criterion 308, the structural variant recognition array determination system 106 selects a structural variant haplotype 312 from among the candidate structural variants 302 based on a region occurrence threshold 310. The region occurrence threshold 310 provides another example of a threshold amount of occurrence. For example, the structural variant recognition array determination system 106 selects the structural variant haplotype 312 by selecting candidate structural variants that occur at a threshold frequency or greater in the target genomic region 314. In some cases, the threshold frequency constitutes a particular percentage (e.g., 10%, 25%) of genomic samples represented by a contiguous sequence (or other nucleotide sequence) within the genomic sample database 300 for the target genomic region 314 (e.g., at least one overlapping genomic coordinate with the target genomic region 314). In addition to, or instead of, this, the structural variant recognition array determination system 106 selects the structural variant haplotype 312 by selecting candidate structural variants that occur at a threshold count or greater within a contiguous sequence (or other nucleotide sequence) within the genomic sample database 300 for the target genomic region 314 (e.g., at least one overlapping genomic coordinate). In some cases, the threshold count constitutes a particular number (e.g., 3, 10, 15) of contiguous sequences or other nucleotide sequences corresponding to the target genomic region 314.

[0077] By selecting structural variant haplotypes based on one or both of the fading criterion 308 and the region occurrence threshold 310, in some cases, the structural variant recognition array determination system 106 improves the computational speed and memory of an array determination system that uses a specific graph reference genome. In contrast to a general-purpose graph reference genome, where most of the target genomic region will contain alternative contiguous sequences for irrelevant or excessive alleles, the structural variant recognition array determination system 106 reduces the memory required to store a relatively small structural polymorphism graph genome with respect to more targeted alternative contiguous sequences and corresponding structural variant haplotypes. In some embodiments, the structural variant recognition array determination system 106 intelligently selects a targeted alternative contiguous sequence representing a structural variant haplotype based on one or both of the fading criterion 308 and the region occurrence threshold 310, rather than an indiscriminate number of alternative contiguous sequences in the general-purpose graph reference genome.

[0078] After selecting the structural variant haplotypes and other alternative haplotypes, the structural variant recognition array determination system 106 can generate a structural polymorphism graph genome using a digital tissue structure. According to one or more embodiments, FIG. 4 shows the structural variant recognition array determination system 106 that uses a graph hash table to combine a reference sequence from a reference genome, selected structural variant haplotypes, and selected alternative haplotypes into a structural polymorphism graph genome. As will be described below, the graph hash table associates the encoded nucleotide sequence for the reference sequence, the selected structural variant haplotypes, and the selected alternative haplotypes with genomic coordinates.

[0079] In addition to selecting a structural variant haplotype as shown in FIG. 2B or FIG. 3, in some embodiments, the structural variant recognition array determination system 106 identifies or selects alternative haplotypes for inclusion within a structural polymorphic graph genome. For example, in some cases, the structural variant recognition array determination system 106 selects one or more of SNPs, deletions of less than a threshold number of base pairs (e.g., >50 base pairs), or insertions of less than a threshold number of base pairs from a genomic sample database. Such alternative haplotypes differ in size and (optionally) type from the structural variant haplotypes. Consistent with the above disclosure, in some such cases, the structural variant recognition array determination system 106 selects alternative haplotypes based on a region occurrence threshold for a target genomic region of a linear reference genome. When an alternative haplotype is selected, in some embodiments, the structural variant recognition array determination system 106 generates a structural polymorphic graph genome that includes (i) a reference sequence representing a reference haplotype, (ii) an alternative contiguous sequence representing the selected structural variant haplotype, and (iii) alternative nucleotides or additional alternative contiguous sequences representing the selected alternative haplotype.

[0080] To organize and associate such reference arrays, alternative nucleobases, and alternative contiguous arrays, in some embodiments, the structural variant recognition sequencing system 106 generates a digital organization structure that associates the aforementioned reference arrays and alternative arrays with genomic coordinates. For example, in certain implementations, the structural variant recognition sequencing system 106 generates an alignment file that maps a selected structural variant haplotype to the genomic coordinates of a selected reference haplotype within a linear reference genome. In some cases, the alignment file constitutes a Sequence Alignment / Map (SAM) lift-over file. By leveraging the alignment file, the structural variant recognition sequencing system 106 generates a structural polymorphism graph genome by associating, within an organization structure (e.g., a hash table), an identifier (e.g., a single-character code, a binary code) of an alternative contiguous array representing a structural variant haplotype with the value of the genomic coordinates of the reference haplotype.

[0081] To integrate a reference array representing a reference haplotype and alternative nucleobases or additional alternative contiguous arrays, in some embodiments, the structural variant recognition sequencing system 106 further generates a file representing the nucleobases or nucleotide sequences of the reference haplotype and the selected alternative haplotypes. For example, the structural variant recognition sequencing system 106 generates an array file representing a reference genome including the reference haplotype and a variant call file representing the selected alternative haplotypes. By leveraging the array file, the alignment file, and the variant call file, in some embodiments, the structural variant recognition sequencing system 106 associates, within a hash table, (i) a reference array representing a reference haplotype, (ii) an alternative contiguous array representing a selected structural variant haplotype, and (iii) a nucleobase identifier for an alternative nucleobase or additional alternative contiguous arrays with a value representing the genomic coordinates of the reference haplotype to generate a structural polymorphism graph genome.

[0082] FIG. 4 shows a structural variant recognition array determination system 106 that generates a graph hash table 422 as such an organizational structure based on the corresponding file. As shown in FIG. 4, for example, the structural variant recognition array determination system 106 identifies a reference genome 402 such as a linear reference genome. For example, the structural variant recognition array determination system 106 identifies GRCh38 (or other versions of the reference genome) from the Genome Reference Consortium as the reference genome 402. Based on the reference genome 402, the structural variant recognition array determination system 106 generates a reference genome sequence file 404 that includes an encoded version of the reference genome 402. For example, in some embodiments, the structural variant recognition array determination system 106 generates a FASTA format file as the reference genome sequence file 404. Such a FASTA file includes text having a single-letter code (e.g., A, C, T, G, U, R, Y, M, S, W) that represents the nucleobases (e.g., A, C, T, G) of the nucleotide sequence of the reference genome 402.

[0083] In addition to the reference genome 402, as further shown in FIG. 4, the structural variant recognition array determination system 106 identifies candidate structural variants 406 from the genome sample database and selects a structural variant haplotype 408 from among the candidate structural variants 406 for inclusion in the structural polymorphic graph genome. For example, the structural variant recognition array determination system 106 selects the structural variant haplotype 408 using the method shown in FIG. 3 and described above. Thus, in some cases, the structural variant haplotype 408 includes a structural variant haplotype that is in phase with adjacent variants (e.g., SNPs or indels) within a contiguous sequence.

[0084] Based on the structural variant haplotype 408, the structural variant recognition array determination system 106 generates a structural variant (SV) haplotype alignment file 410. For example, the structural variant recognition array determination system 106 generates an array alignment / map (SAM) lift-over file that maps the structural variant haplotype 408 to the genomic coordinates of the corresponding reference haplotype within the reference genome 402. By generating the SAM lift-over file, the structural variant recognition array determination system 106 generates a file that maps the structural variant haplotype 408 to the genomic coordinates where the alternative contiguous sequences form a lift-over group in the structural polymorphism graph genome. Alternatively, the structural variant recognition array determination system 106 generates a binary alignment map (BAM) file that is compressed into a binary format such as the mapping of the structural variant haplotype to the genomic coordinates of the corresponding reference haplotype.

[0085] Based on the structural variant haplotype 408, as further shown in FIG. 4, the structural variant recognition array determination system 106 generates a structural variant (SV) haplotype array file 412. For example, in some embodiments, the structural variant recognition array determination system 106 generates a FASTA format file as the SV haplotype array file 412. Such a FASTA file contains text having a single-letter code representing the individual nucleotides of the nucleotide sequence of the structural variant haplotype 408. In some cases, the FASTA file includes a descriptor or other header that identifies the target genomic region for the individual structural variant haplotypes.

[0086] As further shown in FIG. 4, the structural variant recognition array determination system 106 identifies candidate alternative haplotypes 414. For example, in some cases, the structural variant recognition array determination system 106 selects SNPs or indels with fewer than a threshold number of base pairs in the low-confidence call regions of the reference genome 402. By way of illustration, the low-confidence call regions can include genomic regions that (wholly or partially) include variable number tandem repeats (VNTRs), insertions or deletions, or regions with various different polymorphisms. The low-confidence call regions can similarly include genomic regions that have historically resulted in nucleotide calls that exhibit low-quality sequencing metrics, such as below a threshold base call quality metric (e.g., Q20, Q30, Q37) or a threshold mapping quality metric (e.g., relative MAPQ score or MAPQ 40). Consistent with the above disclosure, in some embodiments, the structural variant recognition array determination system 106 selects alternative haplotypes 416 based on an area occurrence threshold for a target genomic region of the reference genome 402, such as a low-confidence call region.

