The segmentation-reading comparison method is improved by intelligently identifying and scoring candidate segmentation groups.

Abstract

This disclosure relates to systems, non-transitory computer-readable media, and methods for efficiently identifying and selecting segment groups corresponding to one or more nucleotide reads. Typically, a segment group comprises strands of fragments that form a segment alignment for a read. The disclosed system utilizes dynamic programming to generate and evaluate candidate segment groups. The disclosed system generates a segment group score for each candidate segment group. To generate the segment group score, the disclosed system considers fragment alignment scores and geometry within the candidate segment group. The disclosed system selects a predicted segment group from the candidate segment groups based on the segment group score.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 367,002, filed June 24, 2022, entitled “Improving Split-Read Aligment by Intelligently Identifying and Scoring Candidate Split Groups”. The entire contents of the above application are incorporated herein by reference. Background Technology

[0003] In recent years, biotechnology companies and research institutions have improved the hardware and software used for nucleotide sequencing of genome samples and for determining nucleotide base detection. For example, some existing sequencers and sequencing data analysis software (collectively referred to as "existing sequencing systems") predict individual nucleotide bases within a sequence using conventional Sanger sequencing or sequencing-by-synthesis (SBS) methods. When using SBS, existing sequencing systems monitor thousands of oligonucleotides synthesized in parallel from a template to predict nucleotide base detection in the growing nucleotide reads. Cameras in many existing sequencing systems capture images of irradiated fluorescent tags incorporated into the oligonucleotides. After capturing such images, some existing sequencing systems determine the nucleotide base detection of the nucleotide reads corresponding to the oligonucleotides and send the base detection data to a computing device with sequencing data analysis software that aligns the nucleotide reads to a reference genome. Based on the differences between the aligned nucleotide reads and the reference genome, existing systems (e.g., variant detectors) determine nucleotide base detection in genomic regions and identify variants in the genome sample.

[0004] Despite recent advances, existing sequencing systems often fail to accurately identify split reads and align them to a reference genome, and consequently, cannot identify variants or other nucleobase detections or may identify inaccurate nucleobase detections. Typically, a split read represents a nucleotide read with one segment mapped to (or aligned to) a region of the reference genome and one or more other segments mapped to (or aligned to) a different region of the reference genome. For example, a nucleotide read that covers a structural variant, a different side of a deletion, a different side of a gene fusion, or a simple random mapping of a segment can result in a split read. In practice, within a split read, one segment from a nucleotide read may best align to a genomic region on one chromosome, and another segment from the same nucleotide read may best align to a genomic region on another chromosome. Because such split-read alignments on two different chromosomes (or different genomic regions on the same chromosome) can accurately reflect variants in a genomic sample or incorrectly suggest a split read that should align to a single genomic region, existing sequencing systems have developed computational models to identify and distinguish between correct and incorrect split-read alignments.

[0005] While existing computational models can accurately identify some segment-read alignments, these models include design flaws that often lead to incorrect identification of segment-read alignments. For example, some existing sequencing systems determine the primary alignment for a segmented read based on the highest-scoring alignment of a single read from candidate alignments. However, such existing sequencing systems fail to account for the possibility of segment alignments and cannot explain how to score multiple read alignments together relative to other candidate alignments. To further illustrate, many existing sequencing systems identify the primary alignment by trimming reads (or different ends of reads) and thus leaving gaps between read alignments. To fill such gaps, some existing sequencing systems iteratively select additional read alignments that overlap with the gap. By simply filling gaps without considering read alignments together, such existing systems fail to account for the relative position or orientation of nucleotide reads relative to the reference genome or other segment alignment geometry.

[0006] Partly due to inaccuracies in read alignment, existing sequencing systems often determine inaccurate variant detections or other base detections based on inaccurate segment-read alignments. For example, by prioritizing major alignments over segment alignments from nucleotide reads as a whole, some existing sequencing systems may incorrectly ignore alignments that correctly reflect structural variants and fill in missing gaps, as well as other segment alignments. Conversely, the major alignment of a read may itself optimally map to incorrect genomic regions of the reference genome. By prioritizing major alignments, some existing sequencing systems ignore correct genomic regions that are better reflected by alignments from multiple segments of nucleotide reads, leading to false-negative variant detections or other incorrect variant detections. Therefore, existing sequencing systems frequently misalign, incorrectly match, or incorrectly detect a large number of variants in a large number of samples, and increase the probability of read mismatch alignments from genomic samples.

[0007] To compensate for the shortcomings of some existing sequencing systems in accurately detecting segment-read alignments indicating structural variants, some existing systems not only use SBS (or other technologies) for whole-genome sequencing (WGS) but also employ microarrays with genotyping probes targeting specific structural variants. In fact, microarrays have been specifically designed to target structural variants that are difficult to detect using existing sequencing equipment. By running WGS and multiple microarrays, and sometimes using different dedicated sequencing and microarray devices, existing sequencing systems significantly increase computational processing time to determine accurate variant detection, targeting both single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels), as well as structural variants. Summary of the Invention

[0008] This disclosure describes specific embodiments of methods, non-transitory computer-readable media, and systems that can solve one or more of the aforementioned problems (or other problems) in the art. For example, the disclosed system can determine a score for the alignment of one or more fragments from nucleotide reads in a candidate segmentation group, and select a predicted segmentation group from the candidates for base detection based on such scores. Specifically, the disclosed system can identify fragment alignments comprising reads from a genomic sample with candidate local alignments to a reference genome. The disclosed system then groups such fragment alignments into candidate segmentation groups and determines a segmentation group score for each of these candidate segmentation groups. Based on the segmentation group scores, the disclosed system identifies a predicted segmentation group from the candidate segmentation groups for base detection.

[0009] Additional features and advantages of one or more embodiments of this disclosure will be outlined in the following description and will be apparent in part from that description or may be learned by practice of such exemplary embodiments. Attached Figure Description

[0010] The specific embodiments are provided with additional specificity and detail through the use of the accompanying drawings, as briefly described below.

[0011] Figure 1 A diagram illustrating the environment in which a segment-read comparison system, based on one or more specific implementations, can operate.

[0012] Figure 2 An overview of a segment-read alignment system is provided, illustrating the scoring of alignments of one or more fragments from nucleotide reads in a candidate segmentation group based on one or more specific implementations, and the selection of predicted segmentation groups from candidates based on such scores for use in base detection.

[0013] Figures 3A to 3B An overview of a segmentation-read alignment system for generating candidate segmentation groups of single-terminal and double-terminal nucleotide reads according to one or more specific implementations is provided.

[0014] Figure 4 An example is given of a segment-read alignment system that utilizes dynamic programming to generate and evaluate candidate segment groups, based on one or more specific implementations.

[0015] Figure 5 An overview of a segment-read alignment system that generates segment group scores based on one or more specific implementations is illustrated.

[0016] Figures 6A to 6B An example is illustrated of a segment-read alignment system that determines a score based on one or more specific implementations and selects a predicted segment group based on the score.

[0017] Figure 7 An example is given of a segment-read alignment system that generates a score for the alignment of read segments with alternative continuous sequences based on one or more specific implementations, and selects the alternative continuous segment alignment as the score for the replacement segment group.

[0018] Figure 8 An example is illustrated by a segment-read alignment system that removes candidate segment groups based on threshold segment alignment scores according to one or more specific implementations.

[0019] Figure 9 An example is illustrated by a segment-read alignment system that uses minimum alignment scores to identify candidate segment groups on which no alignment has been reported, based on one or more specific implementations.

[0020] Figure 10 An example is illustrated of a segment-read alignment system for variant detection of genome samples generated from one or more specific implementations based on predicted segment groups.

[0021] Figures 11A to 11DAn example is illustrated by reading stacking within a graphical user interface of one or more specific implementations, which displays the detection of true negative variants of candidate gene fusion events by a segmentation-read alignment system. This is an improvement over existing sequencing systems that incorrectly identify such variants as gene fusion events.

[0022] Figures 12A to 12D The diagram illustrates coverage plots based on one or more specific implementations, demonstrating higher coverage of nucleotide reads mapped and aligned to genomic regions of chromosome M using a segment-read alignment system, relative to such coverage of nucleotide reads mapped and aligned using existing sequencing systems.

[0023] Figure 13 Variant detection tables based on one or more specific implementations are illustrated, demonstrating superior accuracy of SNP and indel detections at genomic regions of chromosome M obtained via segmentation-read alignment systems compared to such SNP and indel detections obtained via existing sequencing systems.

[0024] Figures 14A to 14B Tables based on one or more specific implementations are illustrated, demonstrating improved accuracy in structural variant detection obtained through segmentation-read alignment systems compared to existing sequencing systems.

[0025] Figure 15 A flowchart illustrating a series of actions based on one or more specific implementations for determining candidate segment groups and selecting a predicted segment group based on the segment group score is provided.

[0026] Figure 16 A block diagram illustrating an exemplary computing device for implementing one or more specific embodiments of the present disclosure is shown. Detailed Implementation

[0027] This disclosure describes one or more specific embodiments of a segmentation-read alignment system that selects a segment from candidate segmentation groups based on generating candidate segmentation groups of read fragment alignments and scoring said candidate segmentation groups. Typically, the segmentation-read alignment system identifies single-end or paired-end reads corresponding to genomic regions of a genome sample and analyzes candidate segmentation groups that together include one or more read fragments, rather than finding isolated single fragments with the highest alignment score. More specifically, the segmentation-read alignment system identifies candidate local alignments of read fragments and forms the strands of the aligned fragments into candidate segmentation groups. The segmentation-read alignment system scores the candidate segmentation groups and selects a predicted segmentation group for base detection based on the candidate segmentation group scores.

[0028] As mentioned, a segment-read alignment system can determine candidate segment groups. Typically, candidate segment groups can include (i) one or more fragment alignments of a single-terminal nucleotide read or (ii) one or more fragment alignments of a pair of double-terminal nucleotide reads. In some embodiments, the segment-read alignment system efficiently determines candidate segment groups by using dynamic programming. Typically, in dynamic programming, the segment-read alignment system does not consider every possible combination of fragment alignments, but rather iterates from the outermost fragment alignment to the innermost fragment alignment to determine the segment group and segment group score. By using dynamic programming, the segment-read alignment system efficiently considers all possible or potential combinations of fragment alignments from nucleotide reads.

[0029] The segment-read alignment system can also generate segment group scores for fragment alignments within candidate segment groups. Typically, the segment group score indicates the likelihood that a fragment alignment within a candidate segment group represents a correct alignment with the reference genome. The segment group score describes the probability of a segment alignment and the geometry of the alignment. Therefore, by determining segment group scores, rather than just alignment scores for isolated fragment alignments, the segment-read alignment system increases the likelihood of selecting the correct fragment alignment or combination of fragment alignments to complete the template.

[0030] In some implementations, the segment-read alignment system generates a segment group score for candidate segment groups based on one or more of the following: (i) a fragment alignment score, (ii) a breakage penalty, (iii) an overlap penalty, or other penalties for fragment alignments within the candidate segment group. As part of the segment group score, for example, the segment-read alignment system determines a fragment alignment score for each fragment within the candidate segment group. As another part of the segment group score, in some implementations, the segment-read alignment system determines a breakage penalty (e.g., to penalize breaks between fragment alignments) for the relative geometry of fragment alignments within the candidate segment group. As yet another part of the segment group score, in some implementations, the segment-read alignment system determines an overlap penalty for overlaps between or within fragment alignments within the candidate segment group. As described below, the segment-read alignment system may combine (i), (ii), and (iii) to determine the segment group score.

[0031] For paired-terminal nucleotide reads, the segment-read alignment system can also identify candidate segment pairs and score them. Typically, in some implementations, the segment-read alignment system also considers and determines pair scores for paired-terminal couples to identify possible segment pairs from the candidate segment pairs of the paired-terminal couples. For example, the segment-read alignment system can sum the segment scores for corresponding candidate segment pairs from paired-terminal couples and estimate the insert size between the innermost fragment alignments of the candidate segment pairs. The segment-read alignment system can then generate a pair score for the candidate segment pairs based on the total segment score and the estimated insert size. For illustration, the segment-read alignment system may include a pair score penalty for insert sizes that are unlikely to be estimated.

[0032] In addition to scoring and selecting segment groups, in some implementations, the segment-read alignment system can also identify fragment alignments to alternative continuous sequences within the reference genome by reporting the corresponding segment alignments using segment group reports. When the segment-read alignment system determines the optimal alignment of a nucleotide read to an alternative continuous sequence based on the segment group score, in some implementations, the segment-read alignment system reports the segment alignments corresponding to the alternative continuous sequences in the primary assembly via liftover relationships. For example, in some cases, the segment-read alignment system determines the alternative continuous fragment alignment score for the fragment alignment to an alternative continuous sequence representing a structural variant. The segment-read alignment system can also determine the segment group score for the corresponding segment alignment to the fragment alignment in the primary assembly of the reference genome. The segment-read alignment system can utilize higher-scoring alternative continuous fragment alignment scores as replacement segment alignment scores to guide the selection of the corresponding segment group relative to other candidate segment groups. For example, if the score of the alternative contiguous segment alignment exceeds the score of the segmentation group of other candidate segmentation groups, the segmentation-read alignment system selects and reports the segmentation alignment with the primary assembly corresponding to the alternative contiguous sequence, rather than the segmentation alignment represented by other candidate segmentation groups that might have a better score in the absence of an alternative contiguous segment alignment score.

[0033] As mentioned, the segment-read alignment system selects a predicted segment group from candidate segment groups for nucleotide base detection based on one or both of the segment group score and the pair score. For example, in some embodiments, the segment-read alignment system selects the predicted segment group with the highest segment group score for each pair of nucleotide reads. In another example, the segment-read alignment system selects a predicted segment group for each pair of nucleotide reads based on the highest pair score among all pair scores generated from the pairs of scored segment groups. As a result of selecting the predicted segment group, the segment-read alignment system improves the accuracy of nucleotide base detection and predicted variant detection in the output file (e.g., a variant detection file).

[0034] As described above, segment-read alignment systems offer several technical advantages and benefits over existing sequencing systems and methods. For example, by considering the alignment probabilities within various candidate segment groups corresponding to nucleotide reads, segment-read alignment systems improve the alignment accuracy of segmented reads compared to existing sequencing systems. By determining segment group scores for candidate segment groups that include fragment alignments from nucleotide reads and selecting predicted segment groups from candidates based on these scores, segment-read alignment systems identify fragment alignments of segmented reads with better accuracy than existing sequencing systems. For example, as... Figures 11A to 11D As illustrated, compared to existing sequencing systems, the segmentation-read alignment system determines better mapping and alignment for transcriptome reads and more accurate detection of true negative variants for candidate gene fusion events. Figures 12A to 12D As shown, the segment-read alignment system also determines better mappings and alignments of nucleotide reads onto genomic regions of chromosome M in mitochondrial DNA, resulting in improved coverage relative to existing sequencing systems. Unlike primary alignments that only find the single fragment with the highest alignment score, the segment-read alignment system considers and scores candidate fragment alignments from nucleotide reads as part of a segmentation group.

[0035] In addition to considering segment alignments together rather than in isolation, in some implementations, segment-read alignment systems also utilize other computational model improvements to enhance the accuracy of segment-read alignment. For example, for a given segment group, the segment-read alignment system determines a breakage penalty based on the relative geometry of segment alignments in the candidate segment group. In some cases, the segment-read alignment system effectively identifies and scores such segment groups and rapidly identifies possible segment-read alignments by utilizing dynamic processing to comprehensively consider candidate segment groups. For each candidate segment group, in some implementations, the segment-read alignment system generates a segment group score based on segment alignment scores, breakage penalties, and overlap penalties, thereby comprehensively assessing the likelihood that a given candidate segment group includes a segment alignment.

[0036] Partly due to improved segment-read alignment, the segment-read alignment system also improves the accuracy of corresponding nucleotide detection. Based on more accurate segment-read alignment, the system can accurately identify and report segment alignments when reads are aligned with substituted contiguous sequences. The system can report segment alignments corresponding to substituted contiguous sequences in the primary assembly to further guide the selection of predicted segment groups. Due to the improved alignment, the system can also determine more accurate variant detection or other nucleotide detection with higher confidence levels than existing sequencing systems. Figures 11A to 11DAs illustrated, for example, segmentation-read alignment systems determine more accurate detection of true negative variants for candidate gene fusion events compared to existing sequencing systems. Additionally, as... Figure 13 and Figures 14A to 14B As shown, compared with existing sequencing systems, the segment-read alignment system determines more accurate SNP detection, indel detection, and variant detection.

[0037] In addition to improved alignment and enhanced base detection accuracy, in some implementations, segment-read alignment systems improve computational efficiency by reducing the number of sequencing assays and computational devices required to determine structural variant detection. As mentioned above, some existing sequencing systems consume significant amounts of computing power and time by running (i) a dedicated sequencing device (WGS) to generate nucleotide reads of a genome sample and (ii) multiple genotyping microarrays on a microarray device. By comparing nucleotide reads to a reference genome from the WGS and analyzing the light signals from DNA probes in the microarrays, existing sequencing systems can determine accurate variant detection of SNPs and smaller indels based on the reference genome on the one hand, and target structural variants based on the DNA probes on the other. In contrast to such existing sequencing systems, in some implementations, segment-read alignment systems facilitate a more computationally efficient approach by using dedicated sequencing devices to determine nucleotide reads with candidate segment groups—without using or with fewer genotyping microarrays for targeting structural variants—to determine variant detection corresponding to primary assembly regions of the structural variant or the reference genome. Therefore, by determining segment group scores for candidate segment groups that include fragment alignments from nucleotide reads and selecting predicted segment groups from candidates based on such segment group scores, segment-read alignment systems can eliminate some or all of the genotyping microarrays used for structural variants.

[0038] As illustrated in the above discussion, this disclosure utilizes various terms to describe the features and advantages of segment-read alignment systems. Additional details regarding the meaning of such terms are now provided. For example, as used herein, the term "nucleotide read" (or simply "read") refers to a sequence of one or more nucleobases (or nucleobase pairs) inferred from all or part of a sample nucleotide sequence (e.g., a sample genomic sequence, cDNA). In particular, a nucleotide read includes a sequence of nucleobases detected from a nucleotide sequence (or a set of monoclonal nucleotide sequences) determined or predicted based on a sample library fragment corresponding to the genomic sample. For example, in some cases, sequencing devices determine nucleotide reads by generating nucleobases that pass through nanopores in a nucleotide sample slide, by adding fluorescent tags, or based on clusters in a flow cell.

