Improved segmentation-read alignment by intelligently identifying candidate segmentation groups and scoring

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for efficiently identifying and selecting segmentation groups corresponding to one or more nucleotide reads. Typically, the segmentation group includes a chain of segmented aligned segments that form one read. The disclosed system utilizes dynamic programming to generate and evaluate candidate segmentation groups. The disclosed system may generate a segmentation group score for each candidate segmentation group. To generate segmentation group scores, the disclosed system takes into account segment alignment scores and geometries for segment alignment within candidate segmentation groups. The disclosed system selects a predicted segmentation group from the candidate segmentation groups based on the segmentation group score.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 367,002, filed on June 24, 2022, entitled “IMPROVING SPLIT-READ ALIGNMENT BY INTELLIGENTLY IDENTIFYING AND SCORING CANDIDATE SPLIT GROUPS”, which is hereby incorporated by reference in its entirety. Background Art

[0003] In recent years, biotechnology companies and research institutions have improved the hardware and software for nucleotide sequencing and determining the nucleobase detection of genomic samples. For example, some existing sequencers and sequencing data analysis software (collectively referred to as "existing sequencing systems") predict each nucleobase in a sequence by using conventional Sanger sequencing or sequencing by synthesis (SBS) methods. When using SBS, existing sequencing systems can monitor thousands of oligonucleotides synthesized in parallel from templates to predict the nucleobase detection of growing nucleotide reads. The camera in many existing sequencing systems captures images of irradiated fluorescent labels incorporated into oligonucleotides. After capturing such images, some existing sequencing systems determine the nucleobase detection of the nucleotide reads corresponding to the oligonucleotides and send the base detection data to a computing device with sequencing data analysis software, which compares the nucleotide reads with a reference genome. Based on the difference between the compared nucleotide reads and the reference genome, existing systems (e.g., variant detectors) determine the nucleobase detection of genomic regions and identify variants of genomic samples.

[0004] Despite these recent advances, existing sequencing systems are generally unable to accurately identify segmented reads and compare them to a reference genome, and therefore, variants or other nucleobase calls cannot be determined or inaccurate nucleobase calls can be determined. Typically, segmented reads represent nucleotide reads with a read segment mapped to a region of a reference genome (or compared to it) and one or more other read segments mapped to different regions of the reference genome (or compared to it). For example, nucleotide reads covering different sides of structural variants, deletions, different sides of gene fusions, or simple random mappings of read segments can result in segmented reads. In fact, in segmented reads, a read segment from a nucleotide read can be optimally aligned with a genomic region on a chromosome, and another read segment from the same nucleotide read can be optimally aligned with a genomic region on another chromosome. Because such segmentation-read alignments on two different chromosomes (or different genomic regions on the same chromosome) can accurately reflect variants of genomic samples or erroneously suggest segmented reads that should be aligned with a single genomic region, existing sequencing systems have developed computational models to identify and distinguish correct and incorrect segmentation-read alignments.

[0005] Although existing computational models can accurately identify some segmentation-read alignments, such computational models include design flaws that often lead to erroneous identification of segmentation-read alignments. For example, some existing sequencing systems determine the main alignment of segmented reads based on the highest scoring alignment of a single read segment of a candidate alignment from a candidate read segment fragment. However, such existing sequencing systems cannot consider the possibility of segmentation alignments and cannot explain how to score the alignments of multiple fragments together relative to other candidate alignments. To further illustrate, many existing sequencing systems determine the main alignments that trim read segments (or the different ends of reads) and thus leave spaces between fragment alignments. In order to fill such spaces, some existing sequencing systems iteratively select other fragment alignments that overlap with the spaces. By only filling spaces without considering the fragment alignments together, such existing systems cannot consider the relative fragment positions or orientations of nucleotide reads relative to the reference genome or other segmentation alignment geometries.

[0006] Due in part to the inaccuracy of aligning read segments, existing sequencing systems often determine inaccurate variant calls or other base calls based on inaccurate segmentation-read alignments. For example, by giving priority to the main alignment without considering the segment alignment from the nucleotide read segment as a whole, some existing sequencing systems may incorrectly ignore the segment alignment that correctly reflects the structural variant and fills the gap indicating the missing, as well as other segment alignments. On the contrary, the main alignment of the read segment segment itself may be optimally mapped to an incorrect genomic region of the reference genome. By giving priority to the main alignment, some existing sequencing systems ignore the correct genomic region that is better reflected by the alignment of multiple segments from the nucleotide read segment, resulting in false negative variant calls or other incorrect variant calls. Therefore, existing sequencing systems often misalign, incorrectly match or misdetect variants of a large number of samples, and increase the probability of mismatched alignments with read segments from genomic samples.

[0007] In order to make up for the defects of some existing sequencing systems that cannot correctly detect the segmentation-read alignment indicating structural variants, some existing systems not only use SBS (or other technologies) for whole genome sequencing (WGS), but also use genotyping probes targeting specific structural variants for microarrays. In fact, microarrays have been specially 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 equipment and microarray equipment, existing sequencing systems greatly increase computer processing and time to determine accurate variant calls, which are for single nucleotide polymorphisms (SNPs) and smaller insertions and deletions (indels) on the one hand, and for structural variants on the other hand. Summary of the invention

[0008] The present disclosure describes specific implementations 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 the scores of the alignments of one or more fragments from nucleotide reads in a candidate segmentation group, and select a predicted segmentation group from the candidates for base calling based on such scores. In particular, the disclosed system can identify segment alignments of candidate local alignments of fragments of reads from a genomic sample with a reference genome. The disclosed system then groups such segment 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 predicted segmentation groups from the candidate segmentation groups for base calling.

[0009] Additional features and advantages of one or more implementations of the present disclosure will be outlined in the description which follows, and in part will be obvious from the description, or may be learned by practice of such exemplary implementations. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] DETAILED DESCRIPTION One or more implementations are provided with additional specificity and detail through the use of the accompanying drawings, as briefly described below.

[0011] Figure 1 A diagram illustrating an environment in which a segmentation-read alignment system may operate according to one or more implementations.

[0012] Figure 2 An overview of a segment-read alignment system for determining scores for alignments of one or more fragments from nucleotide reads in candidate segmentation groups and selecting predicted segmentation groups from the candidates for base calling based on such scores is illustrated in accordance with one or more implementations.

[0013] FIG. 3A to FIG. 3B An overview of a segmentation-read alignment system for generating candidate segmentation sets of single-end and double-end nucleotide reads according to one or more implementations is illustrated.

[0014] Figure 4 A segment-read alignment system utilizing dynamic programming to generate and evaluate candidate segmentation groups is illustrated in accordance with one or more implementations.

[0015] Figure 5 An overview of a segment-read alignment system for generating segment group scores according to one or more implementations is illustrated.

[0016] FIG. 6A to FIG. 6B A segment-read alignment system for determining pair scores and selecting predicted segmentation groups based on the pair scores according to one or more implementations is illustrated.

[0017] Figure 7 A segment-read alignment system is illustrated that generates alternative contiguous segment alignment scores for alignments between read segments and alternative contiguous sequences and selects the alternative contiguous segment alignment as an alternative segment group score according to one or more implementations.

[0018] Figure 8 A segment-read alignment system for removing candidate segment groups using a threshold segment alignment score according to one or more implementations is illustrated.

[0019] Fig. 9

[0013] A segment-read alignment system utilizing a minimum alignment score to identify candidate segment groups for which no alignments are reported is illustrated according to one or more implementations.

[0020] Fig.10 A segmentation-read alignment system for generating variant calls for a genomic sample based on predicted segmentation groups according to one or more specific implementations is illustrated.

[0021] FIG. 11A to FIG. 11DIllustrated is a read stacking within a graphical user interface showing that a segmentation-read alignment system determines true negative variant calls for candidate gene fusion events, which is an improvement over existing sequencing systems that incorrectly determine such variant calls as gene fusion events, according to one or more specific implementations.

[0022] FIG. 12A to FIG. 12D Illustrated is a coverage graph showing higher coverage of nucleotide reads mapped and aligned to a genomic region of chromosome M using a split-read alignment system relative to such coverage of nucleotide reads mapped and aligned using existing sequencing systems, according to one or more specific implementations.

[0023] Fig.13 A variant call table according to one or more specific implementations is illustrated, which demonstrates the superior accuracy of SNP calls and indel calls obtained by the split-read alignment system at the genomic region of chromosome M relative to such SNP calls and indel calls obtained by existing sequencing systems.

[0024] FIG. 14A to FIG. 14B A table illustrating the improved accuracy of structural variant calling by the segmentation-read alignment system relative to existing sequencing systems is illustrated according to one or more specific implementations.

[0025] Fig.15 A flow chart illustrating a series of actions for determining candidate segment groups and selecting a predicted segment group based on a segment group score according to one or more specific implementations.

[0026] Fig.16 A block diagram of an exemplary computing device for implementing one or more implementations of the present disclosure is illustrated. DETAILED DESCRIPTION

[0027] The present disclosure describes one or more specific implementations of a segmentation-read alignment system that can select a segmentation group from such candidate segmentation groups based on generating candidate segmentation groups of read segment fragment alignments and scoring the candidate segmentation groups. Typically, the segmentation-read alignment system identifies single-end reads or double-end reads corresponding to genomic regions of a genomic sample, and analyzes candidate segmentation groups that include alignments of one or more read segments together, rather than finding isolated single segments with the highest alignment scores. More specifically, the segmentation-read alignment system can identify candidate local alignments of fragments of reads, and form chains of fragment alignments into candidate segmentation groups. The segmentation-read alignment system scores candidate segmentation groups, and selects predicted segmentation groups for base calling based on the candidate segmentation group scores.

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

[0029] The segmentation-read alignment system can also generate a segmentation group score for the segment alignment of a candidate segmentation group. Typically, the segmentation group score indicates that the segment alignment in the candidate segmentation group represents the probability of a correct alignment with a reference genome. The segmentation group score illustrates the probability of the segmentation alignment and the geometry of the segmentation alignment. Therefore, by determining the segmentation group score rather than just the alignment score of the isolated segment alignment, the segmentation-read alignment system improves the probability of selecting the correct segment alignment or segment alignment combination to complete the template.

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

[0031] For double-end nucleotide reads, the segmentation-read alignment system can also identify candidate segmentation groups and score the candidate segmentation groups. Typically, in some specific implementations, the segmentation-read alignment system also considers and determines the pair scores of double-end partners to identify possible segmentation groups from the candidate segmentation groups of double-end partners. For example, the segmentation-read alignment system can sum the segmentation group scores of the corresponding candidate segmentation groups from the double-end partners, and estimate the insert size between the innermost fragment alignments of the candidate segmentation groups. The segmentation-read alignment system can then generate the pair scores of the candidate segmentation groups based on the segmentation group scores and the estimated insert size of the total. For illustration, the segmentation-read alignment system may include a pair score penalty for the insert size that is unlikely to be estimated.