[0087] Based on the alternative haplotypes 416, as further shown in FIG. 4, the structural variant recognition array determination system 106 generates an alternative haplotype variant call file 418. For example, the structural variant recognition array determination system 106 generates a VCF-formatted file that identifies the alternative haplotypes with single-letter codes (e.g., A, T, C, G) for comparison with the single-letter codes for the corresponding reference haplotypes at specific genomic coordinates. In some embodiments, the structural variant recognition array determination system 106 generates a VCF file that includes over 400,000 such alternative haplotypes for the low-confidence call regions.

[0088] Based on one or more of the reference genome sequence file 404, the SV haplotype alignment file 410, the SV haplotype sequence file 412, or the alternative haplotype variant call file 418, the structural variant recognition array determination system 106 generates a graph hash table 422. The graph hash table 422 represents an embodiment of the structural polymorphic graph genome. For example, the structural variant recognition array determination system 106 (i) a reference array representing a reference haplotype from the reference genome sequence file 404, (ii) an alternative contiguous array representing the structural variant haplotype 408 from the SV haplotype sequence file 412, and (iii) each of the alternative nucleotides or additional alternative contiguous arrays from the alternative haplotype variant call file 418, by associating with the genomic coordinates of the reference haplotype, generates the graph hash table 422. The structural variant recognition array determination system 106 uses the SV haplotype alignment file 410 to map the structural variant haplotype 408 to the genomic coordinates where the alternative contiguous array forms a lift-over group in the graph hash table 422. Thus, the graph hash table 422 represents an organizational structure that maps (i) the reference haplotype from the reference genome 402, (ii) the structural variant haplotype 408, and (iii) the nucleotide identifiers (e.g., single-letter codes) of the alternative haplotype 416 to specific genomic coordinates.

[0089] In addition to the foregoing files, in some embodiments, the structural variant recognition sequencing system 106 generates a masking file 420. The masking file 420 partially masks the sequences or nucleotide identifiers (e.g., A, T, C, G) of the structural variant haplotype 408 or alternative haplotype 416 with "N" from the FASTA file. By masking the sequences or nucleotides of either or both of the structural variant haplotype 408 or alternative haplotype 416, the structural variant recognition sequencing system 106 can create a genome file masked based on custom annotations or mask (e.g., hide) the target genomic region when aligning sequence data from nucleotide reads. By using the masking file 420 to partially mask specific sequences such as repetitive sequences or low-complexity genomic regions, the structural variant recognition sequencing system 106 can selectively hide or mask the reference sequence or alternative contiguous sequence for alignment, thereby ensuring that nucleotide reads do not align with such hidden nucleotide sequences. In some cases, the structural variant recognition sequencing system 106 generates a Browser Extensible Data (BED) file as the masking file 420. Thus, in some embodiments, specific nucleotide sequences within the graph hash table 422 are masked.

[0090] In addition to, or instead of, generating a structural polymorphic graph genome in an organizational structure, in some embodiments, the structural variant recognition sequencing system 106 implements a structural polymorphic graph genome to determine variant calls or other nucleotide calls for a genomic sample. According to one or more embodiments, FIG. 5 shows that the structural variant recognition sequencing system 106 (i) aligns nucleotide reads of a genomic sample with a structural polymorphic graph genome, and (ii) determines nucleotide calls for the genomic sample based on the aligned nucleotide reads. As described below, the structural variant recognition sequencing system 106 can determine variant calls (or other nucleotide calls) based on aligning a subset of nucleotide reads with alternative contiguous sequences representing structural variant haplotypes or alternative haplotypes.

[0091] As shown in FIG. 5, the structural variant recognition sequencing system 106 identifies or receives nucleotide reads 502 for a genomic sample. In some cases, for example, the structural variant recognition sequencing system 106 receives base call data (e.g., a BCL file or a FASTQ file) from a sequencing device. In some such cases, the base call data takes the form of a base call data file that organizes single-end reads or paired-end reads according to an index sequence added to oligonucleotides extracted from the genomic sample. As shown above, the structural variant recognition sequencing system 106 can sequence or analyze short nucleotide reads (e.g., less than 300 base pairs or less than 10,000 base pairs) as nucleotide reads 502 in some implementations, or long nucleotide reads (e.g., more than 300 base pairs or more than 10,000 base pairs) as nucleotide reads 502 in other implementations.

[0092] As further shown in FIG. 5, the structural variant recognition array determination system 106 aligns the nucleotide read 502 with different sequences within the structural polymorphism graph genome 504. For example, the structural variant recognition array determination system 106 aligns subsets of the nucleotide reads 506a, 506c, and 506e with the reference sequences 508a, 508b, and 508c, respectively, either wholly or partially. As described above, each of the reference sequences 508a-508c represents a different reference haplotype derived from a reference genome (e.g., GRCh38). As a further example, the structural variant recognition array determination system 106 aligns a subset of the nucleotide read 506b with an alternative nucleotide or alternative contiguous sequence 510 that represents an alternative haplotype. Finally, the structural variant recognition array determination system 106 aligns a subset of the nucleotide read 506d with an alternative contiguous sequence 512a (or alternative contiguous sequence 512b) that represents a structural variant haplotype, either wholly or partially.

[0093] For illustrative purposes and space constraints, FIG. 5 shows subsets of the nucleotide reads 506a-506e, the reference sequences 508a-508c, the alternative nucleotide or alternative contiguous sequence 510, and the alternative contiguous sequences 512a and 512b as merely examples. As described above, the sequencing device may generate a number of additional subsets of nucleotide reads, and the structural polymorphism graph genome 504 may include a number of other types of reference sequences, alternative nucleotides, or alternative contiguous sequences. In fact, the structural polymorphism graph genome 504 shown in FIG. 5 is merely one example for visualizing the reference sequences and alternative contiguous sequences of the structural polymorphism graph genome embodied by a hash table, matrix, or other digital organizational structure.

[0094] As shown in FIG. 5, the structural variant recognition array determination system 106 determines that a subset of the nucleotide read 506d overlaps entirely or partially with an alternative contiguous array 512a that represents a structural variant haplotype. For example, the structural variant recognition array determination system 106 determines that an alignment score (e.g., a Smith-Waterman score or a modified version of the Smith-Waterman score) exceeds other alignment scores for an alternative alignment of a subset of the nucleotide read 506a with a corresponding reference sequence. Based in part on the alignment score for the alignment with the alternative contiguous array 512a that exceeds other alignment scores for the subset of the nucleotide read 506d, in some embodiments, the structural variant recognition array determination system 106 generates a variant call indicating that the genomic sample exhibits the structural variant haplotype represented by the alternative contiguous array 512a.

[0095] As shown immediately above, in some embodiments, the structural variant recognition alignment determination system 106 determines an alternative contig fragment alignment score (e.g., a Smith-Waterman score or a modified version of the Smith-Waterman score) for an alignment of a subset of nucleotide reads 506d with an alternative contiguous sequence 512a. The structural variant recognition alignment determination system 106 can also determine a split group score for a split alignment of a subset of nucleotide reads 506d with one or more reference sequences. If the alternative contig fragment alignment score exceeds the split group score for the split alignment of the subset of nucleotide reads 506d and also exceeds the alignment score (e.g., a Smith-Waterman score) for other alignments with other alternative contiguous sequences such as alternative contiguous sequence 512b, the structural variant recognition alignment determination system 106 selects and reports a split alignment with the primary assembly of the reference genome corresponding to the alternative contiguous sequence 512a by virtue of a lift-over relationship. By selecting and reporting such a split alignment, the structural variant recognition alignment determination system 106 can use the reported split alignment to determine nucleotide base calls based on the alignment of the subset of nucleotide reads 506d with the alternative contiguous sequence 512a. However, if the split group score for the split alignment of the subset of nucleotide reads 506d exceeds the alternative contig fragment alignment score, the structural variant recognition alignment determination system 106 determines nucleotide base calls based on a different split alignment with one or more reference sequences of the reference genome that may not represent an alignment with the alternative contiguous sequence 512a.In some embodiments, the structural variant recognition array determination system 106 determines alternative contig fragment alignment scores and split group scores as described in Improving Split-Read Alignment by Intelligently Identifying and Scoring Candidate Split Groups, U.S. Patent Application No. 63 / 367002 (filed Jun. 24, 2022), which is hereby incorporated by reference in its entirety.

[0096] Based on aligning subsets of nucleotide reads 506a - 506e with different sequences of the structural polymorphism graph genome 504, as further shown in FIG. 5, the structural variant recognition array determination system 106 generates nucleotide base calls 514. For example, in some embodiments, the structural variant recognition array determination system 106 determines nucleotide base calls for subsets of nucleotide reads 506a, 506c, and 506e based on the alignment of subsets of nucleotide reads 506a, 506c, and 506e with reference sequences 508a, 508b, and 508c, respectively. In some such cases, the nucleotide base call may indicate a reference base (e.g., represented as 0) within the variant call file 516. As a further example of nucleotide base calls 514, the structural variant recognition array determination system 106 determines one or more variant calls for a subset of nucleotide read 506b based on the alignment between the subset of nucleotide read 506b and an alternative nucleotide or alternative contiguous sequence 510.