[0039] Nucleotide reads may include genomic nucleotide reads based on DNA sequences and transcriptomic nucleotide reads based on ribonucleic acid (RNA). As used herein, the term “genomic read” refers to a nucleotide read representing the inferred sequence of nucleosides (or nucleobase pairs) of genomic DNA (gDNA) derived from a sample. For example, a genomic read includes a read containing gDNA (i) extracted or derived from a sample and (ii) part of a sample library fragment corresponding to the sample. In some cases, a genomic read includes a read containing an adaptor sequence for transposase-accessible chromatin (ATAC) read determination, also referred to as an ATAC read. In some embodiments, a genomic read may include, but is not limited to, DNase 1 hypersensitive site (DNase) sequencing reads, formaldehyde-assisted separation (FAIRE) sequencing reads of regulatory elements, or Tet-assisted bisulfite (TAB) sequencing reads.

[0040] Conversely, as used herein, the term "transcriptome read" refers to a nucleotide read that represents a deduced sequence of nucleosides (or nucleobase pairs) complementary to or representing RNA extracted from a sample. For example, a transcriptome read includes a read containing cDNA (i) synthesized from or derived from RNA extracted from a sample by single-stranded messenger RNA (mRNA) or microRNA (miRNA), and (ii) is part of a sample library fragment corresponding to the sample. As another example, a transcriptome read includes a read containing RNA (e.g., mRNA, miRNA, transfer RNA (tRNA)) that (i) is extracted from or derived from RNA extracted from a sample, and (ii) is part of a sample library fragment corresponding to the sample.

[0041] Additionally, as used herein, the term "genomic coordinates" refers to a specific location or orientation of a nucleotide base within a genome (e.g., the genome of an organism or a reference genome). In some cases, genomic coordinates include an identifier of a specific chromosome within the genome and an identifier of the orientation of a nucleotide base within that specific chromosome. For example, one or more genomic coordinates may include a chromosome number, name, or other identifier (e.g., chr1 or chrX) and one or more specific locations, such as a numbered position following the chromosome identifier (e.g., chr1:1234570 or chr1:1234570-1234870). Furthermore, in some specific implementations, genomic coordinates refer to the source of a reference genome (e.g., mt of a mitochondrial DNA reference genome or SARS-CoV-2 of a SARS-CoV-2 virus reference genome) and the location of a nucleotide base within that source of the reference genome (e.g., mt:16568 or SARS-CoV-2:29001). In contrast, in some cases, genomic coordinates refer to the location of a nucleotide base within a reference genome without referencing a chromosome or source (e.g., 29727).

[0042] As used herein, "genomic region" refers to the extent of genomic coordinates. Similar to genomic coordinates, in some implementations, genomic regions can be identified by chromosome identifiers and one or more specific orientations, such as the numbered orientation following the chromosome identifier (e.g., chr1:1234570-1234870). In various implementations, genomic coordinates include orientations within a reference genome. In some cases, genomic coordinates are specific to a particular reference genome.

[0043] Furthermore, as used herein, the term "genome sample" refers to a target genome or a portion of a genome that has undergone sequencing. For example, a sample genome comprises nucleotide sequences (or copies of such isolated or extracted sequences) isolated or extracted from a sample organism. Specifically, a sample genome comprises a whole genome isolated or extracted (in whole or in part) from a sample organism and composed of nitrogenous heterocyclic bases. A sample genome may include fragments of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or other aggregated forms of nucleic acids or chimeric or hybrid forms of nucleic acids as described below. In some cases, the sample genome is present in a sample prepared or isolated by a kit and received by a sequencing device.

[0044] As used herein, the term "segmentation set" refers to a group of one or more fragment alignments corresponding to a nucleotide read. Specifically, a segmentation set comprises one or more chains of fragment alignments that form a segment alignment of a nucleotide read relative to a reference genome. For example, a segmentation set may include fragment alignments of one or more segments of a nucleotide read. Such fragment alignments may represent alignments of read segments from a single-terminal nucleotide read or from a pair of double-terminal nucleotide reads (e.g., a pairing). Relatedly, the term "candidate segmentation set" refers to a potential fragment alignment of a nucleotide read.

[0045] Furthermore, the term "predicted segment group" refers to a selected segment group representing the alignment of nucleotide reads. Specifically, the predicted segment group includes the segment group with the highest segment group score among the candidate segment groups corresponding to the nucleotide reads. In some implementations, the predicted segment group thus represents a prediction that the corresponding segment alignment is most likely to represent the true alignment of the nucleotide read to the reference genome. For example, in some cases described below, the predicted segment group may represent a segment read alignment corresponding to a true structural variant in the sequenced genome sample.

[0046] As used herein, the term "segment group score" refers to a numerical score, metric, or other quantitative measure that indicates the accuracy of fragment alignments within a segment group. For example, a segment group score indicates the probability that a given segment alignment within one or more fragment alignments in a candidate segment group is correct relative to a reference genome. For example, as explained below, a segment group score can reflect a combination of fragment alignment scores, breakage penalties, overlap penalties, and, in some cases, gap penalties for fragment alignments within a segment group.

[0047] As used herein, the term "fragment alignment" refers to a candidate local alignment of a given fragment of a nucleotide read relative to a reference genome. For example, fragment alignment indicates the genomic region or genomic coordinates of the reference genome to which the read is aligned.

[0048] As used further herein, the term "alignment score" refers to a numerical score, metric, or other quantitative measure that assesses the accuracy of an alignment between a nucleotide read or fragment of a nucleotide read and another nucleotide sequence from a reference genome. Specifically, an alignment score includes a measure indicating the degree to which the nuclei of a nucleotide read (or fragment of a nucleotide read) match or are similar to a reference sequence or alternative continuous sequence from a reference genome. In some specific implementations, the alignment score takes the form of a Smith-Waterman score or a variant or version of the Smith-Waterman score for local alignments, such as the various settings or configurations used by DRAGEN, Inc., for the Smith-Waterman score. Therefore, the term "fragment alignment score" refers to the alignment score of a fragment alignment of a nucleotide read. Thus, in a segmentation group comprising multiple fragment alignments, a fragment alignment score can be determined for each fragment alignment within the segmentation group.

[0049] Relatedly, the term "alternative continuous sequence" (or simply "alternative continuous") refers to a continuous sequence representing a population haplotype added to a linear reference genome (or other reference genome) at one or more specific genomic coordinates (e.g., uplifted to the linear reference genome). In some implementations, a graph reference genome may include alternating continuous sequences of genomic coordinates mapped to the primary assembly of the linear reference genome. For example, alternating continuous sequences may represent a population haplotype comprising an uplifted structural variant having two or more genomic coordinates corresponding to two or more flanking ends of the structural variant in the linear reference genome. In some cases, the hash table used for the graph reference genome includes identifiers that associate alternating continuous sequences representing structural variant haplotypes with genomic coordinates representing reference haplotypes from the primary assembly of the linear reference genome.

[0050] Accordingly, the term "alternative contiguous segment alignment score" refers to the alignment score between one or more read segments and an alternative contiguous sequence. Specifically, the alternative contiguous segment alignment score may include the alignment score of one or more inner read segments and one or more outer read segments of a nucleotide read with an alternative contiguous sequence. As explained below, in some cases, the alternative contiguous segment alignment score may replace or be used as a segmentation score.

[0051] As used further herein, the term "break penalty" refers to a numerical score, metric, or other quantitative measure that penalizes segment alignments that exhibit breaks between or within segment alignments within a segment group. Specifically, a break penalty may include a measure of penalizing segment alignments within a segment group to the extent (or proportionally) the degree to which a segment alignment exhibits a nucleobase break between segment alignments at the breakpoint. Thus, in some embodiments, the segment-read alignment system determines a relatively high break penalty for breaks between or within segment alignments of relatively large size or distance.

[0052] Relatedly, the term "breakpoint" refers to a break or space between nucleotide reads and / or fragments of nucleotide reads, where the nucleotide read is aligned to a different location within a reference genome. For example, a split alignment contains a breakpoint because when a fragment of a nucleotide read is aligned to a different location, resulting in a break or breakpoint between fragments of the nucleotide read, the fragment exhibits the highest score alignment (e.g., highest pair score) with the reference genome.

[0053] As used further herein, the term "overlap penalty" refers to a numerical score, metric, or other quantitative measure that penalizes fragment alignments that overlap within a nucleotide read within a segment group. Specifically, an overlap penalty can include a measure of penalizing fragment alignments of a segment group to the extent (or proportionally) to the degree to which the fragment alignments exhibit overlapping nucleotide bases within the nucleotide read. For example, a 150-base-pair nucleotide read may have at least two fragment alignments. A first alignment might align the leftmost 100 base pairs to one chromosome (e.g., Chr1) in a reference genome, and a second alignment might align the rightmost 100 base pairs to another chromosome (e.g., Chr2). Although the exemplary fragment alignments do not overlap within the reference genome, the first and second alignments may overlap by 50 base pairs within the nucleotide read. Therefore, an overlap penalty can represent a measure of penalizing such 50-base-pair overlap (or other exemplary overlaps of nucleotide bases) within a nucleotide read from the foregoing example.

[0054] As used further herein, the term "vacancy penalty" refers to a numerical score, metric, or other quantitative measure that penalizes a pair of fragment alignments based on vacancy between the two alignments within a nucleotide read. Specifically, a vacancy penalty can include a measure of how much (or proportionally) the size of vacancy exists between the fragment alignments within a nucleotide read, penalizing the alignment of a segment group. For example, a 150-base-pair nucleotide read may have at least two fragment alignments. A first alignment might align the leftmost 50 base pairs to a first set of genomic coordinates of a reference genome, and a second alignment might align the rightmost 50 base pairs to a second set of genomic coordinates of a reference genome. In contrast to the overlapping example described above, a nucleotide read may include a 50-base-pair vacancy between a first fragment corresponding to the first alignment and a second fragment corresponding to the second alignment within the nucleotide read. Therefore, a vacancy penalty can represent a measure of penalizing such a 50-base-pair vacancy between the first and second fragment alignments within a nucleotide read.

[0055] As used in this article, the term "segmentation alignment" refers to the alignment of different segments of a read with different regions in a reference genome. For example, segmentation alignment can refer to segmented reads or chimeric alignment.

[0056] As used further herein, the term "pair score" refers to a numerical score, metric, or other quantitative measure that assesses the accuracy of alignment between a candidate split pair and a nucleotide sequence from a reference genome. Specifically, a pair score includes a measure indicating the degree to which a candidate split pair is accurately aligned with a nucleotide sequence from a reference genome. More specifically, in some embodiments, the pair score indicates the likelihood that a candidate split pair includes a true pair of paired nucleotide reads. In practice, in some embodiments, the pair score represents the sum of the split scores for the respective candidate split pair minus a pairing penalty.

[0057] As used herein, the term "pairing penalty" refers to a numerical score, metric, or other quantitative measure that penalizes a pair of fragment alignments that are unlikely to be paired as duplicated-end reads. Specifically, the term pairing penalty refers to a measure indicating the probability or impossibility of a correct pairing of fragment alignments based on the geometry of two or more fragment alignments relative to a reference genome. For example, the pairing penalty may represent the logarithmic probability of the insert size between the two innermost fragment alignments based on an empirical insert distribution, or alternatively, a logarithmic P-value.

[0058] As used herein, the term “reference genome” refers to a digital nucleic acid sequence assembled as a representative example (or multiple representative examples) of genes and other genetic sequences of an organism. Regardless of sequence length, in some cases, a reference genome representation is defined as an exemplary set of genes or a collection of nucleic acid sequences in a digital nucleic acid sequence representing an organism. For example, a linear human reference genome could be GRCh38 (or another version of the reference genome) from the Genome Reference Consortium. While GRCh38 may include alternating continuous sequences representing alternating haplotypes, such as SNPs and small insertions and deletions (e.g., 10 or fewer base pairs, 50 or fewer base pairs), GRCh38 includes alternating haplotypes with restricted representations of population structural variants. In fact, the structural variants represented in GRCh38 include only those represented by the 11 individuals on which their library GRCh38 was constructed. Relatedly, the term “reference region” refers to a portion or a small portion of the reference genome. For example, a reference region could be a selected number of nucleobases (e.g., 150 bases) from the reference genome.

[0059] As used herein, the term "variant" refers to one or more nucleobases that do not align to, differ from, or vary from the corresponding nucleobase (or nucleotide base) in the reference sequence or reference genome. For example, variants include SNPs, indels, or structural variants, which indicate nucleobases in the sample nucleotide sequence that differ from the reference nucleobase at the corresponding genomic coordinates of the reference sequence. Similarly, "variant nucleobase detection" refers to the detection of a variant nucleobase at a specific genomic coordinate. Conversely, "non-variant nucleobase detection" refers to the detection of a non-variant (or matching reference) nucleobase at a genomic coordinate.

[0060] Additionally, as used herein, the term "nucleobase detection" (or sometimes simply "nucleotide base detection" or "base detection") refers to the determination or prediction of specific nucleobases (or nucleobase pairs) at genomic coordinates of oligonucleotides (e.g., nucleotide reads) or sample genomes during a sequencing cycle. Specifically, nucleobase detection may indicate: (i) determining or predicting the type of nucleobases within oligonucleotides incorporated into a nucleotide sample slide (e.g., read-based nucleobase detection); or (ii) determining or predicting the type of nucleobases present at genomic coordinates or regions within the genome, including variant or non-variant detection in digital output files. In some cases, for nucleotide reads, nucleobase detection includes determining or predicting nucleobases based on intensity values ​​produced by fluorescently tagged nucleotides of oligonucleotides added to a nucleotide sample slide (e.g., clusters in a flow cell). Alternatively, nucleobase detection includes determining or predicting nucleobases based on chromatographic peaks or current changes produced by nucleotides passing through nanopores in a nucleotide sample slide. In contrast, based on nucleotide reads corresponding to genomic coordinates, nucleobase detection may also include nucleosides at genomic coordinates of the sample genome in the final predicted variant detection file (VCF) or other base detection output file. Therefore, nucleobase detection can include base detections corresponding to genomic coordinates and a reference genome, such as indications of variants or non-variants corresponding to specific locations in the reference genome. In practice, nucleobase detection can refer to variant detection, including but not limited to single nucleotide variants (SNVs), insertions or deletions (indels), or base detections as part of structural variants. As presented above, a single nucleobase detection can be adenine (A), cytosine (C), guanine (G), thymine (T), or uracil (U).

[0061] As used further herein, the term "alignment file" refers to a digital file indicating a relative alignment or mapping of a nucleotide read to a nucleotide sequence of a reference genome or other reference nucleotide sequences. Specifically, an alignment file may include data indicating the relative mapping positions of the nucleotide read and the nucleotide sequence of the reference genome. In some embodiments, the alignment file includes or constitutes a Sequence Alignment / Map (SAM) file, a Binary Alignment Map (BAM) file, a FAST-All (FASTA) file, or a FASTQ file.

[0062] As used herein, the term "variant detection file" refers to a digital file that indicates or represents one or more nucleobase detections (e.g., variant detections) compared to a reference genome, along with other information about those nucleobase detections (e.g., variant detections). For example, a variant detection format (VCF) file is a text file format that contains information about variants at specific genomic coordinates, including metadata lines, header lines, and data lines, where each data line contains information about a single nucleobase detection (e.g., a single variant).

[0063] In some implementations, the segmentation-read alignment system or the corresponding sequencing system utilizes a detection generation model to determine nucleotide base detection (e.g., variant detection or genotype detection). As used herein, the term "detection generation model" refers to a probabilistic model that generates sequencing data based on nucleotide reads from a sample nucleotide sequence, including nucleotide base detection, variant detection, and / or genotype detection, as well as associated metrics. Therefore, in some cases, the detection generation model can be a variant detection generation model. For example, in some cases, the detection generation model refers to a Bayesian probabilistic model that generates variant detection based on nucleotide reads from a sample nucleotide sequence. Such a model can process or analyze sequencing metrics corresponding to read stacking (e.g., multiple nucleotide reads corresponding to a single genome coordinate), including mapping quality, base quality, and various hypotheses, including foreign reads, missing reads, co-detection, etc. The detection generation model can also include multiple components, including but not limited to different software applications or components for mapping and alignment, sorting, repeat labeling, calculating read stacking depth, and variant detection. In some cases, the detection generative model refers to the ILLUMINA DRAGEN model (e.g., DRAGEN variant detector or "DRAGEN VC") used for variant detection functions as well as mapping and alignment functions.

[0064] As used herein, for example, the term "configurable processor" refers to a circuit or chip that can be configured or customized to perform a particular application. For example, a configurable processor includes, for instance, an integrated circuit chip designed to be field-configured or customized by an end-user's computing device to perform a particular application. Configurable processors include, but are not limited to, ASICs, ASSPs, coarse-grained reconfigurable arrays (CGRAs), or FPGAs. In contrast, configurable processors do not include CPUs or GPUs. In some implementations, segment-read alignment systems use configurable processors (e.g., FPGAs) or processors (e.g., CPUs) to perform the various implementations described herein.

[0065] The following paragraphs describe the segment-read comparison system with illustrative drawings depicting exemplary specific implementations and schemes. For example, Figure 1A schematic diagram illustrating a computing system 100 in which a segmentation-read alignment system 106, according to one or more specific implementations, operates is shown. As illustrated, the computing system 100 includes one or more server devices 102 connected via a network 112 to a user client device 108, a local device 118, and a sequencing device 114. The network 112 may include any suitable network on which the computing devices can communicate. Reference is made below. Figure 16 The exemplary network is discussed in more detail.

[0066] like Figure 1 As shown, the computing system 100 includes a server device 102. In various embodiments, the server device 102 can generate, receive, analyze, store, and transmit digital data, such as data for nucleotide detection or sequenced nucleic acid polymers. In some embodiments, the server device 102 receives various data from the sequencing device 114, such as data from sample genomes and / or nucleotide reads. The server device 102 can also communicate with a user client device 108. In particular, the server device 102 can send data on nucleotide reads, direct nucleotide detection, nucleotide detection, and / or sequencing metrics to the user client device 108.

[0067] As shown in the figure, server device 102 includes a sequencing system 104. Typically, sequencing system 104 analyzes data received from sequencing device 114 or elsewhere (e.g., detection data) to determine the nucleotide sequence of nucleic acid polymers. For example, sequencing system 104 may receive raw data from sequencing device 114 and determine the nucleotide sequence of a sample genome or nucleic acid strand. In some specific implementations, sequencing system 104 determines the sequence of nucleotides in DNA and / or RNA strands or oligonucleotides.

[0068] As also shown in the figure, sequencing system 104 includes a segmentation-read alignment system 106. As described below, segmentation-read alignment system 106 determines segmentation-read alignments of nucleotide reads with reference genome 116. For example, in some embodiments, segmentation-read alignment system 106 identifies one or more nucleotide reads corresponding to genomic regions of the genome sample. Segmentation-read alignment system 106 further (i) determines candidate segment groups comprising fragment alignments corresponding to one or more nucleotide reads, and (ii) generates segment group scores for segmentation alignments of candidate segment groups with reference genome 116. Based on the segment group scores, segmentation-read alignment system 106 selects predicted segment groups from the candidate segment groups for nucleotide detection.