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

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

[0034] As described above, the segmentation-read alignment system provides several technical advantages and benefits over existing sequencing systems and methods. For example, by considering the likelihood of segmentation alignments within various candidate segmentation groups corresponding to nucleotide reads, the segmentation-read alignment system improves the alignment accuracy of segmented reads relative to existing sequencing systems. By determining segmentation group scores for candidate segmentation groups that include segment alignments of fragments from nucleotide reads and selecting predicted segmentation groups from the candidates based on such segmentation group scores, the segmentation-read alignment system identifies segment alignments of segmented reads with better accuracy than existing sequencing systems. For example, FIG. 11A to FIG. 11D As illustrated, the segmentation-read alignment system determines better mapping and alignment for transcriptome reads and more accurate true negative variant calls for candidate gene fusion events compared to existing sequencing systems. FIG. 12A to FIG. 12D As shown in , the segment-read alignment system also determines better mapping and alignment of nucleotide reads on the genomic region of chromosome M of mitochondrial DNA, resulting in improved coverage relative to existing sequencing systems. Unlike only finding the primary alignment of a single segment with the highest alignment score, the segment-read alignment system considers and scores candidate segment alignments from nucleotide reads together as part of a segment group.

[0035] In addition to considering the fragment alignment together rather than in isolation, in some specific implementations, the segmentation-read alignment system also utilizes other computational model improvements to improve the accuracy of the segmentation-read alignment. For example, for a given segmentation group, the segmentation-read alignment system determines the breakage penalty for the relative geometry of the segment alignment in the candidate segmentation group. In some cases, the segmentation-read alignment system effectively identifies this type of segmentation group and scores the segmentation group, and considers the candidate segmentation group in detail by utilizing dynamic processing to quickly identify possible segmentation read alignments. For each candidate segmentation group, in some embodiments, the segmentation-read alignment system generates a segmentation group score based on the segment alignment score, the breakage penalty and the overlap penalty, thereby comprehensively assessing the possibility that a given candidate segmentation group includes a segment alignment.

[0036] Due in part to the improved segment-read alignment, the segment-read alignment system also improves the accuracy of corresponding nucleobase calls. Based on the more accurate segment-read alignment, the segment-read alignment system can accurately identify and report segment alignments when reads are aligned to alternative contiguous sequences. The segment-read alignment system can report segment alignments corresponding to alternative contiguous sequences in the primary assembly to further guide the selection of predicted segmentation groups. Due to the improved alignment, the segment-read alignment system can also determine more accurate variant calls or other nucleobase calls with a higher confidence rate than existing sequencing systems. FIG. 11A to FIG. 11DAs exemplified, for example, the segmentation-read alignment system determines more accurate true negative variant calls for candidate gene fusion events compared to existing sequencing systems. Fig.13 and FIG. 14A to FIG. 14B As shown, the split-read alignment system determines more accurate SNP calls, indel calls, and variant calls compared to existing sequencing systems.

[0037] In addition to improved alignment and increased base calling accuracy, in some embodiments, the segmentation-read alignment system improves computational efficiency by reducing the number of sequencing assays and computing devices used to determine structural variant calls. As described above, some existing sequencing systems consume a large amount of computer processing and time by running (i) WGS on a dedicated sequencing device to generate nucleotide reads of a genomic sample and (ii) multiple genotyping microarrays on a microarray device. By comparing the nucleotide reads to the reference genome of the WGS and analyzing the optical signals from the DNA probes in the microarray, the existing sequencing system can determine accurate variant calls of SNPs and smaller indels based on the reference genome on the one hand, and can determine targeted structural variants based on the DNA probes on the other hand. In contrast to such existing sequencing systems, in some embodiments, the segmentation-read alignment system promotes a more computationally efficient method by using a dedicated sequencing device to determine nucleotide reads with candidate segmentation groups—without or using fewer genotyping microarrays for targeting structural variants—to determine variant calls corresponding to structural variants or primary assembly regions of the reference genome. Thus, by determining segmentation group scores for candidate segmentation groups comprising segment alignments of segments from nucleotide reads and selecting predicted segmentation groups from the candidates based on such segmentation group scores, the segment-read alignment system can eliminate some or all genotyping microarrays for structural variants.

[0038] As exemplified in the above discussion, the present disclosure utilizes a variety of terms to describe the features and advantages of the segmentation-read alignment system. Additional details about 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 determined or predicted based on a sample library fragment corresponding to a genomic sample based on the nucleobase call of a nucleotide sequence (or a monoclonal nucleotide sequence group). For example, in some cases, a sequencing device determines a nucleotide read by generating a nucleobase call of a nucleobase that passes through a nanopore of a nucleotide sample slide, determined via fluorescent labeling, or determined based on a cluster in a circulation pool.

[0039] Nucleotide reads may include genomic nucleotide reads based on DNA sequences and transcriptome nucleotide reads based on ribonucleic acid (RNA). As used herein, the term "genomic reads" refers to nucleotide reads representing the inferred sequence of the nucleobase (or nucleobase pair) of the genomic DNA (gDNA) extracted from the sample. For example, genomic reads include reads comprising gDNA, which (i) extracts or derives from the gDNA extracted from the sample, and (ii) is a part of the sample library fragment corresponding to the sample. In some cases, genomic reads include reads comprising adapter sequences for transposase accessible chromatin (ATAC) read determinations, which are also referred to as ATAC reads. In some embodiments, genomic reads may include but are not limited to DNA enzyme 1 hypersensitive site (DNase) sequencing reads, formaldehyde-assisted separation of regulatory elements (FAIRE) sequencing reads, or Tet-assisted bisulfite (TAB) sequencing reads.

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

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

[0042] As used herein, "genomic region" refers to the scope of genomic coordinates. Like genomic coordinates, in some specific implementations, genomic region can be identified by the identifier of chromosome and one or more specific positions, such as the numbering position after the chromosome identifier (e.g., chr1:1234570-1234870). In various specific implementations, genomic coordinates are included in the position within the reference genome. In some cases, genomic coordinates are specific to a specific reference genome.

[0043] In addition, as used herein, the term "genomic sample" refers to a target genome or a portion of a genome that is subjected to sequencing. For example, a sample genome includes a nucleotide sequence (or a copy of such a separated or extracted sequence) isolated or extracted from a sample organism. Specifically, a sample genome includes a whole genome that is isolated or extracted (in whole or in part) from a sample organism and consists of nitrogen-containing heterocyclic bases. The sample genome may include fragments of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or other polymeric forms of nucleic acids or chimeric or hybrid forms of nucleic acids described below. In some cases, the sample genome is present in a sample prepared or separated by a kit and received by a sequencing device.

[0044] As used herein, the term "segmentation group" refers to a group of one or more fragment alignments corresponding to a nucleotide read. In particular, a segmentation group includes a chain of one or more fragment alignments that forms a segmentation alignment of a nucleotide read relative to a reference genome. For example, a segmentation group may include a fragment alignment of one or more fragments of a nucleotide read. Such fragment alignments may represent alignments of read fragments from a single-end nucleotide read or from a pair of double-end nucleotide reads (e.g., a mate). Relatedly, the term "candidate segmentation group" refers to a potential fragment alignment of a nucleotide read.

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

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

[0048] As further used herein, the term "alignment score" refers to a numerical score, a metric or other quantitative measurement of the accuracy of the alignment between a nucleotide read or a fragment of a nucleotide read and another nucleotide sequence from a reference genome. In particular, the alignment score includes a measure of the degree to which the nucleobase of an indication nucleotide read (or a fragment of a nucleotide read) matches or replaces a continuous sequence from a reference genome. In some specific implementations, the alignment score takes the form of a variation or version of a Smith-Waterman score or a Smith-Waterman score for local alignment, such as various settings or configurations used by Illumina, Inc. for DRAGEN for Smith-Waterman scoring. Therefore, the term "fragment alignment score" refers to the alignment score of the fragment alignment of a nucleotide read. Therefore, in a segmentation group including a plurality of fragment alignments, a fragment alignment score can be determined for each fragment alignment within the segmentation group.

[0049] Relatedly, the term "alternative contiguous sequence" (or simply "alternative contiguous") refers to a contiguous sequence representing a population haplotype added to a linear reference genome (or other reference genome) at one or more specific genomic coordinates (e.g., promoted to a linear reference genome). In some embodiments, a graph reference genome may include an alternating contiguous sequence of genomic coordinates mapped to a primary assembly of a linear reference genome. For example, an alternating contiguous sequence may represent a population haplotype comprising a promoted structural variant having two or more genomic coordinates corresponding to two or more flanks of a structural variant breakend in a linear reference genome. In some cases, a hash table for a graph reference genome includes an identifier that associates an alternating contiguous sequence representing a structural variant haplotype with a genomic coordinate representing a reference haplotype from a primary assembly of a linear reference genome.

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

[0051] As further used herein, the term "break penalty" refers to a numerical score, metric, or other quantitative measurement that penalizes a fragment alignment that exhibits a break between or among fragment alignments within a segmentation group. In particular, a break penalty may include a metric that penalizes the fragment alignment of a segmentation group to the extent (or in proportion to) that the fragment alignment exhibits a break in a nucleobase between the fragment alignments at a breakpoint. Thus, in some embodiments, the segmentation-read alignment system determines a relatively high break penalty for breaks between or among fragment 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, wherein the nucleotide reads are aligned to different positions within a reference genome. For example, a split alignment contains a breakpoint because when a fragment of nucleotide reads is aligned to different positions, thereby having a break or breakpoint between the fragments of nucleotide reads, the fragments exhibit the highest scoring alignment (e.g., highest alignment score) to a reference genome.

[0053] As further used herein, the term "overlap penalty" refers to a numerical score, metric or other quantitative measurement that penalizes the fragment alignments that overlap within a nucleotide read in a segmentation group. In particular, an overlap penalty may include a metric that penalizes the fragment alignments of a segmentation group to the extent that the fragment alignments exhibit overlapping nucleotide bases within a nucleotide read (or in proportion thereto). For example, a nucleotide read of 150 base pairs may have at least two fragment alignments. The first fragment alignment may align the 100 base pairs on the far left with a chromosome (e.g., Chr1) within a reference genome, and the second fragment alignment may align the 100 base pairs on the far right with another chromosome (e.g., Chr2). Although the exemplary fragment alignments do not overlap within the reference genome, the first fragment alignment and the second fragment alignment may overlap 50 base pairs within the nucleotide read. Therefore, an overlap penalty may represent a metric that penalizes such 50 base pairs of overlaps (or other exemplary overlaps of nucleotide bases) within the nucleotide reads from the aforementioned examples.