[0097] Unlike existing array determination systems, the structural variant recognition array determination system 106 can also determine variant calls corresponding to structural variants based on a structural polymorphic graph genome. Based on the alignment of a subset of nucleotide reads 506a and alternative contiguous sequences 512a, for example, the structural variant recognition array determination system 106 generates one or more variant calls indicating that the genomic sample exhibits a structural variant haplotype represented by the alternative contiguous sequence 512a. In some cases, the structural variant recognition array determination system 106 generates a variant call file 516 or an alignment file 518 that includes (i) an annotation indicating that one or more variant calls or other nucleotide calls represent a structural variant haplotype, and / or (ii) an annotation indicating an alignment that reflects the structural variant haplotype within the genomic sample. Consistent with the above disclosure, a variant call or nucleotide call may correspond to a structural variant haplotype that includes a deletion of a base pair exceeding a threshold number, an insertion of a base pair exceeding a threshold number, a duplication of a base pair exceeding a threshold number, an inversion, a translocation, or a copy number variant (CNV).

[0098] By aligning a subset of nucleotide reads 506a-506e with alternative contiguous sequences of the structural polymorphic graph genome 504 representing a structural variant haplotype, the structural variant recognition array determination system 106 can recover nucleotide calls that would otherwise not have been reported in the output file. For example, in some embodiments, the structural variant recognition array determination system 106 determines that the alignment score for a subset of nucleotide reads 506d does not meet a threshold alignment score for a candidate alignment between a subset of nucleotide reads 506a and the primary assembly region of the linear reference genome within the structural polymorphic graph genome 504.

[0099] To illustrate such recovery, the alignment scores for candidate alignments of a subset of nucleotide read 506a with various reference sequences may fall below a threshold alignment score. In contrast, the alternative contig fragment alignment scores for the alignment of a subset of nucleotide read 506d with alternative contiguous sequence 512a may meet the threshold alignment score. Thus, in some embodiments, the structural variant recognition alignment determination system 106 generates the variant call file 516 or the alignment file 518 using one or more nucleotide base calls of the genomic sample based on the subset of nucleotide read 506d aligned with alternative contiguous sequence 512a, without nucleotide base calling of the genomic sample based on candidate alignments of the subset of nucleotide read 506d with various reference sequences that do not meet the threshold alignment score.

[0100] As described above, the structural variant recognition alignment determination system 106 can generate a variant call file 516 or an alignment file 518 that includes an annotation indicating information about the structural variant haplotype detected in the genomic sample. For example, in one or more embodiments, the structural variant recognition alignment determination system 106 generates a variant call file 516 or an alignment file 518 that includes one or more of: (i) an annotation indicating that a variant call or other nucleotide base call corresponds to a structural variant haplotype; (ii) an annotation indicating the frequency of the structural variant haplotype (e.g., the frequency within a genomic sample database of the structural variant haplotype); (iii) an annotation indicating the genomic coordinates for the structural variant haplotype corresponding to the nucleotide base call; or (iv) an annotation indicating an alignment that reflects the structural variant haplotype within the genomic sample.

[0101] After generating data for one or more such annotations, in some embodiments, the structural variant recognition sequencing system 106 provides a variant call file 516 or an alignment file 518 for display on a computing device. In accordance with one or more embodiments, FIG. 6 shows a client device 114 that displays a graphical user interface 602 that includes variant calls for a structural variant haplotype. Although FIG. 6 shows the graphical user interface 602 that is displayed when the client device 114 implements the computer-executable instructions of the sequencing application 116, rather than repeatedly referring to the client device 114 that causes the client device 114 to perform specific operations for the structural variant recognition sequencing system 106, the present disclosure describes the client device 114 or the structural variant recognition sequencing system 106 that performs those operations in the following paragraphs. In some embodiments, the variant call file 516 or the alignment file 518 provides some of the computer-executable instructions and data presented within the graphical user interface 602.

[0102] As shown in FIG. 6, for example, client device 114 presents variant calls 604a and 604b that reflect different structural variant haplotypes indicated by a genomic sample. Consistent with the above disclosure, variant calls 604a and 604b represent graphical displays of nucleotide calls corresponding to the above structural variant haplotypes. As part of or in addition to each of variant calls 604a and 604b, client device 114 presents a reference sequence indicator (e.g., REF: GGGGCC 30X or REF: ACGTTAA...) for the reference sequence of the reference haplotype, and an alternative sequence indicator (e.g., ALT: GGGGCC 101X or ALT: duplication-inversion-inversion-deletion) for part or all of the alternative contiguous sequence corresponding to the structural variant haplotype. Further, as part of or in addition to each of variant calls 604a and 604b, client device 114 presents the genomic coordinates of variant calls 604a and 604b (e.g., Chr9: 614260 or Chr6: 156,776,025-157).

[0103] As further shown in FIG. 6, in some embodiments, client device 114 presents annotations regarding the genes and variant frequencies corresponding to variant calls 604a and 604b. For example, client device 114 presents genes 606a and 606b (e.g., c9orf72 or ARID1B) corresponding to variant calls 604a and 604b, respectively. In addition to specific gene identification, client device 114 presents variant frequencies 608a and 608b (e.g., 1.2% or 0.6%) indicating the frequencies of the structural variant haplotypes represented by variant calls 604a and 604b (e.g., from a genomic sample database), respectively. By providing reference sequence identifiers, alternative sequence identifiers, genomic coordinates, genes, and variant frequencies, the structural variant recognition sequencing system 106 provides important information indicating structural variant calls for specific genes to clinicians, test subjects, or other people.

[0104] As described above, the structural variant recognition sequencing system 106 improves the accuracy of read alignment and nucleobase calling by generating or utilizing a structural polymorphic graph genome representing the structural variant. To test the accuracy of read alignment and nucleobase calling of the structural variant recognition sequencing system 106, the researchers compared the accuracy with which the sequencing system detects structural variants using an existing graph reference genome and the accuracy with which the structural variant recognition sequencing system 106 identifies structural variants using the structural polymorphic graph genome. According to one or more embodiments, FIG. 7 shows a table 700 showing different accuracy measurements of (i) the sequencing system determining a variant call for deletions and insertions greater than 50 base pairs using an existing graph reference genome lacking alternative contiguous sequences representing the structural variant, and (ii) the structural variant recognition sequencing system 106 determining a variant call for such deletions and insertions using the structural polymorphic graph genome. As shown in table 700, the structural variant recognition sequencing system 106 improves the true positive genotype call, false negative genotype call, recall rate, and F-score for determining variant calls for deletions and insertions greater than 50 base pairs by using the structural polymorphic graph genome instead of the existing graph reference genome.

[0105] As shown by FIG. 7, the researchers input data on nucleotide reads from a query call set containing new deletions and insertions greater than 50 base pairs into the sequencing system and the structural variant recognition sequencing system 106. The sequencing system aligned data on nucleotide reads from the query call set to an existing graph reference genome, here the Illumina DRAGEN Graph Reference Genome hg19, and determined variant calls based on the aligned nucleotide read data. The structural variant recognition sequencing system 106 also aligned data on nucleotide reads within the query call set to one embodiment of a structural polymorphism graph genome and determined variant calls based on the aligned nucleotide read data.

[0106] To evaluate the accuracy of genotype calls for the query call set, the researchers compared the genotype calls of the sequencing system and the structural variant recognition sequencing system 106 for the query call set to a true call set. The true call set contains known deletions and insertions greater than 50 base pairs. For example, the true call set contains a list of structural variant events identified by other techniques or manually verified.

[0107] As shown by Table 700, the researchers further determined (i) the number of true positive (TP) genotype calls in which the sequencing system or the structural variant recognition sequencing system 106 accurately determined the corresponding insertions and deletions, and (ii) the number of false negative (FN) genotype calls in which the sequencing system or the structural variant recognition sequencing system 106 failed to determine the corresponding insertions and deletions. Based on the number of true positive and false negative genotype calls, the researchers also determined the recall rate, precision rate, and F-score, as shown in Table 700.

[0108] As shown by Table 700, by using a structural polymorphic graph genome instead of an existing graph reference genome, the structural variant recognition sequencing system 106 improves true positive genotype calls, reduces false negative genotype calls, and improves the reproducibility rate for deletions exceeding 50 base pairs in the true call set. Similarly, by using a structural polymorphic graph genome, the structural variant recognition sequencing system 106 improves true positive genotype calls, reduces false negative genotype calls, improves the precision rate, and improves the F-score for deletions exceeding 50 base pairs in the query call set as compared to the existing graph reference genome of the sequencing system.

[0109] As further shown by Table 700, by using a structural polymorphic graph genome instead of an existing graph reference genome, the structural variant recognition sequencing system 106 improves true positive genotype calls, reduces false negative genotype calls, and improves the reproducibility rate for insertions exceeding 50 base pairs in the true call set. Similarly, by using a structural polymorphic graph genome, the structural variant recognition sequencing system 106 improves true positive genotype calls, reduces false negative genotype calls, improves the precision rate, and improves the F-score for insertions exceeding 50 base pairs in the query call set as compared to the existing graph reference genome of the sequencing system.