[0069] like Figure 1As further illustrated, the computing system 100 includes a user client device 108. In various specific embodiments, the user client device 108 can generate, store, receive, and transmit digital data. In particular, the user client device 108 can receive data from the sequencing device 114. As further illustrated, the user client device 108 includes a sequencing application 110. The sequencing application 110 can be a web application or a native application (e.g., a mobile application, desktop application, or web application) stored and executed on the user client device 108. The sequencing application 110 can receive data from the sequencing system 104 and / or the segment-read alignment system 106. For example, the user client device 108 can receive variant detection files and / or alignment files from the sequencing system 104.

[0070] The sequencing application 110 may also include instructions (when executed) that cause the user client device 108 to receive data from the segmentation-read alignment system 106 and present data from the sequencing device 114 and / or the server device 102. Furthermore, the sequencing application 110 may instruct the user client device 108 to display nucleobase detection data relative to the reference genome 116, such as nucleobase detection or segmentation alignment instructions from variant detection files or alignment files. In practice, the user client device 108 may display nucleobase detection results and / or indications of predicted segmentation groups for a genome sample.

[0071] like Figure 1 As further illustrated, the computing system 100 optionally includes a sequencing device 114. In various embodiments, the sequencing device 114 can sequence genomic samples or other nucleic acid polymers. For example, the sequencing device 114 analyzes nucleic acid segments or oligonucleotides extracted from genomic samples to generate data directly or indirectly on the sequencing device 114. More specifically, the sequencing device 114 receives and analyzes nucleic acid sequences extracted from genomic samples within a nucleotide sample slide (e.g., a flow cell). In one or more embodiments, the sequencing device 114 utilizes SBS to sequence genomic samples or other nucleic acid polymers. As a complement or alternative to communication across network 112, in some embodiments, the sequencing device 114 bypasses network 112 and communicates directly with the user client device 108.

[0072] like Figure 1As further described, in some specific embodiments, server device 102 includes a distributed collection of servers, comprising several server devices distributed across network 112 and located in the same or different physical locations. For example, server device 102 may be implemented wholly or partially on local device 118. For illustration, local device 118 may implement sequencing system 104 and / or segment-read alignment system 106. Furthermore, server device 102 and / or local device 118 may include content servers, application servers, communication servers, web hosting servers, or other types of servers.

[0073] Figure 1 The user client device 108 illustrated herein may include various types of client devices. For example, in some embodiments, user client device 108 includes non-mobile devices such as desktop computers or servers, or other types of client devices. In various embodiments, user client device 108 includes mobile devices such as laptops, tablets, mobile phones, or smartphones. Additional details regarding user client device 108 are incorporated herein by reference. Figure 16 Discussion.

[0074] Furthermore, although the segment-read alignment system 106 is displayed on server device 102 as part of sequencing system 104, in some implementations, the segment-read alignment system 106 is implemented on user client device 108, sequencing device 114, and / or local device 118 (e.g., wholly or partially located thereon). As mentioned, in some implementations, the segment-read alignment system 106 is implemented through one or more other components of computing system 100, such as sequencing device 114. In particular, the segment-read alignment system 106 can be implemented in a variety of different ways across server device 102, network 112, user client device 108, local device 118, and sequencing device 114.

[0075] although Figure 1 The illustration shows components of computing system 100 that communicate via network 112; however, in some embodiments, components of computing system 100 may also communicate directly with each other, bypassing network 112. For example, in some embodiments, user client device 108 communicates directly with sequencing device 114. Additionally, in some embodiments, user client device 108 communicates directly with segment-read alignment system 106 and / or server device 102. In some embodiments, user client device 108 communicates directly with local device 118. Furthermore, segment-read alignment system 106 may access one or more databases located on or accessed by server device 102 or other locations within computing system 100.

[0076] Figure 2 An overview of a segment-read alignment system 106 is provided, which identifies alignment scores for one or more fragments from nucleotide reads in candidate segmentation groups according to one or more embodiments, and selects predicted segmentation groups from candidates based on such scores for base detection. Typically, and as... Figure 2 As illustrated, the segment-read alignment system 106 performs a series of actions 200, including the action 202 of identifying one or more nucleotide reads. The segment-read alignment system 106 also performs the action 204 of determining candidate segment groups for segment alignment of fragments containing nucleotide reads. The segment-read alignment system 106 further performs the action 206 of generating segment group scores for the determined candidate segment groups and the action 208 of selecting predicted segment groups.

[0077] like Figure 2 As illustrated, the segment-read alignment system 106 performs the action 202 of identifying one or more nucleotide reads. Specifically, the segment-read alignment system 106 identifies one or more nucleotide reads corresponding to genomic regions of a genome sample. For example, the segment-read alignment system 106 may identify nucleotide reads corresponding to a template strand or sequence of the genome sample. More specifically, the template comprises a raw, continuous DNA or RNA fragment sequenced using a single-end or paired-end method. In the single-end method, a single read is sequenced starting from one end of the template. Because a single-end read is sequenced starting from one end of the template, this single read represents a complementary sequence to the template. In the paired-end method, a first read (e.g., R1) is sequenced from one end of the template toward the middle, and a second read (e.g., R2) is sequenced starting from the other end. Figure 2 This illustration shows two double-ended readings, R1 and R2, oriented toward each other. As illustrated, there is a gap between R1 and R2; however, there may also be overlap between them. R1 and R2 can be described as double-ended pairs.

[0078] like Figure 2 As further illustrated, the segment-read alignment system 106 performs the action 204 of determining candidate segment groups. Specifically, the segment-read alignment system 106 determines candidate segment groups that include fragment alignments corresponding to one or more nucleotide reads. Typically, fragment alignment refers to candidate local alignments of fragments of a read. Figure 2 A segment-read alignment system 106 is illustrated to determine candidate segment groups for R1. R1 can be a single-end read or one of two paired-end reads. R1 can contain one or more distinct segments.

[0079] To illustrate the comparison between segments, Figure 2A segmentation-read alignment system 106 is shown to identify various fragments of nucleotide reads. As illustrated, the segmentation-read alignment system 106 identifies fragments 218, 220, 222, and 224 corresponding to R1. Figure 2 The illustrated fragments are separated by fractures representing structural variants (or "SVs") breakpoints. Although Figure 2 An example is shown of R1 broken by a single SV breakpoint, but nucleotide reads may not have SV breaks or may have several SV breaks. For example, fragment 220 may be further broken into two or more fragments.

[0080] like Figure 2 As further illustrated, the segment-read alignment system 106 identifies candidate segment groups 214a-214c for R1. As part of action 204, the segment-read alignment system 106 identifies the segment alignment of the identified fragments of the reads. Typically, fragments of the reads can be aligned to different sequences in the reference genome. For example, fragments 218 and 220 can be aligned to nearby genomic regions of the reference genome on the same chromosome. Conversely, fragment 218 can be aligned to the reference genome on one chromosome, while fragment 220 can be aligned to the reference genome on another chromosome.

[0081] Figure 2 Examples of candidate fragment alignments corresponding to R1 as part of a segmentation group are illustrated. More specifically, candidate segmentation groups 214a-214c show candidate local alignments of different combinations of fragments 218-222 of R1 on the reference genome. For example, candidate segmentation group 214a includes candidate fragment alignments of fragments 218 and 220 relative to the reference genome. Figures 3A to 4 A segmentation-read alignment system 106 for determining candidate segmentation groups of single-terminal and double-terminal nucleotide reads according to one or more embodiments is illustrated and further described in detail in the corresponding paragraphs.

[0082] like Figure 2 As further illustrated, the segment-read alignment system 106 performs the action 206 of generating segment group scores. Typically, the segment-read alignment system 106 generates segment group scores based on the alignment of candidate segment groups with the reference genome. The segment-read alignment system 106 can generate segment group scores based on fragment alignment scores, breakage penalties, and overlap penalties. As illustrated, the segment-read alignment system 106 generates a segment group score of 0.98 for candidate segment group 214a and a segment group score of 0.73 for candidate segment group 214b. Figure 5 Examples and corresponding discussions are provided to offer additional details on determining the score for the segmentation group according to one or more implementation schemes.

[0083] After determining the scores for the split groups, such as Figure 2As further shown, the segment-read alignment system 106 performs the action 208 of selecting a predicted segment group. The segment-read alignment system 106 selects a predicted segment group from candidate segment groups based on the segment group score. For illustration, in some embodiments, the segment-read alignment system 106 selects candidate segment group 214a as the predicted segment group based on the candidate segment group 214a with the highest segment group score.

[0084] As mentioned, the segment-read alignment system 106 can generate predicted segment groups for single-end reads and double-end reads. In some implementations, the segment-read alignment system 106 predicts segment groups in part based on the pair scores of candidate segment group pairs. Figures 6A to 6B An example is illustrated by a segment-read comparison system 106 for generating scores based on one or more implementation schemes.

[0085] As previously mentioned, the segment-read alignment system 106 identifies candidate segment groups for single-terminal and double-terminal nucleotide reads. Figure 3A An example is illustrated by a segmentation-read alignment system 106 for determining candidate segmentation groups of single-terminal nucleotide reads according to one or more embodiments, and Figure 3B A segmentation-read alignment system 106 for determining candidate segmentation groups of paired-terminal nucleotide reads according to one or more embodiments is illustrated.

[0086] Figure 3A A segment-read alignment system 106 is illustrated for identifying candidate segments in single-terminal nucleotide reads. As previously mentioned, single-terminal read sequencing involves sequencing DNA or RNA from one direction. Typically, the segment-read alignment system 106 identifies fragments of nucleotide reads. For illustration, the segment-read alignment system 106 identifies fragments 320, 322, 324, and 326. When aligned with a reference genome 334a, the illustrated fragments are separated by potential breakpoints.

[0087] The segmentation-read alignment system 106 identifies candidate segmentation groups 332a-332c for the identified fragments. Typically, candidate segmentation groups 332a-332c include all actual fragment alignments. In other words, candidate segmentation groups 332a-332c include alignments of read fragments with potential fragments in the reference genome 334b. For example, candidate segmentation group 332a includes alignments of fragments 320 and 322 relative to the reference genome 334b. Candidate segmentation group 332b includes overlapping fragment arrangements of fragments 320 and 322. Candidate segmentation group 332c includes alignments of fragments 320 and 326 relative to the reference genome 334b.

[0088] Although Figure 3ACandidate segment groups 332a-332c are illustrated, but other candidate segment groups are possible. For example, a candidate segment group may include a single fragment alignment of a single segment of a nucleotide read. For example, in some embodiments, the fragment may be an entire nucleotide read. Furthermore, a candidate segment group may include more than two fragment alignments. For example, a candidate segment group may include fragment alignments of three or more segments of a nucleotide read.

[0089] As mentioned, Figure 3B A segment-read alignment system 106, according to one or more embodiments, is illustrated for determining candidate segmentation groups of nucleotide reads in paired-end sequencing. Typically, paired-end sequencing of a sequence involves generating paired nucleotide reads starting at different (and opposite) positions in the library template. Specifically, paired-end sequencing generates two mating reads. For example, Figure 3B R1 and R2, as illustrated, include paired mating items. As mentioned, there may be gaps between R1 and R2, or the two-terminal reads may overlap.

[0090] In some cases, one paired-end pair crosses a breakpoint (e.g., the SV breakpoint), while the other paired-end pair does not. For illustration, R2 can cross a breakpoint, while R1 cannot. Therefore, R2 can be segmented into fragments 302 and 304, while R1 remains intact as fragment 316. In this example, the 3' end of R2 (e.g., the inner end of fragment 302) is correctly aligned with the paired end of intact fragment 316, while fragment 304 could potentially align to a different genomic region of the reference genome.

[0091] In another example, R1 and R2 may overlap and both cross a single breakpoint. For illustration, breakpoints 336a and 336b may represent the same breakpoint. In this example, segment 318 of R1 overlaps with segment 302 of R2, and segment 320 of R1 is represented by segment 304 of R2.

[0092] In another example, R1 and R2 cross different breakpoints. For example, breakpoint 336a may represent a different breakpoint than breakpoint 336b. Therefore, R1 is segmented into segments 318 and 320, while R2 is segmented into segments 310 and 312.

[0093] The segment-read alignment system 106 assumes the above situation by generating candidate segment groups for both R1 and R2. For example... Figure 3BAs illustrated, the segment-read alignment system 106 generates candidate segment sets 324a-324c corresponding to R1 relative to the reference genome 327. The segment-read alignment system 106 also generates candidate segment sets 340a, 340b, and 340c corresponding to R2 relative to the reference genome 314. In some specific embodiments, the reference genome 327 and the reference genome 314 represent the same reference genome. Candidate segment sets 324a-324c and candidate segment sets 340a-340c include fragment alignments corresponding to the relevant nucleotide reads (i.e., R1 or R2).

[0094] As previously mentioned Figure 3A As mentioned, in some embodiments, candidate segment sets comprise strands of alignments for a single nucleotide read. As described above, nucleotide reads and fragment alignments can have various nucleobase lengths. As illustrated, the segment-read alignment system 106 can determine candidate segment sets comprising alignments for an entire nucleotide read. For example, candidate segment set 324a comprises an alignment of a complete fragment 316 containing the entire R1 relative to a reference genome 327. In contrast, candidate segment sets can also comprise overlapping fragment alignments. For example, candidate segment sets 324c for R1 and 340c for R2 comprise overlapping fragment alignments. The segment-read alignment system 106 can also determine non-overlapping candidate segment sets. For example, candidate segment sets 324b for R1 and 340a for R2 comprise non-overlapping fragment alignments. Furthermore, the segment-read alignment system 106 can generate candidate fragments comprising strands containing more than two fragment alignments. Additionally, candidate fragments can comprise fragment alignments with different geometric orientations relative to the reference genome.

[0095] Figures 3A to 3B A segment-read alignment system 106 is illustrated, which generates candidate segments for single-terminal and double-terminal nucleotide reads. In some implementations, the segment-read alignment system 106 utilizes dynamic programming to efficiently generate and evaluate all possible segment alignment sequences. Figure 4 An example and corresponding discussion are provided to describe a segment-read alignment system 106 that utilizes dynamic programming to generate and evaluate candidate segment groups according to one or more implementations.

[0096] In some implementations, by utilizing dynamic programming, the segment-read alignment system 106 considers a subset of each possible candidate segmentation group. More specifically, the segment-read alignment system 106 identifies a subset of possible candidate segmentation groups by evaluating fragment alignments in a specific order. For illustration, in some implementations, the segment-read alignment system 106 determines candidate segmentation groups by iteratively grouping the fragment alignments in the order they are aligned from the outermost to the innermost nucleotide read. The segment-read alignment system 106 also iteratively scores the groupings of the fragment alignments in the order they were grouped.

[0097] Typically, each read segment has two ends, a 3' end or a 5' end, where the 3' end is designated as "inner" and the 5' end as "outer". For double-ended reads, the terms inner and outer refer to their expected relative positions in the template. For single-ended or double-ended reads with a forward-backward (FR) pair orientation, the 3' end represents the inner end and the 5' end represents the outer end. The segment alignment system 106 dynamically determines the inner and outer read segments when a backward-forward (RF) or forward-forward (FF) / backward-backward (RR) pair orientation is expected. In particular, the segment alignment system 106 designates the innermost and outermost segment alignments based on the geometry of the pair of correct segment alignments that have the highest total alignment score observed.

[0098] Figure 4 The process of dynamic programming performed by the segment-read alignment system 106 is illustrated. For illustrative purposes, fragment alignments 402-410 are organized in a Smith-Waterman matrix. In addition to depicting the position of the fragment alignments relative to the nucleotide reads and the reference genome, the Smith-Waterman matrix also shows the orientation of fragment alignments 402-410. For example, as depicted, fragment alignment 406 represents a forward alignment, while fragment alignment 408 represents a reverse complementarity alignment. Figure 4 Fragment alignments 402-410 are depicted as perfect, vacancy-free diagonal alignments; however, individual fragment alignments within fragment alignments 402-410 may contain indels (insertions and / or deletions). In some embodiments, such indels are relatively small variants (e.g., <50 base pairs), in contrast to the size of structural variants (e.g., >50 base pairs). Small indels are typically aligned within fragment alignments, while structural variants are typically described or depicted through multi-fragment segment-read alignments.

[0099] like Figure 4As illustrated, fragment alignment 402 represents the innermost fragment alignment, and fragment alignment 410 represents the outermost fragment alignment. As illustrated, the segment-read alignment system 106 begins by grouping the outermost fragment alignment with the next-outermost fragment alignment. For example, the segment-read alignment system 106 groups fragment alignment 410 with fragment alignment 408. The grouping of fragment alignment 410 and fragment alignment 408 constitutes candidate segmentation group 412a.

[0100] After grouping the outermost segment alignment and the second outermost segment alignment (and determining their segmentation group scores), the segmentation-reading alignment system 106 groups the outermost segment alignment and the second-second outermost segment alignment (and determines their segmentation group scores). Therefore, the segmentation-reading alignment system 106 groups segment alignment 410 and segment alignment 406. The grouping of segment alignment 410 and segment alignment 406 constitutes candidate segmentation group 412b.

[0101] In some specific implementations, as just indicated, the segment-read alignment system 106 generates segment group scores by iteratively scoring the groups of each segment alignment according to the order in which the segments are grouped. For example... Figure 4 As illustrated, the segment-read comparison system 106 scores the candidate segment groups in the order they form candidate segment groups 412a and 412b. For example, the segment-read comparison system 106 determines a segment group score 414a for candidate segment group 412a and a segment group score 414b for candidate segment group 412b. In some cases, segment group score 414b is greater than segment group score 414a. As indicated below, a better segment group score can influence the order in which the next candidate segment group is determined (and scored).

[0102] In some implementations, candidate segment groups 412a and 412b represent partial segment groups. Typically, a partial segment group includes one or more fragment alignments that represent fragment alignments of portions rather than entire nucleotide reads. The segment-read alignment system 106 may associate additional fragment alignments with the partial segment groups. For example, in some implementations, as part of dynamic programming, the segment-read alignment system 106 associates additional fragment alignments with the partial segment group that has the highest segment group score. By associating additional fragment alignments with the highest-scoring partial segment group, the segment-read alignment system 106 reduces the processing power required for exhaustively generating candidate segment groups.

[0103] Despite Figure 4Not shown, but after grouping fragment alignments 410 and 406 into candidate segment groups 412b (and determining their segment group scores), the segment-read alignment system 106 groups (and determines their segment group scores) additional candidate segment groups including fragment alignments 410, 408, and 406. If the segment group score 414b of candidate segment group 412b exceeds the additional segment group scores of other candidate segment groups, the segment-read alignment system 106 continues to group (and determine their segment group scores) candidate segment groups including combinations of fragment alignments 410 and other fragment alignments. For example, the segment-read alignment system 106 (i) groups (and determines their segment group scores) fragment alignments 410, 406, and 404, and (ii) groups (and determines their segment group scores) fragment alignments 410 and 404.