[0054] As further used herein, the term "gap penalty" refers to a numerical score, metric or other quantitative measurement that penalizes a pair of fragment alignments based on a gap between a pair of fragment alignments within a nucleotide read. In particular, a gap penalty may include a metric that penalizes the fragment alignment of a segmented group to the extent of the size of the gap between the fragment alignments within the nucleotide read (or in proportion thereto). For example, a nucleotide read of 150 base pairs may have at least two fragment alignments. The first fragment alignment may align the 50 base pairs on the far left with the first set of genome coordinates of the reference genome, and the second fragment alignment may align the 50 base pairs on the far right with the second set of genome coordinates of the reference genome. In contrast to the above-mentioned overlapping example, a nucleotide read may include a gap of 50 base pairs between the first fragment corresponding to the first fragment alignment and the second fragment corresponding to the second fragment alignment within the nucleotide read. Therefore, a gap penalty may represent a metric that penalizes such a gap of 50 base pairs between the first fragment alignment and the second fragment alignment within the nucleotide read.

[0055] As used herein, the term "split alignment" refers to the alignment of different fragments of a read to different regions in a reference genome. For example, a split alignment can refer to a split read or a chimeric alignment.

[0056] As further used herein, the term "pair scoring" refers to a numerical score, metric or other quantitative measurement that evaluates the accuracy of the alignment between a candidate segmentation group pair and a nucleotide sequence from a reference genome. In particular, the pair scoring includes a metric indicating the degree to which a candidate segmentation group pair accurately aligns with a nucleotide sequence from a reference genome. More specifically, in some embodiments, the pair scoring indicates the likelihood that a candidate segmentation group pair includes a true mate for a double-end nucleotide read. In fact, in some embodiments, the pair scoring represents the sum of the segmentation group scores of the corresponding candidate segmentation group pairs minus a pairing penalty.

[0057] As used herein, the term "pairing penalty" refers to a numerical score, metric, or other quantitative measurement that penalizes a pair of fragment alignments that are unlikely partners for double-end reads. In particular, the term pairing penalty refers to a metric that indicates the likelihood or improbability of a fragment alignment being correctly paired based on the geometry of two or more fragment alignments relative to a reference genome. For example, a pairing penalty may represent the logarithmic likelihood or alternatively the log P value of the insert size between the two innermost fragment alignments based on an empirical insert distribution.

[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 the sequence length, in some cases, a reference genome represents an exemplary set of genes or a set of nucleic acid sequences determined to represent a digital nucleic acid sequence of an organism. For example, a linear human reference genome can be GRCh38 (or other versions of a reference genome) from the Genome Reference Alliance. Although GRCh38 may include alternating continuous sequences representing alternating haplotypes, such as SNPs and small insertions and deletions (e.g., 10 or less base pairs, 50 or less base pairs), GRCh38 includes alternating haplotypes with restricted representations of population structural variants. In fact, the structural variants represented in GRCh38 only include those structural variants represented by 11 individuals on which its library GRCh38 is constructed. Relatedly, the term "reference region" refers to a portion or a small portion of a reference genome. For example, a reference region can be a selected number of nucleobases (e.g., 150 bases) from a reference genome.

[0059] As used herein, the term "variant" refers to one or more nucleobases that are not aligned, different or varied from the corresponding nucleobases (or nucleotide bases) in a reference sequence or reference genome. For example, variants include SNPs, indels, or structural variants, which indicate nucleobases in a sample nucleotide sequence that are different from the reference nucleobases in the corresponding genomic coordinates of the reference sequence. Similarly, a "variant nucleobase call" refers to a nucleobase call that contains a variant at a specific genomic coordinate. In contrast, a "non-variant nucleobase call" refers to a nucleobase call that contains a non-variant (or matching reference base) at a genomic coordinate.

[0060] Additionally, as used herein, the term "nucleobase call" (or sometimes simply "nucleotide base call" or "base call") refers to the determination or prediction of a specific nucleobase (or nucleobase pair) of a genomic coordinate of an oligonucleotide (e.g., a nucleotide read) or a sample genome during a sequencing cycle. Specifically, a nucleobase call can indicate: (i) the determination or prediction of the type of nucleobase that has been incorporated into an oligonucleotide on a nucleotide sample slide (e.g., a read-based nucleobase call); or (ii) the determination or prediction of the type of nucleobase present at a genomic coordinate or region within a genome, including variant calls or non-variant calls in a digital output file. In some cases, for a nucleotide read, a nucleobase call includes determining or predicting a nucleobase based on an intensity value generated by a fluorescently labeled nucleotide of an oligonucleotide added to a nucleotide sample slide (e.g., in a cluster of a flow cell). Alternatively, a nucleobase call includes determining or predicting a nucleobase based on chromatographic peaks or current changes generated by nucleotides passing through a nanopore of a nucleotide sample slide. In contrast, based on the nucleotide reads corresponding to the genomic coordinates, the nucleobase call may also include the nucleobase at the genomic coordinates of the sample genome of the final predicted variant call file (VCF) or other base call output file. Therefore, the nucleobase call may include base calls corresponding to the genomic coordinates and the reference genome, such as an indication of a variant or non-variant at a specific position corresponding to the reference genome. In fact, the nucleobase call may refer to a variant call, including but not limited to a single nucleotide variant (SNV), an insertion or deletion (indel), or a base call as part of a structural variant. As mentioned above, a single nucleobase call may be an adenine (A) call, a cytosine (C) call, a guanine (G) call, a thymine (T) call, or a uracil (U) call.

[0061] As further used herein, the term "alignment file" refers to a digital file indicating the relative alignment or mapping of nucleotide reads to a nucleotide sequence of a reference genome or other reference nucleotide sequence. In particular, an alignment file may include data indicating the relative mapping positions of nucleotide reads and the nucleotide sequence of a reference genome. In some embodiments, an 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 call file" refers to a digital file that indicates or represents one or more nucleobase calls (e.g., variant calls) compared to a reference genome and other information about these nucleobase calls (e.g., variant calls). For example, a variant call format (VCF) file refers to a text file format containing information about variants at specific genomic coordinates, the text file format including a meta-information line, a header line, and data lines, where each data line contains information about a single nucleobase call (e.g., a single variant).

[0063] In some embodiments, the segmentation-read segment alignment system or the corresponding sequencing system uses a detection generation model to determine nucleotide base calls (e.g., variant calls or genotype calls). As used herein, the term "detection generation model" refers to a probability model for generating sequencing data based on nucleotide reads of a sample nucleotide sequence, and the sequencing data includes nuclear base calls, variant calls and / or genotype calls and 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 probability model for generating variant calls based on nucleotide reads of 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 genomic coordinate), including mapping quality, base quality, and various assumptions, including exogenous reads, missing reads, joint detection, and the like. 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 marking, calculating read stacking depth, and variant calls. In some cases, the call generation model refers to the ILLUMINA DRAGEN model for variant calling functions and mapping and alignment functions (e.g., DRAGEN variant caller or "DRAGEN VC").

[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 specific application. For example, a configurable processor includes an integrated circuit chip designed to be configured or customized on-site by an end-user's computing device to perform a specific application. Configurable processors include, but are not limited to, ASICs, ASSPs, coarse-grained reconfigurable arrays (CGRAs), or FPGAs. In contrast, configurable processors do not include CPUs or GPUs. In some embodiments, the segmentation-read segment alignment system uses a configurable processor (e.g., FPGA) or a processor (e.g., CPU) to perform various embodiments described herein.

[0065] The following paragraphs describe the segmentation-read alignment system with respect to illustrative drawings depicting exemplary implementations and embodiments. For example, Figure 1A schematic diagram of a computing system 100 in which a segmentation-read alignment system 106 operates according to one or more specific implementations is illustrated. As illustrated, the computing system 100 includes one or more server devices 102 connected to user client devices 108, local devices 118, and sequencing devices 114 via a network 112. The network 112 may include any suitable network over which computing devices can communicate. Fig.16 Exemplary networks are discussed in more detail.

[0066] like Figure 1 As shown, the computing system 100 includes a server device 102. In various implementations, the server device 102 can generate, receive, analyze, store, and transmit digital data, such as data for nucleobase calls or sequenced nucleic acid polymers. In some implementations, the server device 102 receives various data from a sequencing device 114, such as data from a sample genome 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 of nucleotide reads, direct nucleobase calls, nucleobase calls, and / or sequencing metrics to the user client device 108.

[0067] As shown, the server device 102 includes a sequencing system 104. Typically, the sequencing system 104 analyzes data (e.g., call data) received from the sequencing device 114 or elsewhere to determine the nucleobase sequence of the nucleic acid polymer. For example, the sequencing system 104 can receive raw data from the sequencing device 114 and determine the nucleobase sequence of the sample genome or nucleic acid segment. In some specific implementations, the sequencing system 104 determines the sequence of the nucleobases in the DNA and / or RNA segment or oligonucleotide.

[0068] As also shown, the sequencing system 104 includes a segmentation-read alignment system 106. As described below, the segmentation-read alignment system 106 can determine a segmentation-read alignment of a nucleotide read to a reference genome 116. For example, in some embodiments, the segmentation-read alignment system 106 identifies one or more nucleotide reads corresponding to a genomic region of a genomic sample. The segmentation-read alignment system 106 also (i) determines a candidate segmentation group including a segment alignment corresponding to the one or more nucleotide reads, and (ii) generates a segmentation group score for the segmentation alignment of the candidate segmentation group to the reference genome 116. Based on the segmentation group score, the segmentation-read alignment system 106 selects a predicted segmentation group from the candidate segmentation group for nucleobase calling.

[0069] like Figure 1As further shown, the computing system 100 includes a user client device 108. In various specific implementations, the user client device 108 can generate, store, receive, and send digital data. In particular, the user client device 108 can receive data from a 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, a desktop application, or a 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 segmentation-read segment alignment system 106. For example, the user client device 108 can receive a variant call file and / or an alignment file from the sequencing system 104.

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

[0071] like Figure 1 As further shown, the computing system 100 optionally includes a sequencing device 114. In various specific implementations, the sequencing device 114 can sequence a genomic sample or other nucleic acid polymer. For example, the sequencing device 114 analyzes nucleic acid segments or oligonucleotides extracted from a genomic sample to directly or indirectly generate data on the sequencing device 114. More specifically, the sequencing device 114 receives and analyzes nucleic acid sequences extracted from a genomic sample in a nucleotide sample slide (e.g., a circulation pool). In one or more specific implementations, the sequencing device 114 utilizes SBS to sequence a genomic sample or other nucleic acid polymer. In addition or in lieu of communicating across the network 112, in some specific implementations, the sequencing device 114 bypasses the network 112 and communicates directly with the user client device 108.

[0072] like Figure 1As further depicted, in some implementations, the server device 102 comprises a distributed server collection, wherein the server device 102 comprises several server devices distributed across the network 112 and located in the same or different physical locations. For example, the server device 102 can be implemented in whole or in part on a local device 118. For illustration, the local device 118 can implement the sequencing system 104 and / or the segmentation-read alignment system 106. In addition, the server device 102 and / or the local device 118 can include a content server, an application server, a communication server, a web hosting server, or another type of server.

[0073] Figure 1 The user client device 108 illustrated in the example may include various types of client devices. For example, in some implementations, the user client device 108 includes a non-mobile device, such as a desktop computer or a server, or other types of client devices. In various implementations, the user client device 108 includes a mobile device, such as a laptop, a tablet computer, a mobile phone, or a smart phone. Additional details about the user client device 108 are provided below in conjunction with Fig.16 Discussion.