[0110] Referring now to FIG. 8, this figure illustrates a flowchart of a series of operations 800 for generating a structural polymorphic genome according to one or more embodiments of the present disclosure. FIG. 8 shows operations according to one embodiment, although alternative embodiments may omit, add, reorder, and / or modify any of the operations shown in FIG. 8. The operations of FIG. 8 can be implemented as part of a method. Alternatively, a non-transitory computer-readable storage medium can include instructions that, when executed by one or more processors, cause a computing device or system to perform the operations shown in FIG. 8. In still further embodiments, a system includes at least one processor and a non-transitory computer-readable medium that includes instructions that, when executed by the one or more processors, cause the system to perform the operations of FIG. 8. In some cases, the at least one processor includes a configurable processor, and executing the at least one processor includes configuring the configurable processor.

[0111] As shown in FIG. 8, a series of operations 800 includes an operation 810 of identifying candidate structural variants. In particular, in some embodiments, operation 810 includes identifying candidate structural variants that meet an occurrence threshold amount within a genomic sample database.

[0112] For example, in some cases, identifying candidate structural variants includes selecting structural variants that represent one or more of deletions greater than 50 base pairs, insertions greater than 50 base pairs, duplications greater than 50 base pairs, inversions, translocations, or copy number variants (CNVs). For example, in a particular example, identifying candidate structural variants includes selecting structural variants that represent one or more of deletions of a threshold number of base pairs, insertions of a threshold number of base pairs, duplications of a threshold number of base pairs, inversions, translocations, or vi) copy number variants (CNVs).

[0113] As further shown in FIG. 8, operation 800 includes an operation 820 of selecting a structural variant haplotype from candidate structural variants. In particular, in some embodiments, operation 820 includes selecting a structural variant haplotype from candidate structural variants. For example, in some cases, selecting a structural variant haplotype includes selecting a specific structural variant haplotype from candidate structural variants that meets an additional occurrence threshold amount in a specific genomic region.

[0114] Illustratively, in some embodiments, selecting a structural variant haplotype includes selecting a first structural variant haplotype from candidate structural variants that meets an additional occurrence threshold amount in a first genomic region and selecting a second structural variant haplotype from candidate structural variants that meets an additional occurrence threshold amount in a second genomic region.

[0115] Furthermore, or alternatively, in some embodiments, selecting a structural variant haplotype includes selecting a specific structural variant haplotype adjacent to a specific adjacent variant in the nucleotide sequence of a genomic sample database. In some cases, the adjacent variant includes a single nucleotide polymorphism (SNP), a deletion of less than 50 base pairs, or an insertion of less than 50 base pairs. In particular, in a specific implementation, selecting a specific structural variant haplotype includes selecting a first structural variant haplotype in phase with a first adjacent variant in a first nucleotide sequence of a genomic sample database and selecting a second structural variant haplotype in phase with a second adjacent variant in a second nucleotide sequence of a genomic sample database.

[0116] By way of example, in some embodiments, selecting a structural variant haplotype includes selecting a first structural variant haplotype adjacent to a first adjacent variant in a first nucleotide sequence of a genomic sample database and selecting a second structural variant haplotype adjacent to a second adjacent variant in a second nucleotide sequence of the genomic sample database. As described above, in some cases, the first adjacent variant or the second adjacent variant includes a single nucleotide polymorphism (SNP), a deletion of fewer than a threshold number of base pairs, or an insertion of fewer than a threshold number of base pairs.

[0117] As further shown in FIG. 8, operation 800 includes operation 830 of identifying a reference haplotype corresponding to the structural variant haplotype. In particular, in certain implementations, operation 830 includes identifying a reference haplotype corresponding to the structural variant haplotype from a linear reference genome.

[0118] As further shown in FIG. 8, operation 800 includes operation 840 of generating a structural polymorphic graph genome including the structural variant haplotype and the reference haplotype. In particular, in certain implementations, operation 840 includes generating a structural polymorphic graph genome including an alternative contiguous array representing the structural variant haplotype and a reference array representing the reference haplotype. As suggested above, in some embodiments, operation 840 includes generating a structural polymorphic graph genome including a particular alternative contiguous array representing a particular structural variant haplotype and a particular adjacent variant.

[0119] By way of example, in some cases, generating a structural polymorphic graph genome includes generating a structural polymorphic graph genome including a first alternative contiguous array representing a first structural variant haplotype and a first adjacent variant and a second alternative contiguous array representing a second structural variant haplotype and a second adjacent variant. Further, in some cases, generating a structural polymorphic graph genome includes ordering a subset of alternative contiguous arrays corresponding to a genomic region according to their frequencies in the genomic sample database.

[0120] In addition to, or instead of, operations 810 - 840, in certain implementations, operation 800 further includes identifying, from a genomic sample database, an alternative haplotype that includes one or more of a single nucleotide polymorphism (SNP), a deletion less than 50 base pairs, or an insertion less than 50 base pairs, and generating a structural variant graph genome that further includes alternative nucleotides or additional alternative contiguous sequences representing the alternative haplotype.

[0121] As suggested above, in some embodiments, operation 800 includes generating an alignment file that maps a structural variant haplotype to genomic coordinates of a reference haplotype in a linear reference genome, and generating a structural variant graph genome by associating, within a tissue structure, an alternative contiguous sequence representing the structural variant haplotype with an identifier of the genomic coordinates of the reference haplotype. For example, in certain implementations, generating the alignment file includes generating an array alignment / map (SAM) lift-over file that maps the structural variant haplotype to the genomic coordinates of the reference haplotype, and generating the structural variant graph genome includes generating the structural variant graph genome using the tissue structure by associating, within a hash table, nucleotide identifiers for nucleotides from the alternative contiguous sequence with values representing the genomic coordinates of the reference haplotype.

[0122] Referring now to FIG. 9, this figure shows a flowchart of a series of operations 900 for aligning nucleotide reads of a genomic sample with a structural polymorphic graph genome and determining nucleotide base calls for the genomic sample based on the aligned nucleotide reads, according to one or more embodiments of the present disclosure. FIG. 9 shows operations according to one embodiment, although alternative embodiments can omit, add, reorder, and / or modify any of the operations shown in FIG. 9. The operations of FIG. 9 can be implemented as part of a method. Alternatively, a non-transitory computer-readable storage medium can include instructions that, when executed by one or more processors, cause a computing device or system to perform the operations shown in FIG. 9. In still further embodiments, a system can include at least one processor and a non-transitory computer-readable medium that includes instructions that, when executed by the one or more processors, cause the system to perform the operations of FIG. 9. In some cases, the at least one processor includes a configurable processor, and executing the at least one processor includes configuring the configurable processor.

[0123] As shown in FIG. 9, a series of operations 900 includes an operation 910 of identifying nucleotide reads from a genomic sample. As further shown in FIG. 9, operations 900 includes an operation 920 of aligning a subset of the nucleotide reads with structural variant haplotypes within the structural polymorphic graph genome. In particular, in some embodiments, operation 920 includes aligning a subset of the nucleotide reads with alternative contiguous sequences representing structural variant haplotypes within the structural polymorphic graph genome.

[0124] In some cases, the structural variant haplotypes include deletions over 50 base pairs, insertions over 50 base pairs, duplications, inversions, translocations, or copy number variants (CNVs). For example, in certain instances, the structural variant haplotypes include deletions of a threshold number of base pairs, insertions of a threshold number of base pairs, duplications of a threshold number of base pairs, inversions, translocations, or copy number variants (CNVs).

[0125] As further shown in FIG. 9, operation 900 includes an operation 930 of generating nucleotide calls for a genomic sample based on an aligned subset of nucleotide reads. In particular, in certain implementations, operation 930 includes generating one or more nucleotide calls for the genomic sample based on the aligned subset of nucleotide reads.

[0126] In addition to, or instead of, operations 910 - 930, in some embodiments, operation 900 includes generating an alignment file or variant call file that includes an annotation indicating a structural variant haplotype corresponding to one or more nucleotide calls. Additionally, or instead, in some cases, operation 900 includes generating an alignment file or variant call file that includes an annotation indicating the frequency within a genomic sample database of a structural variant haplotype corresponding to one or more nucleotide calls. Additionally, or alternatively, in certain embodiments, operation 900 includes generating an alignment file or variant call file that includes the genomic coordinates of a linear reference genome that is part of a structural polymorphic graph genome and corresponds to one or more nucleotide calls.

[0127] As suggested above, in some embodiments, operation 900 includes determining that a subset of nucleotide reads overlaps a breakpoint of an alternative contiguous sequence that represents a structural variant haplotype, and generating an alignment file or variant call file that includes an annotation indicating an alignment that reflects the structural variant haplotype within the genomic sample.

[0128] Additionally or alternatively, in certain implementations, operation 900 determines that an alignment score for a subset of nucleotide reads does not meet a threshold alignment score for candidate alignments between the subset of nucleotide reads and a primary assembly region of a linear reference genome, and generates a variant call file or alignment file having one or more nucleotide calls for a genomic sample based on an aligned subset of nucleotide reads with an alternative contiguous array and not having nucleotide calls for genomic samples based on candidate alignments that do not meet the threshold alignment score.