[0104] Additionally, as part of considering candidate segment groups, the segment-read alignment system 106 may also consider individual segment alignments. As explained above, in some embodiments, the segment-read alignment system 106 also considers individual segment alignments in order from the outermost segment alignment to the innermost segment alignment. Before or after considering candidate segment group 412a, for example, the segment-read alignment system 106 may identify candidate partial segment groups including segment alignment 410. The segment-read alignment system 106 generates a partial segment group score for segment alignment 410. The segment-read alignment system 106 then compares the partial segment group score with other segment group scores (such as segment group score 414a of candidate segment group 412a). Therefore, in addition to candidate segment groups including new or additional segment alignments, in some embodiments, the segment-read alignment system 106 also identifies candidate partial segment groups including new or additional segment alignments (and determines their segment group scores).

[0105] like Figure 4 As further illustrated, the segment-read alignment system 106 generates candidate segment groups 412n, including segment alignment 402, segment alignment 406, and segment alignment 410. The segment-read alignment system 106 adds segment alignment 402 to the candidate segment group 412b because, in this example, candidate segment group 412b has the highest-scoring segment group score, i.e., segment group score 414b. The segment-read alignment system 106 scores the candidate segment group 412n and assigns segment group score 414n. In this way, the segment-read alignment system 106 iterates from the outermost segment alignment towards the innermost segment alignment. For each segment considered, the segment-read alignment system 106 finds the best next segment alignment (i.e., the next, outer-ward, highest-scoring segment alignment).

[0106] If adding the next outer fragment alignment results in an improved segmentation group score, then the segmentation-read alignment system 106 retains the next outer fragment alignment as part of the candidate segmentation group. If adding the next outer fragment alignment does not result in an improved segmentation group score, then the segmentation-read alignment system 106 discards the next outer fragment alignment from the candidate segmentation group and moves forward to the next outer fragment alignment. By performing dynamic programming, the segmentation-read alignment system 106 accordingly continues to group candidate segmentation groups (and determine their segmentation group scores) in the order of the outermost fragment alignment to the innermost fragment alignment of the nucleotide read, until each candidate segmentation group is deemed unable to improve the highest segmentation group score or is eliminated because it cannot improve the highest segmentation group score.

[0107] As mentioned earlier, the segment-read comparison system 106 determines the segment group score of the candidate segment group. Figure 5 The segment-read alignment system 106 for determining segment group scores for candidate segment groups, according to one or more embodiments, is illustrated and further described in detail in the corresponding discussion. In some specific embodiments, the segment-read alignment system 106 determines the segment group score for a candidate segment group based on a fragment alignment score 502, a breakage penalty 506, and an overlap penalty 508. For example, the segment-read alignment system 106 can generate a segment group score by merging the fragment alignment scores 502 of fragment alignments within a candidate segment group and subtracting the breakage penalty 506 and the overlap penalty 508 from the merged fragment alignment scores.

[0108] As mentioned above, the segment-read alignment system 106 can assign a segment score to each candidate segment group. In some embodiments, a candidate segment group includes any strand of fragment alignments that follow certain rules. For example, a candidate segment group includes strands of one or more fragment alignments of the same read from the head fragment to the tail fragment. According to one embodiment of the rules, the head fragment is closest to the inner end of the nucleotide read, and the tail fragment is closest to the outer end of the nucleotide read. The inner vacancy of a fragment is its distance from the inner end of the nucleotide read, and the outer vacancy of a fragment is its distance from the outer end of the nucleotide read. For consecutive fragment alignments A and B, for example, the rule may be expressed as follows: i) A, inner vacancy B. Inner side_vacancy and (ii)A. Outer side_vacancy B. Outer gap. The same segment can be aligned to multiple segmentation groups.

[0109] like Figure 5As illustrated, the segment-read alignment system 106 generates segment alignment scores 502 for segment alignments A and B. As indicated above, segment alignment scores can include numerical scores, metrics, or other quantitative measures of the alignment accuracy of segment alignments from nucleotide reads. For example, a segment alignment score can indicate the probability that a given alignment of a segment is correct relative to a reference genome. As indicated above, such segment alignment scores can indicate the probability of a nucleotide read matching or being similar to a reference sequence (or an alternative continuous sequence) from a reference genome. For example, the segment-read alignment system 106 can assign segment alignment scores to individual segment alignments within a segment group by determining a Smith-Waterman score or a version of the Smith-Waterman score. In other embodiments, the segment-read alignment system 106 utilizes variations of segment alignment scores. As illustrated, the segment-read alignment system 106 combines (e.g., sums) the segment alignment scores of two segment alignments A and B within a segment group.

[0110] like Figure 5 As a further example, the segment-read comparison system 106 determines the breakage penalty 506. Figure 5 The segmentation-read alignment system 106 is illustrated with three factors for generating a break penalty 506—segment alignment orientation, same reference sequence, and effective indel length. As described above, in some embodiments, the break penalty 506 represents a measure of penalizing segment alignments of a segment group based on the degree to which the relative geometry of the aligned segments exhibits nucleobase breaks. More specifically, the break penalty 506 indicates the relative geometry of segment alignments A and B relative to the reference genome. In some embodiments, and as... Figure 5 As illustrated, the segment-read alignment system 106 determines a breakage penalty 506 based on the segment alignment orientation. For example, segment alignment orientation refers to whether the segment alignment has a forward or reverse orientation. For illustration, in some cases, the expected orientation of the dual-end template is that the two segment alignments point towards each other. For example, the segment-read alignment system 106 determines the breakage penalty 506 based on whether segment alignments A and B have opposite orientations or are reversed.

[0111] In some implementations, if fragment alignments A and B have opposite orientations, the split-read alignment system 106 determines a reverse penalty (e.g., denoted as a split-inv-pen). If fragment alignments A and B do not have opposite orientations, the split-read alignment system 106 does not specify such a reverse penalty.

[0112] In addition, and as Figure 5As illustrated, the split-read alignment system 106 determines a break penalty 506 based on whether the fragment alignments are located within the same reference sequence of the reference genome. For illustration, if fragment alignments A and B are aligned to different reference sequences of the reference genome, the split-read alignment system 106 may associate a maximum break penalty (e.g., denoted as "split-max-pen"). The maximum break penalty may include predetermined values ​​for DNA and RNA. For example, based on the determination that fragment alignments A and B are aligned to different reference sequences, the split-read alignment system 106 assigns a penalty of 36 points to the DNA fragment alignment and a penalty of 20 points to the RNA fragment alignment when determining the split set score. If fragment alignments A and B are aligned to the same reference sequence, in some embodiments, the split-read alignment system 106 calculates the effective indel length (indelLen) as the absolute difference between the alignment diagonals at their facing ends.

[0113] like Figure 5 As further illustrated, the segment-read alignment system 106 determines the breakage penalty 506 based on the effective indel length. In some specific implementations, the segment-read alignment system 106 reduces the breakage penalty 506 based on the indel length. For example, the segment-read alignment system 106 can reduce the overlap penalty via MIN(overlap, FLOOR(Log4(indelLen)), split-olap-ignore)). In some specific implementations, indelLen is equal to the indel length measured in nucleobase pairs. The segment-read alignment system 106 reduces the overlap penalty because (a) overlap implies sequence similarity at segment alignments A and B, which is common for SV breaks, and (b) many penalties for long-distance breaks come from an exponentially large number of potential breakage sites. However, when only breakage sites with sufficient sequence similarity to cause segment overlap are considered, the number of potential breakage sites is reduced to a smaller set.

[0114] In some implementations, the segment-read alignment system 106 can limit or disable overlap reduction by setting the segment-overlap-ignore value lower or to 0. When overlap reduction is allowed, the segment-read alignment system 106 can set the split-log2-coeff to at least 0.5, so that overlap breaks do not receive penalties that decrease rather than increase with distance.

[0115] Instead of determining the effective indel length, in some implementations, the segment-read alignment system 106 determines the break distance in the chromosome. In one example, the segment-read alignment system 106 determines the distance between the segment alignment start points within a reference genome and compares the distance between the segment alignment start points to the expected break distance. In another example, the segment-read alignment system 106 determines the distance between the nearest termination points of two segment alignments and compares that distance to the expected break distance.

[0116] Furthermore, in the segment alignment instance, the segment-read alignment system 106 determines an initial break penalty (e.g., denoted as a split-open-pen) before considering the effective indel length. In at least one example, the break penalty is equal to the larger of (i) the maximum break penalty or (ii) the break penalty determined based on the reverse penalty (invPen) and the indel length (indelLen). For illustration, the break penalty is equal to MIN(split-max-pen, split-open-pen + invPen + FLOOR(split-log2 - coeff * Log2(indelLen))).

[0117] Figure 5 A segment-read alignment system 106 for determining an overlap penalty 508 is also illustrated. As described above, in some embodiments, the overlap penalty 508 represents a measure of penalizing segment alignments of a segment group for the degree of overlap within nucleotide reads in the segment alignments. For example, in some embodiments, the overlap penalty 508 is equal to the amount of overlap within reads between segment alignments A and B multiplied by a Smith-Waterman match score. As indicated above, segment alignment overlap can occur when a segment includes (and aligns with) overlapping nucleotide read bases from a nucleotide read. By determining the overlap penalty, the segment-read alignment system 106 avoids double counting of nucleotide bases within reads matching the reference genome in both segments of the segment alignment.

[0118] In some implementations, the segment-read alignment system 106 also determines additional penalties as part of determining the segment group score. For illustration, the segment-read alignment system 106 may determine a gap penalty. The gap penalty is complementary to the overlap penalty 508. More specifically, in some implementations, the gap penalty represents a numerical score, metric, or other quantitative measure that penalizes segment alignments of a segment group for the degree to which gaps exist between segment alignments. In some implementations, the gap penalty represents negative overlap, and the overlap penalty represents negative gaps.

[0119] As mentioned above, in some embodiments, the segment-read comparison system 106 generates segment groups and scores said segment groups by using dynamic programming. Therefore, in some embodiments, the segment-read comparison system 106 follows the procedure described above. Figure 4 The order of alignment from the outermost segment to the innermost segment, as illustrated in the example, is as follows: Figure 5 The example shows the segmentation scores for generating candidate segmentation groups.

[0120] In some specific implementations, and as previously mentioned, the segment-read alignment system 106 evaluates candidate segment pairs based on a score. More specifically, the segment-read alignment system 106 evaluates the comparison pairs of candidate segment pairs and selects a predicted segment pair based on the score. Figure 6A An example is illustrated by a segment-read comparison system 106 for generating scores based on one or more implementation schemes. Figure 6B An example is a segment-read alignment system 106 based on a score to determine a predicted segment group, according to one or more implementation schemes.

[0121] Figure 6A A segment-read alignment system 106 is illustrated, which generates pair scores based on segment group score 602 and pairing penalty score 608. In some specific embodiments, the segment-read alignment system 106 identifies candidate segment group pairs from candidate segment groups that include different fragment alignments of paired reads. For example, the segment-read alignment system 106 identifies candidate segment group pairs including segment group 604 and segment group 606. More specifically, segment group 604 includes fragment alignments A and B, and segment group 606 includes fragment alignments C and D. As shown, segment group 604 and segment group 606 are aligned with a reference genome. More specifically, segment group 604 and segment group 606 include candidate paired reads aligned along the reference genome. For example, segment group 604 may represent R1 and segment group 606 may represent R2 of paired reads.

[0122] like Figure 6A As further illustrated, the segment-read alignment system 106 generates a segment group score 602. As described above, in some embodiments, the score assesses the accuracy of the candidate segment group pair against the reference genome. In some specific embodiments, the segment group score 602 includes the sum of the segment group scores of the candidate segment group pair. For illustration, the segment-read alignment system 106 sums the segment group scores of segment group 604 and segment group 606 as some or all of the score.

[0123] like Figure 6AAs further illustrated, the segment-read alignment system 106 generates a pairing penalty 608 for candidate segment pairs. The segment-read alignment system 106 can determine the pairing penalty 608 based on the estimated insert size between the innermost fragment alignments of the candidate segment pairs. In some cases, fragment alignments corresponding to paired-end couples are located relatively close to each other in the reference genome. The segment-read alignment system 106 can determine a known empirical insert size distribution. In some embodiments, the segment-read alignment system 106 determines the known empirical insert size distribution by analyzing the insert sizes in the sequence library. The known empirical insert size distribution typically indicates the most likely insert size in the sequence library. Therefore, based on the empirical insert size distribution, if two innermost fragment alignments are located close together or at a expected distance from each other, the segment-read alignment system 106 can assign a pairing penalty of 0 or a small penalty.

[0124] For example, the segment-read alignment system 106 determines the estimated insert size 610 between the innermost segment alignments B and C. Figure 6A As indicated, the estimated insert size 610 includes the length of the library template from which the mating nucleotide reads are sequenced at each end. The split-read alignment system 106 compares the estimated insert size 610 with the expected insert size based on an empirical insert size distribution. In cases where the estimated insert size 610 is greater than or even smaller than the expected insert size, the split-read alignment system 106 assigns a larger pairing penalty to the candidate split pair. In some embodiments, the split-read alignment system 106 determines a fixed pairing penalty for candidate split pairs outside the expected insert size range. In other specific embodiments, the split-read alignment system 106 utilizes a sliding scale, where the split-read alignment system 106 adjusts the pairing penalty based on the difference between the estimated insert size 610 and the expected insert size.

[0125] In some examples, the estimated insert size is calculated to reflect the estimated total length of the library template strand obtained by sequencing at each end to obtain two paired-terminal nucleotide reads. For example, the two paired-terminal nucleotide reads include fragment alignments A, B, C, and D. In at least one specific embodiment, the insert size is estimated from the reference position of the termination point of the innermost fragment alignments B and C, and extrapolated to account for the outer portions of the two paired-terminal nucleotide reads not covered by fragment alignments B and C. For illustration, the split-read alignment system 106 may extrapolate to account for the outer portions including those covered by fragment alignments A and D. However, in Figure 6AIn the illustrated example, the segment-read alignment system 106 does not consider the reference positions of the outer segment alignments A and D because of the SV break between segment alignments A and B and between segment alignments C and D. Therefore, due to the SV break, the positions of segment alignments A and D do not provide strong information about the actual inserted segment size.

[0126] In some implementations, the segment-read alignment system 106 further adjusts the pairing penalty 608 based on the segment group location and segment group orientation. For example, the segment-read alignment system 106 may assign a larger pairing penalty to segment groups aligned to different chromosomes of the reference genome in candidate segment pairs. As mentioned, the segment-read alignment system 106 may also assign a larger pairing penalty based on the orientation of the segment groups. For example, if the segment alignments are oriented to the same orientation (e.g., both are oriented from 3' to 5' of the reference genome) rather than complementary orientations (e.g., pointing to each other), then the segment-read alignment system 106 assigns a larger pairing penalty to the candidate segment pairs.

[0127] In one or more embodiments, the segment-read alignment system 106 determines a pair score based on segment group scores 602 and pairing penalties 608. For illustration, in some specific embodiments, the segment-read alignment system 106 generates a pair score by subtracting the pairing penalty 608 from the sum of segment group scores 602.

[0128] As mentioned, in some cases, two paired-end reads overlap at the same breakpoint (e.g., the SV breakpoint). When overlapping pairs cross the breakpoint in their overlap region, each pair can be similarly segmented and aligned, each as two separate segments. In some embodiments, the segment-read alignment system 106 detects these “quads” as special cases and assigns a pair score involving only one copy of the break penalty (but two overlap penalties). When such a “quad” of the segmented overlapping alignment exhibits the highest pair score, the segment-read alignment system 106 selects the R1 and R2 segment alignments on the same side of the break as the primary alignments, i.e., a 5' segment alignment and a 3' segment alignment, to support a correct pairing. Typically, the segment-read alignment system 106 selects the higher-scoring 5' segment alignment along with the 3' segment alignment of the pair as the primary alignments.

[0129] In some implementations, the detection of quadruplets is somewhat restrictive. Corresponding fragments in the two matings need to be cut at the same SV break position, which often occurs unless sequencing errors intervene. Gaps or overlaps between fragments in each nucleotide read are allowed, but they must be identical in the two matings of the paired-end reads. If the split-read alignment system 106 cannot detect a perfect quadruplet, it outputs only three fragment alignments, omitting the lowest-scoring 3' fragment alignment.

[0130] As mentioned, in some implementations, the segment-read alignment system 106 selects the predicted segment group based on the score. Figure 6B An example and corresponding paragraph describe a segment-read alignment system 106 that selects predicted segmentation groups based on scores. As an overview, Figure 6B Example of a pair score 622 for candidate segment pairs 626a-626c. Candidate segment pair 626a includes segment 611 and segment 612. The empty boxes within the segment arrows in segment 612 represent breaks (e.g., SV breaks) between the fragment alignments constituting segment 612. In contrast to candidate segment pair 626a, candidate segment pair 626b includes segment 614 and segment 616. Finally, candidate segment pair 626c includes segment 618 and segment 620. As explained below, the segment-read alignment system (i) selects the candidate segment pair with the highest pair score, and (ii) selects the predicted segment for each pair of nucleotide reads from the candidate segment pair with the highest pair score.

[0131] In some cases, a candidate segment set with the highest segment set score may not necessarily exhibit the correct segment alignment. For example, a relatively high segment set score indicates a possible pattern of segment alignment for the nucleotide reads. However, such a relatively high segment set score may involve an unlikely pairing configuration of the two mating elements from a pair of paired terminal nucleotide reads. By generating a pair score in addition to the segment set score, the segment-read alignment system 106 further considers the pairing configuration of the fragment alignment of mating elements from paired terminal nucleotide reads when selecting a predicted segment set.

[0132] For illustration, segment group 614 may have the highest segment group score among segment groups 611-620. The segment-read alignment system 106 generates pair scores 622 for candidate segment group pairs 626a-626c. Based on the determination that the pair score of candidate segment group pair 626a exceeds that of candidate segment group pair 626b, in some cases, the segment-read alignment system 106 selects segment group 611 from candidate segment group pair 626a as the predicted segment group for a specific mate, instead of selecting segment group 614 from candidate segment group pair 626b.

[0133] In some implementations, the segment-read alignment system 106 generates a segment alignment map score (e.g., MAPQ) corresponding to the segment alignment that corresponds to the highest-scoring pair. From the perspective of a map quality metric (e.g., MAPQ), the segment alignment map score represents the confidence that a given segment alignment is part of a true alignment. A segment alignment map score is not conditioned on other segment alignments. The segment alignment map score is proportional to the difference between the highest-scoring pair and the second-highest-scoring pair that does not involve the segment alignment of interest.

