A graph reference genome and base calling approach using imputed haplotypes

Abstract

The present disclosure relates to a system, a non-transitory computer readable medium, and a method for generating a customized graph reference genome for a particular sample genome and utilizing the customized graph reference genome to determine a final nucleotide base call for the sample genome. By way of example, the disclosed system can generate a customized graph reference genome that includes various paths that represent the assigned haplotypes corresponding to a particular genomic region. Additionally or alternatively, the disclosed system can determine and compare direct and assigned nucleotide base calls for the sample genome as a basis for generating a final nucleotide base call. In some such cases, the disclosed system weights (and selects between) the direct and assigned nucleotide base calls for a genomic coordinate based on sequencing metrics that correspond to the direct nucleotide base calls or based on variability of the genomic region that includes the genomic coordinate.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 246,626, entitled "A GRAPH REFERENCE GENOME AND BASE-CALLING APPROACH USING IMPUTED HAPLOTYPES," filed September 21, 2021, the contents of which are incorporated by reference in their entirety herein. [Background technology]

[0002] In recent years, biotechnology companies and research institutions have improved hardware and software platforms for determining the sequence of nucleotide bases (or entire genomes) and identifying variant calls for nucleotide bases that differ from reference bases in a reference genome. For example, some existing nucleic acid sequencing platforms determine individual nucleotide bases in a sequence by using conventional Sanger sequencing or by using sequencing by synthesis (SBS). When using SBS, existing platforms can monitor tens of thousands or more oligonucleotides being synthesized in parallel to detect more accurate nucleotide base calls from a larger base call data set. For example, a camera in an SBS platform can capture images of illuminated fluorescent tags from nucleotide bases incorporated into such oligonucleotides. After capturing such images, existing SBS platforms transmit the base call data (or image data) to a computing device having sequencing data analysis software that aligns the nucleotide reads with a reference genome. Based on the aligned nucleotide-fragment reads, existing SBS platforms can determine nucleotide-base calls for genomic regions and identify variants in the nucleic acid sequence of a sample.

[0003] Despite these recent advances, existing nucleotide base sequencing platforms and sequencing data analysis software (collectively, hereafter, existing sequencing systems) may inaccurately determine base calls, especially for bases in difficult-to-call genomic regions. Such difficult-to-call genomic regions may include genomic regions that historically (or for a given sample) contain nucleotide reads that often fail to align well with a linear reference genome or generate nucleotide base calls that exhibit low-quality sequencing metrics, such as base call quality and mapping quality scores, below normal thresholds. For example, existing sequencing systems frequently generate inaccurate mapping or inaccurate nucleotide base calls for genomic regions that contain rare variants or high variability, such as variable number tandem repeat (VNTR) regions. Despite decades of failure to generate accurate nucleotide base calls in difficult-to-call regions, existing sequencing systems often limit input data for variant callers or other sequencing data analysis software to (i) direct nucleotide base calls from reads compared to a linear reference genome, and (ii) sequencing metrics corresponding to such direct nucleotide base calls.

[0004] Although some existing sequencing systems attempt to solve the problems of alignment accuracy and base calling accuracy with respect to graph reference genomes, existing graph reference genomes often contain an excess of alternative paths for alleles that are sufficiently similar (or unrelated) to alleles represented by many sample genomes. For example, some existing sequencing systems utilize general graph genomes that contain a large number of alternative genome sequences and paths for alleles that are both common and uncommon across populations. Because such alternative sequences and paths may be similar to alleles in many sample genomes but do not match, the general graph genome frequently causes existing sequencing systems to misalign or miss variants for many samples. Thus, by utilizing a general graph reference genome, existing sequencing systems can increase the likelihood of mismatch alignment with reads from genomic samples.

[0005] In addition to alignment accuracy issues, existing graph reference genomes are often bulky and consume significant memory and computational resources. In fact, some existing graph reference genomes may contain countless alternative paths for alternative genome sequences that are unrelated to a given genome sample. These countless alternative paths can consume unnecessary memory. In addition to wasting memory, generic graph reference genomes often increase the computational time of existing sequencing systems to determine whether to include or exclude matches to alternative sequences when making nucleotide-base calls. Summary of the Invention

[0006] The present disclosure describes embodiments of methods, non-transitory computer readable media, and systems that can solve one or more of the above-mentioned (or other) problems in the art. In particular, the disclosed system can generate a customized graph reference genome for a particular sample genome and utilize the customized graph reference genome to determine nucleotide base calls for the sample genome. For example, the disclosed system can determine variant nucleotide base calls (e.g., single nucleotide polymorphisms) surrounding a genomic region of the sample genome and assign haplotypes corresponding to the genomic region based on the variant nucleotide base calls. The disclosed system can then generate a graph reference genome of the sample genome that includes paths representing the assigned haplotypes. Based on comparing nucleotide fragment reads of the sample genome to the paths representing the assigned haplotypes of the genomic region, the disclosed system can determine nucleotide base calls within the genomic region.

[0007] In addition to or as an alternative to the sample customized graph genome, in one or more embodiments, the disclosed system determines and compares direct and imputed nucleotide base calls for the sample genome as a basis for generating final nucleotide base calls. For example, the disclosed system can determine direct nucleotide base calls (and corresponding sequencing metrics) based on nucleotide fragment reads aligned with a linear or graph reference genome. Such direct nucleotide base calls may include variant nucleotide base calls surrounding the genomic region. Based on such variant-nucleotide-base calls, the disclosed system can impute haplotypes for the genomic region and determine imputed nucleotide-base calls based on the imputed haplotypes. Based on the direct nucleotide base calls, corresponding sequencing metrics, and imputed nucleotide base calls, the disclosed system determines a final nucleotide base call for the sample genome with respect to the reference genome. For example, the disclosed system can utilize a weighting model (e.g., a base calling machine learning model) to assign weights to both direct and imputed nucleotide-base calls to determine a final nucleotide-base call for the sample genome.

[0008] Additional features and advantages of one or more embodiments of the present disclosure will be set forth in the description that follows, and in part will be obvious from the description, or may be learned by practice of such exemplary embodiments. [Brief description of the drawings]

[0009] The detailed description provides additional specificity and detail to one or more embodiments through the use of the accompanying drawings, as briefly described below. [Figure 1] 1 shows a diagram of an environment in which a customized sequencing system can operate in accordance with one or more embodiments. [Figure 2A]1 shows an overview of a customized sequencing system that generates and utilizes a graph reference genome in accordance with one or more embodiments. [Figure 2B] 1 shows an overview of a customized sequencing system that determines final nucleotide-base calls based on imputed nucleotide-base calls, direct nucleotide-base calls, and sequencing metrics, according to one or more embodiments. [Figure 3A] 1 shows an example of a customized sequencing system that utilizes a haplotype database to assign haplotypes corresponding to genomic regions, according to one or more embodiments. [Figure 3B] 1 shows an example of a customized sequencing system that utilizes a haplotype database to assign haplotypes corresponding to genomic regions, according to one or more embodiments. [Figure 4A] 1 illustrates a customized sequencing system that generates a graph reference genome and aligns nucleotide fragment reads of a sample genome to the graph reference genome, according to one or more embodiments. [Figure 4B] 1 illustrates a customized sequencing system that generates a graph reference genome and aligns nucleotide fragment reads of a sample genome to the graph reference genome, according to one or more embodiments. [Diagram 5] 1 shows a graph depicting non-reference genotype concordance rate for a customized sequencing system using a sample-specific graph reference genome versus allele frequency, in accordance with one or more embodiments. [Figure 6] FIG. 1 illustrates a customized sequencing system that utilizes direct nucleotide base calls, sequencing metrics, and assigned nucleotide base calls to determine a final nucleotide base call in accordance with one or more embodiments. [Figure 7A] 1 illustrates a customized sequencing system that trains and utilizes a base calling machine learning model, according to one or more embodiments. [Figure 7B]1 illustrates a customized sequencing system that trains and utilizes a base calling machine learning model, according to one or more embodiments. [Figure 8] 1 illustrates a flowchart of a sequence of actions for generating and utilizing a graph reference genome in accordance with one or more embodiments. [Figure 9] FIG. 1 shows a flowchart of a series of operations for determining final nucleotide-base calls based on imputed nucleotide-base calls, direct nucleotide-base calls, and sequencing metrics in accordance with one or more embodiments. [Figure 10] FIG. 1 shows a flowchart of a series of operations for determining final nucleotide-base calls based on imputed nucleotide-base calls, direct nucleotide-base calls, and sequencing metrics in accordance with one or more embodiments. [Figure 11] 1 illustrates a block diagram of an exemplary computing device for implementing one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present disclosure describes one or more embodiments of a customized sequencing system that can generate a graph reference genome with customized haplotype paths for a particular sample genome and utilize the customized graph reference genome to determine nucleotide base calls for the sample genome. For example, the customized sequencing system can determine single nucleotide polymorphisms (SNPs) or other variant nucleotide base calls surrounding a target genomic region of the sample genome, and then assign haplotypes corresponding to the genomic region based on the surrounding variant nucleotide base calls. From such assigned haplotypes and the linear reference genome, the customized sequencing system can generate a graph reference genome for the sample genome, including paths representing the assigned haplotypes. Based on comparing the nucleotide fragment reads of the sample genome to the paths representing the assigned haplotypes for the target genomic region - and other such regions in the graph reference genome - the disclosed system can determine nucleotide base calls within the genomic region and other such regions. In some cases, the customized sequencing system also determines nucleotide base calls by aligning the nucleotide fragment reads to the linear reference genome included in the customized graph reference genome.

[0011] Prior to identifying such target genomic regions, in one or more embodiments, the customized sequencing system receives data representing nucleotide fragment reads of the sample genome sequenced by the sequencing machine. Such data for the nucleotide fragment reads includes a sequence of nucleotide base calls determined by the sequencing machine. After receiving the read data, the customized sequencing system can align the nucleotide fragment reads with a linear reference genome. Based on the aligned nucleotide fragment reads, the customized sequencing system can determine genomic coordinates and direct nucleotide base calls for the regions of the sample genome in response to the linear reference genome.

[0012] As described above, when determining nucleotide-base calls, some difficult-to-call genomic regions may exhibit alignment accuracy or base calling accuracy issues, among other sequencing challenges. In some embodiments, the customized sequencing system identifies difficult-to-call genomic regions (and sometimes non-difficult genomic regions) in the sample genome as target genomic regions. For example, the customized sequencing system identifies genomic regions of poor quality, such as low-confidence called genomic regions, where nucleotide base calls and / or nucleotide fragment reads indicate poor base call quality metrics, mapping quality metrics, and / or depth metrics below corresponding thresholds. As a further example, the customized sequencing system can identify genomic regions lacking nucleotide fragment reads covering a portion (or all) of the genomic region.

[0013] Once the target genomic regions are identified, in one or more embodiments, the customized sequencing system determines variant-nucleotide-base calls surrounding each target genomic region. For example, the customized sequencing system determines variant calls within a threshold distance of the target genomic region. To illustrate, the customized sequencing system can determine SNPs or other variants within a threshold number of base pairs (e.g., 600 base pairs; 10,000 base pairs, or 50,000 base pairs) from the target genomic region. As described further below, the customized sequencing system can determine SNPs (or other variants) that are part of one or more haplotypes corresponding to the target genomic region.

[0014] Based on the variant-nucleotide-base calls, the customized sequencing system assigns a haplotype for each target region. Illustratively, in one or more embodiments, the customized sequencing system statistically infers haplotypes for target regions from a haplotype database based on variant nucleotide-base calls flanking the target genomic region. For example, the customized sequencing system assigns haplotypes for difficult-to-call regions (e.g., low-confidence call regions) from the corresponding haplotype reference panel in the database based on SNPs or other variant-nucleotide-base calls. Thus, the customized sequencing system can compare SNPs or other variant-nucleotide-base calls to the haplotype reference panel to identify haplotypes that are likely to correspond to the target genomic region.

[0015] Based on the imputed haplotypes for the genomic regions, in one or more embodiments, the customized sequencing system generates a customized graph reference genome for the sample genome. To illustrate, the customized sequencing system can generate a graph reference genome that includes both a linear reference genome and paths representing the imputed haplotypes for the target genomic regions discussed above. In addition to the difficult to call regions, the graph reference genome can also add or include paths representing the imputed haplotypes for the non-difficult genomic regions.

[0016] By using the customized graph reference genome, the customized sequencing system can determine a final nucleotide base call for the target genomic region of the sample genome. To do so, in one or more embodiments, the customized sequencing system aligns the nucleotide fragment reads with the graph reference genome. For example, the customized sequencing system can align the nucleotide fragment reads with a path of the graph reference genome or a portion of the linear reference genome that has the highest quality mapping metrics for the corresponding nucleotide fragment reads. In some embodiments, the customized sequencing system determines a final nucleotide base call for the genomic coordinates of the sample genome based on the nucleotide fragment reads aligned with any path representing the assigned haplotype for the target genomic region or portion of the linear reference genome included in the graph reference genome.

[0017] As described above, in addition to or instead of using a customized graph reference genome, the customized sequencing system can determine a final nucleotide base call based on the direct nucleotide base calls, corresponding sequencing metrics, and the imputed nucleotide base calls. For example, the customized sequencing system can determine a direct nucleotide base call (and corresponding sequencing metrics) based on nucleotide fragment reads aligned with a linear or graph reference genome. Such direct nucleotide base calls can include variant nucleotide base calls surrounding the genomic region. Based on the variant-nucleotide-base calls, the customized sequencing system can impute haplotypes for the genomic region and determine the imputed nucleotide-base calls based on the imputed haplotypes. As described above, in some cases, the customized sequencing system further generates a graph reference genome with paths representing the imputed haplotypes and further determines direct nucleotide base calls for the sample genome using the graph reference genome. Based on the direct nucleotide base calls, corresponding sequencing metrics, and the imputed nucleotide base calls, the disclosed system determines a final nucleotide base call. For example, a customized sequencing system can utilize a weighting model or base calling machine learning model to assign weights to both direct and imputed nucleotide-base calls to determine the final nucleotide-base calls for the sample genome.

[0018] As indicated immediately above, in some embodiments, the customized sequencing system aligns the nucleotide fragment reads with a reference genome and determines a direct nucleotide base call for the sample genome based on the aligned nucleotide fragment reads. For example, the customized sequencing system determines a direct nucleotide base call based on aligning the nucleotide fragment reads with a linear reference genome or a graph reference genome. With the base calls of the aligned nucleotide fragment reads covering the genome coordinates, in some cases, the customized sequencing system applies a probability model (e.g., a Bayesian probability model) to determine a direct nucleotide base call (e.g., a direct variant nucleotide base call) for the genome coordinates of the sample genome.

[0019] During the determination of the direct nucleotide base calls, the customized sequencing system can determine and utilize various sequencing metrics corresponding to the direct nucleotide base calls. By way of example, in one or more embodiments, the customized sequencing system determines depth metrics that quantify the read depth of the nucleotide base calls at the genomic coordinates of the sample genome. As another example, in some embodiments, the customized sequencing system determines mapping quality metrics that quantify the quality of the alignment of the nucleotide fragment reads with the reference genome. As yet another example, the customized sequencing system can determine call data quality metrics that summarize the quality or confidence of the nucleotide base calls.

[0020] In addition to direct nucleotide base calling based on a reference genome, the customized sequencing system can determine an assigned nucleotide base call based on an assigned haplotype corresponding to one or more genomic regions. As described above, in one or more embodiments, the customized sequencing system determines SNPs (or other variant nucleotide base calls) surrounding a genomic region of the sample genome and assigns a haplotype corresponding to the genomic region based on the surrounding variant nucleotide base calls. Based on the assigned haplotypes, in certain cases, the customized sequencing system statistically infers possible haplotypes to determine an assigned nucleotide base call for the genomic region.

[0021] Based on the direct nucleotide base calls, corresponding sequencing metrics, and imputed nucleotide base calls, the disclosed system determines a final nucleotide base call. In one or more embodiments, for example, the customized sequencing system utilizes a weighting model to determine the weights of each of the direct and imputed nucleotide base calls. In one or more embodiments, the customized sequencing system can determine the weights based on the sequencing metrics corresponding to the direct nucleotide base calls and other factors described below. From the weighted direct and imputed nucleotide base calls for the genomic coordinates, the customized sequencing system can select or otherwise determine a final nucleotide base call. For example, in some cases, the customized sequencing system uses a base calling machine learning model to determine the final nucleotide base call from the direct and imputed nucleotide base calls (e.g., by weighting).

[0022] As alluded to above, the customized sequencing system provides several technical advantages and benefits over existing sequencing systems and methods. For example, the customized sequencing system improves read alignment accuracy and nucleotide base calling accuracy by utilizing a customized graph reference genome for the sample genome. More specifically, the customized sequencing system generates a graph reference genome that includes paths representing the assigned haplotypes for genomic regions of the sample genome. By utilizing a graph reference genome with paths for alternative contigs selected for a particular sample, the customized sequencing system can align nucleotide fragment reads to the graph reference genome more accurately, especially for more complex or "difficult" regions (e.g., low confidence call regions), than a generic graph reference genome cluttered with irrelevant or too many alternative paths. Due to the improved alignment with the customized graph reference genome, the customized sequencing system can also determine more accurate nucleotide base calls, with a higher confidence that such calls match or differ from the reference bases of the reference genome, than existing sequencing systems.

[0023] In addition to improving alignment and base calling accuracy, the customized sequencing system improves the computational speed and memory of the sequencing system using the graph reference genome. In contrast to the generic graph reference genome that includes paths for irrelevant or excessive alleles, the customized sequencing system reduces the memory required to store a significantly smaller graph reference genome with fewer paths representing haplotypes that are assigned based on variants of the sample genome. Rather than inefficiently using computational resources such as processing and memory storage, when deciding between an excessive number of possible read alignment matches with generic haplotype paths or allele paths, the customized sequencing system saves computational processing and other resources by using a customized graph reference genome with fewer (and more relevant) paths representing haplotypes assigned for the genomic region of the sample and more efficient mapping with fewer path matches.