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

[0075] although Figure 1 The components of the computing system 100 are illustrated as communicating via the network 112, but in some implementations, the components of the computing system 100 may also communicate directly with each other, bypassing the network 112. For example, in some implementations, the user client device 108 communicates directly with the sequencing device 114. Additionally, in some implementations, the user client device 108 communicates directly with the segmentation-read alignment system 106 and / or the server device 102. In some implementations, the user client device 108 communicates directly with the local device 118. Additionally, the segmentation-read alignment system 106 may access one or more databases hosted on or accessed by the server device 102 or elsewhere in the computing system 100.

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

[0077] like Figure 2 As illustrated, the segmentation-read alignment system 106 performs an action 202 of identifying one or more nucleotide reads. In particular, the segmentation-read alignment system 106 identifies one or more nucleotide reads corresponding to the genomic region of the genomic sample. For example, the segmentation-read alignment system 106 can identify nucleotide reads corresponding to the template strand or sequence of the genomic sample. More specifically, the template includes an original continuous DNA or RNA fragment sequenced by a single-end or double-end method. In the single-end method, a single read is sequenced from one end of the template. Because the single-end read is sequenced from one end of the template, the single read represents the complementary sequence of the template. In the double-end method, a first read (e.g., R1) is sequenced from one end of the template to the middle, and a second read (e.g., R2) is sequenced from the other end. Figure 2 Two double-end reads R1 and R2 are illustrated, oriented toward each other. As illustrated, there is a gap between R1 and R2, however, there may also be overlap between R1 and R2. R1 and R2 can be described as double-end partners.

[0078] like Figure 2 As further illustrated, the segmentation-read alignment system 106 performs an action of determining candidate segmentation groups 204. In particular, the segmentation-read alignment system 106 determines candidate segmentation groups that include fragment alignments corresponding to one or more nucleotide reads. Typically, a fragment alignment refers to a candidate local alignment of a fragment of a read. Figure 2 The segment-read alignment system 106 is illustrated to determine a candidate segment group for R1. R1 may be a single-end read or one of two paired-end reads. R1 may include different one or more segments.

[0079] To illustrate the segment-to-segment comparison, Figure 2The segment-read alignment system 106 is shown identifying various fragments of nucleotide reads. As illustrated, the segment-read alignment system 106 identifies fragment 218, fragment 220, fragment 222, and fragment 224 corresponding to R1. Figure 2 The exemplified fragments are separated by breaks representing structural variant (or "SV") breakpoints. Figure 2 R1 broken by a single SV breakpoint is illustrated, but the nucleotide read may have no SV breakpoint or may have several SV breakpoints.For example, fragment 220 may be further broken into two or more fragments.

[0080] like Figure 2 Further illustrated, the segmentation-read alignment system 106 determines the candidate segmentation group 214a-214c of R1. As part of performing action 204, the segmentation-read alignment system 106 identifies the segment alignment of the identified segments of the read. Generally, the segments of the read can be aligned with different sequences in the reference genome. For example, segments 218 and 220 can be aligned with nearby genomic regions of the reference genome on the same chromosome. In contrast, segment 218 can be aligned with a reference genome on one chromosome, while segment 220 can be aligned with a reference genome on another chromosome.

[0081] Figure 2 Candidate segment 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 segments 218-222 of R1 on a reference genome. For example, candidate segmentation group 214a includes candidate segment alignments of segments 218 and 220 relative to a reference genome. FIG. 3A to FIG. 4 A segmentation-read alignment system 106 for determining candidate segmentation groups of single-end and double-end nucleotide reads according to one or more embodiments is illustrated and the corresponding paragraphs further describe in detail.

[0082] like Figure 2 As further illustrated, the segmentation-read alignment system 106 performs an action 206 of generating a segmentation group score. Typically, the segmentation-read alignment system 106 generates a segmentation group score for the segmentation alignment of the candidate segmentation group with the reference genome. The segmentation-read alignment system 106 can generate a segmentation group score for the segmentation group based on the fragment alignment score, the break penalty, and the overlap penalty. As illustrated, the segmentation-read alignment system 106 generates a segmentation group score of 0.98 for the candidate segmentation group 214a and a segmentation group score of 0.73 for the candidate segmentation group 214b. Figure 5 The examples and corresponding discussion provide additional details regarding determining segmentation group scores according to one or more embodiments.

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

[0084] As mentioned, the segment-read alignment system 106 can generate predicted segmentation groups for single-end reads and paired-end reads. In some implementations, the segment-read alignment system 106 predicts segmentation groups based in part on pair scores of candidate segmentation group pairs. FIG. 6A to FIG. 6B A segment-read alignment system 106 for generating pair scores is illustrated according to one or more embodiments.

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

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

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

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

[0089] As mentioned, Figure 3B The segmentation-read alignment system 106 for determining candidate segmentation groups of nucleotide reads in double-end sequencing according to one or more embodiments is illustrated. In general, double-end sequencing of a sequence includes generating paired nucleotide reads starting at different (and opposite) positions of a library template. In particular, double-end sequencing generates two mate reads. For example, Figure 3B The R1 and R2 exemplified in include paired partners. As mentioned, there may be a gap between R1 and R2, or the paired-end reads may overlap.

[0090] In some cases, one double-end partner crosses a breakpoint (e.g., an SV breakpoint) while the other double-end partner does not. To illustrate, R2 may cross a breakpoint while R1 does not. Thus, R2 may be segmented into fragment 302 and fragment 304 while R1 remains intact fragment 316. In this example, the 3' end of R2 (e.g., the inner end of fragment 302) is in a correctly paired position relative to the partner alignment of intact fragment 316 while fragment 304 may potentially be aligned at 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, break 336a and break 336b may represent the same breakpoint. In this example, segment 318 of R1 overlaps segment 302 of R2, and segment 320 of R1 is represented by segment 304 of R2.

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

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

[0094] As previously mentioned Figure 3A As mentioned, in some embodiments, the candidate segmentation group includes a strand of fragment alignment of a nucleotide read. As described above, nucleotide reads and fragment alignments may have various nucleobase lengths. As illustrated, the segmentation-read alignment system 106 may determine that the candidate segmentation group includes an alignment of the entire nucleotide read. For example, the candidate segmentation group 324a includes an alignment of a complete fragment 316 containing a complete R1 relative to a reference genome 327. In contrast, the candidate segmentation group may also include overlapping fragment alignments. For example, the candidate segmentation group 324c of R1 and the candidate segmentation group 340c of R2 include overlapping fragment alignments. The segmentation-read alignment system 106 may also determine non-overlapping candidate segmentation groups. For example, the candidate segmentation group 324b of R1 and the candidate segmentation group 340a of R2 include non-overlapping fragment alignments. In addition, the segmentation-read alignment system 106 may generate candidate segments including strands of more than two fragment alignments. In addition, the candidate segments may also include fragment alignments with different geometric orientations relative to the reference genome.

[0095] FIG. 3A to FIG. 3B The segment-read alignment system 106 that generates candidate segmentation sets of single-end and double-end nucleotide reads is illustrated. In some implementations, the segment-read alignment system 106 utilizes dynamic programming to efficiently generate and evaluate all possible segment alignment sequences. Figure 4 The illustration and corresponding discussion describe a segment-read alignment system 106 that utilizes dynamic programming to generate and evaluate candidate segmentation groups according to one or more embodiments.

[0096] In some embodiments, by utilizing dynamic programming, the segmentation-read alignment system 106 considers a subset of each possible candidate segmentation group. More specifically, the segmentation-read alignment system 106 identifies a subset of possible candidate segmentation groups by evaluating the segment alignments in a particular order. To illustrate, in some implementations, the segmentation-read alignment system 106 determines candidate segmentation groups by iteratively grouping the segment alignments in the order of the outermost segment alignment to the innermost segment alignment of the nucleotide reads. The segmentation-read alignment system 106 also iteratively scores the groupings of the segment alignments in the order in which the segment alignments are grouped.

[0097] Typically, each read has two ends, a 3' end or a 5' end, where the 3' is designated as "inside" and the 5' end is designated as "outside". For double-end reads, the terms inside and outside refer to the expected relative positions in the template. For single-end or double-end reads with a forward-reverse (FR) pair orientation, the 3' end represents the inside end and the 5' end represents the outside end. When a reverse-forward (RF) or forward-forward (FF) / reverse-reverse (RR) pair orientation is expected, the split-read alignment system 106 dynamically determines the inside and outside read ends. In particular, the split-read alignment system 106 specifies the innermost fragment alignment and the outermost fragment alignment based on the geometry of the observed pair of correct fragment alignments with the highest total alignment score.

[0098] Figure 4 The process of dynamic programming performed by the segmentation-read segment alignment system 106 is illustrated. As shown for illustrative purposes, the segment alignments 402-410 are organized in the Smith-Waterman matrix. In addition to depicting the position of the segment alignments relative to the nucleotide reads and the reference genome, the Smith-Waterman matrix also illustrates the orientation of the segment alignments 402-410. For example, as depicted in the figure, the segment alignment 406 represents a forward alignment, and the segment alignment 408 represents a reverse complementary alignment. Figure 4 The fragment alignments 402-410 are depicted as perfect ungapped diagonal alignments, but each of the fragment alignments 402-410 may contain indels (insertions and / or deletions). In some embodiments, such indels are relatively small-sized variants (e.g., <50 base pairs), as opposed 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 by multi-fragment segmentation-read alignments.

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

[0100] After grouping the outermost segment alignments and the next-next-outer segment alignments (and determining their segment group scores), the segment-read alignment system 106 groups the outermost segment alignments and the next-next-outer segment alignments (and determines their segment group scores). Thus, the segment-read alignment system 106 groups the segment alignments 410 with the segment alignments 406. The grouping of the segment alignments 410 and the segment alignments 406 constitutes the candidate segment group 412b.

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

[0102] In some embodiments, candidate segmentation group 412a and candidate segmentation group 412b represent partial segmentation groups. Typically, a partial segmentation group includes one or more fragment alignments, and the fragment alignments represent the fragment alignments of a portion rather than the entire nucleotide read. Segmentation-read alignment system 106 can associate other fragment alignments with partial segmentation groups. For example, in some embodiments, as a part of dynamic programming, segmentation-read alignment system 106 associates other fragment alignments with the partial segmentation group with the highest segmentation group score. By associating other fragment alignments with the highest-scoring partial segmentation group, segmentation-read alignment system 106 reduces the processing power required to exhaustively generate candidate segmentation groups.

[0103] Despite Figure 4406 into candidate segmentation group 412 b (and determine its segmentation group score), the segmentation-read alignment system 106 groups (and determines its segmentation group score) additional candidate segmentation groups including segmentation group 410, segmentation group 408, and segmentation group 406. If the segmentation group score 414 b of candidate segmentation group 412 b exceeds the additional segmentation group score of the additional candidate segmentation group, the segmentation-read alignment system 106 continues to group (and determine its segmentation group score) candidate segmentation groups including segmentation group 410 and other segment alignment combinations. For example, the segmentation-read alignment system 106 (i) groups (and determines its segmentation group score) segmentation group 410, segmentation group 406, and segmentation group 404, and (ii) groups (and determines its segmentation group score) segmentation group 410 and segmentation group 404.