[0134] In some implementations, the segment-read alignment system 106 can identify segments aligned with alternative contiguous (or "alternative contiguous") sequences within the reference genome. Figure 7 An example is illustrated of a segmentation-read alignment system 106 that scores the alignment of substituted contiguous fragments corresponding to nucleotide reads having substituted contiguous sequences according to one or more embodiments. As an overview, Figure 7 An example of a series of actions 700 is illustrated, including action 702 of determining an alternative contiguous segment alignment score, action 704 of determining a segmentation group score, and action 708 of selecting an alternative contiguous segment alignment score. If the alternative contiguous segment alignment score exceeds the segmentation group score of the segment alignment, the segmentation-read alignment system 106 reports a segment alignment with the segment alignment corresponding to the primary assembly of the alternative contiguous sequence.

[0135] Typically, the segment-read alignment system 106 identifies alternative continuous sequences representing structural variants. The segment-read alignment system 106 determines which fragments of a nucleotide read exhibit the highest segment alignment score against the alternative continuous sequence and therefore reports the segment alignment in the corresponding primary assembly region. For example, if the segment-read alignment system 106 determines that a segment alignment of a nucleotide read exhibits an alternative continuous fragment alignment score relative to the alternative continuous sequence, where the alternative continuous fragment alignment score exceeds the segmentation group scores of other candidate segmentation groups for the nucleotide read, then the segment-read alignment system 106 uses the alternative continuous fragment alignment score instead of other candidate segmentation group scores to improve the corresponding segment alignment (without any breakage penalty). Therefore, the alternative continuous fragment alignment score guides the segment-read alignment system 106 to select and report a given segment alignment, rather than other candidate segment alignments represented by other segmentation groups that might otherwise receive better scores in the absence of an alternative continuous fragment alignment score.

[0136] When the substituted contiguous sequence represents an SV break, for example, the segment-read alignment system 106 may recognize two major fragment alignments within the same lift group as a single substituted fragment alignment. In some cases, multiple major fragments within a lift group are treated as repetitions of each other, and only the alignment with the best score is retained. However, in cases where the nucleotide read match is a substituted contiguous sequence spanning an SV break, the segment-read alignment system 106 may retain both major fragment alignments and add them to a segment group using the alignment score from the substituted contiguous sequence.

[0137] like Figure 7 As shown, this series of actions 700 exemplifies a segment-read alignment system 106 that uses a scoring system to identify segment alignments representing structural variants when detecting alternative contiguous fragment alignments. Typically, the segment-read alignment system 106 determines when a boost group has two major fragment alignments—a 5' fragment alignment and a 3' fragment alignment—that extend beyond each other in the nucleotide reads. A boost group includes fragment alignments of alternative contiguous sequences to the same genomic region as the primary assembly region or a reference genome. In some specific implementations, the segment-read alignment system 106 determines that the 5' fragment alignment and the 3' fragment alignment exhibit alternative contiguous characteristics.

[0138] To identify such alternative contiguous fragment alignments, in some embodiments, the split-read alignment system 106 determines a split-alt-min-ext, by which the two primary fragment alignments must extend beyond each other in the nucleotide reads. The split-read alignment system 106 uses split-alt-min-ext to identify fragment alignments that qualify for alternative contiguous fragment alignment. In some embodiments, split-alt-min-ext includes a predetermined value (e.g., 20 bases); in other embodiments, the split-read alignment system 106 determines split-alt-min-ext based on user input. Generally, a higher split-alt-min-ext is more restrictive, making it less likely that the split-read alignment system 106 will identify alternative contiguous fragment alignments. In some embodiments, the split-read alignment system 106 sets split-alt-min-ext to 0 to disable lift-guided split alignments. For example, 5' fragment alignment must begin within the first split-alt-min-ext base of the nucleotide read. Compared to the 3' fragment, the 5' fragment must extend at least one split-alt-min-ext base towards the 5' end. Compared to the 5' fragment, the 3' fragment must extend at least one split-alt-min-ext base towards the 3' end. The best-scoring alignment in the boost group must be an alternative sequential alignment.

[0139] To determine whether a segment alignment with an alternative continuous sequence scores better than other candidate segmentation groups of nucleotide reads, the segment-read alignment system 106 can use... Figure 7 The scoring method described in the text. For example... Figure 7 As illustrated, the segment-read alignment system 106 performs action 702 to determine the alignment score of alternative contiguous fragments. The segment-read alignment system 106 determines the alignment scores of alternative contiguous fragments corresponding to the nucleotide reads: inner fragment alignment 712 (3' fragment) and outer fragment alignment 710 (5' fragment). For example... Figure 7 As indicated, both the inner fragment alignment 712 and the outer fragment alignment are aligned with the alternative continuous sequence 714 within the reference genome 718. The alternative continuous sequence 714 includes the alternative sequence of the primary assembly region 716 of the reference genome. Unlike some existing sequencing systems, the split-read alignment system 106 does not treat the inner fragment alignment 712 and the outer fragment alignment 710 as duplicates, but allows them to participate independently for scoring purposes, provided that they meet the requirement that the two fragment alignments must extend beyond each other's minimum length in the read (e.g., the split-alt-min-ext requirement).

[0140] In practice, in some implementations, the segment-read alignment system 106 determines the alternative contiguous segment alignment scores for the inner segment alignment 712 and the outer segment alignment 710 in the same manner as the segment alignment system 106 determines the segment alignment scores. For example, the segment-read alignment system 106 determines the alternative contiguous segment alignment scores by determining the Smith-Waterman score or a variant of the Smith-Waterman score.

[0141] In addition to determining the alternative consecutive fragment alignment score for each fragment alignment, the segment-read alignment system 106 also performs the action 704 of determining the segment group score. Specifically, the segment-read alignment system 106 determines the segment group scores for the inner fragment alignment 712 and the outer fragment alignment 710 of the primary assembly region 716 of the reference genome 718.

[0142] like Figure 7 As further shown, the segment-read alignment system 106 further performs the action 708 of selecting an alternative continuous segment alignment score. Typically, the segment-read alignment system 106 utilizes the best alignment score from the alternative continuous segment alignment score and the segment group score. Therefore, the segment-read alignment system 106 can replace the segment group score with the best alternative continuous segment score. Thus, the alternative continuous segment score becomes the replacement segment group score.

[0143] Based on the determination that the alternative contiguous segment alignment score exceeds the segmentation group score, the segmentation-reading alignment system 106 utilizes the alternative contiguous segment alignment score in the segment alignment process. In some embodiments, the segmentation-reading alignment system 106 further compares and determines that the alternative contiguous segment alignment score exceeds the scores of other segmentation groups for alignments of inner and outer segments with other primary assembly regions.

[0144] If the alternative contiguous fragment alignment score exceeds the segment group score of the fragment alignment, the segment-read alignment system 106 reports the relevant segment alignments, including the outer segment alignment 710 and the inner segment alignment 712. By reporting relevant segment alignments, the segment-read alignment system 106 effectively reports or indicates the alignment of the nucleotide read with the alternative contiguous sequence 714 itself. By utilizing the alternative contiguous fragment alignment score as a replacement segment group score, the segment-read alignment system 106 facilitates the selection of the segment group corresponding to the alternative contiguous sequence 714 rather than other candidate segment groups. In other words, the segment-read alignment system 106 assigns a higher score to the segment group of the primary assembly, inherited from the alternative contiguous sequence corresponding to the primary assembly. By using the alternative contiguous fragment alignment score as a segment group score, the segment-read alignment system 106 further increases the fragment alignment mapping score (e.g., MAPQ) corresponding to the fragment alignments within the segment group.

[0145] In some implementations, the segment-read alignment system 106 filters unreliable segment alignments by utilizing a threshold segment alignment score and a minimum alignment score. According to one or more implementations, Figures 8 to 9 An example of a segment-read alignment system 106 is shown, which removes candidate segment groups and identifies candidate segment groups that do not report alignments by using threshold segment alignment scores and minimum alignment scores, respectively.

[0146] Figure 8 An example is illustrated by a segment-read alignment system 106 that removes malformed candidate segment groups using segment alignment scoring according to one or more embodiments. As an overview, Figure 8 An example of a series of actions 800 is given, including action 802 determining that the segment alignment score fails to meet the threshold segment alignment score and action 804 removing the segment alignment.

[0147] like Figure 8As illustrated, the series of actions 800 includes action 802 determining that a segment alignment score fails to meet a threshold segment alignment score. Specifically, the segment-read alignment system 106 determines that the segment alignment score of a segment alignment corresponding to a candidate segment group fails to meet a threshold segment alignment score. The segment-read alignment system 106 may determine the threshold segment alignment score based on user input. Additionally or alternatively, the segment-read alignment system 106 generates a predetermined segment alignment score. The threshold segment alignment score may include the minimum segment alignment score used for segment alignments participating in the segment-read alignment. For example, the segment alignment score of segment alignment A may drop below the threshold segment alignment score.

[0148] like Figure 8 As further exemplified, the segment-read alignment system 106 performs the action 804 of removing segment alignments. More specifically, the segment-read alignment system 106 removes subthreshold segment alignments from consideration in forming candidate segment groups. For example, based on the determination that the segment alignment score of segment alignment A has dropped below the threshold segment alignment score, the segment-read alignment system 106 removes segment alignment A from consideration. Therefore, the segment-read alignment system 106 never forms a segment group that includes segment alignment A and segment alignment B. By removing subthreshold segment alignments from consideration, the segment-read alignment system 106 effectively filters unreliable segment alignments at the input, thereby ignoring them entirely. The threshold segment alignment score is primarily used for low-scoring inner (3') segments that can be included, as their correct pairing positions benefit from high scores due to low pairing penalties. Furthermore, in some specific implementations, the segment-read alignment system 106 also prevents subthreshold segment alignments from participating in any generated multi-segment alignment segment groups.

[0149] The segment-read comparison system 106 further reduces noise by utilizing minimum comparison score. Figure 9 An example is illustrated by a segment-read alignment system 106 that uses minimum alignment scores to identify candidate segment groups on which no alignments have been reported, according to one or more embodiments. As an overview, Figure 9 An example of a series of actions 900 is given, including action 902 determining that the alignment score of the candidate segmentation group fails to meet the minimum alignment score and action 904 prohibiting the reporting of segmentation alignments.

[0150] like Figure 9As illustrated, the segment-read alignment system 106 performs action 902, determining that the alignment score of a candidate segment group fails to meet the minimum alignment score. The alignment score of a candidate segment group refers to the alignment score of the entire segment group. In some implementations, the alignment score of a candidate segment group includes the segment group score. For example, the segment-read alignment system 106 determines that the segment group score of the candidate segment group 906 has dropped below the minimum alignment score. The segment-read alignment system 106 may determine the minimum alignment score based on user input or may predetermine the minimum alignment score.

[0151] Compared to existing sequencing systems, the segment-read alignment system 106 can report segment alignments even if the constituent fragment alignments have low alignment scores. For illustration, fragment alignment A and / or fragment alignment B may each have individual alignment scores below the minimum alignment score; however, the A+B segmentation score can be higher and exceed the minimum alignment score. In this case, the segment-read alignment system 106 can report the A+B segment alignment. In contrast, existing sequencing systems would filter out one or both of fragment alignments A and / or B for not meeting the minimum alignment score. Essentially, the segment-read alignment system 106 utilizes the generation of segmentation scores by splitting the threshold score into two separate parameters (threshold fragment alignment score and minimum alignment score). The threshold fragment alignment score filters out fragment alignments early by disqualifying sub-threshold fragment alignments from participating in segment alignment. The threshold fragment alignment score utilized by the segment-read alignment system 106 can be higher and more forgiving than the alignment scores utilized by existing sequencing systems. In some implementations, the segment-read alignment system 106 is configured to filter candidate segment groups only after low-scoring fragment alignments have had a chance to participate in candidate segment groups that could potentially achieve higher segment group scores. Therefore, the segment-read alignment system 106 retains the final minimum score, thereby achieving a similar level of noise filtering as existing sequencing systems, but in a manner that provides sensitivity to lower-scoring component fragment alignments that are part of a full read alignment.

[0152] The segment-read alignment system 106 further performs an action 904 to prohibit reporting segment alignments. Specifically, based on an alignment score that fails to meet the minimum alignment score, the segment-read alignment system 106 prohibits reporting segment alignments of candidate segment groups in the alignment file or variant detection file. For illustration, the segment-read alignment system 106 does not report candidate segment groups 906 as predicted segment groups.

[0153] In some implementations, even if the segment-read alignment system 106 does not report the candidate segment group 906, it still considers the candidate segment group 906 as a competitor to other alignments. If the highest pair score includes segment group scores lower than the lowest alignment score, the segment-read alignment system 106 returns an unmapped read. However, even if another alignment or segment group exhibits the highest pair score, the segment-read alignment system 106 may also lower the segment alignment mapping score (e.g., MAPQ) of the segment alignment if the pair score of the failing segment group is second best. As mentioned, from the perspective of mapping quality metrics (e.g., MAPQ), the segment alignment mapping score represents the confidence that a given segment alignment is part of (or mapped to) a true alignment.

[0154] In some implementations, the split-read alignment system 106 generates and stores a configuration registry as part of determining the split-read alignment. Previous discussions described registry entries, including split-log2-coeff, primary-5p, and others. The following table provides an overview of additional configuration registry entries defined by the split-read alignment system 106 according to one or more implementations.

[0155] Name (--Aligner.XXX) DNA default RNA default describe Primary-5p 0 0 The setting prioritizes the 5' segment alignment of the emission segmentation alignment, rather than the correctly matched segment alignment (usually 3'). Split-secondary 0 0 Configured to enable segmentation of secondary alignments, producing records with secondary and supplementary markers. Split-local-dist 0xFFFFFFFF 0xFFFFFFFF For the segmentation comparison break penalty, the maximum effective indel length is considered "locally", thus accepting the second largest penalty. Split-inv-pen 4 4 For segmentation alignment, an additional breakage penalty is used to change the orientation (reverse). Split-open-pen 8 4 For segment alignment, consider the initial breakage penalty before the effective indel length. Split-log-2-coeff 0.875 0.5 For the segmentation alignment break penalty, multiply the value by log2 of the effective indel length. Split-max-pen 36 20 Maximum segmentation comparison breakage penalty Frag-min-score 12 12 The minimum score used for segment alignment in segment-read comparison. This can be lower than the arn-min-score used for full segment-read scoring. Split-alt-min-ext 20 20 For segment alignments that use alternative lifting guidance, the alignment of the two principal segments of minimum length must extend beyond each other within the read segment. Split-olap-ignore 16 16 For interchromosomal breaks, or up to log4 of the effective indel length within a chromosome, no penalty is applied for maximum fragment alignment overlap.

[0156] In some implementations, the segment-read alignment system 106 assigns alignment tags indicating chain orientation to segment alignments. More specifically, the XS tag is defined as the original competing fragment score. In some implementations, the XS of a given segment alignment is the highest score of any other segment alignment that substantially overlaps with a given segment alignment from a nucleotide read (and therefore does not qualify as a segment alignment with a given segment alignment). In other implementations, the segment-read alignment system 106 determines that the XS of all non-minor segment alignments (both primary and supplementary) is the highest fragment score that does not participate in the winning or highest-scoring segment group. The XS of all minor alignments (both non-supplementary and supplementary) is the highest fragment score that participates in the winning segment group.

[0157] In some implementations, the segment-read alignment system 106 determines the nucleobase detection of genomic regions based on the alignment of the predicted segment group with the reference genome. Figure 10 An example is illustrated by a segmentation-read alignment system 106 for generating nucleobase detection and variant detection files according to one or more embodiments. As an overview, Figure 10 A series of actions 1000 are illustrated, including action 1002 of identifying nucleotide reads, action 1004 of aligning nucleotide reads with a reference genome, action 1006 of generating nucleobase detection, and the resulting variant detection file 1008.

[0158] like Figure 10 As illustrated, the segmentation-read alignment system 106 performs action 1002 to identify nucleotide reads. In one or more embodiments, action 1002 includes identifying nucleotide reads from a genomic sample. In some embodiments, sequencing device 114 determines nucleotide reads from a sample genome (e.g., by using SBS) and sends data representing the nucleotide reads (e.g., in a base detection file) to sequencing system 104. In another embodiment, a third-party system determines nucleotide reads from a sample genome and allows sequencing system 104 access to these nucleotide reads.

[0159] Figure 10 The illustrated series of actions 1000 also includes action 1004 of aligning nucleotide reads with a reference genome. As illustrated, the segmentation-read alignment system 106 aligns nucleotide reads 1010 with a reference genome. For example, in various specific embodiments, the sequencing system 104 aligns nucleotide reads 1010 with a reference genome. As part of performing action 1004, the segmentation-read alignment system 106 determines the fragment alignment and determines the predicted segment group.

[0160] like Figure 10 As further illustrated, the segmentation-read alignment system 106 performs the action 1006 of generating nucleobase detection. Typically, based on the alignment of nucleotide reads with a reference genome, nucleobase detection includes nucleobases at genomic coordinates of the sample genome from a predicted variant detection file 1008 (VCF) or other base detection output file. Due to the accuracy of the predicted segmentation, the sequencing system 104 can generate nucleobase detection at genomic coordinates with higher accuracy and confidence than existing sequencing systems.

[0161] In some examples, the segment-read alignment system 106 uses the BAM / SAM file format to report segment alignments. The BAM / SAM file specification provides three different alignment types: primary, secondary, and minor. In some examples, flag bits indicate supplementary and / or minor names. According to the BAM / SAM specification, exactly one primary alignment (without supplementary or minor flag sets) is identified. A segment alignment with N>=2 segments is therefore represented as one primary segment alignment BAM / SAM record and N-1 supplementary segment alignment BAM / SAM records.

[0162] Therefore, typically, unless through special means or encoding, the segment-read alignment system 106 may not output the entire segment group as the primary alignment. The segment-read alignment system 106 determines which of the N segment alignments should be selected for the primary alignment state, with the remaining N-1 segment alignments receiving supplementary alignment states. In some specific implementations, the segment-read alignment system 106 determines the primary alignment output based on the parameter `primary-5p`. When `primary-5p=0`, the primary segment alignment is selected to support a correct match, typically the 3'th segment alignment. Alternatively, the segment-read alignment system 106 sets `primary-5p` to 1 to set the 5'th segment alignment as the primary alignment.