[0129] The methods described herein can be used in conjunction with a variety of nucleic acid sequencing techniques. Particularly applicable techniques are those in which nucleic acids are attached to fixed positions within an array such that their relative positions do not change and the array is imaged repeatedly. For example, embodiments in which images are obtained in different color channels corresponding to different labels used to distinguish one nucleotide type from another are particularly applicable. In some embodiments, the process of determining the nucleotide sequence of a target nucleic acid can be an automated process. Preferred embodiments include sequencing by synthesis (SBS) techniques.

[0130] SBS techniques generally involve the enzymatic extension of a nascent nucleic acid strand by the iterative addition of nucleotides to a template strand. In conventional methods of SBS, single nucleotide monomers can be provided to the target nucleotides in the presence of polymerase in each delivery. However, in the methods described herein, two or more types of nucleotide monomers can be provided to the target nucleic acid in the presence of polymerase during delivery.

[0131] SBS can utilize nucleotide monomers with a terminator part or nucleotide monomers lacking any terminator part. As methods of using nucleotide monomers lacking a terminator, for example, pyrosequencing and sequencing using γ-phosphate labeled nucleotides, as described in more detail below, can be mentioned. In methods of using nucleotide monomers without a terminator, the number of nucleotides added in each cycle is generally variable and depends on the template sequence and the mode of nucleotide delivery. In SBS technology using nucleotide monomers with a terminator part, the terminator can be effectively irreversible under sequencing conditions as used in conventional Sanger sequencing using dideoxynucleotides, or the terminator can be reversible as in the case of the sequencing method developed by Solexa (now Illumina, Inc.).

[0132] SBS technology can use nucleotide monomers with a label part or nucleotide monomers lacking a label part. Thus, incorporation events can be detected based on characteristics of the label such as fluorescence of the label, characteristics of the nucleotide monomer such as molecular weight or charge, by-products of nucleotide incorporation such as the release of pyrophosphate, etc. In embodiments where two or more different nucleotides are present in the sequencing reagent, the different nucleotides can be distinguishable from each other, or alternatively, two or more different labels can be distinguishable under the detection technology used. For example, different nucleotides present in the sequencing reagent can have different labels, and they can be distinguished using an appropriate optical system exemplified by the sequencing method developed by Solexa (now Illumina, Inc.).

[0133] Preferred embodiments include pyrosequencing technology. Pyrosequencing detects the release of inorganic pyrophosphate (PPi) when a specific nucleotide is incorporated into the nascent strand (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996) “Real-time DNA sequencing using detection of pyrophosphate release.” Analytical Biochemistry 242(1), 84-9, Ronaghi, M. (2001) “Pyrosequencing sheds light on DNA sequencing.” Genome Res. 11(1), 3-11, Ronaghi, M., Uhlen, M. and Nyren, P. (1998) “A sequencing method based on real-time pyrophosphate.” Science 281(5375), 363, U.S. Patent No. 6,210,891, U.S. Patent No. 6,258,568 and U.S. Patent No. 6,274,320, the entire disclosures of which are incorporated herein by reference). In pyrosequencing, the released PPi can be detected by its immediate conversion to adenosine triphosphate (ATP) by ATP sulfurylase, and the level of the generated ATP is detected via photons generated by luciferase. The nucleic acid to be sequenced can be attached to features in an array, and the array can be imaged to capture the chemiluminescent signal generated by incorporating nucleotides into the features of the array. An image can be obtained after treating the array with a specific nucleotide type (e.g., A, T, C, or G). The images obtained after the addition of each nucleotide type differ with respect to which features in the array are detected. These differences in the images reflect the different sequence contents of the features on the array. However, the relative positions of each feature remain unchanged within the image. The images can be stored, processed, and analyzed using the methods described herein.For example, the images obtained after processing the array with each different nucleotide type can be processed in the same manner as those exemplified herein for images obtained from different detection channels for reversible terminator-based sequencing methods.

[0134] In another exemplary type of SBS, cycle sequencing is achieved, for example, by stepwise addition of reversible terminator nucleotides that include cleavable or photobleachable dye labels as described in International Publication No. WO 04 / 018497 and U.S. Patent No. 7,057,026, the disclosures of each of which are incorporated herein by reference. This approach has been commercialized by Solexa (now Illumina Inc.) and is also described in International Publication No. WO 91 / 06678 and International Publication No. WO 07 / 123,744, each of which is incorporated herein by reference. The availability of fluorescently labeled terminators whose fluorescence labels can be cleaved, allowing both termini to be reversed, facilitates efficient cyclic reversible termination (CRT) sequencing. The polymerase can also co-operate to efficiently incorporate and extend from these modified nucleotides.

[0135] Preferably, in a reversible terminator-based sequencing embodiment, the label does not substantially inhibit extension under SBS reaction conditions. However, the detection label may be removable, for example, by cleavage or degradation. Images can be taken after incorporation of the label into the arrayed nucleic acid features. In certain embodiments, each cycle involves the simultaneous delivery of four different nucleotide types to the array, and each nucleotide type has spectrally distinct labels. Next, four images can be obtained, each using a detection channel selective for one of the four different labels. Alternatively, the different nucleotide types can be added sequentially, and an image of the array can be obtained between each addition step. In such embodiments, each image shows nucleic acid features that have incorporated a particular type of nucleotide. Because the sequence content of each feature is different, different features will be present in, or absent from, different images. However, the relative positions of the features remain unchanged within the image. Images obtained from such reversible terminator-SBS methods can be stored, processed, and analyzed as described herein. Following the imaging step, the labels can be removed, and the reversible terminator moiety can be removed for subsequent cycles of nucleotide addition and detection. Removing the label after detection in a particular cycle and prior to subsequent cycles has the advantage of reducing background signal and crosstalk between cycles. Examples of useful labels and removal methods are described below.

[0136] In certain embodiments, some or all of the nucleotide monomers can include reversible terminators. In such embodiments, the reversible terminator / cleavable fluor can include a fluor attached to the ribose moiety via a 3'-ester bond (Metzker, Genome Res. 15:1767-1776 (2005), which is incorporated herein by reference). Other approaches separate the chemistry of the terminator from the cleavage of the fluorescent label (Ruparel et al., Proc Natl Acad Sci USA 102:5932-7 (2005), which is incorporated herein by reference in its entirety). Ruparel et al. describe the development of reversible terminators that use a small amount of 3'-allyl groups to block extension, but can be easily de-blocked by treatment with a palladium catalyst for a short time. The fluor is attached to the group via a photocleavable linker that can be easily cleaved by exposure to long wavelength UV light for 30 seconds. Thus, either disulfide reduction or photocleavage can be used as the cleavable linker. Another approach to reversible termini is the use of natural termini following placement of a bulky dye on the dNTP. The presence of a charged bulky dye on the dNTP can act as an effective terminator via steric and / or electrostatic hindrance. The presence of one incorporation event prevents further binding unless the dye is removed. Cleavage of the dye removes the fluor and effectively reverses the termination. Examples of modified nucleotides are also described in U.S. Patent No. 7,427,673 and U.S. Patent No. 7,057,026, the disclosures of which are incorporated herein by reference in their entirety.

[0137] Additional exemplary SBS systems and methods that can be used with the methods and systems described herein are described in U.S. Patent Application Publication No. 2007 / 0166705, U.S. Patent Application Publication No. 2006 / 0188901, U.S. Patent No. 7,057,026, U.S. Patent Application Publication No. 2006 / 0240439, U.S. Patent Application Publication No. 2006 / 0281109, International Publication No. WO05 / 065814, U.S. Patent Application Publication No. 2005 / 0100900, International Publication No. WO06 / 064199, International Publication No. WO07 / 010,251, U.S. Patent Application Publication No. 2012 / 0270305, and U.S. Patent Application Publication No. 2013 / 0260372, the disclosures of which are hereby incorporated by reference in their entirety.

[0138] Some embodiments can utilize the detection of four different nucleotides using fewer than four different labels. For example, SBS can be implemented using the methods and systems described in incorporated reference US Patent Application Publication No. 2013 / 0079232. As a first example, nucleotide type pairs can be detected at the same wavelength, but are distinguishable based on the difference in intensity for one member of the pair, or based on a change (e.g., via chemical modification, photochemical modification, or physical modification) to one member of the pair that results in the appearance or disappearance of a distinct signal as compared to the signal detected for the other member of the pair. As a second example, three of the four different nucleotide types can be detected under certain conditions, while the fourth nucleotide type has no detectable label or is minimally detected under those conditions (e.g., minimal detection by background fluorescence). Incorporation of the first three nucleotide types into a nucleic acid can be determined based on the presence of their respective signals, and incorporation of the fourth nucleotide type into a nucleic acid can be determined based on the absence or minimal detection of any signal. As a third example, one nucleotide type can include a label that is detected in two different channels, while other nucleotide types are detected in one or fewer of the channels. The three exemplary configurations described above are not considered mutually exclusive and can be used in various combinations.An exemplary embodiment combining all three examples is a first nucleotide type detected in a first channel (e.g., dATP having a label detected in the first channel when excited by a first excitation wavelength), a second nucleotide type detected in a second channel (e.g., dCTP having a label detected in the second channel when excited by a second excitation wavelength), a third nucleotide type detected in both the first and second channels (e.g., dTTP having at least one label detected in both channels when excited by the first and / or second excitation wavelength), and a fourth nucleotide type that is not detected in any channel or lacks a label that is minimally detected (e.g., unlabeled dGTP) in a fluorescence-based SBS method.