[0024] In addition to improving accuracy, the customized sequencing system can generate customized graph genomes that are more flexible than traditional graph genomes. As alluded to above, in one or more embodiments, the customized sequencing system assigns haplotypes based on selected variant call data from a variant call file (e.g., VCF). To illustrate, in some cases, the customized sequencing system selectively identifies variant-nucleotide-base calls surrounding difficult-to-call regions (e.g., low-confidence call regions) from the VCF, rather than other genomic regions, as a basis for assigning haplotypes to represent paths in the customized graph reference genome. Rather than generating a graph reference genome using each variant-nucleotide-base call from the variant call file, as some existing sequencing systems do, the customized sequencing system can more selectively identify variant call data for customizing the graph reference genome.

[0025] Additionally or alternatively, in one or more embodiments, the customized sequencing system, when determining the final nucleotide base call based on direct and imputed nucleotide base calls, improves the accuracy of determining base calls over existing sequencing systems in difficult to call genomic regions, genomic regions without read coverage, or other genomic regions. By weighting and selecting between direct and imputed nucleotide base calls, the customized sequencing system can replace direct nucleotide base calls that exhibit sequencing metrics below a quality threshold with imputed nucleotide base calls that are more likely to be accurate in a particular genomic coordinate or region. As described above, the customized sequencing system can determine such imputed nucleotide base calls for a target genomic region based on statistically inferred haplotypes for the target genomic region. Similarly, in some cases, the customized sequencing system can improve accuracy by determining and selecting imputed nucleotide base calls (rather than direct nucleotide base calls) for genomic regions with little or no coverage by nucleotide fragment reads. In addition to relying on direct and imputed nucleotide base calls, in some cases, customized sequencing systems may improve the accuracy of the final nucleotide base calls for genomic regions by relying on further indirect evidence that existing sequencing systems do not consider (e.g., local variants, imputed haplotypes, and variant frequencies).

[0026] As alluded to above, in some embodiments, the customized sequencing system improves the accuracy of the final nucleotide-base call by utilizing a very first base calling machine learning model that analyzes both direct and assigned nucleotide-base calls. To illustrate, the base calling machine learning model can be trained to distinguish whether the assigned nucleotide base call or the direct nucleotide base call for a genomic coordinate is more accurate based on sequencing metrics for the training sample genome and the corresponding ground truth base calls. More specifically, in one or more embodiments, the customized sequencing system trains the base calling machine learning model to determine the final nucleotide-base call based on the direct nucleotide-base call, the sequencing metrics, and the assigned nucleotide-base call. Thus, the customized sequencing system can utilize the base calling machine learning model to efficiently and accurately determine the final nucleotide base call based on a variety of data, including the various data types described above.

[0027] As indicated by the preceding discussion, the present disclosure utilizes various terms to describe the features and advantages of the customized sequencing system. Here, further details regarding the meaning of such terms are provided. For example, as used herein, the term "nucleotide fragment read" or simply "read" refers to a predicted sequence of one or more nucleotide bases (or nucleotide base pairs) from all or a portion of a sample nucleotide sequence. In particular, a nucleotide fragment read includes a determined or predicted sequence of nucleotide base calls for a nucleotide fragment (or a group of monoclonal nucleotide fragments) from a sequencing library corresponding to a genomic sample. For example, in some cases, the sequencing device determines a nucleotide-fragment read by generating a nucleotide-base call for a nucleotide base that has passed through a nanopore in a nucleotide-sample slide, that has been determined via fluorescent tagging, or that has been determined from a well in a flow cell.

[0028] Further, as used herein, the term "nucleotide-base calling" (or sometimes simply "base calling") refers to the determination or prediction of a particular nucleotide base (or nucleotide-base pair) for a genomic coordinate or oligonucleotide of a sample genome during a sequencing cycle. In particular, a nucleotide base call can refer to (i) a determination or prediction of the type of nucleotide base incorporated within an oligonucleotide on a nucleotide sample slide (e.g., a read-based nucleotide base call), or (ii) a determination or prediction of the type of nucleotide base present at a genomic coordinate or region in a sample genome, including a variant or non-variant call in a digital output file. In some cases, for a nucleotide fragment read, a nucleotide base call includes a nucleotide base determination or prediction based on an intensity value resulting from a fluorescently tagged nucleotide added to an oligonucleotide of a nucleotide sample slide (e.g., in a well of a flow cell). Alternatively, a nucleobase call includes a nucleobase determination or prediction from a chromatogram peak or current change resulting from a nucleotide passing through a nanopore of a nucleotide-sample slide. In contrast, a nucleotide-base call may also include a final prediction of a nucleotide base at a genomic coordinate of a sample genome for a variant call file or other base call output file based on a nucleotide-fragment read corresponding to a genomic coordinate or an assigned haplotype. Thus, a nucleotide-base call may include a base call corresponding to a genomic coordinate and a reference genome, such as an indication of a mutant or non-mutant at a particular position corresponding to a reference genome. In fact, a nucleotide-base call may refer to a variant call, including but not limited to a single nucleotide polymorphism (SNP), an insertion or deletion (indel), or a base call that is part of a structural variant. As alluded to above, a single nucleotide base call may include an adenine call, a cytosine call, a guanine call, or a thymine call (abbreviated as A, C, G, T) for DNA, or a uracil call (instead of a thymine call) (abbreviated as U) for RNA.

[0029] As used herein, the term "direct evidence" refers to base call data determined from nucleotide fragment reads aligned with a reference genome. For example, direct evidence includes nucleotide-base calls for nucleotide-fragment reads, corresponding sequencing metrics, or other base call data determined based on nucleotide-fragment reads aligned with a reference genome at a target genome coordinate or region corresponding to the nucleotide-base call. In contrast, the term "indirect evidence" refers to base call data or genomic data related to the surrounding or adjacent genomic regions of a target genome coordinate or region. Such indirect evidence includes, but is not limited to, variant-nucleotide-base calls and assigned haplotypes surrounding the target genome coordinate or region, variant allele frequencies, and / or population haplotypes corresponding to the genome coordinate or region. Indirect evidence does not include base call data from nucleotide fragment reads directly compared to a reference genome at a target genome coordinate or region.

[0030] Relatedly, as used herein, the term "variant-nucleotide-base call" refers to a nucleotide-base call that is different or varies from a reference base (or multiple reference bases) of a reference genome. By way of example, a variant-nucleotide-base call may include (or be part of) a SNP, an indel, or a structural variant that differs from one or more reference bases of a reference genome. Furthermore, as used herein, the term "direct nucleotide base call" refers to a nucleotide base call that is determined based on a comparison of a nucleotide fragment read to a reference genome (e.g., a linear reference genome or a graph reference genome). Thus, a direct nucleotide base call includes a determination or prediction of a type of nucleotide base present at a genomic coordinate or region in a sample genome based on a nucleotide fragment read that covers the genomic coordinate and corresponding sequencing metrics. Furthermore, as used herein, the term "direct invariant nucleotide base call" refers to a nucleotide base call that matches a reference base from a reference genome based on a comparison of a nucleotide fragment read to a reference genome. By way of example, a customized sequencing system can determine a direct invariant nucleotide base call based on a nucleotide fragment read that is directly aligned with a reference genome at a genomic coordinate that corresponds to the nucleotide base call.

[0031] As used herein, the term "imputation" refers to statistically inferring or estimating genotypes for genomic coordinates or genomic regions. More specifically, imputation can refer to statistically inferring haplotypes corresponding to genomic regions of a sample genome. For example, imputation can refer to utilizing variant-nucleotide-base calls surrounding a genomic region to determine haplotypes corresponding to that genomic region. In one or more embodiments, the customized sequencing system also utilizes a reference panel from a haplotype database and a hidden Markov model to impute haplotypes. As further described herein, the customized sequencing system can impute haplotypes for a target genomic region based on SNPs (or other variants) that not only surround or flank the target genomic region but are part of one or more haplotypes corresponding to the target genomic region. For example, if 20 SNPs form a haplotype in a target genomic region, the customized sequencing system can use 15 of such SNPs determined for the target genomic region to identify which haplotypes are present in the sample genome, thereby assigning the remaining 5 SNPs to one or more haplotypes for the target genomic region.

[0032] Further, as used herein, the term "assigned nucleotide base call" refers to a nucleotide base call for a genomic coordinate determined based on an assigned haplotype and / or variant frequency. For example, an assigned nucleotide base call includes a determination or prediction of the type of nucleotide base present at a genomic coordinate or region in a sample genome based on variant nucleotide base calls surrounding or adjacent to the genomic coordinate or region and statistical inference. In some cases, an assigned nucleotide base call represents a nucleotide base for a genomic coordinate or region from the most likely or probable haplotype determined by assignment. To further illustrate, in some embodiments, an assigned nucleotide base call includes variant frequencies, local variant nucleotide base calls, and / or inferred or predicted nucleotide bases for a genomic coordinate or region of a sample genome that reflect a population haplotype corresponding to the genomic coordinate or region.

[0033] Further, as used herein, the term "final nucleotide-base call" refers to a nucleotide-base call determined for a genomic coordinate and included or used in a base call output file (e.g., a variant call file). By way of example, in one or more embodiments, the term final nucleotide-base call includes (i) a nucleotide-base call included in a base call output file for a genomic coordinate, such as a variant-nucleotide-base call in a variant call file, or (ii) a nucleotide-base call for a genomic coordinate that is the same as a reference base and the nucleotide-base call is included in or excluded from the base call output file, such as a final decision to exclude the nucleotide-base call from the variant call file because the nucleotide-base call is the same as the reference base. As described below, the customized sequencing system can select a final nucleotide base call from among (or based on) the direct nucleotide base calls and the imputed nucleotide base calls that correspond to the same genomic coordinate.

[0034] Also, as used herein, the term "sample genome" refers to a target genome or a portion of a genome to be sequenced. For example, a sample genome includes a sequence of nucleotides isolated or extracted from a sample organism (or a copy of such an isolated or extracted sequence). In particular, a sample genome includes an entire genome isolated or extracted (in whole or in part) from a sample organism and composed of nitrogenous heterocyclic bases. For example, a nucleic acid polymer can include segments of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or other polymeric forms of nucleic acid or chimeric or hybrid forms of nucleic acid as described below. In some cases, a sample genome is one that is found in a sample prepared or isolated by a kit and received by a sequencing device.

[0035] Also as used herein, the term "haplotype" refers to a nucleotide sequence present in an organism (or present in an organism from a population) and inherited from one or more ancestors. In particular, a haplotype can include alleles or other nucleotide sequences present in organisms of a population and inherited together by such organisms, each from a single parent. In one or more embodiments, a haplotype includes a set of SNPs on the same chromosome that tend to be inherited together. In some cases, data representing haplotypes or sets of different haplotypes are stored or otherwise accessible in a haplotype database. Furthermore, an "imputed haplotype" refers to a haplotype that is presumed or statistically inferred to be present in a sample genome. For example, an imputed haplotype can be a haplotype that is statistically inferred for a genomic coordinate or region based on SNPs surrounding or adjacent to the genomic coordinate or region. As described above, an imputed haplotype can include SNPs or other variant-nucleotide-base calls that surround a target genomic region and to which a customized sequencing system imputes a haplotype. Relatedly, a "population haplotype" refers to a haplotype that is present within a particular or defined population.

[0036] Further, 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, the genomic coordinate includes an identifier for a particular chromosome of the genome and an identifier for the location of the nucleotide base within the particular chromosome. For example, the genomic coordinate(s) may include a number, name, or other identifier for the chromosome (e.g., chr1 or chrX) and a specific location(s), such as a numbered location following the identifier for the chromosome (e.g., chr1:1234570 or chr1:1234570-1234870). Further, in certain implementations, the genomic coordinate refers to the source of the 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 the location of the nucleotide base within the source for the reference genome (e.g., mt:16568 or SARS-CoV-2:29001). In contrast, in certain cases, genomic coordinates refer to the location of a nucleotide base within a reference genome, without reference to a chromosome or source (e.g., 29727).

[0037] Further, as used herein, a "genomic region" refers to a range of genomic coordinates. Similar to genomic coordinates, in certain embodiments, a genomic region can be identified by a chromosomal identifier and a specific location(s), such as a numbered location following the chromosomal identifier (e.g., chr1:1234570-1234870).

[0038] As described above, a genome coordinate includes a position within a reference genome. Such a position may be within a particular reference genome. As used herein, the term "reference genome" refers to a digital nucleic acid sequence assembled as a representative example(s) of genes for an organism. Regardless of sequence length, in some cases, a reference genome represents an exemplary set of genes or a set of nucleic acid sequences in a digital nucleic acid sequence determined by a scientist or statistical model as representative of a particular species of organism. For example, a linear human reference genome may be GRCh38 or other version of the reference genome from the Genome Reference Consortium.

[0039] Furthermore, as used herein, the term "graph reference genome" may include a reference genome that includes both a linear reference genome and paths that represent haplotypes or other alternative nucleic acid sequences. In particular, a graph reference genome may include a linear reference genome and paths that correspond to the imputed haplotypes identified for a particular sample genome from a haplotype database. By way of example only, a graph reference genome may include Illumina DRAGEN Graph Reference Genome hg19. In contrast, the present disclosure also describes a graph reference genome that includes a linear reference genome and paths that represent the imputed haplotypes selected or customized for a sample genome.

[0040] Furthermore, as used herein, the term "low confidence call region" refers to a range of genomic coordinates corresponding to one or more sequencing metrics that do not meet one or more thresholds for the corresponding sequencing metrics. In particular, a low confidence call region can include a range of genomic coordinates that have corresponding quality metrics or other sequencing metrics that do not meet a threshold for quality or alignment. By way of example, a low confidence call region can include a genomic region that contains (in whole or in part) VNTRs, large insertions or deletions, regions with a variety of different mutations, and / or other types of genomic mutations.

[0041] Also, as used herein, the term "sequencing metrics" refers to quantitative measurements or scores that indicate the degree to which individual nucleotide base calls (or a sequence of nucleotide base calls) align, compare, or quantify to genomic coordinates or genomic regions of a reference genome, or to nucleotide base calls from nucleotide fragment reads. For example, sequencing metrics include quantitative measurements or scores that indicate (i) the degree to which individual nucleotide-base calls align, map, or cover genomic coordinates or reference bases of a reference genome, or (ii) the degree to which nucleotide-base calls compare to reference or alternative nucleotide reads in terms of mapping, mismatches, base call quality, or other raw sequencing metrics. As described below, sequencing metrics can include different types of quality metrics.

[0042] As indicated immediately above, the term "quality metrics" refers to metrics or other quantitative measurements that indicate the accuracy, confidence, or quantity of nucleotide base calls or nucleotide fragment reads corresponding to one or more genomic coordinates. In particular, quality metrics include values ​​that indicate the likelihood that one or more predicted nucleotide base calls are inaccurate or that a nucleotide fragment read is misaligned or is below a quantitative threshold (e.g., depth). For example, in some implementations, the quality metrics can comprise call data quality metrics, read data quality metrics, or mapping quality metrics, as further described below.

[0043] Further, as used herein, the term "read data quality metrics" refers to metrics or other measurements that quantify the quality and / or certainty corresponding to a nucleotide fragment read. In particular, read data quality metrics can include metrics that reflect the total number of nucleotide bases that do not match the nucleotide bases of an exemplary nucleic acid sequence (e.g., a reference genome or an assigned haplotype) at a particular genomic coordinate across multiple reads (e.g., all reads overlapping a particular genomic coordinate) or multiple cycles (e.g., all cycles). Additionally or alternatively, read data quality metrics can include metrics that reflect read position metrics for a sample nucleic acid sequence, for example, by determining the average or median position within sequencing reads of nucleotide bases that cover a genomic coordinate.

[0044] Further, as used herein, the term "call data quality metrics" refers to metrics or other measurements that quantify the accuracy or certainty of a nucleotide base call. Call data quality metrics can include, for example, base call quality metrics, call probability metrics, or body quality metrics. With respect to the first example, "base call quality metrics" refers to a particular score or other measurement that indicates the accuracy of a nucleotide base call. In particular, a base call quality metric includes a value that indicates the likelihood that one or more predicted nucleotide base calls for a genomic coordinate will contain an error. For example, in certain implementations, a base call quality metric can include a Q score (e.g., a Phred quality score) that predicts the probability of error for any given nucleotide-base call. By way of illustration, a quality score (or Q score) can indicate that the probability of an incorrect nucleotide base call at a genomic coordinate is equal to 1 in 100 for a Q20 score, 1 in 1,000 for a Q30 score, 1 in 10,000 for a Q40 score, etc.

[0045] Additionally, as used herein, the term "callability metric" refers to a metric or other measurement that quantifies the indication of a correct nucleotide-base call (e.g., a variant-nucleotide-base call) in a genomic coordinate. By way of example, a callability metric can include the proportion or percentage of non-N reference positions that have a passing genotype call, as implemented by Illumina, Inc. Additionally, in some implementations, the customized sequencing system 104 uses a version of the Genomic Analysis Toolkit (GATK) to determine the callability metric.

[0046] Further, as used herein, the term "somatic quality metrics" refers to metrics or other measurements that estimate the probability of determining a number of aberrant nucleotide fragment reads in a tumor sample genome. For example, somatic quality metrics can represent estimates of the probability of determining a given (or more extreme) number of aberrant reads in a tumor sample genome using Fisher's exact test, given the counts of aberrant and normal reads in the tumor and normal BAM files. In some cases, the customized sequencing system 104 determines somatic quality metrics using a Phred algorithm and represents the somatic quality metrics as a Phred-scaled score, such as a quality score (or Q-score) that ranges from 0 to 60. Such quality scores may be equal to -10 log10, where the random variable is somatic.

[0047] Also, as used herein, the term "mapping quality metric" refers to a metric or other measurement that quantifies the quality or certainty of the alignment of a nucleotide fragment read or other sample nucleotide sequence with a reference genome. In particular, the term mapping quality metric can include a mapping quality (MAPQ) score for a nucleotide base call at a genomic coordinate, where the MAPQ score represents -10 log10 Pr{mapping position incorrect} rounded to the nearest integer. As an alternative to the mean or median mapping quality, in some embodiments, the mapping quality metric refers to the entire distribution of mapping quality for all nucleotide fragment reads that align with the reference genome at a genomic coordinate.