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

[0105] like Figure 4 As further illustrated, the segmentation-read alignment system 106 generates a candidate segmentation group 412n including the segment alignment 402, the segment alignment 406, and the segment alignment 410. The segmentation-read alignment system 106 adds the segment alignment 402 to the candidate segmentation group 412b because, in this example, the candidate segmentation group 412b has the highest-scoring segmentation group score, i.e., the segmentation group score 414b. The segmentation-read alignment system 106 scores the candidate segmentation group 412n and assigns a segmentation group score 414n. In this way, the segmentation-read alignment system 106 iterates from the outermost segment alignment toward the innermost segment alignment. For each segment under consideration, the segmentation-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 peripheral fragment alignment results in an improvement in the segmentation group score, the segmentation-read alignment system 106 will retain the next peripheral fragment alignment as part of the candidate segmentation group. If adding the next peripheral fragment alignment does not result in an improvement in the segmentation group score, the segmentation-read alignment system 106 discards the next peripheral fragment alignment from the candidate segmentation group and moves forward to the next peripheral fragment alignment. By performing dynamic programming, the segmentation-read alignment system 106 accordingly continues to group the candidate segmentation groups (and determine their segmentation group scores) in the order of the outermost segment alignment to the innermost segment alignment of the nucleotide reads until each candidate segmentation group is deemed not to improve the highest segmentation group score or is eliminated because it cannot improve the highest segmentation group score.

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

[0108] As mentioned above, the segmentation-read segment alignment system 106 can specify a segmentation group score for each candidate segmentation group. In some embodiments, the candidate segmentation group includes any chain of fragment alignments that follow certain rules. For example, the candidate segmentation group includes chains of one or more fragment alignments of the same read from the head segment to the tail segment. According to one embodiment of the rule, the head segment is closest to the inner end of the nucleotide read, and the tail segment is closest to the outer end of the nucleotide read. The inner gap of the fragment is its distance from the inner end of the nucleotide read, and the outer gap of the fragment is its distance from the outer end of the nucleotide read. For continuous fragment alignments A and B, for example, the rule can be expressed as follows: i) A, inner_space ≤ B. inner_space and (ii) A. outer_space> B. outer_space. The same fragment alignment can participate in multiple segmentation groups.

[0109] like Figure 5As illustrated, the segmentation-read alignment system 106 generates a segment alignment score 502 for segment alignments A and B. As indicated above, the segment alignment score may include a numerical score, a metric or other quantitative measurement of the alignment accuracy of the segment alignment from the nucleotide read. For example, the segment alignment score may indicate the possibility that a given alignment of a segment is correct relative to a reference genome. As indicated above, if the nucleotide read segment fragment matches or is similar to a reference sequence (or alternative continuous sequence) from a reference genome, such segment alignment score may indicate the probability degree of a nucleobase. For example, the segmentation-read alignment system 106 may specify a segment alignment score for each segment alignment in the segmentation group by determining a Smith-Waterman score or a version of a Smith-Waterman score. In other specific implementations, the segmentation-read alignment system 106 utilizes a variation of the segment alignment score. As illustrated, the segmentation-read alignment system 106 merges (e.g., sums) the segment alignment scores of two segment alignments A and B in the segmentation group.

[0110] like Figure 5 As further illustrated, the split-read alignment system 106 determines a break penalty 506 . Figure 5 The three factors that the segmentation-read alignment system 106 analyzes to generate the break penalty 506 are illustrated - segment alignment orientation, identical reference sequence, and effective indel length. As described above, in some embodiments, the break penalty 506 represents a measure of penalizing the segment alignments of the segment group to the extent that the relative geometry of the segment alignments exhibits nucleobase breaks. More specifically, the break penalty 506 indicates the relative geometry of the segment alignments A and B relative to the reference genome. In some embodiments, and as Figure 5 As illustrated, the segmentation-read alignment system 106 determines the break penalty 506 based on the fragment alignment orientation. For example, the fragment alignment orientation refers to whether the fragment alignment has a forward orientation or a reverse orientation. For illustration, in some cases, the expected orientation of the double-end template will be that the two fragment alignments point toward each other. For example, the segmentation-read alignment system 106 determines the break penalty 506 based on whether the fragment alignments A and B have opposite orientations or are reversed.

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

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

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

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

[0115] Instead of determining the effective indel length, in some embodiments, the segmentation-read alignment system 106 determines the break distance in the chromosome. In one example, the segmentation-read alignment system 106 determines the distance between the starting points of the segment alignment within the reference genome and compares the distance between the starting points of the segment alignment with the expected break distance. In another example, the segmentation-read alignment system 106 determines the distance between the closest end points of the two segment alignments and compares the distance with the expected break distance.

[0116] In addition, in the split alignment example, the split-read alignment system 106 determines an initial break penalty (e.g., expressed as a split-open-pen) before considering the effective indel length. In at least one example, the break penalty is equal to the greater of (i) the maximum break penalty or (ii) a break penalty determined based on the inverse 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 Also illustrated is the segmentation-read alignment system 106 that determines overlap penalty 508.As described above, in some embodiments, overlap penalty 508 represents the metric that the segment alignment of segmentation group is punished in the degree that segment alignment overlaps in nucleotide read.For example, in some embodiments, overlap penalty 508 equals the overlap amount in the read between segment alignment A and B multiplied by Smith-Waterman matching score.As indicated above, when segment includes (and with reference genome alignment) overlapping nucleotide read base from nucleotide read, segment alignment overlap may occur.By determining overlap penalty, segmentation-read alignment system 106 avoids the double counting of the read core base of reference genome in two segments of segment alignment.

[0118] In some implementations, as part of determining the segmentation group score, the segmentation-read alignment system 106 also determines other penalties. For illustration, the segmentation-read alignment system 106 can determine a gap penalty. The gap penalty is complementary to the overlap penalty 508. More particularly, in some embodiments, the gap penalty represents a numerical score, metric, or other quantitative measurement that penalizes the segment alignment of the segmentation group to the extent that there is a gap between the segment alignments. In some implementations, the gap penalty represents a negative overlap, and the overlap penalty represents a negative gap.

[0119] As mentioned above, in some embodiments, the segmentation-read alignment system 106 generates and scores segmentation groups by using dynamic programming. Figure 4 The order of the outermost segment alignment toward the innermost segment alignment shown in the example is as follows Figure 5 Generating segmentation group scores for candidate segmentation groups is exemplified in FIG.

[0120] In some implementations, and as previously mentioned, the segment-read alignment system 106 evaluates candidate segment groups based on pair scores. More specifically, the segment-read alignment system 106 evaluates alignment pairs of candidate segment group pairs and selects a predicted segment group based on the pair scores. Fig. 6A A segment-read alignment system 106 for generating pair scores is illustrated according to one or more embodiments. Figure 6B A segment-read alignment system 106 for determining predicted segmentation groups based on pair scores is illustrated in accordance with one or more embodiments.

[0121] Fig. 6A The segmentation-read alignment system 106 for generating a pair score based on the segmentation group score 602 and the pairing penalty 608 is illustrated. In some specific implementations, the segmentation-read alignment system 106 identifies the candidate segmentation group pairs of different fragment alignments of the partner of the double-end nucleotide read from the candidate segmentation group. For example, the segmentation-read alignment system 106 identifies the candidate segmentation group pairs including the segmentation group 604 and the segmentation group 606. More specifically, the segmentation group 604 includes the fragment alignment A and B, and the segmentation group 606 includes the fragment alignment C and D. As shown, the segmentation group 604 and the segmentation group 606 are aligned with the reference genome. More specifically, the segmentation group 604 and the segmentation group 606 include candidate double-end partners aligned along the reference genome. For example, the segmentation group 604 can represent R1 and the segmentation group 606 can represent the R2 of the double-end read.

[0122] like Fig. 6A Further illustrated, the segmentation-read alignment system 106 generates a segmentation group score 602. As described above, in some embodiments, the score evaluates the accuracy of the comparison of the candidate segmentation group to the reference genome. In some specific implementations, the segmentation group score 602 includes the sum of the segmentation group scores of the candidate segmentation group pairs. For illustration, the segmentation-read alignment system 106 sums the segmentation group score of the segmentation group 604 and the segmentation group score of the segmentation group 606 as some or all of the score.

[0123] like Fig. 6AFurther illustrated, the segmentation-read alignment system 106 generates a pairing penalty 608 for a candidate segmentation group pair. The segmentation-read alignment system 106 can determine the pairing penalty 608 based on the estimated insert size between the innermost fragment alignment of the candidate segmentation group pair. In some cases, the fragment alignment corresponding to the double-end partner is positioned relatively close to each other in the reference genome. The segmentation-read alignment system 106 can determine a known empirical insert size distribution. In some embodiments, the segmentation-read alignment system 106 determines a known empirical insert size distribution by analyzing the insert size in the sequence library. The known empirical insert size distribution generally indicates the most likely insert size of the sequence library. Therefore, based on the empirical insert size distribution, if the two innermost fragment alignments are positioned close together or at an expected distance from each other, the segmentation-read alignment system 106 can be specified as 0 or a small pairing penalty.

[0124] For example, the segment-read alignment system 106 determines an estimated insert size 610 between the innermost segment alignments B and C. Fig. 6A As indicated, the estimated insert size 610 includes the length of the library template, and the mate nucleotide read is sequenced from the template at each end. The segmentation-read segment alignment system 106 compares the estimated insert size 610 with the expected insert size based on the empirical insert size distribution. In the case where the estimated insert size 610 is greater than or even less than the expected insert size, the segmentation-read segment alignment system 106 specifies a larger pairing penalty for the candidate segmentation group. In some embodiments, the segmentation-read segment alignment system 106 determines the fixed pairing penalty for the candidate segmentation group outside the expected insert size range. In other specific implementations, the segmentation-read segment alignment system 106 utilizes a sliding scale, wherein the segmentation-read segment 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 sequenced at each end to obtain two double-end nucleotide reads. For example, the two double-end nucleotide reads include fragment alignments A, B, C, and D. In at least one specific implementation, the insert size is estimated from the reference position of the end points of the innermost fragment alignments B and C, and extrapolated to take into account the outer portions of the two double-end nucleotide reads not covered by the fragment alignments B and C. For illustration, the segmentation-read alignment system 106 can extrapolate to take into account the outer portions including the portions covered by the fragment alignments A and D. However, in Fig. 6AIn the illustrated example, the split-read alignment system 106 does not consider the reference positions of the outer fragment alignments A and D because of the SV break between fragment alignments A and B and the SV break between fragment alignments C and D. Therefore, the positions of fragment alignments A and D do not provide strong information about the true insert size due to the SV break.