[0139] Furthermore, as described in U.S. Patent Application Publication No. 2013 / 0079232, which is incorporated herein by reference, sequencing data can be obtained using a single channel. In such a so-called one-dye sequencing method, the first nucleotide type is labeled, but the label is removed after the first image is generated, and the second nucleotide type is labeled only after the first image is generated. The third nucleotide type retains its label in both the first and second images, and the fourth nucleotide type remains unlabeled in both images.

[0140] Some embodiments can utilize ligation-based sequencing. Such techniques utilize DNA ligase to incorporate oligonucleotides and identify the incorporation of such oligonucleotides. The oligonucleotides typically have different labels that correlate with the identity of specific nucleotides in the sequence to which the oligonucleotide hybridizes. As with other SBS methods, an image can be obtained after processing an array of nucleic acid features with labeled sequencing reagents. Each image shows nucleic acid features that incorporated a particular type of label. Because the sequence content of each feature is different, different features may or may not be present in different images, but the relative positions of the features remain the same within the image. Images obtained from ligation-based sequencing methods can be stored, processed, and analyzed as described herein. Exemplary SBS systems and methods that can be utilized with the methods and systems described herein are described in U.S. Patent No. 6,969,488, U.S. Patent No. 6,172,218, and U.S. Patent No. 6,306,597, the disclosures of which are incorporated herein by reference in their entirety.

[0141] Some embodiments can utilize nanopore sequencing (Deamer, D. W. & Akeson, M., "Nanopores and nucleic acids: prospects for ultrarapid sequencing.", Trends Biotechnol. 18, 147 - 151 (2000); Deamer, D. and D. Branton, "Characterization of nucleic acids by nanopore analysis", Acc. Chem. Res. 35:817 - 825 (2002); Li, J., M. Gershow, D. Stein, E. Brandin, and J. A. Golovchenko, "DNA molecules and configurations in a solid - state nanopore microscope", Nat. Mater. 2:611 - 615 (2003), the disclosures of which are incorporated herein by reference in their entirety). In such embodiments, the target nucleic acid passes through a nanopore. The nanopore can be a synthetic pore such as α - hemolysin or a biomembrane protein. When the target nucleic acid passes through the nanopore, each base pair can be identified by measuring fluctuations in the electrical conductance of the pore. (U.S. Patent No. 7,001,792; Soni, G. V. & Meller, "A. Progress toward ultrafast DNA sequencing using solid - state nanopores.", Clin. Chem. 53, 1996 - 2001 (2007); Healy, K., "Nanopore - based single - molecule DNA analysis.", Nanomed. 2, 459 - 481 (2007); Cockroft, S. L., Chu, J., Amorin, M. & Ghadiri, M. R., "A single - molecule nanopore device detects DNA polymerase activity with single - nucleotide resolution.", J. Am Chem. Soc. 130, 818 - 820 (2008), the disclosures of which are incorporated herein by reference in their entirety).Data obtained from nanopore array determination can be stored, processed, and analyzed as described herein. Specifically, the data can be processed as an image according to the exemplary processing of the optical and other images described herein.

[0142] Some embodiments can utilize methods involving real-time monitoring of DNA polymerase activity. Incorporation of nucleotides can be detected, for example, via fluorescence resonance energy transfer (FRET) interactions between a fluorophore-containing polymerase and a γ-phosphate-labeled nucleotide, as described in, for example, U.S. Patent No. 7,329,492 and U.S. Patent No. 7,211,414, each of which is incorporated herein by reference, or incorporation of nucleotides can be detected, for example, using zero-mode waveguides, as described in, for example, U.S. Patent No. 7,315,019, which is incorporated herein by reference, and fluorescent nucleotide analogs and engineered polymerases, as described in, for example, U.S. Patent No. 7,405,281 and U.S. Patent Application Publication No. 2008 / 0108082, each of which is incorporated herein by reference. Illumination can be restricted to zeptoliter-scale volumes surrounding surface-tethered polymerase so that incorporation of fluorescently labeled nucleotides can be observed with low background (Levene, M. J. et al. “Zero-mode waveguides for single-molecule analysis at high concentrations.” Science, 299, 682 - 686 (2003); Lundquist, P. M. et al. “Parallel confocal detection of single molecules in real time.” Opt. Lett. 33, 1026 - 1028 (2008); Korlach, J. et al. “Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nano structures.” Proc. Natl. Acad. Sci. USA 105, 1176 - 1181 (2008), the disclosures of which are incorporated herein by reference in their entireties).Images obtained from such methods can be stored, processed, and analyzed as described herein.

[0143] Some SBS embodiments involve the detection of protons released upon incorporation of nucleotides into an extension product. For example, sequencing based on the detection of released protons may use an electrical detector and related technology commercially available from Ion Torrent (Guilford, CT, a subsidiary of Life Technologies), or the sequencing methods and systems described in US Patent Application Publication No. 2009 / 0026082 (A1), US Patent Application Publication No. 2009 / 0127589 (A1), US Patent Application Publication No. 2010 / 0137143 (A1), or US Patent Application Publication No. 2010 / 0282617 (A1), each of which is incorporated herein by reference. The methods described herein for amplifying a target nucleic acid using kinetic exclusion can be readily applied to the substrate used to detect protons. More specifically, the methods described herein can be used to generate a clonal population of amplicons used to detect protons.

[0144] The above-described SBS method can be advantageously implemented in a multiplex format such that multiple different target nucleic acids are manipulated simultaneously. In certain embodiments, the different target nucleic acids can be processed in a common reaction vessel or on the surface of a particular substrate. This enables convenient delivery of sequencing reagents, removal of unreacted reagents, and detection of incorporation events in a multiplex manner. In embodiments using surface-bound target nucleic acids, the target nucleic acids can be in an array format. In an array format, the target nucleic acids can typically be bound to the surface in a spatially distinguishable manner. The target nucleic acids can be bound by direct covalent bonding, binding to beads or other particles, or binding to a polymerase or other molecule bound to the surface. The array can contain a single copy of the target nucleic acid at each site (also referred to as a feature), or multiple copies having the same sequence can be present at each site or feature. The multiple copies can be generated by amplification methods such as bridge amplification or emulsion PCR, which are described in more detail below.

[0145] The methods described herein can use arrays having features of any of a variety of densities, for example, including at least about 10 features / cm2, 100 features / cm2, 500 features / cm2, 1,000 features / cm2, 5,000 features / cm2, 10,000 features / cm2, 50,000 features / cm2, 100,000 features / cm2, 1,000,000 features / cm2, 5,000,000 features / cm2, or more.

[0146] The advantages of the methods described herein are to provide rapid and efficient detection of multiple target nucleic acids in parallel. Accordingly, the present disclosure provides an integrated system that can prepare and detect nucleic acids using techniques known in the art such as those exemplified above. Thus, the integrated system of the present disclosure can include a fluid component capable of delivering amplification reagents and / or sequencing reagents to one or more immobilized DNA fragments, and the system can include components such as pumps, valves, reservoirs, fluid lines, and the like. A flow cell can be configured and / or used in the integrated system for detecting target nucleic acids. Exemplary flow cells are described, for example, in U.S. Patent Application Publication No. 2010 / 0111768 (A1) and U.S. Patent Application No. 13 / 273,666, each of which is incorporated herein by reference. As exemplified for the flow cell, one or more of the fluid components of the integrated system can be used in amplification methods and detection methods. Taking an embodiment of nucleic acid sequencing as an example, one or more of the fluid components of the integrated system can be used for the delivery of sequencing reagents in the amplification methods described herein and sequencing methods as exemplified above. Alternatively, the integrated system can include separate fluid systems for performing amplification methods and detection methods. Examples of integrated sequencing systems that can create amplified nucleic acids and also determine the sequence of nucleic acids include, but are not limited to, the MiSeq™ platform (Illumina, Inc., San Diego, CA), and the apparatus described in U.S. Patent Application No. 13 / 273,666, which is incorporated herein by reference.