[0163] If the segment-read alignment system 106 determines that a minor alignment should be output, then the segment-read alignment system 106 selects minor segment alignments in descending order of their scores. Generally, minor alignments include additional alignment records that are not related to the primary alignment but represent alternative alignment candidates. Some minor segment alignments may themselves be important segment groups. The segment-read alignment system 106 may determine the full segment groups from which minor alignments should be output. Each of the full segment groups will simulate the primary / supplementary structure of the winning segment group but with a minor marker. However, if a segment alignment of a minor segment group has already been output (in the highest-scoring segment group or in a higher-scoring minor segment group), the segment-read alignment system 106 prevents the output of supplementary minor segment alignments. More specifically, supplementary alignments include additional alignment records that supplement the primary alignment or present additional portions of the segment alignment.

[0164] As indicated above, the segment-read alignment system 106 improves the alignment of segmented reads and enhances the accuracy of corresponding nucleobase detection (including structural variant detection). According to one or more embodiments, Figures 11A to 11D This illustrates read stacking of candidate gene fusion events generated by the segmentation-read alignment system 106, which exhibits more accurate mapping and alignment—and leads to more accurate variant detection—than existing sequencing systems based on transcriptome reads. Figures 11A to 11D As instructed, by determining a segment group score for candidate segment groups that includes fragment alignments from nucleotide reads (e.g., transcriptome reads), and selecting predicted segment groups from candidates based on such segment group scores, the segment-read alignment system 106 (i) identifies fragment alignments of candidate segment reads with better accuracy than existing sequencing systems, and (ii) identifies true negative variants (where no gene fusion is detected) at genomic coordinates and breakpoints where false positive variants of gene fusion events are detected in existing sequencing systems.

[0165] Figure 11A and Figure 11B By plotting the breakpoints along the chromosome ( Figure 11A ) and through segment-read alignment system 106 and existing sequencing systems ( Figure 11B Different segments determined by the same breakpoint are compared and mapped to each other, thus complementing each other. For example... Figure 11A As shown, for example, chromosome segment 1102a of chromosome 11 contains breakpoint 1104a. Specifically, Figure 11A The breakpoint 1104a shown identifies one or more genomic coordinates where nucleotide reads have been aligned using existing sequencing systems, and subsequently... Figure 11B The nucleotide reads depicted in the diagram have breaks between them. As further described below, the alignment of transcriptome reads with breakpoint 1104a can indicate gene fusion events between the ARL2-SNX15 RNA gene and another gene.

[0166] like Figure 11B As shown, user client device 108 presents a graphical user interface 1100a, which includes alignments and mappings of different read segments determined by the segment-read alignment system 106 and existing sequencing systems regarding breakpoint 1104a. For example, graphical user interface 1100a may represent the graphical user interface of an integrated genome viewer (IGV), which includes read alignments relative to a reference genome. For comparative purposes, graphical user interface 1100a includes an updated alignment window 1106a depicting candidate transcriptome read alignments from the segment-read alignment system 106, a previous alignment window 1108a depicting candidate transcriptome read alignments from existing sequencing systems, and a reference genome window 1110a depicting reference nucleotide bases of the reference genome. Figure 11B In addition, the updated alignment window 1106a also includes a read coverage marker 1120a, which indicates the read coverage (e.g., read depth) at the genomic coordinates that overlap with the breakpoint 1104a.

[0167] As shown in the previous alignment window 1108a, existing sequencing systems map and align transcriptome read 1114a to the reference genome at genomic coordinates corresponding to (or relatively closer to) breakpoint 1104a. As indicated by the light gray shading of transcriptome read 1114a in the previous alignment window 1108a, the detected nucleotide bases of transcriptome read 1114a match the reference nucleotide bases of the reference genome within reference genome window 1110a. In contrast to transcriptome read 1114a, existing sequencing systems map and align the following: (i) mismatched transcriptome read 1112a to the genomic region corresponding to the ARL2 continuum upstream of breakpoint 1104a, and (ii) mismatched transcriptome read 1112b to the genomic region corresponding to the SNX15 continuum downstream of breakpoint 1104a. As indicated by the different gray shading or color of mismatched transcriptome reads 1112a and 1112b in the previous alignment window 1108a, the detected nucleotide bases of mismatched transcriptome reads 1112a and 1112b do not match the reference nucleotide bases of the reference genome in the reference genome window 1110a.

[0168] Because the threshold number of detected nucleotide bases does not match the reference nucleotide bases, existing sequencing systems trim (e.g., soft or hard trim) the nucleotide bases within mismatched transcriptome reads 1112a and 1112b, thus ignoring the nucleotide bases of mismatched transcriptome reads 1112a and 1112b for alignment purposes. However, mismatched transcriptome reads 1112a and 1112b exhibit segmentation alignments of the corresponding transcriptome reads relative to the reference genome. Candidate alignments of mismatched transcriptome reads 1112a and 1112b obtained by existing sequencing systems represent complementary alignments with a positive mapping quality metric (e.g., positive MAPQ) and correspond to a major alignment with another gene (e.g., the AKT3 gene). Based on the scores of the major and complementary alignments of such corresponding transcriptome reads depicted in the previous alignment window 1108a, existing sequencing systems determine the false-positive variant detection of gene fusion events in the genomic sample. For example, in some cases, existing sequencing systems re-align mismatched transcriptome reads 1112a and 1112b with the genomic region of another gene on a different chromosome (e.g., the AKT3 gene on chromosome 1), thereby indicating a gene fusion event.

[0169] As shown in the updated alignment window 1106a, the segment-read alignment system 106 maps and aligns the transcriptome read 1116a with the reference genome at genomic coordinates corresponding to (or relatively closer to) the breakpoint 1104a. As indicated by the light gray shading of transcriptome read 1116a, the detected nucleotide bases of transcriptome read 1116a match the reference nucleotide bases of the reference genome within the reference genome window 1110a. In contrast to transcriptome read 1116a, the segment-read alignment system 106 maps and aligns the mismatched transcriptome read 1118a with the genomic region corresponding to the SNX15 contiguous sequence downstream of the breakpoint 1104a, but does not map or align any mismatched transcriptome reads upstream of the breakpoint 1104a. As indicated by the different gray shading or color of the mismatched transcriptome read 1118a in the updated alignment window 1106a, the detected nucleotide bases of the mismatched transcriptome read 1118a do not match the reference nucleotide bases of the reference genome in the reference genome window 1110a.

[0170] like Figure 11B Further instructions indicate that candidate alignments of mismatched transcriptome reads 1118a obtained by the segmentation-read alignment system 106 exhibit a mapping quality metric of 0 (e.g., MAPQ0), causing the segmentation-read alignment system 106 to filter out candidate alignments of mismatched transcriptome reads 1118a. Because the segmentation-read alignment system 106 filters out mismatched transcriptome reads 1118a that align to genomic regions on one side of breakpoint 1104a, the segmentation-read alignment system 106 avoids the detection of false-positive variants that identify gene fusion events in the same genomic sample (as with existing sequencing systems). By generating improved segmentation scores for candidate segmentation alignments, the segmentation-read alignment system 106 avoids “noisy” segmented reads displayed in the previous alignment window 1108a by candidate alignments from existing sequencing systems. Because it avoids noisy segment-read alignment, the segment-read alignment system 106 also avoids detecting incorrect gene fusion variants and correctly identifies true negative variants of gene fusions.

[0171] Figure 11C and Figure 11D By plotting the breakpoints along the chromosome ( Figure 11C ) and through segment-read alignment system 106 and existing sequencing systems ( Figure 11D Different segments determined by the same breakpoint are compared and mapped to each other, thus complementing each other. For example... Figure 11C As shown, for example, chromosome segment 1102b of chromosome 4 contains breakpoint 1104b. Specifically, Figure 11CThe breakpoint 1104b shown identifies one or more genomic coordinates at which transcriptome reads have been aligned using existing sequencing systems, and subsequently... Figure 11D The read segments depicted have breaks between them. As further illustrated below, the segment-read alignment of transcriptomic reads relative to breakpoint 1104b can indicate gene fusion events between the DCTD gene and another gene.

[0172] like Figure 11D As shown, the user client device 108 presents a graphical user interface 1100b, which includes alignments and mappings of different read segments determined by the segment-read alignment system 106 and existing sequencing systems regarding breakpoint 1104b. As described above, for example, the graphical user interface 1100b represents a graphical user interface from IGV, which includes transcriptome read alignments relative to a reference genome. For comparative purposes, the graphical user interface 1100b includes an updated alignment window 1106b depicting the transcriptome read alignments of the segment-read alignment system 106, a previous alignment window 1108b depicting the transcriptome read alignments of the existing sequencing system, and a reference genome window 1110b depicting the reference nucleotide bases of the reference genome. Figure 11D In the updated alignment window 1106b, a read coverage marker 1120b is also included, which indicates the read coverage (e.g., read depth) at the genomic coordinates that overlap with the breakpoint 1104b.

[0173] As shown in the previous alignment window 1108b, existing sequencing systems map and align transcriptome read 1114b with the reference genome at genomic coordinates corresponding to (or relatively closer to) breakpoint 1104b. Similar to... Figure 11B The graphical user interface 1100a in the middle, Figure 11D The graphical user interface 1100b includes a light gray shading indicating that the detected nucleotide bases of a transcriptome read (e.g., transcriptome read 1114b) match the reference nucleotide bases of the reference genome, and a different gray shading or color indicating that the detected nucleotide bases of mismatched transcriptome reads (e.g., mismatched transcriptome reads 1112c, 1112d, and 1118b) do not match the reference nucleotide bases. In contrast to transcriptome read 1114b, existing sequencing systems map and align the following: (i) mismatched transcriptome read 1112c with the genomic region corresponding to the contiguous sequence upstream of breakpoint 1104b, and (ii) mismatched transcriptome read 1112d with the genomic region corresponding to the contiguous sequence downstream of breakpoint 1104b.

[0174] Because the threshold number of detected nucleotide bases does not match the reference nucleotide bases, existing sequencing systems trim nucleotide bases in mismatched transcriptome reads 1112c and 1112d, thus ignoring nucleotide bases in mismatched transcriptome reads 1112a and 1112b for alignment purposes. For example... Figure 11D The depicted mismatched transcriptome reads 1112c and 1112d show segmentation alignments of the corresponding transcriptome reads relative to the reference genome. Candidate alignments of mismatched transcriptome reads 1112c and 1112d obtained by existing sequencing systems represent supplementary alignments with a positive mapping quality metric (e.g., positive MAPQ) and correspond to a major alignment with another gene (not shown). Based on the scores of the major and supplementary alignments of such corresponding transcriptome reads depicted in the previous alignment window 1108b, existing sequencing systems determine false-positive variant detections of gene fusion events in genomic samples. For example, in some cases, existing sequencing systems re-align mismatched transcriptome reads 1112c and 1112d with genomic regions of another gene on the same chromosome (e.g., chromosome 4) or another gene on a different chromosome, thereby indicating gene fusion events.

[0175] As shown in the updated alignment window 1106b, in contrast, the segment-read alignment system 106 maps and aligns mismatched transcriptome reads 1118a with genomic regions corresponding to contiguous sequences upstream of the breakpoint 1104b, but does not map or align any mismatched transcriptome reads downstream of the breakpoint 1104b. Figure 11D Further indications are given that candidate alignments of mismatched transcriptome reads 1118a obtained by the segmentation-read alignment system 106 exhibit relatively low mapping quality metrics (e.g., MAPQ0), causing the segmentation-read alignment system 106 to filter out candidate alignments of mismatched transcriptome reads 1118a. Because the segmentation-read alignment system 106 filters out mismatched transcriptome reads 1118a that align to a genomic region on one side of breakpoint 1104b, the segmentation-read alignment system 106 is uncertain about the detection of false positive variants of gene fusion events from the same genomic sample. By generating an improved segmentation score for candidate segmentation alignments, the segmentation-read alignment system 106 avoids “noisy” segmented reads presented in the previous alignment window 1108b by candidate alignments from existing sequencing systems. As stated above, because it avoids such noisy segmented read alignments, the segmentation-read alignment system 106 also avoids detecting incorrect gene fusion variants and correctly identifies true negative variants regarding gene fusions.

[0176] In addition to improving the accuracy of mapping and alignment, as well as variant detection of gene fusion events, in some embodiments, the segmentation-read alignment system 106 also improves nucleotide read coverage and variant detection accuracy of human mitochondrial DNA chromosome M by selecting more accurate mappings and alignments based on improved segmentation group scores. According to one or more embodiments, Figures 12A to 12D Coverage diagrams 1200a-1200d illustrate the higher coverage of nucleotide reads mapped and aligned to genomic regions of chromosome M using the segment-read alignment system 106, compared to such coverage from nucleotide reads mapped and aligned using existing sequencing systems. Figures 12A to 12D As shown, the improved nucleotide read coverage extends from the start point of chromosome M to the end point of chromosome M, covering and detecting less accessible genomic regions. According to one or more embodiments, Figure 13 Variant detection table 1300 is illustrated, which demonstrates that, at the genomic region of chromosome M, better SNP and indel detection accuracy is achieved by segment-read alignment system 106 compared to such SNP and indel detection obtained by existing sequencing systems.

[0177] It is well known that the endpoint genomic region of chromosome M is difficult to detect and cover with existing sequencing systems, partly due to the circular nature of mitochondrial DNA. Because existing models used for mapping and alignment represent the circular DNA of chromosome M in a linear manner, existing sequencing systems frequently truncate and incorrectly soft-cleave nucleotide reads aligned to the endpoint genomic region of chromosome M, thus sometimes incorrectly ignoring valuable nucleotide reads associated with the endpoint genomic region of chromosome M. In contrast to existing sequencing systems and as... Figures 12A to 12D As shown, the segment-read alignment system 106 generates improved segment group scores, which penalize segment alignments across different chromosomes, thereby improving the selection of segment groups and the mapping and alignment of chromosome M.

[0178] To test the nucleotide read coverage of fragment alignment from the segment-read alignment system 106, researchers performed segment-read alignment on mitochondrial DNA samples from the Fazzini dataset using both the system and existing sequencing systems, as described in Federica Fazzini et al., “Analyzing Low-Level mtDNA Heteroplasmy-Pitfalls and Challenges from Bench to Benchmarking,” Int'l J. Mol. Sci. 2021 Jan 19; 22(2):935, the full text of which is incorporated herein by reference. For example, researchers sequenced and aligned nucleotide reads from two mtDNA mixtures with different target allele frequencies, where sample mixture M1 consisted of a 1:2 mixture and 50% of the target allele frequency, sample mixture M2 consisted of a 1:10 mixture and 10% of the target allele frequency, and sample mixture M3 consisted of a 1:50 mixture and 2% of the target allele frequency. In some cases, researchers used different versions of Taq polymerase for polymerase chain reaction (PCR), including LA Advantage (provided by Clontech Laboratories), Herculase II Fusion (HERK), and LongAmp Taq polymerase (NEB). Researchers also used two different protocols to sequence nucleotide reads from sample mixtures M1, M2, and M3: PCR amplification before mixing and PCR after mixing. Figures 12A to 12D In addition, researchers further mapped the nucleotide read coverage at the genomic coordinates of the start and end points of chromosome M. Furthermore, researchers used different versions of PCR Taq polymerase and protocols to determine the detection of false positive and false negative variants of SNPs and indels in sample mixtures M1, M2, and M3, such as... Figure 13 As depicted in the text.

[0179] like Figure 12A and Figure 12B As shown, coverage diagrams 1200a and 1200b illustrate the coverage of nucleotide reads sequenced from sample mixture M1 using HERK, mapped and aligned using the segment-read alignment system 106 and existing sequencing systems. Figure 12A and Figure 12BIn the diagram, graph keys 1202a and 1202b show the coverage plot lines for the segment-read alignment system 106 named MapperV2 (i.e., MapperV2_All, MapperV2_60, MapperV2_20, and MapperV2_gvcf) and the coverage plot lines for the existing sequencing system named curMapper (i.e., curMapper_All, curMapper_60, curMapper_20, and curMapper_gvcf). Figure 12A As indicated by coverage diagram 1200a and graphical key 1202a, the segmentation-read alignment system 106 maps and aligns nucleotide reads with consistently higher coverage across the origin genomic region of chromosome M (chrM: 0-100) compared to existing sequencing systems, including all mapped nucleotide reads, as shown by the comparison of the plotting lines of MapperV2_All and curMapper_All. This even extends beyond the origin genomic region of chromosome M, as shown in the diagram. Figure 12B As indicated by coverage diagram 1200b and graph key 1202b, the segment-read alignment system 106 maps and aligns nucleotide reads with a consistently higher coverage across the endpoint genomic region (chrM:16469-16569) of chromosome M compared to existing sequencing systems, including all mapped nucleotide reads, as shown by a comparison of the plotting lines of MapperV2_All and curMapper_All.

[0180] like Figure 12C and Figure 12D As shown, coverage plots 1200c and 1200d similarly illustrate the coverage of nucleotide reads sequenced from sample mixture M1 using Clontech Taq polymerase, mapped and aligned using the segment-read alignment system 106 and existing sequencing systems. Figure 12C and Figure 12D In the diagram, graph keys 1202c and 1202d show the coverage plotting lines for the segmentation-read alignment system 106 named MapperV2 (i.e., MapperV2_All, MapperV2_60, MapperV2_20, and MapperV2_gvcf) and the coverage plotting lines for the existing sequencing system named curMapper (i.e., curMapper_All, curMapper_60, curMapper_20, and curMapper_gvcf). Figure 12CAs indicated by coverage diagram 1200c and graphical key 1202c, the segmentation-read alignment system 106 maps and aligns nucleotide reads with consistently higher coverage across the origin genomic region of chromosome M (chrM: 0-100) compared to existing sequencing systems, including all mapped nucleotide reads, as shown by the comparison of the plotting lines of MapperV2_All and curMapper_All. This even extends beyond the origin genomic region of chromosome M, as shown in the diagram. Figure 12D As indicated by coverage plot 1200d and graph key 1202d, the segment-read alignment system 106 also maps and aligns nucleotide reads with a consistently higher coverage across the endpoint genomic region (chrM:16469-16569) of chromosome M compared to existing sequencing systems, including all mapped nucleotide reads, as shown by a comparison of the plotting lines of MapperV2_All and curMapper_All.

[0181] As indicated above, Figure 13 Variant Detection Table 1300 illustrates the detection of false positive and false negative variants of SNPs and indels at genomic regions of chromosome M for sample mixtures M1, M2, and M3 using different versions of PCR Taq polymerase and different PCR protocols, obtained by segment-read alignment system 106 and existing sequencing systems. On the left, as indicated by the “Dataset_jama_REV7169” column, Variant Detection Table 1300 shows the detection of false positive and false negative variants of SNPs and indels obtained by existing sequencing systems. On the right, as indicated by the “CGM_mapperV2” column, Variant Detection Table 1300 shows the detection of false positive and false negative variants of SNPs and indels obtained by segment-read alignment system 106. As indicated by the “Total” and “Diff” columns of Variant Detection Table 1300, segment-read alignment system 106 consistently identifies fewer total false positive and false negative SNP and indel detections compared to existing sequencing systems. Although the “Diff” column of variant detection table 1300 shows that segment-read alignment system 106 exhibits one less false positive and false negative SNP and indel detection between eight, this reduction in false positive and false negative SNP and indel detection is significant for such short chromosomes (i.e., chromosome M, which is only 16,569 base pairs long).