[0048] As further used herein, the term "depth metric" refers to a metric that quantifies the number of nucleotide fragment reads (or the number of nucleotide base calls from nucleotide fragment reads) that correspond to or overlap with genomic coordinates of a sample genome or other nucleic acid sequence. A depth metric can, for example, quantify the number of nucleotide base calls determined and aligned at a genomic coordinate during sequencing. In some cases, the customized sequencing system uses a scale where a normalized depth of 1 refers to diploidy and a normalized depth of 0.5 refers to haploidy. Additionally or alternatively, the customized sequencing system can utilize a depth metric that quantifies the number of nucleotide base calls below the expected or threshold depth coverage at a genomic coordinate or genomic region.

[0049] Furthermore, as used herein, the term "genotypic variability" refers to the degree of variation in genotypes for nucleotide bases for a particular genomic region. In particular, genotypic variability may include a metric or measurement that quantifies the likelihood that a genomic region and / or haplotype aligns with a graph reference genome. Furthermore, in one or more embodiments, genotypic variability may reflect the number or breadth of possible nucleotide bases (or nucleotide base sequences) in a particular genomic region with respect to a reference genome.

[0050] The following paragraphs describe the customized sequencing system with reference to example diagrams depicting example embodiments and implementations. For example, FIG. 1 shows a schematic diagram of a system environment (or "environment") 100 in which a customized sequencing system 104 operates according to one or more embodiments. As shown, the environment 100 includes one or more server devices 102 connected to user client devices 108 and sequencing devices 114 via a network 112. While FIG. 1 illustrates an embodiment of the customized sequencing system 104, the present disclosure describes alternative embodiments and configurations below.

[0051] 1, server device 102, user client device 108, and sequencing device 114 are connected via network 112. Thus, each of the components of environment 100 can communicate via network 112. Network 112 includes any suitable network with which computing devices can communicate. An exemplary network is described in further detail below in conjunction with FIG.

[0052] As illustrated by FIG. 1, the sequencing device 114 includes a device for sequencing a nucleic acid polymer or other nucleic acid polymer. In some embodiments, the sequencing device 114 analyzes nucleic acid segments or oligonucleotides extracted from a sample to generate data utilizing the computer-implemented methods and systems described herein, either directly or indirectly on the sequencing device 114. More specifically, the sequencing device 114 receives and analyzes nucleic acid segments extracted from a sample in a nucleotide-sample slide. In one or more embodiments, the sequencing device 114 utilizes SBS to sequence the nucleic acid polymer. In some embodiments, the sequencing device 114, in addition to or as an alternative to communicating via the network 112, bypasses the network 112 and communicates directly with the user client device 108. Additionally, as illustrated in FIG. 1, in one or more embodiments, the sequencing device 114 includes a customized sequencing system 104.

[0053] As further illustrated by FIG. 1, the server device 102 can generate, receive, analyze, store, and transmit digital data, such as data for nucleotide base calling or sequencing of a nucleic acid polymer. As illustrated in FIG. 1, the sequencing device 114 can transmit (and the server device(s) 102 can receive) various data from the sequencing device 114, including data representing nucleotide fragment reads. The server device 102 can also communicate with the user client device 108. In particular, the server device(s) 102 can transmit data about nucleotide fragment reads, direct nucleotide base calls, imputed nucleotide base calls, and / or sequencing metrics to the user client device 108. As further illustrated in FIG. 1, the server device 102 can include a sequencing system 104. In one or more embodiments, as further described below, the customized sequencing system 104 generates a customized graph reference genome 106 for the sample genome. Accordingly, the server device 102 can also transmit the graph reference genome 106 to the user client device 108.

[0054] In some embodiments, server device 102 comprises a distributed collection of servers, where server device 102 includes several server devices that are distributed across network 112 and located in different physical locations. Server device 102 may include a content server, an application server, a communication server, a web hosting server, or another type of server.

[0055] 1, the user client device 108 can generate, store, receive, and transmit digital data. In particular, the user client device 108 can receive nucleotide fragment reads, direct nucleotide base calls, assigned nucleotide base calls, sequencing metrics, and / or graph reference genome data from the server device 102 and / or the sequencing device 114. As such, the user client device 108 can present the final nucleotide fragment reads to a user associated with the user client device 108 in a graphical user interface.

[0056] The user client device 108 shown in Figure 1 can include various types of client devices. For example, in some embodiments, the user client device 108 includes a non-mobile device, such as a desktop computer or server, or other type of client device. In yet other embodiments, the user client device 108 includes a mobile device, such as a laptop, tablet, cell phone, or smartphone. Further details regarding the user client device 108 are described below with respect to Figure 11.

[0057] 1, the user client device 108 includes a sequencing application 110. The sequencing application 110 may be a web application or a native application (e.g., a mobile application, a desktop application) stored and executed on the user client device 108. The sequencing application 110 may include instructions that (when executed) cause the user client device 108 to receive data from the customized sequencing system 104 and present data from the sequencing device 114 and / or the server device 102. Additionally, the sequencing application 110 may instruct the user client device 108 to display data about the nucleotide-base calls for the graph reference genome, such as variant-nucleotide-base calls from a variant call file.

[0058] 1, the customized sequencing system 104 may be located on the user client device 108 or on the sequencing device 114 as part of the sequencing application 110. Thus, in some embodiments, the customized sequencing system 104 is implemented (e.g., located completely or partially) on the user client device 108. As noted above, in yet other embodiments, the customized sequencing system 104 is implemented by one or more other components of the environment 100, such as the sequencing device 114. Notably, the customized sequencing system 104 can be implemented in a variety of different ways across the server device 102, the network 112, the user client device 108, and the sequencing device 114.

[0059] 1 depicts components of environment 100 communicating over network 112, in certain implementations, components of environment 100 may also communicate directly with one another, bypassing the network. For example, as previously described, in some embodiments, user client device 108 may communicate directly with sequencing device 114. Additionally, in some embodiments, user client device 108 communicates directly with customized sequencing system 104. Furthermore, customized sequencing system 104 may access one or more databases housed on or accessed by server device 102 or elsewhere in environment 100.

[0060] As shown above, the customized sequencing system 104 can generate a customized graph reference genome for a sample genome (or a group of sample genomes) and use the graph reference genome to determine nucleotide base calls for the sample genome. FIG. 2A shows an overview of a process 200 for generating and utilizing such a customized graph reference genome. As shown in FIG. 2A, the customized sequencing system 104 determines variant-nucleotide-base calls surrounding a particular genomic region in the sample genome. The customized sequencing system 104 then utilizes the variant-nucleotide-base calls to assign haplotypes corresponding to the genomic region. The customized sequencing system 104 further generates a customized graph reference genome that includes paths representing the assigned haplotypes. In some embodiments, the customized sequencing system 104 then determines nucleotide base calls for the sample genome by comparing nucleotide fragment reads for the genomic region to paths in the graph reference genome.

[0061] As shown immediately above and in FIG. 2A, the customized sequencing system 104 can perform operation 202 of determining variant-nucleotide-base calls surrounding genomic regions. To identify such genomic regions, in some cases, the customized sequencing system 104 sequences or receives data representing nucleotide fragment reads for a sample genome (e.g., from one or more sequencing cycles). The customized sequencing system 104 further determines variant-nucleotide-base calls (or other nucleotide-base calls) and sequencing metrics based on a comparison of the nucleotide fragment reads to a reference genome (e.g., a linear reference genome). Upon determining the nucleotide base calls, the customized sequencing system 104 identifies target genomic regions having nucleotide base calls that exhibit sequencing metrics below a corresponding quality threshold.

[0062] Upon identifying the target genomic region, the customized sequencing system 104 can identify variant-nucleotide-base calls surrounding the genomic region. To illustrate, in one or more embodiments, the customized sequencing system 104 searches within a predetermined number of base pairs from the genomic region for variant-nucleotide-base calls. Specifically, in one or more embodiments, the customized sequencing system 104 identifies SNPs or other variant-nucleotide-base calls within a threshold number of base pairs within the genomic region (e.g., 10,000-50,000 base pairs from the genomic region). As described above, such identified SNPs (or other variant-nucleotide-base calls) can be part of a haplotype that the customized sequencing system 104 attributes to be present in the target genomic region. Instead of SNPs, in some cases, the customized sequencing system 104 identifies other variant types surrounding the genomic region, such as insertions, deletions, or inversions.

[0063] 2A, the customized sequencing system 104 can perform an operation 204 of assigning a haplotype for the genomic region based on the variant-nucleotide-base calls. To illustrate, upon determining the variant-nucleotide-base calls surrounding the genomic region, the customized sequencing system 104 can assign a haplotype for the genomic region from a haplotype database 206. In one or more embodiments, the haplotype database 206 includes data representing the nucleotide base sequences of the haplotypes, as well as other data corresponding to the haplotypes (e.g., corresponding genomic coordinates for the haplotypes, common surrounding variant nucleotide base calls for the haplotypes, and / or populations associated with the haplotypes).

[0064] In one or more embodiments, the customized sequencing system 104 assigns haplotypes for genomic regions by statistically inferring with statistical probability which haplotypes are likely to be present in the genomic region. More specifically, in some aspects, the customized sequencing system 104 assigns haplotypes by comparing variant-nucleotide-base calls surrounding the genomic region to common variant-nucleotide-base calls associated with a particular haplotype. The customized sequencing system 104 can compare SNPs surrounding the genomic region to SNPs associated with haplotypes in the haplotype database 206. By way of example, the customized sequencing system 104 can determine SNPs that are common between the genomic region and the haplotypes in the haplotype database 206. Thus, in one or more embodiments, the customized sequencing system 104 utilizes statistical inference and the amount of shared variant-nucleotide-base calls (e.g., SNPs) to identify haplotypes from the haplotype database 206 that are likely to be present in the genomic region.

[0065] In one or more embodiments, the customized sequencing system 104 utilizes the imputed haplotypes of the genomic regions to generate a customized graph reference genome. To illustrate, as shown in FIG. 2A, the customized sequencing system 104 can perform operation 208 of generating a graph reference genome including paths of imputed haplotypes for the genomic regions based on variant-nucleotide-base calls. More specifically, the customized sequencing system 104 can add or generate paths representing the imputed haplotypes corresponding to the genomic regions for inclusion in the graph reference genome. Indeed, the customized sequencing system 104 can add such paths for multiple target genomic regions in the graph reference genome.

[0066] In one or more embodiments, the customized sequencing system 104 assigns haplotypes by utilizing a hidden Markov model to identify associated genotypes. To illustrate, in some embodiments, the hidden Markov model identifies haplotypes by determining the likelihood that a haplotype corresponds to a genomic region. More specifically, the customized sequencing system 104 can utilize a hidden Markov model (HMM) that utilizes a haplotype database and haplotype patterns (e.g., surrounding variants-nucleotide-base calls) to identify haplotypes that are likely to correspond to a genomic region.

[0067] When performing HMM imputation, for example, the customized sequencing system 104 can utilize an imputation model based on the approach described by Na Li and Matthew Stephens, "Modeling Linkage Unequilibrium and Identifying Recombination Hotspot Using Single-Nucleotide Polymorphism Data," 165 Genetics 2213-2233 (2003), which is incorporated by reference in its entirety. For illustration, in some cases, the customized sequencing system 104 models the genotype of the sample genome at the target genomic region or coordinate as a mosaic of haplotypes from a reference panel. The customized sequencing system 104 further determines the probability that the sample genome contains a haplotype pair at the target genomic region or coordinate based on the determined variant nucleotide base calls (e.g., SNPs) surrounding or adjacent to the target genomic region or coordinate. In some such cases, the customized sequencing system 104 accounts for potential linkage between (i) the target genomic region or coordinate and (ii) a nearby genomic region or coordinate by determining the probability that a haplotype is present at the target genomic region or coordinate based on the observed variant nucleotide base calls and the similarity of the haplotypes inferred at the nearby genomic regions or coordinates. After determining the probabilities for the haplotype pair, in some cases, the customized sequencing system 104 selects the haplotype that exhibits the highest probability and / or a probability exceeding a threshold probability as the assigned haplotype for the target genomic region or coordinate. The present disclosure provides further examples and explanations of haplotype assignment below with reference to Figures 3A and 3B.

[0068] As described above, the customized sequencing system 104 can utilize the customized graph reference genome to determine nucleotide base calls for genomic regions. To illustrate, as shown in FIG. 2A, the customized sequencing system 104 performs operation 210 to determine nucleotide base calls for genomic regions in part by comparing nucleotide fragment reads of the sample genome to paths representing assigned haplotypes in the graph reference genome. As alluded to above, the customized sequencing system 104 can similarly determine nucleotide base calls for other genomic regions in the sample genome by comparing nucleotide fragment reads of the sample genome to paths representing either assigned haplotypes in the graph reference genome or portions of a linear reference genome.

[0069] As described above, in one or more embodiments, the customized sequencing system 104 aligns the nucleotide fragment reads to either the linear reference genome or paths representing the assigned haplotypes to determine direct variant or invariant nucleotide base calls. For illustrative purposes, the customized sequencing system 104 can align the nucleotide-fragment reads to nucleotide-base calls that match reference bases from the graph reference genome. More specifically, in one or more embodiments, the customized sequencing system 104 determines direct invariant nucleotide base calls based on the nucleotide fragment reads directly aligned to the reference genome at the genomic coordinates or regions that correspond to the nucleotide base calls. Because the customized sequencing system 104 utilizes statistical inference to determine the different possible haplotype paths contained in the graph reference genome, the customized sequencing system 104 can more accurately determine variant nucleotide base calls (or other nucleotide base calls) for low confidence call regions, genomic regions with little coverage by nucleotide fragment reads, or other genomic regions in the sample.

[0070] In addition to more accurately determining direct nucleotide base calls based on aligned nucleotide fragment reads, the customized sequencing system 104 can also determine and take into account imputed nucleotide base calls. To illustrate, the customized sequencing system 104 may determine nucleotide base calls based on indirect evidence (e.g., variant nucleotide base calls around the target genomic region, population haplotypes, and / or variant frequencies or variant nucleotide base calls adjacent to the target genomic region). Figure 2B shows an overview 220 of a customized sequencing system 104 that determines a final nucleotide base call for a genomic coordinate of a sample genome based on the direct nucleotide base calls for a reference genome, sequencing metrics corresponding to the direct nucleotide base calls, and the imputed nucleotide base calls for a particular genomic region of the sample genome.

[0071] 2B, for example, the customized sequencing system 104 performs operation 222 of determining direct nucleotide base calls and sequencing metrics. In some embodiments, the customized sequencing system 104 receives or determines nucleotide fragment reads corresponding to the sample genome. For example, in some cases, the customized sequencing system 104 performs SBS on the sequencing device 114 to determine nucleotide-base calls for nucleotide-fragment reads corresponding to nucleotide-clusters in a sample slide (e.g., a flow cell). Alternatively, the customized sequencing system 104 receives data from the sequencing device representing nucleotide base calls for such nucleotide fragment reads for the sample genome.

[0072] Regardless of how the customized sequencing system 104 receives the data for the nucleotide fragment reads, in one or more embodiments, the customized sequencing system 104 determines direct nucleotide base calls for genomic coordinates or regions of the sample genome by aligning the nucleotide fragment reads to a reference genome. By way of example, in some embodiments, the customized sequencing system 104 maps the nucleotide fragment reads of a genome sequence to a reference genome and applies a probability model (e.g., a Bayesian probability model) to determine direct nucleotide base calls (e.g., variant nucleotide base calls) for genomic coordinates of the sample genome. As described further below, the customized sequencing system 104 can then use the variant nucleotide base calls as bases for assigning haplotypes for the surrounding genomic regions or as bases for determining final nucleotide base calls.

[0073] In addition to determining direct nucleotide base calls, the customized sequencing system 104 can also receive or determine sequencing metrics corresponding to the direct nucleotide base calls. Such sequencing metrics can indicate various accuracy and / or certainty metrics corresponding to the nucleotide fragment reads (e.g., depth metrics, read data quality metrics, mapping data quality metrics). Additionally, such sequencing metrics can indicate the certainty or quality of the direct nucleotide base calls (e.g., call data quality metrics, base quality drop-off (BQD) scores).

[0074] 2B, in one or more embodiments, operation 222 includes operation 224 utilizing a linear reference genome or operation 226 utilizing a graph reference genome to determine direct nucleotide base calls. As described above, in some embodiments, the customized sequencing system 104 receives or determines nucleotide fragment reads corresponding to the sample genome. Thus, the customized sequencing system 104 can align the nucleotide fragment reads to either the linear reference genome or the graph reference genome to determine direct nucleotide base calls.

[0075] In addition to determining direct variant-nucleotide-base calls (or other nucleotide-base calls), in one or more embodiments, the customized sequencing system 104 determines an assigned nucleotide-base call. By way of example, as shown in FIG. 2B, in one or more embodiments, the customized sequencing system 104 performs operation 228 of assigning a haplotype corresponding to the genomic region. As described above with respect to FIG. 2A, the customized sequencing system 104 can assign a haplotype corresponding to the genomic coordinates of the genomic region based on variant-nucleotide-base calls surrounding or adjacent to the genomic region.

[0076] In one or more embodiments, the customized sequencing system 104 also utilizes other factors to assign haplotypes, including utilizing variant frequencies. In some embodiments, variant frequencies indicate the likelihood that a particular haplotype will occur at a target genomic coordinate or region. As further alluded to above, in some embodiments, the customized sequencing system 104 assigns the most likely haplotype for a genomic region based on "local" variant-nucleotide-base call data that indicates which genomic variants are common to a particular population and / or ethnic group corresponding to the sample genome. The customized sequencing system 104 can filter or narrow down the most likely haplotypes for a genomic region based on SNPs or other variant-nucleotide-base calls within a threshold base pair distance of the target genomic region.