[0126] In some implementations, the segmentation-read alignment system 106 also adjusts the pairing penalty 608 based on the segmentation group position and the segmentation group orientation. For example, the segmentation-read alignment system 106 may specify a larger pairing penalty for the segmentation groups that are aligned to different chromosomes of the reference genome in the candidate segmentation group pair. As mentioned, the segmentation-read alignment system 106 can also specify a larger pairing penalty based on the orientation of the segmentation group. For example, if the fragment alignment is oriented to the same orientation (e.g., both are oriented to 3' to 5' from the reference genome) rather than a complementary orientation (e.g., pointing to each other), the segmentation-read alignment system 106 specifies a larger pairing penalty for the candidate segmentation group pair.

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

[0128] As mentioned, in some cases, two double-end mate reads overlap at the same breakpoint (e.g., SV breakpoint). When overlapping mates cross breakpoints in their overlapping regions, each mate can be similarly segmented and aligned, each as two fragments. In some embodiments, segmentation-read alignment system 106 detects these "quads" as special cases, and specifies a pair score involving only one copy of a break penalty (but two overlap penalties). When such "quads" of segmentation overlapping alignments show the highest pair score, segmentation-read alignment system 106 selects R1 and R2 fragment alignments on the same side of the break as the main alignment, that is, a 5' fragment alignment and a 3' fragment alignment, to support correct pairing. Typically, segmentation-read alignment system 106 selects 5' fragment alignments with higher scores together with 3' fragment alignments of mates as the main alignment.

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

[0130] As mentioned, in some embodiments, the segment-read alignment system 106 selects predicted segmentation groups based on pair scores. Figure 6B The example and corresponding paragraphs describe a segment-read alignment system 106 for selecting predicted segment groups based on pair scores. As an overview, Figure 6B Pair scores 622 for candidate segmentation group pairs 626a-626c are illustrated. Candidate segmentation group pair 626a includes segmentation group 611 and segmentation group 612. The empty box within the segmented arrow in segmentation group 612 represents the break (e.g., SV break) between the fragment alignments that constitute segmentation group 612. In contrast to candidate segmentation group pair 626a, candidate segmentation group pair 626b includes segmentation group 614 and segmentation group 616. Finally, candidate segmentation group pair 626c includes segmentation group 618 and segmentation group 620. As explained below, the segmentation-read alignment system (i) selects the candidate segmentation group pair with the highest pair score, and (ii) selects the predicted segmentation group for each partner of the nucleotide read pair from the candidate segmentation group pair with the highest pair score.

[0131] In some cases, the candidate segmentation group with the highest segmentation group score may not necessarily show the correct segmentation alignment. For example, a relatively high segmentation group score indicates that the nucleotide reads show a possible way of segmentation alignment. However, this relatively high segmentation group score may involve an unlikely pairing configuration of two partners from a pair of double-end nucleotide reads. By generating a pair score in addition to the segmentation group score, the segmentation-read alignment system 106 further considers the pairing configuration of the fragment alignment of the partners from the double-end nucleotide reads when selecting the predicted segmentation group.

[0132] To illustrate, for example, segment group 614 can 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 determining that the pair score of candidate segment group pair 626a exceeds the pair score 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 particular partner instead of selecting segment group 614 from candidate segment group pair 626b.

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

[0134] In some implementations, the segment-read alignment system 106 can determine segment alignments that align to alternative contiguous (or "alternative contiguous") sequences within a reference genome. Figure 7 A segment-read alignment system 106 for scoring alternative contiguous segment alignments corresponding to nucleotide reads having alternative contiguous sequences is illustrated according to one or more embodiments. As an overview, Figure 7 A series of acts 700 are illustrated, including an act 702 of determining an alternative contiguous segment alignment score, an act 704 of determining a segment group score, and an act 708 of selecting an alternative contiguous segment alignment score. If the alternative contiguous segment alignment score exceeds the segment group score of the segment alignment, the segment-read alignment system 106 reports the segment alignment with the segment alignment corresponding to the primary assembly of the alternative contiguous sequence.

[0135] Typically, the segmentation-read alignment system 106 identifies alternative continuous sequences representing structural variants. The segmentation-read alignment system 106 determines that the fragment of the nucleotide read exhibits the highest fragment alignment score with the alternative continuous sequence, and therefore reports the segmentation alignment in the corresponding primary assembly region. For example, if the segmentation-read alignment system 106 determines that the segmentation alignment of the nucleotide read exhibits an alternative continuous fragment alignment score relative to the alternative continuous sequence, wherein the alternative continuous fragment alignment score exceeds the segmentation group score of other candidate segmentation groups of the nucleotide read, then the segmentation-read alignment system 106 uses the alternative continuous fragment alignment score to replace the other candidate segmentation group score for improving the corresponding segmentation alignment (without any break penalty). Therefore, the alternative continuous fragment alignment score can guide the segmentation-read alignment system 106 to select and report a given segmentation alignment, rather than other candidate segmentation alignments represented by other segmentation groups that may otherwise obtain better scores in the absence of an alternative continuous fragment alignment score.

[0136] When the alternative contiguous sequence represents an SV breakpoint, for example, the segmentation-read alignment system 106 may identify two primary segment alignments of the same boosted group as one alternative segment alignment. In some cases, multiple primary segments of an boosted group are treated as duplicates of each other, and only the best-scoring segment alignment is retained. However, in the case where the nucleotide read matches an alternative contiguous sequence that spans the SV break, the segmentation-read alignment system 106 may retain the two primary segment alignments and add them to the segmentation group using the alignment score of the alternative contiguous sequence.

[0137] like Figure 7 As shown, the series of actions 700 illustrates the segmentation-read alignment system 106 using a scoring system to identify segmented alignments representing structural variants when detecting alternative continuous segment alignments. Typically, the segmentation-read alignment system 106 determines when the boosted group has two main segment alignments that extend beyond each other in the nucleotide reads—the 5' segment alignment and the 3' segment alignment. The boosted group includes segment alignments that are alternative continuous sequence alignments to the same genomic region of the primary assembly region or reference genome. In some specific implementations, the segmentation-read alignment system 106 determines that the 5' segment alignment and the 3' segment alignment exhibit alternative continuous characteristics.

[0138] In order to identify such alternative continuous fragment alignments, in some embodiments, the split-read alignment system 106 determines a split-alt-min-ext by which the two main 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 as alternative continuous fragment alignments. In some specific implementations, split-alt-min-ext includes a predetermined value (e.g., 20 bases); in other specific implementations, the split-read alignment system 106 determines split-alt-min-ext based on user input. Typically, a higher split-alt-min-ext is more restrictive, making it less likely that the split-read alignment system 106 identifies alternative continuous fragment alignments. In some embodiments, the split-read alignment system 106 sets split-alt-min-ext to 0 to disable boost-guided split alignments. For example, a 5' fragment alignment must start within the first split-alt-min-ext base of a nucleotide read. A 5' fragment must extend at least split-alt-min-ext bases toward the 5' end compared to the 3' fragment. A 3' fragment must extend at least split-alt-min-ext bases toward the 3' end compared to the 5' fragment. The best scoring alignment in the boosted set must be an alternative contiguous alignment.

[0139] To determine whether a segment alignment with an alternative contiguous sequence scores better than other candidate segmentation groups of nucleotide reads, the segment-read alignment system 106 may use Figure 7 The scoring method described in Figure 7 As illustrated, the segment-read alignment system 106 performs action 702 of determining alternative continuous segment alignment scores. The segment-read alignment system 106 determines alternative continuous segment alignment scores for the inner segment alignment 712 (3' segment) and the outer segment alignment 710 (5' segment) corresponding to the nucleotide read. Figure 7 As indicated, both the inner fragment alignment 712 and the outer fragment alignment are aligned to an alternative contiguous sequence 714 within a reference genome 718. The alternative contiguous sequence 714 includes an alternative sequence to a primary assembly region 716 of the reference genome. In contrast to 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 rather allows both to participate separately for scoring purposes, provided that they meet the requirement that the two fragment alignments must extend beyond a minimum length of each other in the read (e.g., the split-alt-min-ext requirement).

[0140] Indeed, in some embodiments, the segment-read alignment system 106 determines the alternative contiguous segment alignment score for the inner segment alignment 712 and the alternative contiguous segment alignment score for the outer segment alignment 710 in the same manner as the segment alignment score is determined by the segment-read alignment system 106. For example, the segment-read alignment system 106 determines the alternative contiguous segment alignment score by determining the Smith-Waterman score or a variation of the Smith-Waterman score.

[0141] In addition to determining the alternative contiguous segment alignment score for each segment alignment, the segment-read alignment system 106 also performs an action of determining a segment group score 704. In particular, the segment-read alignment system 106 determines a segment group score for the inner segment alignment 712 and the outer segment 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 an action 708 of selecting an alternative contiguous segment alignment score. Typically, the segment-read alignment system 106 utilizes the alternative contiguous segment alignment score and the best alignment score of the boosted group in the segment group score. Thus, the segment-read alignment system 106 can replace the segment group score with the best alternative contiguous segment score. Thus, the alternative contiguous segment score becomes the alternative segment group score.

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

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

[0145] In some embodiments, 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 embodiments, Figures 8 to 9 The segment-read alignment system 106 is illustrated for removing candidate segment groups and identifying candidate segment groups on which no alignments are reported, respectively, using a threshold segment alignment score and a minimum alignment score.

[0146] Figure 8 The segment-read alignment system 106 for removing malformed candidate segment groups using segment alignment scores is illustrated in accordance with one or more embodiments. As an overview, Figure 8 A series of acts 800 are illustrated, including an act 802 of determining that a segment alignment score fails to meet a threshold segment alignment score and an act 804 of removing the segment alignment.

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

[0148] like Figure 8 Further illustrated, the segmentation-read alignment system 106 performs an action 804 of removing the fragment alignment. More particularly, the segmentation-read alignment system 106 removes the subthreshold fragment alignment from the consideration of forming the candidate segmentation group. For example, based on determining that the fragment alignment score of the fragment alignment A drops below the threshold fragment alignment score, the segmentation-read alignment system 106 removes the fragment alignment A from the consideration. Therefore, the segmentation-read alignment system 106 never forms a segmentation group including the fragment alignment A and the fragment alignment B. By removing the subthreshold fragment alignment from the consideration, the segmentation-read alignment system 106 effectively filters the unreliable fragment alignment at the input, thereby completely ignoring them. The threshold fragment alignment score can be mainly used for the low-scoring inner (3') fragments that can be included, because their correct matching positions obtain the benefit of high scores through low matching penalties. In addition, in some specific implementations, the segmentation-read alignment system 106 also prevents the subthreshold fragment alignment from participating in any generated multi-fragment alignment segmentation group.

[0149] The segment-read alignment system 106 further reduces noise by utilizing a minimum alignment score. Fig. 9 A segment-read alignment system 106 is illustrated for identifying candidate segment groups for which no alignments are reported using a minimum alignment score according to one or more embodiments. As an overview, Fig. 9 A series of acts 900 are illustrated, including an act 902 of determining that the alignment score of a candidate segmentation group fails to satisfy a minimum alignment score and an act 904 of inhibiting reporting of the segmentation alignment.