[0182] In addition to improved nucleotide read coverage and improved variant detection for chromosome M, in some embodiments, the segmentation-read alignment system 106 also improves the accuracy of structural variant detection. According to one or more embodiments, Figure 14ATable 1400a depicts the impact of the segment-read alignment system 106 on the recovery of omissions in existing sequencing systems for the insertion detection of genes in acute myeloid leukemia (AML). According to one or more embodiments, Figure 14B Table 1400b illustrates how the segment-read alignment system 106 determines more accurate replication and translocation detection compared to existing sequencing systems.

[0183] like Figure 14A As shown, for example, Table 1400a compares insertion detections within the fms-like tyrosine kinase 3 (FLT3) gene obtained by segment-read alignment system 106 and existing sequencing systems in known genomic samples from normal and tumor tissues. Mutations in the FLT3 gene result in a significant percentage of AML cases, with internal tandem repeats (ITDs) representing the most common type of FLT3 mutation. As indicated in Table 1400a, based on improved segmentation scores and selection of better-improved segmentation groups for variant detection, segment-read alignment system 106 (shown in the "New M / A+ Detection Generation Model" column) correctly identifies insertions longer than at least 50 base pairs for a pair of known genomic samples with FLT3-ITD mutations at genomic coordinates chr13:28034103 and chr13:28034120, but existing sequencing systems (shown only in the "Detection Generation Model" column) incorrectly detect such insertions. As further indicated in Table 1400a, the segment-read alignment system 106 (shown in the “New M / A+ Detection Generation Model” column) also correctly determined the presence or absence of such insertions at other genomic coordinates, which was also correctly determined in existing sequencing systems (shown only in the “Detection Generation Model” column). The detection of such recovered insertions obtained through the segment-read alignment system 106 (along with the preservation of previously accurate insertion detections) confirms the crucial improvement and preservation of accuracy in detecting intragene structural variants, which is important for cancer diagnosis.

[0184] like Figure 14BAs shown in Table 1400b, the accuracy of somatic structural variant detection obtained from sequencing data HCC1954 using the segment-read alignment system 106 and existing sequencing systems is compared. HCC1954 is a cell line exhibiting epithelial breast cancer. As indicated in Table 1400b, based on the improved segment set score and the selection of a better improved segment set for variant detection, the segment-read alignment system 106 (shown in the "New M / A+ Detection Generation Model" row) demonstrates better recall, accuracy, and F-score for duplicate detection in HCC1954 than existing sequencing systems (shown only in the "Detection Generation Model" row). Also as indicated in Table 1400b, the segment-read alignment system 106 (shown in the "New M / A+ Detection Generation Model" row) demonstrates better accuracy and F-score for translocation detection in HCC1954 than existing sequencing systems (shown in the "Detection Generation Model" row). Because the re-detection, accuracy, and F-score reported in Table 1400b were determined without ground-truth detection, the re-detection, accuracy, and F-score of the segment-read alignment system 106 are determined when ground-truth detection is used.

[0185] Figures 1 to 14B The corresponding text and examples provide a number of different methods, systems, devices, and non-transitory computer-readable media for the segmentation-read comparison system 106. In addition to the foregoing, one or more specific implementations may be described based on flowcharts including actions for achieving specific results, such as... Figure 15 As shown. Figure 15 It can be performed with more or fewer actions. Furthermore, these actions can be performed in different orders. Additionally, the actions described herein can be repeated, performed in parallel with each other, or performed in parallel with different instances of the same or similar actions.

[0186] As mentioned, Figure 15 A flowchart illustrating a series of actions 1500 for selecting a predicted segment group from candidate segment groups is provided. Although Figure 15 This example illustrates actions based on a specific implementation, but alternative implementations may be omitted, added, reordered, and / or modified. Figure 15 Any action shown. Figure 15 The action can be performed as part of a method. Alternatively, a non-transitory computer-readable medium may include actions that, when executed by at least one processor, cause a computing device to perform... Figure 15 In some other embodiments, the system includes at least one processor and a non-transitory computer-readable medium that, when executed by one or more processors, causes the system to perform... Figure 15The instructions for the action. In some cases, at least one processor includes a configurable processor, and executing at least one processor includes configuring the configurable processor.

[0187] like Figure 15 As shown, this series of actions 1500 includes action 1502, which identifies one or more nucleotide reads. Specifically, action 1502 includes identifying one or more nucleotide reads corresponding to genomic regions of the genome sample.

[0188] Figure 15 The illustrated series of actions 1500 also includes action 1504 of determining candidate segment groups. Specifically, action 1504 includes determining candidate segment groups comprising fragment alignments corresponding to one or more nucleotide reads. In some embodiments, determining candidate segment groups further includes grouping one or more fragment alignments of single-terminal nucleotide reads into the candidate segment groups; or grouping one or more fragment alignments of double-terminal nucleotide reads from a pair of double-terminal nucleotide reads into the candidate segment groups.

[0189] like Figure 15 As further exemplified, this series of actions 1500 includes action 1506 for generating segmentation group scores. Specifically, action 1506 includes generating segmentation group scores for segment alignments between candidate segmentation groups and the reference genome. In some embodiments, action 1506 further includes the following additional actions: generating fragment alignment scores for each segment alignment between the candidate segmentation group and the reference genome; and generating segmentation group scores for the candidate segmentation groups based on the fragment alignment scores. Additionally, in some embodiments, action 1506 includes generating breakage penalties for the candidate segmentation groups within the candidate segmentation groups, based on the relative geometry of the first and second fragment alignments relative to the reference genome; and generating segmentation group scores for the candidate segmentation groups based on the breakage penalties. Furthermore, in some embodiments, this series of actions 1506 includes generating overlap penalties for the candidate segmentation groups within the candidate segmentation groups, based on the overlap between the first and second fragment alignments within a nucleotide read; and generating segmentation group scores for the candidate segmentation groups based on the overlap penalties.

[0190] In some implementations, action 1506 further includes generating a segmentation group score for a candidate segmentation group by generating a fragment alignment score, a breakage penalty, and an overlap penalty for the fragment alignments of the candidate segmentation group; and merging the fragment alignment scores and subtracting the breakage penalty and overlap penalty from the merged fragment alignment score. In some specific implementations, action 1006 further includes determining candidate segmentation groups by iteratively grouping each fragment alignment in the order of the outermost fragment alignment to the innermost fragment alignment of the nucleotide read; and generating a segmentation group score by iteratively scoring the groupings of each fragment alignment in the order in which the fragment alignments are grouped.

[0191] Figure 15 The illustrated series of actions 1500 also includes action 1508 of selecting predicted segmentation groups. Specifically, action 1508 includes selecting predicted segmentation groups from candidate segmentation groups based on segmentation group scores for nucleotide detection of genomic regions. In some embodiments, action 1508 includes identifying candidate segmentation group pairs from candidate segmentation groups that align different fragments of mating pairs including paired reads; generating pair scores for the candidate segmentation group pairs to evaluate the comparison pairs of the candidate segmentation group pairs with a reference genome; and further selecting a predicted segmentation group based on the pair scores for each mating pair of paired reads. Furthermore, in some embodiments, action 1508 includes determining the sum of the segmentation group scores for the respective candidate segmentation group pairs; generating a pairing penalty based on the estimated insert size between the innermost fragment alignments of the candidate segmentation group pairs; and generating a pair score for the candidate segmentation group pairs based on the sum of the segmentation group scores and the pairing penalty.

[0192] In some implementations, the series of actions 1500 includes the following additional actions: determining an alternative continuous fragment alignment score for the inner and outer fragment alignments of the alternative continuous sequence corresponding to the nucleotide read within the reference genome; determining a segmentation score for the inner and outer fragment alignments of the primary assembly region of the reference genome; and selecting the alternative continuous fragment alignment score as the replacement segmentation score based on the determination that the alternative continuous fragment alignment score exceeds the segmentation score.

[0193] Additionally, in one or more specific implementations, the series of actions 1500 includes an additional action of determining the nucleobase detection of genomic regions based on the alignment of the predicted segment group with the reference genome.

[0194] The series of actions 1500 may also include the following additional actions: determining that the segment alignment score of a segment alignment fails to meet the threshold segment alignment score; and removing the segment alignment from the considerations for forming candidate segmentation groups.

[0195] The series of actions 1500 may include the following additional actions: determining that the alignment score of the candidate segmentation group fails to meet the minimum alignment score; and based on the failure of the alignment score to meet the minimum alignment score, prohibiting the reporting of the segmentation alignment of the candidate segmentation group in the alignment file or variant detection file.

[0196] The methods described herein can be used in conjunction with a variety of nucleic acid sequencing technologies. Particularly suitable technologies are those in which nucleic acids are attached to fixed positions in the array such that their relative positions do not change, and in which the array is repeatedly imaged. Specific embodiments that obtain images in different color channels (e.g., corresponding to different markers used to distinguish one nucleobase type from another) are particularly suitable. In some embodiments, the process of determining the nucleotide sequence of the target nucleic acid (i.e., the nucleic acid polymer) can be automated. Preferred embodiments include sequencing-by-synthesis (SBS) technology.

[0197] SBS technology typically involves the enzymatic elongation of nascent nucleic acid chains through repeated addition of nucleotides to the template strand. In conventional SBS methods, a single nucleotide monomer is provided to the target nucleotide in the presence of polymerase during each delivery. However, in the method described herein, more than one type of nucleotide monomer can be provided to the target nucleic acid in the presence of polymerase during delivery.

[0198] SBS can utilize nucleotide monomers with or without a terminator motif. Methods using nucleotide monomers lacking a terminator include, for example, pyrosequencing and sequencing using γ-phosphate-labeled nucleotides, as described in further detail below. In methods using nucleotide monomers lacking a terminator, the number of nucleotides added in each cycle is typically variable and depends on the template sequence and the method of nucleotide delivery. For SBS techniques using nucleotide monomers with a terminator motif, the terminator can be effectively irreversible under the sequencing conditions used, as in conventional Sanger sequencing using dideoxynucleotides, or the terminator can be reversible, as in sequencing methods developed by Solexa (now Illumina, Inc.).

[0199] SBS technology can utilize nucleotide monomers with or without a labeled moiety. Therefore, incorporation events can be detected based on: the characteristics of the label, such as the fluorescence of the label; the characteristics of the nucleotide monomer, such as molecular weight or charge; byproducts of the incorporated nucleotide, such as the release of pyrophosphate; and so on. In specific implementations where two or more different nucleotides are present in the sequencing reagent, the different nucleotides can be distinguishable from each other, or alternatively, the two or more different labels can be indistinguishable under the detection technique used. For example, the different nucleotides present in the sequencing reagent may have different labels, and they can be distinguished using appropriate optics, as exemplified by the sequencing method developed by Solexa (now Illumina, Inc.).

[0200] Preferred specific implementations include pyrosequencing. Pyrosequencing detects the release of inorganic pyrophosphate (PPi) when specific nucleotides are incorporated into the nascent DNA strand (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996), “Real-time DNA sequencing using detection of pyrophosphate release.”, Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001), “Pyrosequencing sheds light on DNA sequencing.”, Genome Res. 11(1), 3-11; Ronaghi, M., Uhlen, M. and Nyren, P. (1998), “A sequencing method based on real-time pyrophosphate.”, Science 281(5375). The disclosures of U.S. Patent Nos. 6,210,891, 6,258,568, and 6,274,320 (the full text of which is incorporated herein by reference) are cited herein. In pyrosequencing, the released PPi can be detected by being immediately converted to ATP by adenosine triphosphate (ATP) sulfatase, and the level of ATP produced can be detected by photons generated by luciferase. The nucleic acid to be sequenced can be attached to a feature in an array, and the array can be imaged to capture the chemiluminescent signal generated by the incorporation of nucleotides at the feature in the array. Images can be obtained after processing the array with a specific type of nucleotide (e.g., A, T, C, or G). The images obtained after adding each type of nucleotide will differ in which feature in the array is detected. These differences in the images reflect the different sequence contents of the feature on the array. However, the relative position of each feature will remain unchanged in the image. Images can be stored, processed, and analyzed using the methods described herein. For example, the images obtained after processing the array with each different nucleotide type can be processed in the same way as illustrated in this paper for images obtained from different detection channels used in reversible terminator-based sequencing methods.

[0201] In another exemplary type of SBS, cyclic sequencing is accomplished by stepwise addition of reversible terminator nucleotides containing, for example, cleavable or photobleachable dye labels, as described in, for example, WO 04 / 018497 and U.S. Patent No. 7,057,026, the disclosures of which are incorporated herein by reference. This method was commercialized by Solexa (now Illumina Inc.) and is also described in WO 91 / 06678 and WO 07 / 123,744, the disclosures of each of which are incorporated herein by reference. The availability of fluorescently labeled terminators (where not only is the termination reversible, but the fluorescent label is cleavable) facilitates efficient cyclic reversible termination (CRT) sequencing. Polymerases can also be co-engineered to efficiently incorporate and extend from these modified nucleotides.

[0202] Preferably, in a reversible terminator-based sequencing embodiment, the marker does not substantially inhibit elongation under SBS reaction conditions. However, the detection marker can be removable, for example, by cleavage or degradation. Images can be captured after the marker is incorporated into the arrayed nucleic acid signature. In a particular embodiment, each cycle involves the simultaneous delivery of four different nucleotide types to the array, and each nucleotide type has a spectrally distinct marker. Four images are then obtained, each using a detection channel selective for one of the four different markers. Alternatively, different nucleotide types can be added sequentially, and images of the array can be obtained between each addition step. In such embodiments, each image will show a nucleic acid signature incorporating a specific type of nucleotide. Different signatures may or may not be present in different images due to the different sequence contents of each signature. However, the relative positions of the signatures will remain unchanged in the images. Images obtained by such a reversible terminator-SBS method can be stored, processed, and analyzed as described herein. After the image capture step, the marker can be removed, and the reversible terminator portion can be removed for subsequent cycles of nucleotide addition and detection. Removing tags after they have been detected in a specific loop and before subsequent loops can reduce background signal and crosstalk between loops. Examples of available tagging and removal methods are illustrated below.

[0203] In certain specific embodiments, some or all of the nucleotide monomers may include a reversible terminator. In such embodiments, the reversible terminator / cleavable fluorophore may include a fluorophore linked to the ribose moiety via a 3' ester bond (Metzker, Genome Res. 15:1767-1776 (2005), which is incorporated herein by reference). Other methods have separated 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. described the development of reversible terminators that use a small 3' allyl group to block elongation but can be easily deblocked by a short treatment with a palladium catalyst. The fluorophore is attached to the base via a photocleavable linker that can be easily cleaved by exposure to long-wavelength ultraviolet light for 30 seconds. Therefore, disulfide reduction or photocleavage can be used as the cleavable linker. Another method for reversible termination is to use natural termination, which occurs after a bulk dye is placed on the dNTP. The presence of a charged bulk dye on the dNTP can act as an efficient terminator due to steric and / or electrostatic hindrance. The presence of an incorporation event prevents further incorporation unless the dye is removed. The cleavage of the dye removes the fluorophore and effectively reverses the termination. Examples of modified nucleotides are also described in U.S. Patent Nos. 7,427,673 and 7,057,026, the disclosures of which are incorporated herein by reference in their entirety.

[0204] Additional exemplary SBS systems and methods that can be used in conjunction with the methods and systems described herein are described in U.S. Patent Application Publication No. 2007 / 0166705, U.S. Patent Application Publication No. 2006 / 0188901, U.S. Patent No. 7,057,026, U.S. Patent Application Publication No. 2006 / 0240439, U.S. Patent Application Publication No. 2006 / 0281109, PCT Publication No. WO05 / 065814, U.S. Patent Application Publication No. 2005 / 0100900, PCT Publication No. WO 06 / 064199, PCT Publication No. WO07 / 010,251, U.S. Patent Application Publication No. 2012 / 0270305, and U.S. Patent Application Publication No. 2013 / 0260372, the disclosures of which are incorporated herein by reference in their entirety.

[0205] Some specific implementations may use fewer than four different markers to detect four different nucleotides. For example, the methods and systems described in the material of incorporated U.S. Patent Application Publication No. 2013 / 0079232 may be used to perform SBS. As a first example, a pair of nucleotide types may be detected at the same wavelength, but distinguished based on the intensity difference of one member relative to the other member, or based on a change in the presence or absence of a signal in one member of the pair that results in a significantly different signal compared to the detected signal of the other member of that pair (e.g., by chemical modification, photochemical modification, or physical modification). As a second example, three of the four different nucleotide types may be detectable under specific conditions, while the fourth nucleotide type lacks a marker that is detectable under those conditions or is minimally detectable under those conditions (e.g., minimal detection due to background fluorescence, etc.). The incorporation of the first three nucleotide types into the nucleic acid may be determined based on the presence of their respective signals, and the incorporation of the fourth nucleotide type into the nucleic acid may be determined based on the absence of any signal or minimal detection of any signal. As a third example, one nucleotide type may include a marker that is detected in two different channels, while other nucleotide types are detected in no more than one channel. The three exemplary configurations described above are not considered mutually exclusive and can be used in various combinations. An exemplary specific implementation combining all three examples is a fluorescence-based SBS method that uses a first nucleotide type detected in a first channel (e.g., dATP with a label detected in the first channel when excited by a first excitation wavelength), a second nucleotide type detected in a second channel (e.g., dCTP with a label detected in the second channel when excited by a second excitation wavelength), a third nucleotide type detected in both the first and second channels (e.g., dTTP with at least one label detected in both channels when excited by the first and / or second excitation wavelengths), and a fourth nucleotide type lacking a label or minimally detected in either channel (e.g., unlabeled dGTP).

[0206] Furthermore, as described in the incorporated U.S. Patent Application Publication No. 2013 / 0079232, sequencing data can be obtained using a single channel. In this so-called single-dye sequencing method, 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.

[0207] Some specific implementations may utilize ligation-based sequencing (SBS) techniques. These techniques utilize DNA ligases to incorporate oligonucleotides and recognize the incorporation of such oligonucleotides. Oligonucleotides typically have distinct labels associated with the identity of specific nucleotides in the sequence to which they hybridize. As with other SBS methods, images are obtained after treating an array of nucleic acid features with labeled sequencing reagents. Each image will show nucleic acid features incorporated with a specific type of label. Due to the different sequence content of each feature, different features may be present or absent in different images, but the relative positions of the features will remain unchanged within the images. Images obtained by ligation-based sequencing methods can be stored, processed, and analyzed as described herein. Exemplary SBS systems and methods that can be used with the methods and systems described herein are described in U.S. Patent Nos. 6,969,488, 6,172,218, and 6,306,597, the disclosures of which are incorporated herein by reference in their entirety.