[0077] To further illustrate, in one or more embodiments, the customized sequencing system 104 utilizes population haplotype frequencies to assign more likely (or more common) haplotypes to the population corresponding to the sample genome. Thus, the customized sequencing system 104 can utilize various frequency and / or population data indicating the likelihood of a haplotype occurring to determine the assigned haplotype.

[0078] As further shown in FIG. 2B, the customized sequencing system 104 performs operation 230 of determining an assigned nucleotide base call. In one or more embodiments, the customized sequencing system 104 determines the assigned nucleotide base call by identifying a nucleotide base call for each genomic coordinate within the genomic region from the most likely haplotype for the genomic region. In some cases, for example, the customized sequencing system 104 ranks the assigned haplotypes for the genomic region and selects the highest ranked assigned haplotype for identifying an assigned nucleotide base call.

[0079] Further, as shown in FIG. 2B, the customized sequencing system 104 can optionally perform operation 232 of determining direct nucleotide base calls, which includes operation 234 of utilizing a customized graph reference genome. As described above with respect to FIG. 2A, the customized sequencing system 104 can generate and utilize a customized graph reference genome. In some embodiments, the customized sequencing system 104 aligns nucleotide fragment reads to the customized graph reference genome to determine direct nucleotide base calls. Illustratively, the customized sequencing system 104 aligns nucleotide fragment reads to either a linear graph genome in the customized graph reference or an imputed haplotype path in the customized graph reference genome to determine direct nucleotide base calls. In such an embodiment, the customized sequencing system 104 uses the direct nucleotide base calls determined in operation 232 using the customized graph reference genome as the basis for determining the final nucleotide base calls, rather than the direct nucleotide base calls determined in operation 222.

[0080] 2B, the customized sequencing system 104 also performs operation 236 of determining a final nucleotide base call based on the assigned nucleotide base calls, the direct nucleotide base calls, and the sequencing metrics. In one or more embodiments, the customized sequencing system 104 utilizes the sequencing metrics to select a final nucleotide base call for a particular genomic coordinate from either the direct nucleotide base calls or the assigned nucleotide base calls. Although the assigned nucleotide base calls may be limited to certain target genomic regions, in some cases, the customized sequencing system 104 may select a final nucleotide base call for each genomic coordinate in the sample genome from either the direct nucleotide base calls or the assigned nucleotide base calls.

[0081] As noted above, in some embodiments, the customized sequencing system 104 utilizes a weighting model to determine the final nucleotide base call. By way of example, in one or more embodiments, the customized sequencing system 104 weights the direct nucleotide base call based on sequencing metrics that reflect the quality of the direct nucleotide base call and / or the nucleotide fragment reads on which the nucleotide base call is based. Additionally, in some embodiments, the customized sequencing system 104 weights the assigned nucleotide base call based on the variability and / or frequency of the haplotypes used to determine the assigned nucleotide base call.

[0082] In addition to or as an alternative to the weighting model, in some embodiments the customized sequencing system 104 utilizes a machine learning model to determine the final nucleotide base call. As described further below, in some embodiments the customized sequencing system 104 utilizes a base calling machine learning model to determine a nucleotide base call based on the direct nucleotide base calls, sequencing metrics, and the imputed nucleotide base calls. The customized sequencing system 104 can train the base calling machine learning model to predict the final nucleotide base call by selecting either the direct nucleotide base call or the imputed nucleotide base call for the genomic coordinates.

[0083] As discussed above, in one or more embodiments, the customized sequencing system 104 assigns haplotypes for genomic regions of a sample genome. FIGS. 3A-3B show a customized sequencing system 104 that determines whether to assign haplotypes for a genomic region and (potentially) assigns haplotypes for a target genomic region with respect to a linear reference genome. More specifically, FIG. 3A shows a customized sequencing system 104 that decides not to assign haplotypes based on insufficient depth of nucleotide-fragment reads and corresponding variant nucleotide-base calls surrounding the target genomic region. In contrast, FIG. 3A also shows a customized sequencing system 104 that decides to assign haplotypes for a target region based on variant nucleotide base calls (derived from nucleotide fragment reads) surrounding the target genomic region.

[0084] As suggested by FIG. 3A, the customized sequencing system 104 utilizes a sequencing device to determine nucleotide fragment reads for a sample genome or receives data representing nucleotide fragment reads for a sample genome. The customized sequencing system 104 further aligns the nucleotide fragment reads to a linear graph reference genome. Thus, FIG. 3A shows a low-depth region visualization 300 of the nucleotide fragment reads of the sample genome aligned to the linear graph reference genome. Similarly, FIG. 3A shows a high-depth region visualization 308 of the nucleotide fragment reads of the same (or different) sample genome aligned to the linear graph reference genome.

[0085] 3A , low-depth region visualization 300 includes low-confidence called regions 302 and genomic regions 306. In contrast, high-depth region visualization 308 includes low-confidence called regions 310 and genomic regions 312. For purposes of illustration, low-depth region visualization 300 and high-depth region visualization 308 show sample genomic regions (but not all genomic regions) of the sample genome relative to portions of a linear reference genome.

[0086] As further suggested by FIG. 3A, the customized sequencing system 104 determines depth metrics and other sequencing metrics that correspond to nucleotide base calls of nucleotide fragment reads determined during sequencing and aligned at genomic coordinates of the linear reference genome. The customized sequencing system 104 can determine depth metrics utilizing various scales and types. In some embodiments, for example, the customized sequencing system 104 determines depth metrics by quantifying the number of nucleotide fragment reads that overlap or correspond to each genomic coordinate. As further suggested by FIG. 3A, for example, the customized sequencing system 104 determines that (i) genomic coordinates in the low depth region visualization 300 have a depth of 1-15 times per genomic coordinate and (ii) genomic coordinates in the high depth region visualization 308 have a depth of 30 times (or more) per genomic coordinate. Additionally, the low depth region visualization 300 includes shorter nucleotide fragment reads.

[0087] Based on the determined depth metrics, other sequencing metrics, or other factors described below, the customized sequencing system 104 can identify low-confidence called regions or other genomic regions from the sample genome as target genomic regions for attribution. By way of example, in certain embodiments, the customized sequencing system 104 identifies low-confidence called regions corresponding to nucleotide fragment reads having mapping quality metrics that do not meet a quality threshold. For example, the customized sequencing system 104 can identify genomic regions having nucleotide fragment reads with MAPQ scores below a threshold MAPQ as low-confidence called regions, such as by identifying genomic regions having MAPQ scores below a relative threshold based on a distribution of MAPQ scores.

[0088] Additionally or alternatively, in one or more embodiments, the customized sequencing system 104 identifies low confidence call regions corresponding to nucleotide base calls having call data quality metrics that do not meet threshold call data quality metrics. For example, the customized sequencing system 104 can identify genomic regions having nucleotide base calls having base call quality metrics below threshold base call quality metrics (e.g., Q20, Q30). Similarly, the customized sequencing system 104 can identify genomic regions having nucleotide base calls having callability metrics or somatic quality metrics below a threshold callability metric or threshold somatic quality metric, respectively.

[0089] In addition to (or instead of) the mapping quality metrics or the call data quality metrics, in some cases, the customized sequencing system 104 identifies a genomic region as a low-confidence called region if the nucleotide fragment reads covering or overlapping the genomic region exhibit a depth metric that does not meet a threshold depth metric. For example, the customized sequencing system 104 can identify a genomic region as a low-confidence called region if the nucleotide fragment reads covering or overlapping the genomic region have a depth metric that is less than an average of 20 or 30 nucleotide fragment reads deep.

[0090] As alluded to above, the customized sequencing system 104 can also identify genomic regions as low-confidence called regions based on a combination of quality metrics. For example, the customized sequencing system 104 identifies a genomic region as a low-confidence called region if a portion, percentage, or range of the corresponding nucleotide fragment reads or nucleotide base calls meets a threshold percentage (e.g., 2 / 3) of a threshold quality metric or each threshold quality metric from a set of threshold quality metrics (e.g., threshold mapping quality metric, threshold call data quality metric, threshold depth metric). For example, based on one or more of the quality metrics and corresponding threshold quality metrics described above, the customized sequencing system 104 identifies a low-confidence called region 302 shown in the low-depth region visualization 300 and a low-confidence called region 310 shown in the high-depth region visualization 308.

[0091] In addition to low-confidence call regions, in some embodiments, the customized sequencing system 104 identifies other target genomic regions for attribution or to identify alternative haplotypes. For example, in some cases, the customized sequencing system 104 may identify (as target genomic regions) genomic regions where a sequencing machine or sequencing pipeline has historically produced sequencing metrics that do not meet a threshold quality metric or has historically identified alternative haplotypes above a threshold percentage (e.g., 20% or 30% of sample genomes demonstrate alternative haplotypes). As a further example, the customized sequencing system 104 may identify (as target genomic regions) genomic regions from sample genomes of a particular ethnicity or geographic region that have historically produced sequencing metrics that do not meet a threshold quality metric or have historically identified alternative haplotypes above a threshold percentage.

[0092] Based on one or more of the above historical factors, for example, the customized sequencing system 104 identifies (as target genomic regions) the genomic regions 304 shown in the low depth region visualization 300 and the genomic regions 312 shown in the high depth region visualization 308. To illustrate, in one or more embodiments, the customized sequencing system 104 utilizes historical sequencing data corresponding to a particular geographic region, haplotype group, ethnicity, etc. Thus, the customized sequencing system 104 can identify low confidence called regions where the sequencing machine generated nucleotide base calls having sequencing metrics below a quality metric threshold, mapping quality threshold, or other corresponding quality threshold. Thus, in one or more embodiments, the customized sequencing system 104 includes one or more paths in the customized graph genome that represent imputed haplotypes for historically low confidence called regions, even if the current genomic sample does not exhibit low quality in such genomic regions.

[0093] However, due to differences in depth metrics, the low depth region visualization 300 and the high depth region visualization 308 include genomic regions where the customized sequencing system 104 can assign haplotypes in some cases but not in other cases. For example, the low depth region visualization 300 for the sample genome shows insufficient depth for nucleotide-fragment reads corresponding to variant-nucleotide-variant calls to perform haplotype assignment. In particular, nucleotide fragment reads corresponding to (or covering) nucleotide variant calls 301a, 301b, and 301c surrounding low confidence call region 302, and nucleotide fragment reads corresponding to (or covering) nucleotide variant calls 301c and 301d surrounding genomic region 304 have insufficient depth. In other words, the low depth region visualization 300 lacks sufficient depth (e.g., greater than 30x) in SNP or other variant-nucleotide-base calls surrounding the low confidence called region 302 or genomic region 304 to assign haplotypes.

[0094] In contrast, high depth region visualization 308 for the sample genome shows sufficient depth for nucleotide-fragment reads corresponding to variant-nucleotide-variant calls to assign haplotypes for low confidence called region 310. In particular, nucleotide fragment reads corresponding to (or covering) nucleotide variant calls 301e, 301f, and 301g surrounding low confidence called region 310, and nucleotide fragment reads corresponding to (or covering) nucleotide variant calls 301g and 301h surrounding genomic region 312 show sufficient depth. In other words, high depth region visualization 308 shows sufficient depth (e.g., greater than 30-fold) in SNP or other variant-nucleotide-base calls surrounding low confidence called region 310 and genomic region 312 to assign haplotypes.

[0095] To illustrate, in one or more embodiments, the customized sequencing system 104 aligns the nucleotide fragment reads to a linear reference genome to determine variant nucleotide base calls as the basis for a set of potential haplotypes from the haplotype database. Based on the aligned nucleotide fragment reads, in one or more embodiments, the customized sequencing system 104 determines SNPs from the sample genome at 30x read coverage or by utilizing an initial read of sequence data. As an example using an initial read, the first or first 50 base pairs of a 2x150 base pair sequencing run is equivalent to about 6x read coverage for a regular 35x whole genome sequencing run. Once the first or first 50 base pairs of such a sequencing run are determined, in some embodiments, the customized sequencing system 104 can assign haplotypes for the target genomic region, thus generating a customized graph reference genome for the particular sample genome. Using coverage as outlined above, the customized sequencing system 104 can perform low pass assignments up to about 1x read depth to assign haplotypes. Thus, in some embodiments, the customized sequencing system 104 can utilize the initial reads to perform low-pass haplotype imputation.

[0096] After identifying the low-confidence call regions 310 and the genomic region 312 as target genomic regions and determining that the corresponding depth metrics are sufficient for assignment, the customized sequencing system 104 may perform an operation 316 of assigning haplotypes using the haplotype database 314. In some embodiments, the customized sequencing system 104 uses the haplotype database 314 to assign haplotypes for the low-confidence call regions 310 but not for the genomic region 312. In contrast, in some aspects, the customized sequencing system 104 uses the haplotype database 314 to determine haplotypes for both the low-confidence call regions 310 and the genomic region 312.

[0097] In one or more embodiments, the haplotype database 314 includes various haplotypes and associated data. By way of example, the haplotype database 314 includes haplotype genomic sequences and corresponding genomic coordinates. Additionally, in some embodiments, the haplotype database 314 also includes metadata corresponding to the haplotype sequences (e.g., the haplotype, surrounding variant nucleotide base calls common to a population or ethnic group associated with the haplotype, and / or other data related to the haplotype).

[0098] As discussed above, in one or more embodiments, the customized sequencing system 104 utilizes the haplotype database 314 to assign haplotypes. More specifically, the customized sequencing system 104 can assign haplotypes for genomic regions by identifying haplotypes from the haplotype database 314 that have a sufficient likelihood of being present in the genomic region. To illustrate, the customized sequencing system 104 can compare variant-nucleotide-base calls surrounding the low-confidence call region 310 to variant-nucleotide-base calls associated with haplotypes in the haplotype database 314. To illustrate, the customized sequencing system 104 can determine SNPs that are common between the low-confidence call region 310 and the haplotypes in the haplotype database 314. Based on the SNPs (or other variant nucleotide base calls) common between the low-confidence call region 310 and the candidate haplotypes, the customized sequencing system 104 statistically infers which haplotypes are more likely to be present in the low-confidence call region 310.

[0099] For example, in some embodiments, the customized sequencing system 104 applies a hidden Markov model (HMM) to assign haplotypes for the low-confidence call regions 310. To illustrate, in some embodiments, the customized sequencing system 104 can utilize a hidden Markov model to identify assigned haplotypes from the haplotype database 314. More specifically, the customized sequencing system 104 can utilize a hidden Markov model to compare haplotype patterns (e.g., surrounding variant-nucleotide-base calls) corresponding to genomic regions and haplotypes in the haplotype database 314 to identify potential haplotypes corresponding to the genomic regions. In some embodiments, for example, the customized sequencing system 104 uses hidden Markov models to impute haplotypes as described in Genetic Variants Predictive of Cancer Risk, International Publication No. WO 2013 / 035 / 114a1 (published March 14, 2013), or A. Kong et al., Detection of Sharing by Descent, Long-Range Phasing and Haplotype Imputation, Nat. Genet. 40, 1068-75 (2008). Additionally or alternatively, the customized sequencing system 104 uses hidden Markov models to impute haplotypes using available software such as fastPHASE, BEAGLE, MACH, or IMPUTE.

[0100] 3A, in addition to assigning haplotypes, the customized sequencing system 104 performs an operation 318 of identifying additional haplotypes. More specifically, in some embodiments, the customized sequencing system 104 identifies alternative haplotypes from the haplotype database 314 for alleles in the genomic region 312. For example, in one or more embodiments, the system identifies highly common haplotypes for the genomic region 312 for inclusion in the graph reference genome. In some embodiments, the customized sequencing system 104 identifies haplotypes present above a specified threshold (e.g., 20% or 30%) for one or more ethnic and / or geographic regions corresponding to the sample genome.

[0101] As described above, the customized sequencing system 104 can assign haplotypes for various genomic regions. For example, the customized sequencing system 104 can assign haplotypes for genomic regions that include (in whole or in part) a VNTR, structural variant, insertion, deletion, or inversion. Thus, the target genomic region can include some or all of a set of nucleotide bases (or a set of deleted nucleotide bases) that correspond to or represent a VNTR, structural variant, insertion, deletion, or inversion. FIG. 3B shows an example of a low-confidence call region to which the customized sequencing system 104 assigns a haplotype. More specifically, FIG. 3B shows reference data and sequencing metrics for a portion of the sample genome 321. In particular, FIG. 3B shows a genomic coordinate marker 322 from a linear reference genome corresponding to a portion of the sample genome 321 and a gene coding region 324 from a linear reference genome corresponding to a portion of the sample genome 321. The portion of the sample genome 321 is 20 kilobases long with genomic coordinates ranging from about 155,180 kilobases to 155,200 kilobases, as indicated by genomic coordinate markers 322. Within this range, the reference genome includes genes for TRIM46 326a, MUC1 326b, MIR92B 326c, and THBS3 326d.

[0102] In addition to the reference data, FIG. 3B shows a base call quality graphic 328 for a base call quality metric and a mapping quality graphic 332 for a mapping quality metric corresponding to a portion of the sample genome 321. For illustration, the base call quality graphic 328 shows a proportion or percentage of nucleotide base calls within the portion of the sample genome 321 that meet a threshold metric (e.g., Q30 or Q37), with the length of the darker bar indicating a greater proportion or percentage of nucleotide base calls having a base call quality metric that does not meet the threshold metric. In addition to the base call quality graphic 328, FIG. 3B shows a mapping quality graphic 332. The mapping quality graphic 332 shows a proportion or percentage of nucleotide fragment reads corresponding to the portion of the sample genome 321 that meet a threshold metric (e.g., relative MAPQ score or MAPQ 40), with the length of the darker bar indicating a greater proportion or percentage of nucleotide fragment reads having a mapping quality metric that does not meet the threshold metric.

[0103] As described above, in some embodiments, the customized sequencing system 104 can utilize base call quality metrics and / or mapping quality metrics to identify low-confidence called regions corresponding to one or more low quality metrics. As shown in FIG. 3B, for example, the customized sequencing system 104 identifies a low-confidence called region 330 corresponding to lower quality metrics for both base call quality metrics and mapping quality metrics. Specifically, the low-confidence called region 330 includes (in whole or in part) a VNTR within gene 326b of MUC1.