[0150] like Fig. 9As illustrated, the segmentation-read alignment system 106 performs an action 902 of determining that the alignment score of the candidate segmentation group fails to meet the minimum alignment score. The alignment score of the candidate segmentation group refers to the alignment score of the entire segmentation group. In some specific implementations, the alignment score of the candidate segmentation group includes the segmentation group score. For example, the segmentation-read alignment system 106 determines that the segmentation group score of the candidate segmentation group 906 falls below the minimum alignment score. The segmentation-read alignment system 106 can determine the minimum alignment score based on user input or can predetermine the minimum alignment score.

[0151] Compared to existing sequencing systems, the segmentation-read alignment system 106 can report segmentation alignments, even if the constituent fragment alignments have low fragment alignment scores. For illustration, fragment alignment A and / or fragment alignment B may have individual alignment scores below the minimum alignment score; however, the A+B segmentation group score may be higher and exceed the minimum alignment score. In this case, the segmentation-read alignment system 106 may report the A+B segmentation alignment. In contrast, the existing sequencing system will filter out one or both of the fragment alignment A and / or the fragment alignment B due to failure to meet the minimum alignment score. In essence, the segmentation-read alignment system 106 utilizes the generation of the segmentation group score by splitting the threshold score into two separate parameters (threshold fragment alignment score and minimum alignment score). The threshold fragment alignment score filters the fragment alignment in advance by making the subthreshold fragment alignment ineligible for participating in the segmentation alignment. The threshold fragment alignment score utilized by the segmentation-read alignment system 106 may be higher and more tolerant than the alignment score utilized by the existing sequencing system. In some embodiments, the segment-read alignment system 106 configures the minimum alignment score to filter candidate segment groups only after low-scoring segment alignments have had a chance to participate in candidate segment groups that can potentially achieve higher segment group scores. Thus, the segment-read alignment system 106 retains the final minimum score, thereby achieving a target level of noise filtering similar to existing sequencing systems, but in a manner that provides sensitivity to lower-scoring component segment alignments that are part of a full read alignment.

[0152] The segment-read alignment system 106 additionally performs an action of prohibiting reporting of the segment alignment 904. In particular, based on the alignment score failing to meet the minimum alignment score, the segment-read alignment system 106 prohibits reporting of the segment alignment of the candidate segment group in the alignment file or the variant call file. For illustration, the segment-read alignment system 106 does not report the candidate segment group 906 as a predicted segment group.

[0153] In some embodiments, even if the segmentation-read alignment system 106 does not report a candidate segmentation group 906, the segmentation-read alignment system 106 still considers the candidate segmentation group 906 as a competitor to other alignments. If the highest pair score includes a segmentation group score that is lower than the minimum alignment score, the segmentation-read alignment system 106 returns the unmapped read. But even if another alignment or segmentation group exhibits the highest pair score, if the failed segmentation group's pair score is suboptimal, then the segmentation-read alignment system 106 can also reduce the segment alignment mapping score (e.g., MAPQ) of the segment alignment. As mentioned, from the perspective of a mapping quality metric (e.g., MAPQ), the segment alignment mapping score represents the confidence that a given segment alignment is part of (or maps to) a true alignment.

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

[0155]

[0156]

[0157] In some implementations, the segmentation-read alignment system 106 assigns an alignment tag representing a strand orientation to the segment alignment. More specifically, the XS tag is defined as the original competitive segment score. In some implementations, the XS of a given segment alignment is the highest score (and therefore does not meet the condition of the segmentation alignment with a given segment alignment) of any other segment alignment that overlaps most of the given segment alignment from the nucleotide reads. In other embodiments, the segmentation-read alignment system 106 determines that the XS of all non-minor segment alignments (both primary and supplementary) is the highest segment score that does not participate in the winning or highest-scoring segmentation group. The XS of all minor alignments (both non-supplementary and supplementary) is the highest segment score that participates in the winning segmentation group.

[0158] In some embodiments, the segment-read alignment system 106 determines nucleobase calls for the genomic regions based on alignment of the predicted segmentation groups to a reference genome. Fig.10 The segmentation-read alignment system 106 for generating nucleobase call and variant call files according to one or more embodiments is illustrated. As an overview, Fig.10A series of acts 1000 are illustrated, including an act 1002 of identifying nucleotide reads, an act 1004 of aligning the nucleotide reads to a reference genome, an act 1006 of generating nucleobase calls, and a resulting variant call file 1008 .

[0159] like Fig.10 As illustrated, the segmentation-read alignment system 106 performs an action 1002 of identifying nucleotide reads. In one or more embodiments, the action 1002 includes identifying nucleotide reads from a genomic sample. In some implementations, the sequencing device 114 determines nucleotide reads from the sample genome (e.g., by using SBS) and sends data representing the nucleotide reads (e.g., in a base call file) to the sequencing system 104. In an alternative implementation, a third-party system determines nucleotide reads from the sample genome and allows the sequencing system 104 to access these nucleotide reads.

[0160] Fig.10 The series of actions 1000 illustrated in 1000 also includes an action 1004 of aligning the nucleotide reads to the reference genome. As illustrated, the segmentation-read alignment system 106 aligns the nucleotide reads 1010 to the reference genome. For example, in various specific implementations, the sequencing system 104 aligns the nucleotide reads 1010 to the reference genome. As part of performing action 1004, the segmentation-read alignment system 106 determines the segment alignment and determines the predicted segmentation group.

[0161] like Fig.10 As further illustrated, the segmentation-read alignment system 106 performs an action of generating nucleobase calls 1006. Typically, the nucleobase calls include nucleobases at genomic coordinates of the sample genome in a predicted variant call file 1008 (VCF) or other base call output file based on the alignment of the nucleotide reads to the reference genome. Due to the accuracy of the predicted segmentation groups, the sequencing system 104 can generate nucleobase calls for genomic coordinates with higher accuracy and confidence than existing sequencing systems.

[0162] In some examples, the segmentation-read segment alignment system 106 reports the segmentation alignment using the BAM / SAM file format. The BAM / SAM file specification provides three different alignment types: primary, auxiliary, and secondary. In some examples, the flag bit (FLAGbit) indicates the supplementary and / or secondary name. According to the BAM / SAM specification, exactly one primary alignment (without supplementary or secondary flag set) is identified. A segmentation alignment with N>=2 segments is therefore represented as 1 primary segment alignment BAM / SAM record and N-1 supplementary segment alignment BAM / SAM records.

[0163] Therefore, typically, unless by special means or encoding, the segmentation-read alignment system 106 may not output the entire segmentation group as the primary alignment. The segmentation-read alignment system 106 identifies which of the N fragment alignments should be selected for the primary alignment state, and the remaining N-1 fragment alignments receive the supplemental alignment state. In some specific implementations, the segmentation-read alignment system 106 determines the primary alignment output based on the parameter primary-5p. When primary-5p=0, the primary fragment alignment is selected to support correct pairing, typically the 3'-most fragment alignment. Additionally or alternatively, the segmentation-read alignment system 106 sets primary-5p to 1 to set the 5'-most fragment alignment as the primary alignment.

[0164] If the segmentation-read alignment system 106 determines to output the secondary alignment, the segmentation-read alignment system 106 selects the secondary fragment alignment in descending order of the score. In general, the secondary alignment includes other alignment records that are not related to the primary alignment but represent alternative alignment candidates. Some secondary fragment alignments can be important segmentation groups themselves. The segmentation-read alignment system 106 can determine the full segmentation group of the output secondary alignment. Each of the full segmentation groups will simulate the main / supplementary structure of the winning segmentation group, but with secondary markers. However, in the case where the segment alignment of the secondary segmentation group has been output (in the segmentation group with the highest score or in the secondary segmentation group with a higher score), the segmentation-read alignment system 106 prevents the output of the supplementary secondary fragment alignment. More specifically, the supplementary alignment includes additional alignment records that supplement the primary alignment or present the other parts of the segmentation alignment.

[0165] As indicated above, the segment-read alignment system 106 improves the alignment of segmented reads and increases the accuracy of corresponding nucleobase calls (including structural variant calls). According to one or more embodiments, FIG. 11A to FIG. 11D The read stacking of candidate gene fusion events generated by the segmentation-read alignment system 106 is illustrated, which exhibits more accurate mapping and alignment—and leads to more accurate variant calling—than existing sequencing systems based on transcriptome reads. FIG. 11A to FIG. 11D As indicated, by determining segmentation group scores for candidate segmentation groups comprising segment alignments of segments from nucleotide reads (e.g., transcriptome reads), and selecting predicted segmentation groups from the candidates based on such segmentation group scores, the segmentation-read alignment system 106 (i) identifies segment alignments of candidate segmentation reads with better accuracy than existing sequencing systems, and (ii) determines true negative variant calls (here, no gene fusion) at genomic coordinates and breakpoints where existing sequencing systems determine false positive variant calls for gene fusion events.

[0166] Fig.11A and Fig. 11B By delineating the breakpoints along the chromosome ( Fig.11A ) and by segmentation-read segment alignment system 106 and existing sequencing system ( Fig. 11B ) Different read segments determined about the same breakpoint are aligned and mapped to complement each other. Fig.11A As shown, for example, chromosome segment 1102a of chromosome 11 includes breakpoint 1104a. In particular, Fig.11A The breakpoints 1104a shown identify one or more genomic coordinates at which nucleotide reads have been aligned by existing sequencing systems and are subsequently sequenced. Fig. 11B There is a break between the nucleotide read segments depicted in . As described further below, segmented alignment of transcriptome reads relative to breakpoint 1104a can indicate a gene fusion event of the ARL2-SNX15 RNA gene with another gene.

[0167] like Fig. 11B As shown, the user client device 108 presents a graphical user interface 1100a that includes different read segment alignments and mappings about breakpoints 1104a determined by the segmentation-read alignment system 106 and the existing sequencing system. For example, the graphical user interface 1100a may represent a graphical user interface of an integrated genome viewer (IGV) that includes read alignments relative to a reference genome. For comparison purposes, the graphical user interface 1100a includes an updated alignment window 1106a depicting candidate transcriptome read alignments of the segmentation-read alignment system 106, a previous alignment window 1108a depicting candidate transcriptome read alignments of the existing sequencing system, and a reference genome window 1110a depicting reference nucleotide bases of the reference genome. Fig. 11B , the updated alignment window 1106a also includes a read coverage marker 1120a that indicates the read coverage (eg, read depth) at the genomic coordinates overlapping the breakpoint 1104a.