[0147] The above-described array determination system determines the sequence of nucleic acid polymers present in a sample received by an array determination device. As defined herein, "sample" and its derivatives are used in the broadest sense and include any sample, culture, etc. that is suspected of containing a target. In some embodiments, the sample contains nucleic acids in the form of DNA, RNA, PNA, LNA, chimeras or hybrids. The sample can include any biological sample, clinical sample, surgical sample, agricultural sample, air sample or water sample that contains one or more nucleic acids. The term also includes any isolated nucleic acid sample, e.g., genomic DNA, freshly frozen or formalin-fixed paraffin-embedded nucleic acid samples. The sample can be from a single individual, a collection of nucleic acid samples from genetically related members, nucleic acid samples from genetically unrelated members, nucleic acid samples (matched) from a single individual such as tumor samples and normal tissue samples, or a sample from a single source that contains two different forms of genetic material such as maternal and fetal DNA obtained from a maternal subject, or can be derived from the presence of contaminating bacterial DNA in a sample that contains plant or animal DNA. In some embodiments, the source of the nucleic acid material can include nucleic acids obtained from a neonate, such as is typically used in neonatal screening.

[0148] The nucleic acid sample can contain high molecular weight substances such as genomic DNA (gDNA). The sample can contain low molecular weight substances such as nucleic acid molecules obtained from FFPE or stored DNA samples. In another embodiment, the low molecular weight substance contains enzymatically or mechanically fragmented DNA. The sample can include cell-free circulating DNA. In some embodiments, the sample can contain nucleic acid molecules obtained from biopsies, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microdissection, surgical excisions, and other clinical or laboratory-derived samples. In some embodiments, the sample can be an epidemiological, agricultural, forensic, or pathogenic sample. In some embodiments, the sample can contain nucleic acid molecules obtained from animals such as human or mammalian sources. In another embodiment, the sample can contain nucleic acid molecules obtained from non-mammalian sources such as plants, bacteria, viruses, or fungi. In some embodiments, the source of the nucleic acid molecule can be a preserved or extinct sample or species.

[0149] Furthermore, the methods and compositions disclosed herein may be useful for amplifying nucleic acid samples having low-quality nucleic acid molecules such as degraded and / or fragmented genomic DNA from forensic samples. In one embodiment, the forensic sample can include nucleic acids obtained from a crime scene, nucleic acids obtained from a missing persons DNA database, nucleic acids obtained from a laboratory associated with a forensic investigation, or forensic samples obtained by law enforcement agencies, one or more military services, or any such personnel. The nucleic acid sample can be, for example, a purified sample or a crude DNA containing a lysate derived from a buccal swab, paper, cloth, or other substrate that can be impregnated with saliva, blood, or other body fluids. Thus, in some embodiments, the nucleic acid sample can contain a small amount of DNA or a fragmented portion of DNA, such as genomic DNA. In some embodiments, the target sequence can be present in one or more body fluids including, but not limited to, blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequence can be obtained from the hair, skin, tissue sample, autopsy, or cadaver of a victim. In some embodiments, the nucleic acid containing one or more target sequences can be obtained from a deceased animal or human. In some embodiments, the target sequence can contain nucleic acids obtained from non-human DNA such as microbial, plant, or entomological DNA. In some embodiments, the target sequence or the amplified target sequence is for the purpose of human identification. In some embodiments, the present disclosure generally relates to methods for characterizing forensic samples. In some embodiments, the present disclosure generally relates to methods of human identification using one or more target-specific primers disclosed herein, or one or more target-specific primers designed using the primer design criteria outlined herein. In one embodiment, a forensic sample or a human identification sample containing at least one target sequence can be amplified using any one or more of the target-specific primers disclosed herein, or using the primer criteria outlined herein.

[0150] The components of the structural variant recognition array determination system 106 can include software, hardware, or both. For example, the components of the structural variant recognition array determination system 106 can include one or more instructions stored on a computer-readable storage medium and executable by a processor of one or more computing devices (e.g., client device 114). When executed by one or more processors, the computer-executable instructions of the structural variant recognition array determination system 106 can cause the computing device to implement the bubble detection method described herein. Alternatively, the components of the structural variant recognition array determination system 106 can include hardware such as a dedicated processing device for implementing a particular function or group of functions. Additionally or alternatively, the components of the structural variant recognition array determination system 106 can include a combination of computer-executable instructions and hardware.

[0151] Furthermore, the components of the structural variant recognition array determination system 106 that implement the functions described herein with respect to the structural variant recognition array determination system 106 can be implemented, for example, as part of a stand-alone application, as a module of an application, as a plug-in of an application, as a library function or functions that can be called by other applications, and / or as a cloud computing model. Thus, the components of the structural variant recognition array determination system 106 can be implemented as part of a stand-alone application on a personal computing device or a mobile device. Additionally or alternatively, the components of the structural variant recognition array determination system 106 can be implemented in any application that provides a sequencing service, including but not limited to Illumina BaseSpace, Illumina DRAGEN, or Illumina TruSight software. "Illumina", "BaseSpace", "DRAGEN", and "TruSight" are registered trademarks or trademarks of Illumina, Inc. in the United States and / or other countries.

[0152] Embodiments of the present disclosure may include, or utilize, a special purpose or general purpose computer including, for example, computer hardware such as one or more processors and system memory, as will be discussed in more detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. In particular, one or more of the processes described herein may be embodied in a non-transitory computer-readable medium and at least partially implemented as executable instructions by one or more computing devices (e.g., any of the media content access devices described herein). Generally, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., memory, etc.), executes those instructions, and thereby performs one or more processes including one or more of the processes described herein.

[0153] A computer-readable medium can be any available medium that can be accessed by a general purpose or special purpose computer system. A computer-readable medium storing computer-executable instructions is a non-transitory computer-readable storage medium (device). A computer-readable medium carrying computer-executable instructions is a transmission medium. Thus, by way of example and not limitation, embodiments of the present disclosure can include at least two distinctly different types of computer-readable media, namely non-transitory computer-readable storage media (devices) and transmission media.

[0154] A non-transitory computer-readable storage medium (device) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (SSDs, e.g., based on RAM), flash memory, phase-change memory (PCM), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general purpose or special purpose computer.

[0155] "Network" is defined as one or more data links that enable the transfer of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer via a network or another communication connection (either hardwired, wireless, or a combination of hardwired or wireless), the computer properly recognizes the connection as a transmission medium. The transmission medium can be used to carry desired program code means in the form of computer-executable instructions or data structures and can include a network and / or data link that can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media.

[0156] Further, when reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be automatically transferred from a transmission medium to a non-transitory computer-readable storage medium (device) (or vice versa). For example, computer-executable instructions or data structures received via a network or data link are buffered in RAM within a network interface module (e.g., NIC), and then ultimately can be transferred to the computer system RAM and / or a less volatile computer storage medium (device) in the computer system. Thus, it should be understood that the non-transitory computer-readable storage medium (device) can be included in computer system components that also (or further primarily) utilize the transmission medium.

[0157] Computer-executable instructions, for example, when executed on a processor, include instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a particular function or group of functions. In some embodiments, the computer-executable instructions are executed on a general-purpose computer and convert the general-purpose computer into a special-purpose computer implementing the elements of the present disclosure. The computer-executable instructions may be, for example, binary, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts above. Rather, the described features and acts are disclosed as exemplary forms for implementing the claims.

[0158] Those skilled in the art will understand that the present disclosure can be implemented in a network computing environment having many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cellular telephones, PDAs, tablets, pagers, routers, switches, and the like. The present disclosure can also be implemented in a distributed system environment where local and remote computer systems, linked via a network (either by a hardwired data link, a wireless data link, or a combination of hardwired and wireless data links), both perform tasks. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0159] Embodiments of the present disclosure can also be implemented in a cloud computing environment. As used herein, "cloud computing" is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be adopted in the marketplace to provide ubiquitous and convenient on-demand access to a shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization, exposed with low management effort or service provider interaction, and then scaled accordingly.

[0160] The cloud computing model can be composed of various characteristics such as, for example, on-demand self-service, wide area network access, resource pooling, rapid elasticity, measured services, etc. The cloud computing model can also expose various service models such as, for example, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The cloud computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this specification and the claims, a "cloud computing environment" is an environment in which cloud computing is adopted.

[0161] FIG. 10 shows a block diagram of a computing device 1000 that can be configured to perform one or more of the above processes. It will be understood that one or more computing devices such as the computing device 1000 can implement the structure variant recognition array determination system 106 and the structure variant recognition array determination system 106. As shown by FIG. 10, the computing device 1000 can include a processor 1002, a memory 1004, a storage device 1006, an I / O interface 1008, and a communication interface 1010, which can be communicatively coupled by a communication infrastructure 1012. In certain embodiments, the computing device 1000 can include fewer or more components than those shown in FIG. 10. The following paragraphs will describe the components of the computing device 1000 shown in FIG. 10 in more detail.

[0162] In one or more embodiments, the processor 1002 includes hardware for executing instructions, such as instructions that make up a computer program. By way of example and not limitation, in order to execute instructions for dynamically modifying a workflow, the processor 1002 can retrieve (or fetch) instructions from internal registers, internal caches, the memory 1004, or the storage device 1006, decode them, and execute them. The memory 1004 may be a volatile or non-volatile memory used to store data, metadata, and programs for execution by the processor. The storage device 1006 includes storage such as a hard disk, a flash disk drive, or other digital storage device for storing data or instructions for implementing the methods described herein.