[0104] As alluded to above, the customized sequencing system 104 can utilize the haplotype database 314 to perform operation 316 of assigning a haplotype for the low-confidence call region 330. Illustratively, the customized sequencing system 104 can assign a haplotype for the low-confidence call region 330 by determining from the haplotype database 314 a haplotype that is likely to be present in the low-confidence call region 330. As noted above, in some embodiments, the customized sequencing system 104 can determine SNPs (or other variant-nucleotide-base calls) surrounding both the low-confidence call region 330 and a haplotype in the haplotype database 314 that corresponds to (or is within) the low-confidence call region 330. For example, the customized sequencing system 104 assigns a haplotype for the low-confidence call region 330 based on SNPs within a threshold number of base pairs of the low-confidence call region 330 that match a haplotype from the haplotype database 314.

[0105] As described above, the customized sequencing system 104 can generate a customized graph reference genome for a particular sample genome by using the imputed haplotypes for the target genomic regions. Figure 4A shows an overview of the customized sequencing system 104 generating such a customized graph reference genome for a particular sample genome. More specifically, Figure 4A shows the customized sequencing system 104 generating a graph reference genome 402 that includes both a linear reference genome 400 and paths 404a-404d that represent the imputed haplotypes corresponding to various genomic regions of the sample genome.

[0106] As described above, the graph reference genome 402 includes the linear reference genome 400. Thus, the customized sequencing system 104 generates the graph reference genome 402 using the linear reference genome 400 as a baseline for backward compatibility. In other words, the customized sequencing system 104 can align nucleotide fragment reads from a sample genome with any portion of the linear reference genome 400 before determining final nucleotide base calls.

[0107] In addition to the linear reference genome 400, the graph reference genome 402 includes paths 404a-404d that represent haplotypes corresponding to genomic regions. Thus, paths 404a-404d represent assigned haplotypes that are different from haplotypes already present in the linear reference genome 400 for a particular genomic region. Illustratively, path 404a represents a deletion to the linear reference genome 400, path 404b includes a single nucleotide variant that is different from a reference base of the linear reference genome 400, path 404c includes a duplication (or an insertion of a duplication) of a nucleotide subsequence from the linear reference genome 400, and path 404d includes an inversion of a nucleotide subsequence from the linear reference genome 400. Thus, each of paths 404a-404d represents an assigned haplotype for a genomic region that is different from a haplotype already present in the linear reference genome 400.

[0108] As shown in FIG. 4A, paths 404a-404d are shown as examples, and the customized sequencing system 104 can determine various paths from various imputed haplotypes. Although not shown in FIG. 4A, the customized sequencing system 104 can include paths representing different imputed haplotypes for a single genomic region in the graph reference genome. For example, the customized sequencing system 104 can include two or three most likely alternative haplotypes for the genomic region. To illustrate, the customized sequencing system 104 determines that a first haplotype and a second haplotype are each present in 30% of the sample genomes having the same surrounding variant-nucleotide-base calls observed in the sample genome. The customized sequencing system 104 can include paths in the graph reference genome representing the first haplotype and the second haplotype based on their respective probabilities considering the variant-nucleotide-base calls.

[0109] As described above, the customized sequencing system 104 can align nucleotide fragment reads from a sample genome to the graph reference genome 402 to determine final nucleotide base calls for genomic regions. Because the graph reference genome 402 includes both a linear reference genome and paths 404a-404d based on imputed haplotypes, the customized sequencing system 104 can align nucleotide fragment reads to either or both of the linear reference genome 400 and the paths 404a-404d.

[0110] 4B illustrates a customized sequencing system 104 that aligns nucleotide fragment reads from a sample genome with a graph reference genome 402 along several genomic regions that include paths representing assigned haplotypes. As shown in FIG. 4B, the customized sequencing system 104 aligns nucleotide fragment reads 406a and 406b with the graph reference genome 402, in part, by aligning variants from nucleotide fragment reads 406a and 406b with paths 404a-404d that correspond to assigned haplotypes.

[0111] As shown by FIG. 4B, the sample genome is heterozygous in some genomic regions. As shown by the alignment of nucleotide fragment read 406a, the sample genome includes alleles that align with paths 404a and 404c, but not with path 404b. In contrast, as shown by the alignment of nucleotide fragment read 406b, the sample genome includes alleles that align with paths 404b and 404d, but not with paths 404a and 404c. Because the graph reference genome 402 includes both the linear reference genome 400 and paths 404a-404d, the customized sequencing system 104 successfully aligns each read from nucleotide fragment reads 406a and 406b to the graph reference genome 402.

[0112] Because the sample genome includes different alleles at the genomic coordinates or regions shown in FIG. 4Be, the customized sequencing system 104 is likely to misalign or align with less accuracy one or more of the nucleotide fragment reads 406a or 406b with the linear reference genome 400 itself. Therefore, the customized sequencing system 104 improves the alignment by utilizing a graph reference genome 402 that includes paths 404a-404d that represent the assigned haplotypes for specific genomic regions of the sample genome. Because the graph reference genome 402 includes assigned haplotypes that are more likely to be present in the sample genome in low confidence call regions (or other genomic regions) than other excluded haplotypes, the customized sequencing system 104 increases the probability of accurate alignment to the conventional linear reference genome.

[0113] Due in part to such improved alignment, the customized sequencing system 104 can likewise improve the confidence with which it determines variant-nucleotide-base calls (or other final nucleotide-base calls) with respect to the graph reference genome 402. With the nucleotide fragment reads 406a and 406b better aligned with the graph reference genome 402, the customized sequencing system 104 is more likely to accurately determine whether the sample genome contains a nucleotide base that alters or matches any of the reference bases of the linear reference genome 400 or the assigned haplotypes represented by the paths 404a-404d.

[0114] As part of the alignment and base calling accuracy improvement, in some embodiments, the customized sequencing system 104 uses a haplotype database that includes a panel of haplotypes from different sample sizes. According to one or more embodiments, FIG. 5 shows a graph 500 with a receiver operating characteristic (ROC) curve that defines the area under the curve (AUC) for the non-reference match rate at which the sequencing system correctly assigns SNPs of various allele frequencies based on a reference panel of different sample sizes. As shown by FIG. 5, the ROC curve shows that the customized sequencing system 104 more accurately assigns SNPs as the sample size of the reference panel in the haplotype database increases.

[0115] To test the accuracy of imputation for different reference panels, for example, the researchers removed about 20% of SNPs from the data representing samples sequenced by a sequencing machine. The customized sequencing system 104 then imputed haplotypes for SNPs from samples based on reference panels of various sample sizes. As shown in FIG. 5, the first reference panel 502a included about 200 haplotypes from 100 samples, the second reference panel 502b included about 1,000 haplotypes from 500 samples, the third reference panel 502c included about 2,000 haplotypes from 1,000 samples, and the fourth reference panel 502d included about 5,006 haplotypes from 2,503 samples.

[0116] As shown in the graph 500, the ROC curve for the customized sequencing system 104 using the first reference panel 502a with 100 samples shows the lowest non-reference match rate for imputing the removed SNP across allele frequencies for the SNP. In contrast, the ROC curve for the customized sequencing system 104 using the fourth reference panel 502d with 2,503 samples shows the highest non-reference match rate for imputing the removed SNP across allele frequencies for the SNP. However, regardless of the ROC curve, the non-reference match rate increases with allele frequency and then reaches a plateau with maximum match at an allele frequency slightly above 0.10. Thus, in some embodiments, the customized sequencing system 104 uses a haplotype database with a reference panel of 2,503 or more samples to increase the accuracy of imputed haplotypes.

[0117] In addition to using a haplotype database with a reference panel of a relatively high sample size or any sample size as described above, the customized sequencing system 104 increases the accuracy of assigning haplotypes for genomic regions as the depth of nucleotide fragment reads increases for genomic coordinates with SNPs surrounding the target genomic region. For example, in some embodiments, the customized sequencing system 104 uses SNPs based on nucleotide fragment reads with a 30-fold depth to assign haplotypes. Even with the same reference panel, SNPs from nucleotide fragment reads with a 30-fold depth provide approximately 3 times more mutation information from SBS of the whole genome than low-pass whole genome sequencing (lpWGS).

[0118] As discussed above, in one or more embodiments, the customized sequencing system 104 determines a final nucleotide base call for the sample genome based on the direct nucleotide base calls, sequencing metrics, and indirect nucleotide base calls. Figure 6 shows an example of a customized sequencing system 104 that weights the direct and imputed nucleotide base calls in a weighting model to determine a final nucleotide base call with respect to a reference genome. Additionally, as discussed below with respect to Figures 7A-7B, the customized sequencing system 104 can utilize machine learning models to determine such final nucleotide base calls.

[0119] As shown in Figure 6, the customized sequencing system 104 can perform an operation 608 of aligning nucleotide fragment reads to a reference genome. As described above with respect to Figures 4A-4B, the customized sequencing system 104 can align the nucleotide fragment reads sequenced from the sample genome to either a linear reference genome or a graph reference genome.

[0120] As alluded to above, the customized sequencing system 104 aligns each nucleotide fragment read to a reference genome to determine a direct nucleotide base call 602 for the reference genome including variant nucleotide base calls. For illustrative purposes, the customized sequencing system 104 determines a direct nucleotide base call 602 based on the nucleotide fragment read and an alignment to either a linear reference genome or a graph reference genome. Thus, the customized sequencing system 104 determines a direct nucleotide base call 602 based on "direct" evidence from the sample genome. As alluded to above, in some embodiments, this direct evidence includes alignment to paths representing haplotypes in the graph reference genome.

[0121] In addition to such direct nucleotide base calls, the customized sequencing system 104 determines sequencing metrics 604 corresponding to the nucleotide fragment reads and / or direct nucleotide base calls, including for mapping. In some cases, the sequencing metrics 604 reflect the quality and / or certainty of the nucleotide fragment reads, the nucleotide base calls, and / or their alignments. By way of example, as shown in FIG. 6, the sequencing metrics 604 can include depth metrics 610, read data quality metrics 612, call data quality metrics 614, and / or mapping quality metrics 616.

[0122] For example, the customized sequencing system 104 can determine a depth metric 610 as a quantification of the depth of nucleotide base calls determined and aligned at a particular genomic coordinate during sequencing. Indeed, in some embodiments, the customized sequencing system 104 determines a depth metric 610 for a genomic region of a sample genome based on an average of the depths of the genomic coordinates within the genomic region. As discussed above, the customized sequencing system 104 can also utilize various scales and metric types for the depth metric 610. For example, in some embodiments, the customized sequencing system 104 determines a depth metric that quantifies the number of nucleotide base calls that are below a threshold depth coverage.

[0123] As described above, the customized sequencing system 104 can also determine read data quality metrics 612 for nucleotide fragment reads from the sample genome. To illustrate, in one or more embodiments, the customized sequencing system 104 determines the read data quality metrics 612 based on a total number of nucleotide bases in the sample genome that do not match nucleotide bases of the reference genome, including one or more paths of the graph reference genome. Additionally or alternatively, the customized sequencing system 104 can determine the read data quality metrics 612 over multiple cycles during sequencing. Furthermore, the customized sequencing system 104 can determine the read data quality metrics 612 based on read position metrics for the sample genome by determining the average or median position within the nucleotide fragment reads that cover genomic coordinates within the sample genome.

[0124] In some embodiments, the customized sequencing system 104 further determines call data quality metrics 614 corresponding to either the nucleotide base calls for the nucleotide bases in the nucleotide fragment reads or the direct nucleotide base calls to the reference genome. In some embodiments, the customized sequencing system 104 determines the call data quality metrics 614 by quantifying the quality and / or certainty corresponding to the nucleotide base calls. For example, the customized sequencing system 104 can determine a base call quality metric (e.g., a Phred quality score or Q score) that predicts the error probability of any given nucleotide base call within a sequencing cycle for a nucleotide fragment read or any given direct nucleotide-base call to a genomic coordinate against a reference genome. By way of example, in some embodiments, the customized sequencing system 104 determines the call data quality metric 614 as the percentage or subset of nucleotide base calls in a genomic region that meet a threshold quality score, such as Q20. Additionally or alternatively, the customized sequencing system 104 determines callability metrics or somatic quality metrics as call data quality metrics 614 for either the nucleotide bases in the nucleotide fragment reads or the direct nucleotide base calls.

[0125] As further described above, the customized sequencing system 104 can determine mapping quality metrics 616 for nucleotide fragment reads from the sample genome. In some embodiments, the customized sequencing system 104 determines mapping quality metrics 616 by quantifying the quality and / or certainty of the alignment of the nucleotide fragment reads with the reference genome. In some embodiments, the customized sequencing system 104 determines a mapping quality (MAPQ) score for the nucleotide base calls of the nucleotide fragment reads at the genomic coordinates. By way of example, in one or more embodiments, the customized sequencing system 104 determines a MAPQ score that represents -10 log10 Pr{mapping position incorrect} rounded to the nearest integer. In some embodiments, the customized sequencing system 104 determines the mean or median of the mapping quality metrics of the nucleotide fragment reads within the genomic region of the sample region.

[0126] In addition to determining direct nucleotide base calls 602, the customized sequencing system 104 determines an imputed nucleotide base call 606. By way of example, in one or more embodiments, the customized sequencing system 104 determines the imputed nucleotide base call 606 based on "indirect" evidence corresponding to statistical information associated with variants for a particular sample genome. As shown in FIG. 6, in one or more embodiments, determining the imputed nucleotide base call 606 can include an operation 618 of determining the imputed nucleotide base call 606 based on local nucleotide base calls, population haplotypes, and variant frequencies.

[0127] More specifically, in one or more embodiments, the customized sequencing system 104 determines and utilizes population data corresponding to the sample genome. To illustrate, in some embodiments, the customized sequencing system 104 identifies or receives data regarding a population and / or ethnic group corresponding to a particular sample genome. Thus, the customized sequencing system 104 can identify local nucleotide base calls common to the population. To illustrate, in one or more embodiments, the customized sequencing system 104 utilizes a reference genome corresponding to the identified population or ethnic group corresponding to the sample genome. Further, in some embodiments, the customized sequencing system 104 identifies nucleotide base calls at genomic coordinates of genomic regions in the sample genome. Thus, the customized sequencing system 104 can utilize the identified nucleotide base calls as a reference point for haplotypes to determine the assigned nucleotide base calls 606.

[0128] As alluded to and mentioned above, the customized sequencing system 104 determines or receives population data corresponding to the sample genome. Thus, the customized sequencing system 104 can determine population haplotype frequencies corresponding to the sample genome by identifying haplotypes corresponding to a population specific to the sample genome. In one or more embodiments, the customized sequencing system 104 identifies population haplotypes, such as by utilizing a haplotype database to identify a reference panel specific to a geographic region or an ethnic group.

[0129] Additionally, the customized sequencing system 104 can utilize the mutation frequencies to determine the imputed nucleotide base calls 606. In one or more embodiments, the customized sequencing system 104 identifies genomic variants that correspond to the populations identified for the sample genome. More specifically, the customized sequencing system 104 can identify genomic variants that correspond to genomic coordinates of genomic regions (e.g., low confidence called genomic regions) identified for the sample genome. Thus, the customized sequencing system 104 can identify nucleotide base calls for the populations and that correspond to high frequency mutations for particular genomic regions. Thus, in one or more embodiments, the customized sequencing system 104 utilizes nucleotide base calls from the identified variants as the imputed nucleotide base calls 606.

[0130] As described above, in some embodiments, the customized sequencing system 104 utilizes population haplotypes to assign haplotypes for genomic coordinates or target genomic regions of the sample genome based on reference panel or other population haplotypes. For illustrative purposes, the customized sequencing system 104 can assign haplotypes corresponding to genomic regions based on surrounding variant-nucleotide-base calls. Furthermore, in some embodiments, the customized sequencing system 104 utilizes mutation frequency and population data to determine the assigned haplotypes. Furthermore, the customized sequencing system 104 can determine the assigned nucleotide base calls based on the assigned haplotypes. More specifically, in some embodiments, the customized sequencing system 104 ranks the assigned haplotypes according to their likelihood for the genomic coordinate or region and determines the assigned nucleotide base call from the highest ranked haplotype for the genomic coordinate or region.

[0131] In some embodiments, the customized sequencing system 104 determines the imputed nucleotide base call 606 based on one or more of the nucleotide base calls corresponding to the local nucleotide base calls, the nucleotide base calls corresponding to the population haplotypes, and the nucleotide base calls corresponding to the high frequency variants. Illustratively, in one or more embodiments, the customized sequencing system 104 selects the imputed nucleotide base call 606 based on the nucleotide base call that has the highest likelihood based on the frequency of one or more of the local nucleotide base calls, the population haplotypes, and the variant frequencies. For example, the customized sequencing system 104 can utilize statistical inference that utilizes the respective frequencies of the local nucleotide base calls, the population haplotypes, and the high frequency variants.

[0132] As described above, in some embodiments, the customized sequencing system 104 generates a customized graph reference genome that includes paths representing the assigned haplotypes for the target genomic region. Thus, in one or more embodiments, the customized sequencing system 104 determines variant nucleotide base calls (e.g., SNPs) surrounding or adjacent to the target genomic region when first determining the direct nucleotide base calls, and then uses the variant nucleotide base calls to assign haplotypes. In some embodiments, the graph reference genome includes assigned haplotypes determined utilizing variant frequencies, local variant-nucleotide-base calls, and population haplotypes. When using a customized graph reference genome, rather than using the direct nucleotide base calls determined initially, the customized sequencing system 104 determines direct nucleotide base calls based on a comparison of nucleotide fragment reads from the sample genome to the customized graph reference genome. In such embodiments, the customized sequencing system 104 uses direct nucleotide base calls determined using the customized graph reference genome, rather than direct nucleotide base calls determined using a linear reference genome or a generic graph reference genome, as the basis for determining the final nucleotide base calls, as described below.