[0208] Some implementations utilize nanopore sequencing (Deamer, DW, and 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 asolid-state nanopore microscope.”, Nat. Mater., 2: 611-615 (2003), the full text of which is incorporated herein by reference). In such implementations, the target nucleic acid passes through a nanopore. The nanopore can be a synthetic pore or a biomembrane protein, such as α-hemolysin. As the target nucleic acid passes through the nanopore, each base pair can be identified by measuring fluctuations in the pore's conductivity. (US Patent No. 7,001,792; Soni, GV, and 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.", Nanoded., 2, 459-481 (2007); Cockroft, SL, Chu, J., Amorin, M., and Ghadiri, MR, "A single-molecule nanopore device detects DNA polymerase activity with single-nucleotide resolution.", J. Am. Chem. Soc. 130, 818-820 (2008), the full text of which is incorporated herein by reference). Data obtained from nanopore sequencing can be stored, processed, and analyzed as described herein.Specifically, based on the exemplary processing of optical images and other images described herein, data can be processed as if it were an image.

[0209] Some specific implementations may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation can be detected by fluorescence resonance energy transfer (FRET) interaction between a polymerase carrying a fluorophore and a γ-phosphate-labeled nucleotide, as described, for example, in U.S. Patent Nos. 7,329,492 and 7,211,414 (each of which is incorporated herein by reference), or by using a zero-mode waveguide, as described, for example, in U.S. Patent No. 7,315,019 (which is incorporated herein by reference), and by using fluorescent nucleotide analogs and engineered polymerases, as described, for example, in U.S. Patent No. 7,405,281 and U.S. Patent Application Publication No. 2008 / 0108082 (each of which is incorporated herein by reference). Illumination can be limited to a volume of approximately 1 / 2 psi around the surface-tethered polymerase, allowing observation of the incorporation of fluorescently labeled nucleotides against a 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 nanostructures.”, Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the full text of which is incorporated herein by reference). Images obtained by such methods can be stored, processed, and analyzed as described herein.

[0210] Some SBS implementations involve detecting protons released during nucleotide incorporation into the extension product. For example, proton-based sequencing can utilize electrical detectors and related technologies commercially available from Ion Torrent (Guilford, CT, a subsidiary of Life Technologies) or the sequencing methods and systems described in US 2009 / 0026082 A1, US 2009 / 0127589 A1, US 2010 / 0137143 A1, or US 2010 / 0282617 A1, each of which is incorporated herein by reference. The method described herein for amplifying target nucleic acids using kinetic exclusion can be readily applied to substrates for proton detection. More specifically, the method described herein can be used to generate a population of amplicon clones for proton detection.

[0211] The SBS method described above can be advantageously performed in a variety of formats, enabling the simultaneous manipulation of multiple different target nucleic acids. In specific implementations, different target nucleic acids can be processed in a common reaction vessel or on the surface of a specific substrate. This allows for convenient delivery of sequencing reagents, removal of unreacted reagents, and detection of incorporation events in a variety of ways. In implementations 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 bind to the surface in a spatially distinguishable manner. Target nucleic acids can bind via direct covalent attachment, attachment to beads or other particles, or binding to polymerases or other molecules attached to the surface. The array can comprise a single copy of the target nucleic acid at each site (also referred to as a feature region), or multiple copies having the same sequence can be present at each site or feature region. Multiple copies can be generated by amplification methods such as, for example, bridging amplification or emulsion PCR, which are described in further detail below.

[0212] The method described herein may use an array of features having a density of, for example, at least about 10 features / cm². 2 100 feature parts / 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 Or higher.

[0213] The advantages of the methods described herein are that they provide rapid and efficient detection of multiple target nucleic acids in parallel. Therefore, this disclosure provides an integrated system capable of preparing and detecting nucleic acids using techniques known in the art, such as those exemplified above. Thus, the integrated system of this disclosure may include fluid components capable of delivering amplification reagents 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 in the integrated system may be configured for and / or for detecting target nucleic acids. Exemplary flow cells are described, for example, in US 2010 / 0111768 A1 and US Serial No. 13 / 273,666, each of which is incorporated herein by reference. As illustrated with respect to a flow cell, one or more fluid components of the integrated system may be used for both amplification and detection methods. Taking a nucleic acid sequencing implementation as an example, one or more fluid components of the integrated system may be used for the amplification methods described herein and for delivering sequencing reagents in sequencing methods, such as those exemplified above. Alternatively, the integrated system may include separate fluid systems for performing amplification and detection methods. Examples of integrated sequencing systems capable of generating amplified nucleic acids and determining their sequences include, but are not limited to, MiSeq. ™ The platform (Illumina, Inc., San Diego, CA) and the device described in U.S. Serial No. 13 / 273,666 are incorporated herein by reference.

[0214] The sequencing system described above sequences nucleic acid polymers present in samples received by the sequencing equipment. As defined herein, “sample” and its derivatives are used in their broadest sense to include any specimen, culture, etc., suspected of containing a target. In some embodiments, a sample includes nucleic acids in the form of DNA, RNA, PNA, LNA, chimeric, or hybrid forms. A sample may include any biological, clinical, surgical, agricultural, atmospheric, or aquatic plant or animal specimen containing one or more nucleic acids. The term also includes any isolated nucleic acid sample, such as genomic DNA, freshly frozen, or formalin-fixed paraffin-embedded nucleic acid specimens. It is also envisioned that a sample may originate from: a single individual, a collection of nucleic acid samples from genetically related members, nucleic acid samples from genetically unrelated members, a (matched) nucleic acid sample from a single individual (such as tumor samples and normal tissue samples), or a sample from a single source containing two different forms of genetic material (such as maternal DNA and fetal DNA obtained from a maternal subject), or a sample containing contaminating bacterial DNA in the presence of plant or animal DNA. In some embodiments, the source of nucleic acid material may include nucleic acids obtained from newborns, such as those commonly used for newborn screening.

[0215] Nucleic acid samples may include high molecular weight substances, such as genomic DNA (gDNA). Samples may include low molecular weight substances, such as nucleic acid molecules obtained from FFPE samples or archived DNA samples. In another embodiment, low molecular weight substances include enzymatically fragmented or mechanically fragmented DNA. Samples may include cell-free circulating DNA. In some embodiments, the sample may include nucleic acid molecules obtained from biopsy tissue, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microscopy, surgical resection, and other clinical or laboratory samples. In some embodiments, the sample may be an epidemiological sample, an agricultural sample, a forensic sample, or a pathogenic sample. In some embodiments, the sample may include nucleic acid molecules obtained from animals, such as humans or mammalian sources. In another embodiment, the sample may 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 molecules may be archived or extinct samples or species.

[0216] Additionally, the methods and compositions disclosed herein can be used to amplify nucleic acid samples containing 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 acids 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 law enforcement agencies, one or more military services, or any such personnel. The nucleic acid sample may be a purified sample or a lysate containing crude DNA, such as derived from oral swabs, paper, fabric, or other substrates that can be impregnated with saliva, blood, or other bodily fluids. Thus, in some embodiments, the nucleic acid sample may contain 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 the victim's hair, skin, tissue samples, autopsy, or remains. 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 include nucleic acids obtained from non-human DNA, such as microbial, plant, or insect DNA. In some embodiments, the target sequence or amplified target sequence is for human identification purposes. In some embodiments, this disclosure relates in its entirety to methods for identifying the characteristics of forensic samples. In some embodiments, this disclosure relates in its entirety to methods for human identification using one or more target-specific primers disclosed herein or designed using one or more target-specific primers with the primer design standards outlined herein. In one embodiment, a forensic sample or human identification sample containing at least one target sequence may be amplified using any one or more target-specific primers disclosed herein or using the primer standards outlined herein.

[0217] The components of the segment-read comparison system 106 may include software, hardware, or both. For example, the components of the segment-read comparison system 106 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., user client device 108). When executed by one or more processors, the computer-executable instructions of the segment-read comparison system 106 may cause the computing device to perform the bubble detection method described herein. Alternatively, the components of the segment-read comparison system 106 may include hardware, such as a dedicated processing device for performing a function or group of functions. Additionally or alternatively, the components of the segment-read comparison system 106 may include a combination of computer-executable instructions and hardware.

[0218] Furthermore, components of the segmentation-read alignment system 106 that perform the functions described herein with respect to the segmentation-read alignment system 106 may, for example, be implemented as part of a standalone application, a module of an application, a plugin of an application, one or more library functions detectable by other applications, and / or a cloud computing model. Thus, components of the segmentation-read alignment system 106 may be implemented as part of a standalone application on a personal computing device or mobile device. Additionally or alternatively, components of the segmentation-read alignment system 106 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 U.S. and / or other countries.

[0219] As discussed in more detail below, specific embodiments of this disclosure may include or utilize a dedicated or general-purpose computer including computer hardware such as, for example, one or more processors and system memory. Specific embodiments within the scope of this disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Specifically, one or more processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). Generally, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., memory, etc.) and executes those instructions, thereby performing one or more processes, including one or more processes described herein.

[0220] Computer-readable media can be any available medium accessible by a general-purpose or special-purpose computer system. A computer-readable medium storing computer-executable instructions is a non-transitory computer-readable storage medium (device). A computer-readable medium carrying computer-executable instructions is a transmission medium. Therefore, by way of example and not limitation, specific embodiments of this disclosure may include at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media.

[0221] Non-transitory computer-readable storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid-state drive (SSD) (e.g., RAM-based), flash memory, phase-change memory (PCM), other types of memory, other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of computer-executable instructions or data structures and that is accessible by a general-purpose or special-purpose computer.

[0222] A “network” is defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer via a network or another communication connection (hardwired, wireless, or a combination of hardwired and wireless), the computer appropriately considers that connection as a transmission medium. The transmission medium may include networks and / or data links that can be used to carry desired program code in the form of computer-executable instructions or data structures, and which are accessible to general-purpose or special-purpose computers. Combinations of the above should also be included within the scope of computer-readable media.

[0223] Furthermore, upon arrival at various computer system components, program code in the form of computer-executable instructions or data structures can be automatically transferred from the transmission medium to a non-transitory computer-readable storage medium (device) (or vice versa). For example, computer-executable instructions or data structures received via a network or data link can be buffered in RAM within a network interface module (e.g., NIC) and then eventually transferred to the computer system RAM and / or to a less transient computer storage medium (device) at the computer system. Therefore, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize the transmission medium.

[0224] Computer-executable instructions include, for example, instructions and data that, when executed at a processor, cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a function or group of functions. In some embodiments, the computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special-purpose computer that implements the elements of this disclosure. The computer-executable instructions may be, for example, binary numbers, 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 actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or actions. Rather, the described features and actions are disclosed as exemplary forms for implementing the claims.

[0225] Those skilled in the art will understand that this disclosure can be practiced in networked computing environments with many types of computer system configurations, including personal computers, desktop computers, portable computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframes, mobile phones, PDAs, tablets, pagers, routers, switches, etc. This disclosure can also be practiced in distributed system environments, where both local and remote computer systems perform tasks via network links (via hardwired data links, wireless data links, or a combination of hardwired and wireless data links). In a distributed system environment, program modules can reside on both local and remote memory storage devices.

[0226] Specific implementations of this disclosure can 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 adopted 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 rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then thus scaled.

[0227] Cloud computing models can be composed of various characteristics, such as on-demand self-service, widespread network access, resource pooling, rapid elasticity, and metered services. Cloud computing models can also exhibit various service models, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud computing models can also be deployed using different deployment models, such as private clouds, community clouds, public clouds, and hybrid clouds. In this specification and in the claims, a "cloud computing environment" means an environment in which cloud computing is employed.

[0228] Figure 16 A block diagram illustrating a computing device 1600 that can be configured to perform one or more of the processes described above is shown. It will be understood that one or more computing devices, such as computing device 1600, can implement a segmentation-read alignment system 106 and a sequencing system 104. Figure 16 As shown, computing device 1600 may include a processor 1602, a memory 1604, a storage device 1606, an I / O interface 1608, and a communication interface 1610, which can be communicatively coupled via communication infrastructure 1612. In some specific embodiments, computing device 1600 may include a processor 1602, a memory 1604, a storage device 1606, an I / O interface 1608, and a communication interface 1610. Figure 16 The examples shown have fewer or more components. The following paragraphs describe this in more detail. Figure 16 The components of the computing device 1600 shown.

[0229] In one or more embodiments, processor 1602 includes hardware for executing instructions such as those constituting a computer program. By way of example, and not limitation, to execute instructions for dynamically modifying workflows, processor 1602 may retrieve (or obtain) instructions from internal registers, internal cache, memory 1604, or storage device 1606, and decode and execute them. Memory 1604 may be volatile or non-volatile memory for storing data, metadata, and programs executed by the processor. Storage device 1606 includes storage means for storing data or instructions for performing the methods described herein, such as a hard disk, flash drive, or other digital storage device.

[0230] I / O interface 1608 allows a user to provide input to computing device 1600, receive output from the computing device, and otherwise pass data to and receive data from the computing device. I / O interface 1608 may include a mouse, keypad or keyboard, touchscreen, camera, optical scanner, network interface, modem, other known I / O devices, or combinations of such I / O interfaces. I / O interface 1608 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 screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In some implementations, I / O interface 1608 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 available for a particular implementation.

[0231] The communication interface 1610 may include hardware, software, or both. In any case, the communication interface 1610 may provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device 1600 and one or more other computing devices or networks. By way of example and not limitation, the communication interface 1610 may include a network interface controller (NIC) or network adapter for communicating with Ethernet or other wired-based networks, or a wireless NIC (WNIC) or wireless adapter for communicating with wireless networks such as Wi-Fi.

[0232] Additionally, the communication interface 1610 facilitates communication with various types of wired or wireless networks. The communication interface 1610 also facilitates communication using various communication protocols. The communication infrastructure 1612 may also include hardware, software, or both, that couples components of the computing device 1600 to each other. For example, the communication interface 1610 may use one or more networks and / or protocols to enable multiple computing devices connected through a specific infrastructure to communicate with each other to perform one or more aspects of the processes described herein. For illustration, the sequencing process may allow multiple devices (e.g., client devices, sequencing devices, and server devices) to exchange information such as sequencing data and error notifications.

[0233] In the foregoing specification, this disclosure has been described with reference to specific exemplary embodiments thereof. Various embodiments and aspects of this disclosure have been described with reference to the details discussed herein, and various embodiments are illustrated in the accompanying drawings. The above description and figures are illustrative of this disclosure and should not be construed as limiting it. Numerous specific details have been described to provide a thorough understanding of various embodiments of this disclosure.

[0234] This disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The specific embodiments described herein should be considered in all respects as exemplary rather than restrictive. For example, the methods described herein may be performed with fewer or more steps / actions, or the steps / actions may be performed in a different order. Additionally, the steps / actions described herein may be repeated or performed in parallel with each other or in parallel with different instances of the same or similar steps / actions. Therefore, the scope of this application is indicated by the appended claims rather than the foregoing description. All modifications within the equivalent meaning and scope of the claims are to be included within their scope.

Claims

1. A computer-implemented method for identifying and selecting a split group corresponding to one or more nucleotide reads, the computer-implemented method comprising: identifying one or more paired-end nucleotide reads corresponding to a genomic region of a genomic sample; determining candidate split groups comprising fragment alignments of the one or more paired-end nucleotide reads; identifying, from the candidate split groups, pairs of candidate split groups comprising different fragment alignments of mates of the one or more paired-end nucleotide reads; generating split group scores for split alignments of the candidate split groups, wherein the split group scores measure accuracy of fragment alignments in the split groups relative to a reference genome; for a pair of candidate split groups and based on the split group scores, generating a pair score evaluating alignment of the pair of candidate split groups to a reference genome; and based on the pair score, selecting a predicted split group from the candidate split groups for nucleobase calling of the genomic region.

2. The computer-implemented method of claim 1, further comprising determining a candidate split group in the candidate split groups by grouping one or more fragment alignments of paired-end nucleotide reads of a pair of paired-end nucleotide reads from the one or more paired-end nucleotide reads to the candidate split group.

3. The computer-implemented method of claim 1, further comprising: generating fragment alignment scores for individual fragment alignments of a candidate split group to the reference genome, wherein the fragment alignment scores measure accuracy of fragment alignments relative to a reference genome; and generating a split group score for the candidate split group based on the fragment alignment scores.

4. The computer-implemented method of claim 1, further comprising: for a candidate split group in the candidate split groups, generating a penalty for relative geometry of a first fragment alignment relative to a first alignment orientation of the reference genome and a second fragment alignment relative to a second alignment orientation of the reference genome; and generating a split group score for the candidate split group based on the penalty for relative geometry of the first fragment alignment and the second fragment alignment.

5. The computer-implemented method of claim 1, further comprising: for a candidate split group in the candidate split groups, generating an overlap penalty for overlap between a first fragment alignment and a second fragment alignment within a nucleotide read; and generating a split group score for the candidate split group based on the overlap penalty.

6. The computer-implemented method of claim 1, further comprising generating a split group score for a candidate split group in the candidate split groups by: generating fragment alignment scores, penalties for relative geometry, and overlap penalties for fragment alignments of the candidate split group; and combining the fragment alignment scores and subtracting the penalties for relative geometry and the overlap penalties from the combined fragment alignment scores.

7. The computer-implemented method of claim 1, further comprising: ​ ​ ​ determining the candidate partition scores by iteratively grouping possible fragment alignment sequences in order of outermost fragment alignment alignment to innermost fragment alignment alignment; and generating the partition score by iteratively scoring the grouping of possible fragment alignment sequences in the order in which the possible fragment alignment sequences are grouped.

8. The computer-implemented method of claim 1, further comprising selecting the predicted partition group from the candidate partition groups by: selecting, from the candidate partition pairs, a candidate partition pair having a highest pair score; and selecting, from the candidate partition pair, the predicted partition group for each partner of the pair of nucleotide reads.

9. The computer-implemented method of claim 8, further comprising: determining a sum of partition scores for respective candidate partition pairs; generating a pairing penalty based on an estimated insert size between innermost fragment alignments of the candidate partition pairs; and generating the pair score for the candidate partition pair based on the sum of partition scores and the pairing penalty.

10. The computer-implemented method of claim 8, further comprising: determining an alternative consecutive fragment alignment score for innermost and outermost fragment alignments of an alternative consecutive sequence within the reference genome corresponding to a nucleotide read; determining a partition score for the innermost fragment alignment and the outermost fragment alignment of a primary assembly region of the reference genome; and based on determining that the alternative consecutive fragment alignment score exceeds the partition score, selecting the alternative consecutive fragment alignment score as a replacement partition score.

Images