[0163] The I / O interface 1008 enables a user to provide input to, receive output from, transfer data to, or receive data from the computing device 1000. The I / O interface 1008 can include a mouse, a keypad or keyboard, a touch screen, a camera, an optical scanner, a network interface, a modem, other known I / O devices, or a combination of such I / O interfaces. The I / O interface 1008 can include one or more devices for presenting output to the user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I / O interface 1008 is configured to provide graphical data to a display for presentation to the user. The graphical data may represent one or more graphical user interfaces and / or any other graphical content that may be useful in a particular implementation.

[0164] The communication interface 1010 can include hardware, software, or both. In any case, the communication interface 1010 can provide one or more interfaces for communication (such as packet-based communication) between the computing device 1000 and one or more other computing devices or a network. By way of non-limiting example, the communication interface 1010 can include a network interface controller (NIC) or network adapter for communicating with Ethernet or other wired-based networks, or a wireless NIC (WNIC) or wireless adapter for communicating with wireless networks such as WI-FI.

[0165] Additionally, the communication interface 1010 can facilitate communication with various types of wired or wireless networks. The communication interface 1010 can also facilitate communication using various communication protocols. The communication infrastructure 1012 can also include hardware, software, or both that couples the components of the computing device 1000 to each other. For example, the communication interface 1010 can enable multiple computing devices connected by a particular infrastructure to communicate with each other to perform one or more aspects of the processes described herein using one or more networks and / or protocols. By way of illustration, the sequencing process can enable multiple devices (such as client devices, sequencing devices, and server devices) to exchange information such as sequencing data and error notifications.

[0166] In the foregoing specification, the present disclosure has been described with reference to its specific exemplary embodiments. The various embodiments and aspects of the present disclosure are described with reference to the details considered herein, and the accompanying drawings illustrate the various embodiments. The above description and drawings are examples of the present disclosure and should not be construed as limiting the present disclosure. Numerous specific details are described to provide a complete understanding of the various embodiments of the present disclosure.

[0167] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be implemented using fewer or more steps / operations, or the steps / operations may be performed in a different order. Additionally, the steps / operations described herein may be repeated or performed in parallel with each other, or in parallel with different occurrences of the same or similar steps / operations. Accordingly, the scope of the present application is shown not by the foregoing description, but by the appended claims. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. A non-temporary computer-readable medium, when executed by at least one processor, to a computing device, Identifying candidate structural variants that meet the occurrence threshold in the genome sample database, From the candidate structural variants, select a structural variant haplotype that includes one or more structural variants that are in phase with adjacent variants in the nucleotide sequence of the genome sample database. Identifying reference haplotypes corresponding to the aforementioned structural variant haplotypes from a linear reference genome, To map and align nucleotide reads of a genome sample, a structural polymorphism graph genome is generated that includes alternative sequences representing the structural variant haplotype and a reference sequence representing the reference haplotype. A non-temporary computer-readable medium containing instructions to execute something.

2. The non-temporary computer-readable medium according to claim 1, further comprising instructions, when executed by the at least one processor, causing the computing device to select a structural variant haplotype by selecting a specific structural variant haplotype from the candidate structural variants that satisfies an additional threshold amount of occurrence in a particular genomic region.

3. The non-temporary computer-readable medium according to claim 2, further comprising instructions, when executed by the at least one processor, causing the computing device to select a structural variant haplotype by selecting a specific structural variant haplotype that is in phase with a specific adjacent variant in a sequence sequence of the genome sample database.

4. When executed by the at least one processor, the computing device: Selecting a first structural variant haplotype that is in phase with a first adjacent variant in a first sequence of the genome sample database, The non-temporary computer-readable medium according to claim 3, further comprising an instruction to select a specific structural variant haplotype by selecting a second structural variant haplotype that is in phase with a second adjacent variant in a second sequence of the genome sample database.

5. The non-temporal computer-readable medium according to claim 3, further comprising instructions, when executed by the at least one processor, causing the computing device to generate the structural polymorphism graph genome, which includes a specific structural variant haplotype and a specific alternative sequence representing the specific adjacent variant.

6. The non-temporary computer-readable medium according to claim 1, wherein adjacent variants include single nucleotide polymorphisms (SNPs), deletions of less than 50 base pairs, or insertions of less than 50 base pairs.

7. When executed by the at least one processor, the computing device: From the aforementioned genome sample database, identify alternative haplotypes containing one or more of the following: single nucleotide polymorphisms (SNPs), deletions of less than 50 base pairs, or insertions of less than 50 base pairs. To generate the structural polymorphism graph genome which further includes alternative nucleobases representing the alternative haplotype or additional alternative sequences, A non-temporary computer-readable medium according to claim 1, further comprising an instruction to execute a command.

8. The non-temporary computer-readable medium according to claim 1, further comprising instructions, when executed by the at least one processor, causing the computing device to identify candidate structural variants by selecting structural variants representing one or more of the following: deletions of more than 50 base pairs, insertions of more than 50 base pairs, duplications of more than 50 base pairs, inversions, translocations, or copy number variations (CNVs).

9. The non-temporary computer-readable medium according to claim 1, wherein the at least one processor includes a configurable processor, and executing the at least one processor includes configuring the configurable processor.

10. It is a system, At least one processor, A non-temporary computer-readable medium is provided, and when the non-temporary computer-readable medium is executed by the at least one processor, the system Identifying candidate structural variants that meet the occurrence threshold in the genome sample database, From the candidate structural variants, select a structural variant haplotype that includes one or more structural variants that are in phase with adjacent variants in the nucleotide sequence of the genome sample database, Identifying reference haplotypes corresponding to the aforementioned structural variant haplotypes from a linear reference genome, To map and align nucleotide reads of a genome sample, a structural polymorphism graph genome is generated that includes alternative sequences representing the structural variant haplotype and a reference sequence representing the reference haplotype. A system that includes instructions to execute something.

11. When executed by the at least one processor, the system From the candidate structural variants, select a first structural variant haplotype that satisfies an additional threshold amount for occurrence in the first genomic region, The system according to claim 10, further comprising an instruction to select a structural variant haplotype by selecting a second structural variant haplotype from the candidate structural variants that satisfies the additional occurrence threshold amount in a second genomic region.

12. When executed by the at least one processor, the system Selecting a first structural variant haplotype that is in phase with a first adjacent variant in a first sequence of the genome sample database, Selecting a second structural variant haplotype that is in phase with a second adjacent variant in a second sequence of the genome sample database, The system according to claim 10, further comprising an instruction to select the particular structural variant haplotype.

13. The system according to claim 12, wherein the first adjacent variant or the second adjacent variant includes a single nucleotide polymorphism (SNP), a deletion of fewer than a threshold number of base pairs, or an insertion of fewer than a threshold number of base pairs.

14. The system according to claim 12, further comprising instructions, when executed by the at least one processor, causing the system to identify candidate structural variants by selecting structural variants that represent one or more of the following: deletion of base pairs beyond a threshold number, insertion of base pairs beyond a threshold number, duplication of base pairs beyond a threshold number, inversion, translocation, or copy number variation (CNV).

15. When executed by the at least one processor, the system A first alternative sequence representing a first structural variant haplotype and a first adjacent variant, The system according to claim 12, further comprising instructions for generating a structural polymorphism graph genome comprising a second structural variant haplotype and a second alternative sequence representing a second adjacent variant.

16. When executed by the at least one processor, the system The process involves generating an alignment file that maps the structural variant haplotype to the genomic coordinates of the reference haplotype within the linear reference genome, Within the tissue structure, the structural polymorphism graph genome is generated by associating the alternative continuous sequence representing the structural variant haplotype with the identifier of the genomic coordinates of the reference haplotype. The system according to claim 10, further comprising an instruction to execute.

17. When executed by the at least one processor, the system The alignment file is generated by generating a sequence alignment / map (SAM) liftover file that maps the structural variant haplotype to the genomic coordinates of the reference haplotype, In a hash table, the nucleobase identifiers for the nucleobases from the alternative sequence are associated with values ​​representing the genomic coordinates of the reference haplotype, thereby generating the structural polymorphism graph genome using the tissue structure. The system according to claim 16, further comprising an instruction to perform the following:

18. The system according to claim 10, further comprising, when executed by the at least one processor, instructions to cause the system to generate the structural polymorphism graph genome by ordering a subset of alternative contiguous sequences corresponding to genomic regions according to their frequency in the genomic sample database.

19. A computer implementation method, Identifying candidate structural variants that meet the occurrence threshold in the genome sample database, From the candidate structural variants, select a structural variant haplotype that includes one or more structural variants that are in phase with adjacent variants in the nucleotide sequence of the genome sample database, Identifying reference haplotypes corresponding to the aforementioned structural variant haplotypes from a linear reference genome, To map and align nucleotide reads of a genome sample, a structural polymorphism graph genome is generated that includes alternative sequences representing the structural variant haplotype and a reference sequence representing the reference haplotype. A computer implementation method including

20. The computer-aided method according to claim 19, wherein selecting the structural variant haplotype comprises selecting a specific structural variant haplotype from the candidate structural variants that satisfies an additional threshold amount of occurrence in a specific genomic region.

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