[0133] In addition to determining the direct nucleotide base calls 602 and the assigned nucleotide base calls 606, as further shown in FIG. 6, the customized sequencing system 104 can perform operation 620 of determining a final nucleotide base call based on the direct nucleotide base calls 602, the sequencing metrics 604, and the assigned nucleotide base calls 606. In some cases, for example, the customized sequencing system 104 weights the direct and assigned nucleotide base calls for the genomic coordinate and selects either the direct or assigned nucleotide base call as the final nucleotide base call for the genomic coordinate in operation 620. For illustrative purposes, the customized sequencing system 104 weights the direct nucleotide base calls 602 based on the corresponding data quality and weights the assigned nucleotide base calls 606 based on the mutational difficulty of the genomic region.

[0134] As alluded to immediately above, the customized sequencing system 104 can weight the direct nucleotide base calls from the direct nucleotide base calls 602 based on corresponding sequencing metrics. To illustrate, in some embodiments, the customized sequencing system 104 weights the direct nucleotide base calls based on the quality of the nucleotide fragment reads used to determine the direct nucleotide base calls and / or the quality of the call and alignment process utilized to determine the direct nucleotide base calls. For example, the customized sequencing system 104 can weight the direct nucleotide base calls utilizing depth metrics, read data quality metrics, call data quality metrics, and / or mapping quality metrics. As shown in FIG. 6, the customized sequencing system 104 weights the direct nucleotide base calls in proportion to the quality of the corresponding data. Similarly, the customized sequencing system 104 can weight the direct nucleotide base calls for each genomic coordinate in a genomic region (or for each genomic coordinate in a sample genome) using the methods described immediately above.

[0135] Additionally, the customized sequencing system 104 can weight the imputed nucleotide-base calls from the imputed nucleotide-base calls 606 based on the corresponding variant confidence difficulty. In one or more embodiments, the customized sequencing system 104 determines a variant “confidence difficulty” corresponding to a genomic coordinate or genomic region based on one or more of the frequency of variance in the genomic coordinate or genomic region, the likelihood of a variant (or variant type) in the genomic coordinate or region, and / or the length of the genomic region. To illustrate, the customized sequencing system 104 is less likely to accurately assign nucleotide base calls in genomic regions or coordinates that have relatively more frequent variations as measured by allele frequency in genomic coordinates or regions that have a relatively high degree of variants (or variant types) as represented by haplotypes in the genomic coordinate or region, and / or in relatively large genomic regions. The imputed nucleotide base calls for such genomic coordinates or regions exhibit a relatively high variant confidence difficulty. Thus, in some embodiments, the customized sequencing system 104 weights the imputed nucleotide base calls inversely proportional to the variant confidence difficulty corresponding to the genomic coordinate or region. Similarly, the customized sequencing system 104 can weight the assigned nucleotide base calls for each genomic coordinate in a genomic region (or for each genomic coordinate in a sample genome) using the method just described.

[0136] In some embodiments, the customized sequencing system 104 determines a final nucleotide base call for each genomic coordinate of the target genomic region by weighting the direct nucleotide base calls and the imputed nucleotide base calls for each coordinate. For example, in some cases, the customized sequencing system 104 determines a direct nucleotide base call that corresponds to a relatively high data quality and a relatively high variant confidence difficulty for the genomic coordinate. In such an example, the customized sequencing system 104 is more likely to select the direct nucleotide base call that corresponds to the high data quality as the final nucleotide base call for the genomic coordinate rather than the imputed nucleotide base call that corresponds to the high variant confidence difficulty.

[0137] In another example, the customized sequencing system 104 determines a direct nucleotide base call for a genomic coordinate that corresponds to a relatively low data quality and a relatively low variant difficulty. In this example, the customized sequencing system 104 is more likely to select as the final nucleotide base call the assigned nucleotide base call that corresponds to the low variant difficulty rather than a direct nucleotide base call that corresponds to a sequencing metric that indicates low data quality.

[0138] In some embodiments, the customized sequencing system 104 can implement thresholds for sequencing metrics that, if not met, result in automatic selection of an assigned nucleotide base call for a genomic coordinate. Illustratively, in these embodiments, the customized sequencing system 104 requires a minimum data quality for any potential selection of a direct nucleotide base call. For example, the customized sequencing system 104 can determine and utilize a minimum Q score or a minimum MAPQ.

[0139] In addition to the weighting model, in one or more embodiments, the customized sequencing system 104 can utilize a machine learning model to determine the final nucleotide base call. Figures 7A-7B respectively show the training and application of a base calling machine learning model to determine a final nucleotide-base call. More specifically, Figures 7A-7B show the training and application of a machine learning model to determine a final nucleotide base call based on the direct nucleotide base calls, sequencing metrics, and the assigned nucleotide base calls.

[0140] As an overview of training in Figure 7A, the customized sequencing system 104 can iteratively input into the base calling machine learning model 708 the training direct nucleotide base calls, the training sequencing metrics corresponding to the training direct nucleotide base calls, and the training imputed nucleotide base calls for the genomic coordinates. Based on the training data, the base calling machine learning model generates predicted nucleotide base calls for the genomic coordinates in each training iteration, such as by selecting either the direct nucleotide base calls or the imputed nucleotide base calls for the genomic coordinates. The customized sequencing system 104 then compares the predicted nucleotide-base calls to the ground truth base calls for the genomic coordinates to determine losses and adjusts the base calling machine learning model based on the losses.

[0141] As shown in Figure 7A, customized sequencing system 104 receives training direct nucleotide base calls 701 for genomic coordinates, training sequencing metrics 703 corresponding to the training direct nucleotide base calls 701, and training assigned nucleotide base calls 705 for the genomic coordinates. For example, customized sequencing system 104 can utilize the types of sequencing metrics discussed above with respect to Figure 6, including depth metrics, read data quality metrics, call data quality metrics, and / or mapping quality metrics.

[0142] As further shown in FIG. 7A, the customized sequencing system 104 provides training direct nucleotide-base calls 701, training sequencing metrics 703, and training assigned nucleotide-base calls 705 to a base calling machine learning model 708. Based on the input calls and metrics, the base calling machine learning model generates predicted nucleotide base calls 707 for the genomic coordinates, as shown in FIG. 7A. In some cases, for example, the base calling machine learning model selects either the training direct nucleotide base calls 701 or the training assigned nucleotide base calls 705 as the predicted nucleotide base calls 707. To select either the training direct nucleotide base calls 701 or the training assigned nucleotide base calls 705, in some embodiments the base calling machine learning model 708 can weight the training direct nucleotide base calls differently than the training assigned nucleotide base calls for the genomic coordinates.

[0143] 7A , the customized sequencing system 104 compares the predicted nucleotide base call 707 for the genomic coordinate to the ground truth base call 710 for the genomic coordinate. In one or more embodiments, the customized sequencing system 104 utilizes a loss function 711 to compare the predicted nucleotide base call 707 to the ground truth base call 710. By using the loss function 711, the customized sequencing system 104 determines a difference or loss between the predicted nucleotide base call 707 and the ground truth base call 710. In some embodiments, the customized sequencing system 104 can back-propagate the loss to adjust one or more weights in the base calling machine learning model 708.

[0144] As further suggested by FIG. 7A, the customized sequencing system 104 can perform training iterations. Illustratively, the customized sequencing system 104 can utilize a loss function 711 to iteratively adjust weights of the base calling machine learning model 708 based on a comparison of the predicted nucleotide base calls to the ground truth base calls for each genomic coordinate. After adjustment, the base calling machine learning model 708 can generate improved predicted nucleotide base calls. In some cases, the customized sequencing system 104 performs training iterations until the customized sequencing system 104 determines that a subsequent loss from the loss function 711 is within a minimum threshold or a threshold number of training iterations has been reached.

[0145] The base calling machine learning model 708 can take a variety of forms. For example, in one or more embodiments, the base calling machine learning model 708 can include various types of decision trees, support vector machines (SVMs), Bayesian networks, or neural networks such as convolutional neural networks (CNNs). In some embodiments, the customized sequencing system 104 utilizes a convolutional deep neural network or a recurrent neural network with many layers as the base calling machine learning model 708. In embodiments in which the base calling machine learning model 708 is a neural network, the customized sequencing system 104 can utilize a cross-entropy loss function, an L1 loss function, or a mean squared error loss function as the loss function 711. In one or more further embodiments, the customized sequencing system 104 utilizes a random forest model, a multi-layer perceptron, or a linear regression, a deep tabular learning architecture, a deep learning transformer (e.g., a self-attention based tabular transformer), or a logistic regression as the base calling machine learning model 708.

[0146] In addition to the forms identified above, in some cases, the base calling machine learning model 708 includes an ensemble of gradient boosted trees. For the latter embodiment of gradient boosted trees, the customized sequencing system 104 can utilize a mean squared error loss function (e.g., for regression) as the loss function 711. Additionally or alternatively, the customized sequencing system 104 can utilize a logarithmic loss function (e.g., for classification) as the loss function 711. In some embodiments, the customized sequencing system 104 makes modifications or adjustments to the base calling machine learning model 708 to reduce the measure of loss from the loss function 711 for subsequent training iterations.

[0147] In the case of gradient boosted trees, for example, the customized sequencing system 104 trains the base calling machine learning model 708 on the gradient of error determined by the loss function 711. For example, the customized sequencing system 104 solves a (e.g., infinite dimensional) convex optimization problem while regularizing the objective function to avoid overfitting. In certain embodiments, the customized sequencing system 104 scales the gradient to emphasize correction for under-represented classes (e.g., when there are significantly more imputed nucleotide base calls than direct nucleotide base calls).

[0148] In some embodiments, the customized sequencing system 104 adds a new weak learner (e.g., a new boosted tree) to the base calling machine learning model 708 for each successive training iteration as part of solving the optimization problem. For example, the customized sequencing system 104 finds a feature (e.g., a sequencing metric) that minimizes the loss from the loss function 711 and adds that feature to the current iteration's tree or starts building a new tree with that feature.

[0149] In addition to or without training, in some embodiments, the customized sequencing system 104 applies a trained version of the base calling machine learning model 708. FIG. 7B shows the customized sequencing system 104 applying the trained base calling machine learning model 712 to determine a final nucleotide base call 714 for the genomic coordinate. As shown in FIG. 7B, the customized sequencing system 104 inputs into the trained base calling machine learning model 712: the direct nucleotide base call 702 for the genomic coordinate, the sequencing metrics 704 corresponding to the direct nucleotide base call 702, and the imputed nucleotide base call 706 for the genomic coordinate. Based on the direct nucleotide base call 702, the sequencing metrics 704, and the imputed nucleotide base call 706, the trained base calling machine learning model 712 generates the final nucleotide base call 714 for the genomic coordinate. To select between a direct nucleotide base call 702 or an imputed nucleotide base call 706, in some embodiments, a trained base calling machine learning model 712 can weight the direct nucleotide base call differently than the imputed nucleotide base call for a genomic coordinate.

[0150] As further shown in Figure 7B, in one or more embodiments, the customized sequencing system 104 system can use the trained base calling machine learning model 712 to determine a final nucleotide base call for each genomic coordinate within one or more target genomic regions of the sample genome or for each genomic coordinate within the sample genome. To illustrate, the customized sequencing system 104 can utilize the trained base calling machine learning model 712 to select between an imputed nucleotide base call and a direct nucleotide base call for each genomic coordinate within the genomic region. Additionally, in one or more embodiments, the customized sequencing system 104 utilizes the trained base calling machine learning model 712 to determine a final base call for each genomic coordinate throughout the sample genome.

[0151] 1-7B, corresponding text, and examples provide several different methods, systems, devices, and non-transitory computer-readable media of sequencing systems. In addition to the above, one or more embodiments may also be described in terms of flow charts that include operations for achieving particular results as illustrated in FIGS. 8-10. FIGS. 8-10 may be performed with more or fewer operations. Additionally, operations may be performed in different orders. Additionally, operations described herein may be repeated or performed in parallel with each other or with different instances of the same or similar operations.

[0152] As discussed above, FIG. 8 illustrates a flowchart of a series of operations 800 for determining nucleotide base calls based on comparing nucleotide fragment reads to a graph reference genome, according to one or more embodiments. Although FIG. 8 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in FIG. 8. The operations of FIG. 8 may be performed as part of a method. Alternatively, a non-transitory computer-readable medium may include instructions that, when executed by one or more processors, cause a computing device to perform the operations of FIG. 8. In some embodiments, a system may perform the operations of FIG. 8.

[0153] As shown in FIG. 8, the set of operations 800 includes an operation 802 for determining a subset of variant nucleotide base calls surrounding a genomic region from a subset of nucleotide fragment reads. In particular, operation 802 can include determining a subset of variant-nucleotide-base calls surrounding a genomic region in a sample genome from a subset of nucleotide-fragment reads of the sample genome. Specifically, operation 802 can include determining that a quality metric of the subset of nucleotide base calls in the genomic region does not meet a quality metric threshold, and identifying the genomic region as a low confidence call region based on the quality metric of the subset of nucleotide base calls that does not meet the quality metric threshold. Further, operation 802 can include the genomic region including at least a portion of a variable number of tandem repeats (VNTRs), structural variants, insertions, or deletions. As described above, when performing operation 802, determining the subset of variant nucleotide base calls surrounding the genomic region can be based on a subset of nucleotide fragment reads from the first 50 base pairs of a 2×150 sequencing run, or at about 1× read depth.

[0154] Further, the set of operations 800 includes an operation 804 for assigning a haplotype for the genomic region based on the subset of variant nucleotide base calls. In particular, operation 804 can include assigning a haplotype for the genomic region corresponding to the sample genome based on the subset of variant-nucleotide-base calls. Specifically, operation 804 can include determining the subset of variant-nucleotide-base calls surrounding the genomic region by determining single nucleotide polymorphisms (SNPs) surrounding the genomic region, and assigning a haplotype corresponding to the sample genome based on the SNPs. Also, in one or more embodiments, operation 804 includes assigning a haplotype for the genomic region from a haplotype database of population haplotypes.

[0155] Further, the set of operations 800 includes an operation 806 for generating a graph reference genome including paths representing the assigned haplotypes corresponding to the genomic regions. In particular, operation 806 can include generating, for the sample genome, a graph reference genome including paths representing the assigned haplotypes corresponding to the genomic regions. Specifically, operation 806 can include determining variant-nucleotide-base calls corresponding to additional genomic regions in the sample genome, determining additional assigned haplotypes for the additional genomic regions based on the variant-nucleotide-base calls, and generating a graph reference genome including additional paths representing the additional assigned haplotypes. Further, operation 806 can include determining genomic coordinates for the genomic regions from the linear reference genome, and generating a graph reference genome including paths representing the assigned haplotypes corresponding to the linear reference genome and the genomic regions located at the genomic coordinates of the linear reference genome.

[0156] The series of operations 800 also includes an operation 808 for determining nucleotide base calls in the genomic region based on comparing the nucleotide fragment reads of the sample genome to paths representing haplotypes. In particular, operation 808 can include determining nucleotide base calls in the genomic region of the sample genome based on comparing the nucleotide fragment reads of the sample genome to paths representing the assigned haplotypes in the graph reference genome. For example, operation 808 can include determining nucleotide base calls in the genomic region of the sample genome based on aligning the nucleotide fragment reads of the sample genome to paths representing the assigned haplotypes in the graph reference genome. In particular, operation 808 can include determining direct nucleotide base calls for genomic coordinates in the genomic region based on comparing the nucleotide fragment reads of the sample genome to paths representing the assigned haplotypes, determining assigned nucleotide base calls for genomic coordinates in the genomic region based on the assigned haplotypes for the genomic region, and determining final nucleotide base calls for genomic coordinates in the genomic region based on the direct nucleotide base calls and the assigned nucleotide base calls.

[0157] Further, operation 808 can include determining sequencing metrics corresponding to the direct nucleotide base calls for the genomic coordinates, and determining a final nucleotide base call for the genomic coordinate by assigning a first weight to the direct nucleotide base call and a second weight to the imputed nucleotide base call based on the sequencing metrics and variability of the genomic region.

[0158] As discussed above, Figure 9 illustrates a flow chart of a series of operations 900 for determining nucleotide base calls based on imputed nucleotide base calls, direct nucleotide base calls, and sequencing metrics, according to one or more embodiments. Although Figure 9 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 9. The operations of Figure 9 may be performed as part of a method. Alternatively, a non-transitory computer-readable medium may include instructions that, when executed by one or more processors, cause a computing device to perform the operations of Figure 9. In some embodiments, a system may perform the operations of Figure 9.

[0159] 9, the series of operations 900 includes an operation 902 for determining a subset of variant nucleotide base calls surrounding a genomic region from a subset of nucleotide fragment reads of the sample genome. In particular, operation 902 can include determining a subset of variant-nucleotide-base calls surrounding a genomic region in the sample genome from a subset of nucleotide-fragment reads of the sample genome. As described above, when performing operation 902, determining the subset of variant nucleotide base calls surrounding a genomic region can be based on a subset of nucleotide fragment reads from the first 35 base pairs, the first 50 base pairs, the first 75 base pairs, or another first number of base pairs of a 2×150 sequencing run, or at about 1× read depth.

[0160] 9, the set of operations 900 includes an operation 904 for assigning haplotypes corresponding to genomic regions based on the subset of variant nucleotide base calls for the sample genome. In particular, operation 904 can include assigning haplotypes corresponding to genomic regions based on the subset of variant-nucleotide-base calls for the sample genome.

[0161] 9, the set of operations 900 includes an operation 906 for determining an assigned nucleotide base call for a genomic region based on a haplotype. In particular, operation 906 can include determining, for the sample genome, an assigned nucleotide base call for the genomic region based on the assigned haplotype.

[0162] 9, the set of operations 900 includes an operation 908 for determining direct nucleotide base calls for genomic regions and sequencing metrics corresponding to the direct nucleotide base calls. In particular, operation 908 can include determining, for the sample genome, direct nucleotide base calls for genomic regions and sequencing metrics corresponding to the direct nucleotide base calls. Specifically, operation 908 can include determining sequencing metrics corresponding to the direct nucleotide base calls by determining depth metrics, read data quality metrics, call data quality metrics, or mapping quality metrics for the direct nucleotide base calls.

[0163] As shown in FIG. 9, the series of operations 900 includes an operation 910 for determining a final nucleotide base call for a genomic region based on the imputed nucleotide base calls, the direct nucleotide base calls, and the sequencing metrics. In particular, operation 910 can include determining a final nucleotide-base call for a genomic region based on the imputed nucleotide-base calls, the direct nucleotide-base calls, and the sequencing metrics. Specifically, operation 910 can include determining a subset of variant-nucleotide-base calls surrounding a genomic region in a sample genome from a subset of nucleotide-fragment reads of the sample genome, assigning a haplotype corresponding to the genomic region based on the subset of variant-nucleotide-base calls for the sample genome, determining an imputed nucleotide-base call for the genomic region based on the imputed haplotype for the sample genome, determining a direct nucleotide-base call for the genomic region and sequencing metrics corresponding to the direct nucleotide-base calls for the sample genome, and determining a final nucleotide-base call for the genomic region based on the imputed nucleotide-base calls, the direct nucleotide-base calls, and the sequencing metrics.

[0164] Further, operation 910 can include determining a final nucleotide-base call for the genomic region by utilizing a base calling machine learning model to determine a final nucleotide-base call based on the imputed nucleotide-base calls, the direct nucleotide-base calls, and the sequencing metrics. Further, operation 910 can include determining a final nucleotide base call for the genomic region by weighting one or more of the direct nucleotide base calls differently than one or more of the imputed nucleotide base calls based on one or more of the sequencing metrics corresponding to the variability and direct nucleotide base calls of the genomic region. Also, operation 910 can include the variability of the genomic region including a genotype variability of the genomic region and a length of the genomic region, and the one or more of the sequencing metrics including a read data quality metric or a mapping quality metric for the direct nucleotide base calls corresponding to the nucleotide fragment reads, and a call data quality metric for the direct nucleotide base calls corresponding to the nucleotide fragment reads.

[0165] In one or more embodiments, the series of operations 900 can include generating a graph reference genome for the sample genome, the graph reference genome including a linear reference genome and paths representing assigned haplotypes corresponding to the genomic regions, and determining direct variant-nucleotide-base calls for genomic coordinates inside or outside the genomic regions based on identifying mismatches between nucleotide-base-fragment reads corresponding to the genomic coordinates and corresponding nucleotide bases in the genomic coordinates in the linear reference genome. The series of operations 900 can also include generating a graph reference genome for the sample genome, the graph reference genome including paths representing assigned haplotypes corresponding to the genomic regions, and determining direct nucleotide base calls for the genomic regions based on comparing the nucleotide fragment reads of the sample genome to the paths representing the assigned haplotypes in the graph reference genome. In particular, comparing the nucleotide fragment reads of the sample genome to the paths can include aligning the nucleotide fragment reads of the sample genome to the paths representing the assigned haplotypes in the graph reference genome.

[0166] Additionally, in one or more embodiments, the series of operations 900 includes determining nucleotide base calls directly by determining nucleotide base calls based on a first subset of nucleotide fragment reads from the sample genome aligned with a linear reference genome in the graph reference genome, and determining nucleotide base calls based on a second subset of nucleotide fragment reads from the sample genome aligned with paths representing one or more assigned haplotypes from the graph reference genome.

[0167] As discussed above, Figure 10 illustrates a flow chart of a series of operations 1000 for determining nucleotide base calls based on direct nucleotide base calls, sequencing metrics, and imputed nucleotide base calls, according to one or more embodiments. Although Figure 10 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in Figure 10. The operations of Figure 10 may be performed as part of a method. Alternatively, a non-transitory computer-readable medium may include instructions that, when executed by one or more processors, cause a computing device to perform the operations of Figure 10. In some embodiments, a system may perform the operations of Figure 10.

[0168] As shown in FIG. 10, the series of operations 1000 includes an operation 1002 for determining direct nucleotide base calls for genomic regions and sequencing metrics corresponding to the direct nucleotide base calls. In particular, operation 1002 can include determining, for a sample genome, direct nucleotide base calls for genomic regions and sequencing metrics corresponding to the direct nucleotide base calls. Determining the direct nucleotide base calls can include determining the direct nucleotide base calls based on alignments between nucleotide fragment reads from the sample genome and the reference genome. In particular, operation 1002 can include determining sequencing metrics corresponding to the direct nucleotide base calls by determining depth metrics, read data quality metrics, call data quality metrics, or mapping quality metrics for the direct nucleotide base calls.

[0169] 10, the series of operations 1000 includes an operation 1004 for assigning a haplotype corresponding to a genomic region based on variant nucleotide base calls surrounding the genomic region. In particular, operation 1004 can include assigning, for the sample genome, a haplotype corresponding to the genomic region based on variant-nucleotide-base calls surrounding the genomic region.

[0170] 10, the series of operations 1000 includes an operation 1006 for determining an assigned nucleotide base call for a genomic region based on a haplotype. In particular, operation 1006 can include determining, for the sample genome, an assigned nucleotide base call for the genomic region based on the assigned haplotype.

[0171] 10, the series of operations 1000 includes an operation 1008 for determining a final nucleotide base call for the genomic region based on the direct nucleotide base calls, the sequencing metrics, and the imputed nucleotide base calls. In particular, operation 1008 can include determining a final nucleotide base call for the genomic region based on the direct nucleotide base calls, the sequencing metrics, and the imputed nucleotide base calls. Specifically, operation 1008 can include utilizing a base calling machine learning model to determine the final nucleotide-base call based on the imputed nucleotide-base calls, the direct nucleotide-base calls, and the sequencing metrics.

[0172] Further, operation 1008 can include determining a final nucleotide base call for the genomic region includes weighting the direct nucleotide base calls differently from the imputed nucleotide base calls based on genotype variability of the genomic coordinate for the direct nucleotide base calls and one or more of read data quality metrics for the direct nucleotide base calls corresponding to the nucleotide fragment reads or call data quality metrics for the direct nucleotide base calls corresponding to the nucleotide fragment reads. Further, operation 1008 can include utilizing a base calling machine learning model to weight the direct nucleotide base calls differently from the imputed nucleotide base calls for the genomic coordinate and selecting one of the direct nucleotide base calls or the imputed nucleotide base calls as the final nucleotide base call for the genomic coordinate.

[0173] The methods described herein can be used in conjunction with a variety of nucleic acid sequencing techniques. Particularly applicable techniques are those in which the 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 that correspond to different labels used to distinguish one nucleotide base type from another are particularly applicable. In some embodiments, the process of determining the nucleotide sequence of the target nucleic acid can be an automated process. A preferred embodiment includes sequencing-by-synthesis ("SBS") techniques.

[0174] SBS technology generally involves the enzymatic extension of nascent nucleic acid strand by repeated addition of nucleotide to template strand.In the conventional method of SBS, a single nucleotide monomer can be provided to target nucleic acid in the presence of polymerase in each delivery.However, in the method described herein, multiple types of nucleotide monomers can be provided to target nucleic acid in the presence of polymerase during delivery.

[0175] SBS can utilize nucleotide monomers with terminator moieties or nucleotide monomers that lack any terminator moieties. Methods that utilize nucleotide monomers that lack terminators include, for example, pyrosequencing and sequencing using γ-phosphate-labeled nucleotides, as described in more detail below. In methods that use nucleotide monomers that do not contain terminators, 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 techniques that utilize nucleotide monomers with terminator moieties, the terminator can be effectively irreversible under the sequencing conditions used, as in the case of conventional Sanger sequencing that utilizes dideoxynucleotides, or the terminator can be reversible, as in the case of the sequencing method developed by Solexa (now Illumina).

[0176] SBS techniques can use nucleotide monomers with or without a label moiety. Thus, incorporation events can be detected based on the properties of the label, such as the fluorescence of the label, the properties of the nucleotide monomer, such as the molecular weight or charge, the by-products of incorporation of the nucleotide, such as the release of pyrophosphate, and the like. In embodiments in which 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 distinguished under the detection technique used. For example, the different nucleotides present in the sequencing reagent can have different labels, which can be distinguished using appropriate optical systems, as exemplified by the sequencing method developed by Solexa (now Illumina).

[0177] Preferred embodiments include pyrosequencing techniques, which detect 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. "A sequencing method based on real-time pyrophosphate" (1998), Science 281(5375),363, U.S. Patent Nos. 6,210,891, 6,258,568, and 6,274,320, the disclosures of which are incorporated herein by reference in their entireties). In pyrosequencing, the released PPi can be detected by its immediate conversion to adenosine triphosphate (ATP) by ATP sulfurase, and the level of ATP generated is detected via luciferase-generated photons. The nucleic acid to be sequenced can be bound to features in an array, and the array can be imaged to capture chemiluminescent signals generated by incorporation of nucleotides into the features of the array. Images can be obtained after treatment of the array with a particular nucleotide type (e.g., A, T, C, or G). Images obtained after 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 content of the features on the array. However, the relative position of each feature remains unchanged in the image. The images can be stored, processed, and analyzed using the methods described herein.For example, images obtained after treating the array with each different nucleotide type can be processed in the same manner as exemplified herein for images obtained from different detection channels for reversible terminator-based sequencing methods.

[0178] In another exemplary type of SBS, cycle sequencing is accomplished by stepwise addition of reversible terminator nucleotides containing cleavable or photobleachable dye labels, for example as described in WO 04 / 018497 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated by reference. This approach has been commercialized by Solexa (now Illumina Inc.) and is also described in WO 91 / 06678 and WO 07 / 123,744, each of which is incorporated by reference herein. The availability of fluorescently labeled terminators, both of which can be reversed and have their fluorescent labels cleaved, facilitates efficient cyclic reversible termination (CRT) sequencing. Polymerases can also be co-engineered to efficiently incorporate and extend from these modified nucleotides.

[0179] Preferably, in reversible terminator-based sequencing embodiments, the label does not substantially inhibit extension under SBS reaction conditions. However, the detection label can 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 simultaneous delivery of four different nucleotide types to the array, each nucleotide type having a spectrally distinct label. Four images can then be obtained, each using a detection channel selective for one of the four different labels. Alternatively, different nucleotide types can be added sequentially, and images of the array can be obtained during each addition step. In such embodiments, each image shows nucleic acid features that incorporate a particular type of nucleotide. Different features are present or absent in different images, since the sequence content of each feature is different. However, the relative positions of the features remain unchanged within the images. Images obtained from such reversible terminator-SBS methods can be stored, processed, and analyzed as described herein. Following the image taking step, the label can be removed, and the reversible terminator moiety can be removed for subsequent cycles of nucleotide addition and detection. Removal of the label after detection in a particular cycle and before the subsequent cycle has the advantage of reducing background signal and crosstalk between cycles. Examples of useful labeling and removal methods are described below.

[0180] In certain embodiments, some or all of the nucleotide monomers can include reversible terminators. In such embodiments, the reversible terminator / cleavable fluorophore can include a fluorophore 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 terminator chemistry 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 group to block extension, but can be easily deblocked by brief treatment with a palladium catalyst. The fluorophore was attached to the group via a photocleavable linker that can be easily cleaved by 30 seconds of exposure to long-wavelength UV light. Thus, either disulfide reduction or photocleavage can be used as a cleavable linker. Another approach to reversible termination is the use of a natural terminus followed by placement of a bulky dye on the dNTP. The presence of a charged bulky dye on the dNTP can act as an effective terminator through 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, effectively reversing the terminus. Examples of modified nucleotides are also described in U.S. Pat. No. 7,427,673 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated herein by reference in their entirety.

[0181] Additional exemplary SBS systems and methods that may be utilized 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. Pat. No. 7,057,026, U.S. Patent Application Publication No. 2006 / 0240439, U.S. Patent Application Publication No. 2006 / 0281109, WO 05 / 065814, U.S. Patent Application Publication No. 2005 / 0100900, WO 06 / 064199, WO 07 / 010,251, U.S. Patent Application Publication No. 2012 / 0270305, and U.S. Patent Application Publication No. 2013 / 0260372, the disclosures of which are incorporated herein by reference in their entireties.

[0182] Some embodiments may utilize detection of four different nucleotides using fewer than four different labels. For example, SBS may be performed using the methods and systems described in incorporated document US Patent Application Publication No. 2013 / 0079232. As a first example, pairs of nucleotide types may be detected at the same wavelength but may be distinguished based on differences in intensity for one member of the pair or based on a change to one member of the pair (e.g., via making a chemical modification, photochemical modification, or physical modification) that results in the appearance or disappearance of a distinct signal compared to the signal detected for the other member of the pair. As a second example, three of the four different nucleotide types may be detected under certain conditions, while the fourth nucleotide type may have no detectable label under those conditions or may be minimally detected under those conditions (e.g., minimal detection due to background fluorescence, etc.). Incorporation of the first three nucleotide types into a nucleic acid may be determined based on the presence of their corresponding signals, and incorporation of the fourth nucleotide type into a nucleic acid may 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 the other nucleotide type is detected in no more than one of the channels. The three exemplary configurations above are not considered mutually exclusive and can be used in various combinations.An exemplary embodiment combining all three examples is a fluorescence-based SBS method that uses a first nucleotide type that is detected in a first channel (e.g., dATP having a label that is detected in the first channel when excited by a first excitation wavelength), a second nucleotide type that is detected in a second channel (e.g., dCTP having a label that is detected in the second channel when excited by a second excitation wavelength), a third nucleotide type that is detected in both the first and second channels (e.g., dTTP having at least one label that is detected in both channels when excited by the first and / or second excitation wavelengths), and a fourth nucleotide type that is not detected in any channel or that is minimally devoid of a label (e.g., unlabeled dGTP).

[0183] Furthermore, as described in incorporated document U.S. Patent Application Publication No. 2013 / 0079232, sequencing data can be obtained using a single channel. In such so-called one-dye sequencing methods, a first nucleotide type is labeled but the label is removed after the first image is generated, and a second nucleotide type is labeled only after the first image is generated. A third nucleotide type retains its label in both the first and second images, and a fourth nucleotide type remains unlabeled in both images.

[0184] Some embodiments may utilize sequencing by ligation techniques. 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 a particular nucleotide in the sequence to which the oligonucleotide hybridizes. As with other SBS methods, images can be obtained after treating an array of nucleic acid sequences with labeled sequencing reagents. Each image shows nucleic acid features that incorporate a particular type of label. Because the sequence content of each feature is different, different features are present or absent in different images, but the relative positions of the features remain unchanged within the images. Images obtained from ligation-based sequencing methods can be stored, processed, and analyzed as described herein. Exemplary SBS systems and methods that may be utilized with the methods and systems described herein are described in U.S. Pat. No. 6,969,488, U.S. Pat. No. 6,172,218, and U.S. Pat. No. 6,306,597, the disclosures of which are incorporated herein by reference in their entireties.

[0185] Some embodiments may utilize nanopore sequencing (Deamer, DW & 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 JA 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 entireties). In such embodiments, the target nucleic acid passes through the nanopore. The nanopore may be a synthetic pore or a biological membrane protein, such as α-hemolysin. As the target nucleic acid passes through the nanopore, each base pair can be identified by measuring the variation in the electrical conductance of the pore. (U.S. Pat. No. 7,001,792; Soni, GV & 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, SL, Chu, J., Amorin, M. & Ghadiri, MR "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 entireties).Data obtained from nanopore sequencing can be stored, processed, and analyzed as described herein. In particular, the data can be processed as images according to the exemplary processing of optical and other images described herein.

[0186] Some embodiments may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation may be detected via fluorescence resonance energy transfer (FRET) interactions between fluorophore-containing polymerases and γ-phosphate-labeled nucleotides, for example, as described in U.S. Pat. No. 7,329,492 and U.S. Pat. No. 7,211,414, each of which is incorporated herein by reference, or nucleotide incorporation may be detected using zero-mode waveguides, for example, as described in U.S. Pat. No. 7,315,019, each of which is incorporated herein by reference, and fluorescent nucleotide analogs and engineered polymerases, for example, as described in U.S. Pat. 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 a zeptoliter-scale volume around the surface-tethered polymerase so that incorporation of fluorescently labeled nucleotides can be observed with low background (Levene, MJ et al. "Zero-mode waveguides for single-molecule analysis at high concentrations." Science, 299, 682-686 (2003); Lundquist, PM 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.

[0187] Some SBS embodiments include detection of protons released upon incorporation of a nucleotide into an extension product. For example, sequencing based on detection of released protons may use electrical detectors and related technology available from Ion Torrent (Guilford, CT, a subsidiary of Life Technologies), or the sequencing methods and systems described in U.S. Patent Application Publication Nos. 2009 / 0026082(A1), 2009 / 0127589(A1), 2010 / 0137143(A1), or 2010 / 0282617(A1), each of which is incorporated herein by reference. The methods described herein for amplifying target nucleic acids using kinetic exclusion can be easily adapted to substrates used to detect protons. More specifically, the methods described herein can be used to generate clonal populations of amplicons used to detect protons.

[0188] The SBS method described above can be advantageously performed 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 allows for convenient delivery of sequencing reagents, removal of unreacted reagents, and detection of incorporation events in a multiplexed 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 a surface in a spatially distinguishable manner. The target nucleic acids can be bound by direct covalent binding, binding to beads or other particles, or binding to a polymerase or other molecule bound to the surface. The array can include a single copy of the target nucleic acid at each site (also referred to as a feature), or multiple copies with 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.

[0189] The methods described herein can be used to fabricate, for example, at least about 10 features / cm2 , 100 features / cm 2 , 500 features / cm 2 , 1,000 features / cm 2 , 5,000 features / cm 2 , 10,000 features / cm 2 , 50,000 features / cm 2 , 100,000 features / cm 2 , 1,000,000 features / cm 2 , 5,000,000 features / cm 2 Arrays having features of any of a variety of densities, including 100 nm to 150 nm, 100 nm to 200 nm, or greater, can be used.

[0190] An advantage of the methods described herein is that they provide rapid and efficient detection of multiple target nucleic acids in parallel. Thus, 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 fluidic components that can deliver amplification and / or sequencing reagents to one or more immobilized DNA fragments, the system including components such as pumps, valves, reservoirs, fluid lines, etc. 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 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 fluidic components of the integrated system can be used for amplification and detection methods. Taking the nucleic acid sequencing embodiment as an example, one or more of the fluidic components of the integrated system can be used for delivery of sequencing reagents in the amplification methods described herein and in the sequencing methods as exemplified above. Alternatively, an integrated system can include separate fluidic systems for performing the amplification method and for performing the detection method. Examples of integrated sequencing systems that can generate amplified nucleic acids and sequence the nucleic acids include, but are not limited to, the MiSeq™ platform (Illumina Inc., San Diego, Calif.) and the devices described in U.S. Patent Application No. 13 / 273,666, which is incorporated herein by reference.

[0191] The sequencing system described above sequences the nucleic acid polymers present in the sample received by the sequencing device. As defined herein, "sample" and its derivatives are used in the broadest sense and include any specimen, culture, etc. suspected of containing a target. In some embodiments, the sample includes DNA, RNA, PNA, LNA, chimeric or hybrid forms of nucleic acid. A sample can include any biological, clinical, surgical, agricultural, air or water sample containing one or more nucleic acids. The term also includes any isolated nucleic acid sample, such as genomic DNA, fresh frozen or formalin-fixed paraffin-embedded nucleic acid samples. It is also envisioned that the sample can be derived from a single individual, a collection of nucleic acid samples from genetically related members, nucleic acid samples from genetically unrelated members, nucleic acid samples from a single individual such as a tumor sample and a normal tissue sample (matched), or a sample from a single source containing two different forms of genetic material such as maternal and fetal DNA obtained from a maternal subject, or the presence of contaminating bacterial DNA in a sample containing plant or animal DNA. In some embodiments, the source of nucleic acid material can include nucleic acid obtained from a newborn, for example, as typically used for newborn screening.

[0192] The nucleic acid sample can include high molecular weight material such as genomic DNA (gDNA). The sample can include low molecular weight material such as nucleic acid molecules obtained from FFPE or archived DNA samples. In another embodiment, the low molecular weight material includes enzymatically or mechanically fragmented DNA. The sample can include cell-free circulating DNA. In some embodiments, the sample can include nucleic acid molecules obtained from biopsies, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microdissection, surgical resection, and other clinical or laboratory obtained samples. In some embodiments, the sample can be an epidemiological, agricultural, forensic, or pathogenic sample. In some embodiments, the sample can include nucleic acid molecules obtained from animals, such as human or mammalian sources. In another embodiment, the sample can include 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 an archived or extinct sample or species.

[0193] Additionally, 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 may include nucleic acid obtained from a crime scene, from a missing persons DNA database, from a laboratory associated with a forensic investigation, or may include forensic samples obtained by a law enforcement agency, one or more military services, or members thereof. The nucleic acid sample may be crude DNA, including purified samples or lysates, for example, from buccal swabs, paper, cloth, or other substrates that may be impregnated with saliva, blood, or other bodily fluids. Thus, in some embodiments, the nucleic acid sample may include small amounts of DNA, such as genomic DNA, or fragmented portions of DNA. In some embodiments, the target sequence may be present in one or more bodily fluids, including, but not limited to, blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequence may be obtained from hair, skin, tissue samples, autopsies, or remains of a victim. In some embodiments, the nucleic acid including one or more target sequences may be obtained from a deceased animal or human. In some embodiments, the target sequence may comprise nucleic acid 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 human identification. In some embodiments, the present disclosure generally relates to a method for identifying features of a forensic sample. In some embodiments, the present disclosure generally relates to a human identification method 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 human identification sample comprising at least one target sequence may be amplified using any one or more of the target specific primers disclosed herein or using the primer criteria outlined herein.

[0194] The components of the customized sequencing system 104 may include software, hardware, or both. For example, the components of the customized sequencing system 104 may 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., the user client device 108). When executed by one or more processors, the computer-executable instructions of the customized sequencing system 104 may cause the computing device to perform the bubble detection methods described herein. Alternatively, the components of the customized sequencing system 104 may include hardware, such as a dedicated processing device for performing a particular function or group of functions. Additionally or alternatively, the components of the customized sequencing system 104 may include a combination of computer-executable instructions and hardware.

[0195] Further, the components of the customized sequencing system 104 that perform the functions described herein with respect to the customized sequencing system 104 may 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(s) that can be called by other applications, and / or as a cloud computing model. Thus, the components of the customized sequencing system 104 may 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 customized sequencing system 104 may be implemented in any application that provides sequencing services, 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.

[0196] Embodiments of the present disclosure may include or utilize special purpose or general purpose computers including, for example, computer hardware such as one or more processors and system memory, as 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 implemented at least in part as instructions executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer readable medium (e.g., a memory, etc.) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.

[0197] A computer-readable medium may be any available medium that can be accessed by a general-purpose or special-purpose computer system. A computer-readable medium that stores computer-executable instructions is a non-transitory computer-readable storage medium (device). A computer-readable medium that carries computer-executable instructions is a transmission medium. Thus, by way of example and not limitation, embodiments of the present disclosure may include at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage medium (device) and transmission media.

[0198] Non-transitory computer-readable storage media (devices) include RAM, ROM, EEPROM, CD-ROM, (e.g., RAM-based) solid-state drives (SSD), 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.

[0199] A "network" is defined as one or more data links that enable the transport of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless), the computer properly recognizes the connection as a transmission medium. A transmission medium may include a network and / or data links that may be used to carry desired program code means in the form of computer-executable instructions or data structures and that may 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.

[0200] Furthermore, upon 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 over a network or data link can be buffered in a RAM in a network interface module (e.g., a NIC) and then eventually transferred to the computer system RAM and / or to a less volatile computer storage medium (device) in the computer system. It should therefore be understood that a non-transitory computer readable storage medium (device) can be included in a computer system component that also (or even primarily) utilizes a transmission medium.

[0201] Computer-executable instructions include, for example, instructions and data that, when executed by a processor, cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to transform the general-purpose computer into a special-purpose computer that implements elements of the present disclosure. Computer-executable instructions may be, for example, binaries, 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 operations, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or operations described above. Rather, the described features and operations are disclosed as example forms of implementing the claims.

[0202] Those skilled in the art will appreciate that the present disclosure may be implemented in a networked 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, cell phones, PDAs, tablets, pagers, routers, switches, etc. The present disclosure may also be implemented in a distributed system environment where both local and remote computer systems perform tasks that are linked through a network (either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links). In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0203] Embodiments of the present disclosure may also be implemented in a cloud computing environment. In this specification, "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 used in the market 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 quickly configured through virtualization, exposed with low management effort or service provider interaction, and then scaled accordingly.

[0204] The cloud computing model may consist of various characteristics such as, for example, on-demand self-service, wide area network access, resource pooling, rapid elasticity, measured service, etc. The cloud computing model may 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 may also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this specification and claims, a "cloud computing environment" is an environment in which cloud computing is employed.

[0205] FIG. 11 illustrates a block diagram of a computing device 1100 that may be configured to perform one or more of the processes described above. It will be understood that one or more computing devices, such as the computing device 1100, may implement the customized sequencing system 104. As illustrated by FIG. 11, the computing device 1100 may include a processor 1102, a memory 1104, a storage device 1106, an I / O interface 1108, and a communication interface 1110, which may be communicatively coupled by a communication infrastructure 1112. In certain embodiments, the computing device 1100 may include fewer or more components than those illustrated in FIG. 11. The following paragraphs describe in more detail the components of the computing device 1100 illustrated in FIG. 11.

[0206] In one or more embodiments, the processor 1102 includes hardware for executing instructions, such as those that make up a computer program. By way of example and not limitation, to execute instructions for dynamically modifying a workflow, the processor 1102 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 1104, or storage device 1106, decode them, and execute them. The memory 1104 may be a volatile or non-volatile memory used to store data, metadata, and programs for execution by the processor. The storage device 1106 includes storage devices, such as a hard disk, flash disk drive, or other digital storage device, for storing data or instructions for performing the methods described herein.

[0207] The I / O interface 1108 enables a user to provide input to, receive output from, and otherwise transfer data to and receive data from the computing device 1100. The I / O interface 1108 may 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 1108 may include one or more devices for presenting output to a 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 a particular embodiment, the I / O interface 1108 is configured to provide graphical data to a display for presentation to a 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.

[0208] Communications interface 1110 may include hardware, software, or both. In any case, communications interface 1110 may provide one or more interfaces for communications (e.g., packet-based communications, etc.) between computing device 1100 and one or more other computing devices or networks. By way of example and not limitation, communications interface 1110 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as WI-FI.

[0209] Further, the communication interface 1110 can facilitate communication with various types of wired or wireless networks. The communication interface 1110 can also facilitate communication using various communication protocols. The communication infrastructure 1112 can also include hardware, software, or both that couples the components of the computing device 1100 to one another. For example, the communication interface 1110 can enable multiple computing devices connected by a particular infrastructure to communicate with one another to perform one or more aspects of the processes described herein using one or more networks and / or protocols. To illustrate, a sequencing process can enable multiple devices (e.g., a client device, a sequencing device, and a server device) to exchange information such as sequencing data and error notifications.

[0210] In the foregoing specification, the present disclosure has been described with reference to certain exemplary embodiments thereof. Various embodiments and aspects of the present disclosure are described with reference to the details discussed herein, and the accompanying drawings illustrate various embodiments. The above description and drawings are illustrative of the present disclosure and should not be construed as limiting the present disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure.

[0211] 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 as illustrative only and not restrictive. For example, methods described herein may be performed with fewer or more steps / actions, or steps / actions may be performed in a different order. Further, steps / actions described herein may be repeated or performed in parallel with each other, or with different instances of the same or similar operations. The scope of the present application is therefore indicated by the appended claims, rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are intended to be embraced within their scope.

Claims

1. 1. A system comprising: at least one processor; When executed by the at least one processor, the system: aligning a subset of nucleotide-fragment reads of a sample genome from a single individual to a reference genome; determining a subset of direct variant-nucleotide-base calls surrounding a genomic region within the sample genome from the single individual from a comparison of the aligned subset of nucleotide-fragment reads of the sample genome from the single individual to the reference genome; assigning a haplotype for the genomic region corresponding to the sample genome from the single individual based on the subset of direct variant-nucleotide-base calls; generating, for the sample genome from the single individual, a customized graph reference genome that includes the reference genome and paths representing the imputed haplotypes that correspond to the genomic regions in the sample genome from the single individual; determining a final nucleotide-base call within the genomic region of the sample genome from the single individual based on comparing one or more nucleotide-fragment reads of the sample genome from the single individual to paths representing assigned haplotypes within the customized graph reference genome; a non-transitory computer-readable medium containing instructions to cause Including, the system.

2. When executed by the at least one processor, the system: determining the subset of direct variant-nucleotide-base calls surrounding the genomic region by determining single nucleotide polymorphisms (SNPs) surrounding the genomic region; assigning the haplotype of the genomic region by assigning the haplotype corresponding to the sample genome from the single individual based on the SNPs; The system of claim 1 further comprising instructions to:

3. When executed by the at least one processor, the system: assigning said haplotype for said genomic region from a haplotype database of population haplotypes.

3. The system of claim 1 or 2, further comprising instructions to:

4. When executed by the at least one processor, the system: determining variant-nucleotide-base calls corresponding to additional genomic regions within the sample genome from the single individual; determining additional assigned haplotypes for the additional genomic regions based on the variant-nucleotide-base calls; generating the customized graph reference genome including additional paths representing the additional imputed haplotypes; The system of claim 1 further comprising instructions to:

5. When executed by the at least one processor, the system: determining that a quality metric for a subset of nucleotide base calls within the genomic region does not meet a quality metric threshold; identifying the genomic region as a low-confidence called region based on the quality metric for the subset of nucleotide base calls that do not meet the quality metric threshold. The system of claim 1 further comprising instructions to:

6. When executed by the at least one processor, the system: determining direct nucleotide base calls for genomic coordinates within the genomic region based on a comparison of the one or more nucleotide-fragment reads of the sample genome from the single individual to the path representing the assigned haplotype; determining an assigned nucleotide base call for the genomic coordinate within the genomic region based on the assigned haplotype for the genomic region; determining a final nucleotide base call among the final nucleotide base calls by determining the final nucleotide-base call for the genomic coordinate within the genomic region based on the direct nucleotide base call and the assigned nucleotide base call; The system of claim 1 further comprising instructions to:

7. When executed by the at least one processor, the system: determining sequencing metrics corresponding to the direct nucleotide base calls for the genomic coordinates; and determining the final nucleotide base calls for the genomic coordinates by assigning first weights to the direct nucleotide base calls and second weights to the imputed nucleotide base calls based on the sequencing metrics and variability of the genomic region; The system of claim 6 further comprising instructions to:

8. 2. The system of claim 1, wherein the genomic region comprises at least a portion of a variable number tandem repeat (VNTR), a structural variant, an insertion, or a deletion.

9. When executed by the at least one processor, the system: determining genomic coordinates of the genomic region from a linear reference genome; generating the customized graph reference genome, the graph reference genome including the linear reference genome and the paths representing the assigned haplotypes corresponding to the genomic regions located at the genomic coordinates of the linear reference genome; The system of claim 1 further comprising instructions to:

10. A non-transitory computer-readable storage medium comprising instructions, The instructions, when executed by at least one processor, cause a computing device to: aligning a subset of nucleotide-fragment reads of a sample genome from a single individual to a reference genome; determining a subset of direct variant-nucleotide-base calls surrounding a genomic region within the sample genome from the single individual from a comparison of the aligned subset of nucleotide-fragment reads of the sample genome from the single individual to the reference genome; assigning a haplotype for the genomic region corresponding to the sample genome from the single individual based on the subset of direct variant-nucleotide-base calls; generating, for the sample genome from the single individual, a customized graph reference genome that includes the reference genome and paths representing the imputed haplotypes that correspond to the genomic regions in the sample genome from the single individual; determining a final nucleotide-base call within the genomic region of the sample genome from the single individual based on comparing one or more nucleotide-fragment reads of the sample genome from the single individual to paths representing assigned haplotypes within the customized graph reference genome; A non-transitory computer-readable storage medium that causes 11. When executed by the at least one processor, the computing device: determining the subset of direct variant-nucleotide-base calls surrounding the genomic region by determining single nucleotide polymorphisms (SNPs) surrounding the genomic region; 11. The non-transitory computer-readable storage medium of claim 10, further comprising instructions to: assign the haplotype of the genomic region by assigning the haplotype corresponding to the sample genome from the single individual based on the SNPs.

12. When executed by the at least one processor, having the computing device assign the haplotype for the genomic region from a haplotype database of population haplotypes.

12. The non-transitory computer-readable storage medium of claim 10 or 11, further comprising instructions to:

13. When executed by the at least one processor, the computing device: determining variant-nucleotide-base calls corresponding to additional genomic regions within the sample genome from the single individual; determining additional assigned haplotypes for the additional genomic regions based on the variant-nucleotide-base calls; generating the customized graph reference genome including additional paths representing the additional imputed haplotypes; The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:

14. When executed by the at least one processor, the computing device: determining that a quality metric for a subset of nucleotide base calls within the genomic region does not meet a quality metric threshold; identifying the genomic regions as low-confidence called regions based on the quality metrics for the subset of nucleotide base calls that do not meet the quality metric threshold; The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:

15. When executed by the at least one processor, the computing device: determining direct nucleotide base calls for genomic coordinates within the genomic region based on a comparison of the one or more nucleotide-fragment reads of the sample genome from the single individual to the path representing the assigned haplotype; determining an assigned nucleotide base call for the genomic coordinate within the genomic region based on the assigned haplotype for the genomic region; determining a final nucleotide base call among the final nucleotide base calls by determining the final nucleotide-base call for the genomic coordinate within the genomic region based on the direct nucleotide base call and the assigned nucleotide base call; The non-transitory computer-readable storage medium of claim 10 , further comprising instructions to:

16. When executed by the at least one processor, the computing device: determining sequencing metrics corresponding to the direct nucleotide base calls for the genomic coordinates; and determining the final nucleotide base calls for the genomic coordinates by assigning first weights to the direct nucleotide base calls and second weights to the imputed nucleotide base calls based on the sequencing metrics and variability of the genomic region; 20. The non-transitory computer-readable storage medium of claim 15, further comprising instructions to:

17. A computer-implemented method comprising: aligning a subset of nucleotide fragment reads of a sample genome from a single individual to a reference genome; determining a subset of direct variant-nucleotide-base calls encompassing genomic regions within the sample genome from the single individual from a comparison of the aligned subset of nucleotide-fragment reads of the sample genome from the single individual to the reference genome; assigning a haplotype for the genomic region corresponding to the sample genome from the single individual based on the subset of direct variant-nucleotide-base calls; generating, for the sample genome from the single individual, a customized graph reference genome that includes the reference genome and paths representing the imputed haplotypes that correspond to the genomic regions in the sample genome from the single individual; determining a final nucleotide-base call within the genomic region of the sample genome from the single individual based on comparing one or more nucleotide-fragment reads of the sample genome from the single individual to paths representing assigned haplotypes within the customized graph reference genome; 11. A computer-implemented method comprising:

18. A method for determining the subset of direct variant-nucleotide-base calls surrounding the genomic region by determining single nucleotide polymorphisms (SNPs) surrounding the genomic region; assigning the haplotype of the genomic region by assigning the haplotype corresponding to the sample genome from the single individual based on the SNPs; 20. The computer-implemented method of claim 17, further comprising:

19. The computer-implemented method of claim 17, wherein the genomic region comprises at least a portion of a variable number tandem repeat (VNTR), a structural variant, an insertion, or a deletion.

20. The method of claim 1, further comprising determining genomic coordinates of the genomic region from a linear reference genome; generating the customized graph reference genome, the graph reference genome including the linear reference genome and the paths representing the assigned haplotypes corresponding to the genomic regions located at the genomic coordinates of the linear reference genome; The computer-implemented method of claim 17 further comprising:

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