[0168] As shown in the previous alignment window 1108a, the existing sequencing system maps and aligns the transcriptome read fragment 1114a with the reference genome at the genomic coordinates corresponding to (or relatively closer to) the breakpoint 1104a. As indicated by the light gray shading of the transcriptome read fragment 1114a in the previous alignment window 1108a, the detected nucleotide bases of the transcriptome read fragment 1114a match the reference nucleotide bases of the reference genome within the reference genome window 1110a. In contrast to the transcriptome read fragment 1114a, the existing sequencing system maps and aligns the following: (i) the mismatched transcriptome read fragment 1112a with the genomic region corresponding to the ARL2 continuous sequence located upstream of the breakpoint 1104a, and (ii) the mismatched transcriptome read fragment 1112b with the genomic region corresponding to the SNX15 continuous sequence located downstream of the breakpoint 1104a. As indicated by the different grey shades or colors of the mismatched transcriptome read fragments 1112a and 1112b in the previous alignment window 1108a , the called nucleotide bases of the mismatched transcriptome read fragments 1112a and 1112b do not match the reference nucleotide bases of the reference genome within the reference genome window 1110a .

[0169] Because the threshold number of detected nucleotide bases does not match the reference nucleotide bases, the existing sequencing system trims (e.g., soft trimming or hard trimming) the nucleotide bases within the mismatched transcriptome read segments 1112a and 1112b, thereby ignoring the nucleotide bases of the mismatched transcriptome read segments 1112a and 1112b for the purpose of comparison. However, the mismatched transcriptome read segments 1112a and 1112b show the segmented comparison of the corresponding transcriptome reads relative to the reference genome. The candidate comparisons of the mismatched transcriptome read segments 1112a and 1112b obtained by the existing sequencing system all represent supplementary comparisons with positive mapping quality metrics (e.g., positive MAPQ), and correspond to the main comparison with another gene (e.g., AKT3 gene). Based on the scores of the main and supplementary comparisons of such corresponding transcriptome reads depicted in the previous comparison window 1108a, the existing sequencing system determines the false positive variant detection of the gene fusion event of the genomic sample. For example, in some cases, existing sequencing systems realign mismatched transcriptome read fragments 1112a and 1112b to genomic regions of another gene on a different chromosome (eg, the AKT3 gene on chromosome 1), indicating a gene fusion event.

[0170] As shown in the updated alignment window 1106a, the segmentation-read alignment system 106 maps and aligns the transcriptome read segment 1116a with the reference genome at the genomic coordinates corresponding to (or relatively closer to) the breakpoint 1104a. As indicated by the light gray shading of the transcriptome read segment 1116a, the detected nucleotide bases of the transcriptome read segment 1116a match the reference nucleotide bases of the reference genome within the reference genome window 1110a. In contrast to the transcriptome read segment 1116a, the segmentation-read alignment system 106 maps and aligns the mismatched transcriptome read segment 1118a with the genomic region corresponding to the SNX15 contiguous sequence located downstream of the breakpoint 1104a, but does not map or align any mismatched transcriptome read segment located upstream of the breakpoint 1104a. As indicated by the different grey shade or color of the mismatched transcriptome read fragment 1118a in the updated alignment window 1106a, the called nucleotide bases of the mismatched transcriptome read fragment 1118a do not match the reference nucleotide bases of the reference genome within the reference genome window 1110a.

[0171] like Fig. 11B It is further indicated that the candidate alignment of the mismatched transcriptome read fragment 1118a obtained by the split-read alignment system 106 exhibits a mapping quality metric (e.g., MAPQ0) of 0, thereby causing the split-read alignment system 106 to filter out the candidate alignment of the mismatched transcriptome read fragment 1118a. Because the split-read alignment system 106 filters out the mismatched transcriptome read fragment 1118a aligned to the genomic region on one side of the breakpoint 1104a, the split-read alignment system 106 avoids false positive variant calls that determine gene fusion events of the same genomic sample (as existing sequencing systems do). By generating an improved split group score for the candidate split alignment, the split-read alignment system 106 avoids the "noisy" split reads exhibited by the candidate alignment of the existing sequencing system in the previous alignment window 1108a. Because it avoids such noisy segmented read alignments, the segmented-read alignment system 106 also avoids calling incorrect gene fusion variants and correctly identifies true negative variants of gene fusions.

[0172] Fig. 11C and Fig.11D By delineating the breakpoints along the chromosome ( Fig. 11C ) and by segmentation-read segment alignment system 106 and existing sequencing system ( Fig.11D ) Different read segments determined about the same breakpoint are aligned and mapped to complement each other. Fig. 11C As shown, for example, chromosome segment 1102b of chromosome 4 includes breakpoint 1104b. In particular, Fig. 11CThe breakpoints 1104b shown identify one or more genomic coordinates at which transcriptome reads have been aligned by existing sequencing systems and subsequently in Fig.11D There is a break between the read segments depicted in . As further exemplified below, a split-read alignment of transcriptome reads relative to breakpoint 1104b may indicate a gene fusion event of the DCTD gene with another gene.

[0173] like Fig.11D As shown, the user client device 108 presents a graphical user interface 1100b that includes alignments and mappings of different read segments with respect to breakpoints 1104b determined by the segmentation-read alignment system 106 and the existing sequencing system. As described above, for example, the graphical user interface 1100b represents a graphical user interface from IGV that includes transcriptome read alignments relative to a reference genome. For comparison purposes, the graphical user interface 1100b includes an updated alignment window 1106b depicting the transcriptome read alignments of the segmentation-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 reference nucleotide bases of the reference genome. Fig.11D , the updated alignment window 1106b also includes a read coverage marker 1120b that indicates the read coverage (eg, read depth) at the genomic coordinates overlapping the breakpoint 1104b.

[0174] As shown in the previous alignment window 1108b, the existing sequencing system maps and aligns the transcriptome read fragment 1114b to the reference genome at the genomic coordinates corresponding to (or relatively closer to) the breakpoint 1104b. Fig. 11B The graphical user interface 1100a in Fig.11D The graphical user interface 1100b in includes a light gray shade indicating that the detected nucleotide bases of the transcriptome read fragment (e.g., transcriptome read fragment 1114b) match the reference nucleotide bases of the reference genome and a different gray shade or color indicating that the detected nucleotide bases of the mismatched transcriptome read fragments (e.g., mismatched transcriptome read fragments 1112c, 1112d, and 1118b) do not match the reference nucleotide bases. In contrast to the transcriptome read fragment 1114b, the existing sequencing system maps and aligns the following: (i) the mismatched transcriptome read fragment 1112c with the genomic region corresponding to the continuous sequence located upstream of the breakpoint 1104b, and (ii) the mismatched transcriptome read fragment 1112d with the genomic region corresponding to the continuous sequence located downstream of the breakpoint 1104b.

[0175] Because the threshold number of detected nucleotide bases does not match the reference nucleotide bases, existing sequencing systems trim the nucleotide bases within the mismatched transcriptome read fragments 1112c and 1112d, thereby ignoring the nucleotide bases of the mismatched transcriptome read fragments 1112a and 1112b for alignment purposes. Fig.11D Depicted, mismatched transcriptome reads fragments 1112c and 1112d show the segmentation alignment of corresponding transcriptome reads relative to the reference genome. The candidate alignments of mismatched transcriptome reads fragments 1112c and 1112d obtained by the existing sequencing system all represent the supplementary alignments with positive mapping quality metrics (e.g., positive MAPQ), and correspond to the main alignments with another gene (not shown). Based on the scores of the main and supplementary alignments of such corresponding transcriptome reads depicted in the previous alignment window 1108b, the existing sequencing system determines the false positive variant detection of the gene fusion event of the genomic sample. For example, in some cases, the existing sequencing system re-aligns the genomic regions of mismatched transcriptome reads fragments 1112c and 1112d with another gene on the same chromosome (e.g., chromosome 4) or another gene on different chromosomes, thereby indicating a gene fusion event.

[0176] As shown in the updated alignment window 1106b, in contrast, the segment-read alignment system 106 maps and aligns the mismatched transcriptome read segment 1118a to the genomic region corresponding to the contiguous sequence located upstream of the breakpoint 1104b, but does not map or align any mismatched transcriptome read segments located downstream of the breakpoint 1104b. Fig.11D It is further indicated that the candidate alignment of the mismatched transcriptome read fragment 1118a obtained by the segmentation-read alignment system 106 exhibits a relatively low mapping quality metric (e.g., MAPQ0), thereby causing the segmentation-read alignment system 106 to filter out the candidate alignment of the mismatched transcriptome read fragment 1118a. Because the segmentation-read alignment system 106 filters out the mismatched transcriptome read fragment 1118a aligned with the genomic region on one side of the breakpoint 1104b, the segmentation-read alignment system 106 is uncertain about the false positive variant detection of the gene fusion event of the same genomic sample. By generating an improved segmentation group score for the candidate segmentation alignment, the segmentation-read alignment system 106 avoids the "noisy" segmentation reads displayed by the candidate alignment of the existing sequencing system in the previous alignment window 1108b. As described above, because it avoids such noisy segmentation read alignments, the segmentation-read alignment system 106 also avoids detecting incorrect gene fusion variants and correctly identifies true negative variants about gene fusions.

[0177] In addition to improving the accuracy of mapping and alignment and variant calling of gene fusion events, in some embodiments, the segmentation-read alignment system 106 also improves the nucleotide read coverage and variant calling accuracy of chromosome M of human mitochondrial DNA by selecting more accurate mapping and alignment based on improved segmentation group scores. According to one or more embodiments, FIG. 12A to FIG. 12D Coverage graphs 1200a-1200d are illustrated, which demonstrate higher coverage of nucleotide reads mapped and aligned to the genomic region of chromosome M using the split-read alignment system 106 relative to such coverage from nucleotide reads mapped and aligned using existing sequencing systems. FIG. 12A to FIG. 12D As shown, the improved nucleotide read coverage extends from the start of chromosome M to the harder to cover and harder to detect genomic region at the end of chromosome M. According to one or more embodiments, Fig.13 A variant call table 1300 is illustrated, demonstrating that better SNP calling and indel calling accuracy is achieved by the segmentation-read alignment system 106 at the genomic region of chromosome M relative to such SNP calling and indel calling obtained by existing sequencing systems.

[0178] It is well known that the terminal genomic region of chromosome M is difficult to detect and cover for existing sequencing systems, partly due to the circular nature of mitochondrial DNA. Because existing models for mapping and alignment represent the circular DNA of chromosome M in a linear manner, existing sequencing systems often cut off and incorrectly soft-clip nucleotide reads that align to the terminal genomic region of chromosome M, and therefore sometimes incorrectly ignore valuable nucleotide read data associated with the terminal genomic region of chromosome M. Compared with existing sequencing systems and such FIG. 12A to FIG. 12D As shown, the segment-read alignment system 106 generates an improved segment group score that penalizes segment alignments across different chromosomes and thereby improves the selection of segment groups and mapping and alignment to chromosome M.

[0179] To test the nucleotide read coverage of the segment alignment from the segment-read alignment system 106, the researchers performed the segment-read alignment system 106 and the existing sequencing system on mitochondrial DNA samples from the Fazzini dataset, as described by 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, which is hereby incorporated by reference in its entirety. For example, the researchers sequenced and aligned nucleotide reads from mtDNA mixtures of two people with different target allele frequencies, where sample mixture M1 included a 1:2 mixture and a 50% target allele frequency, sample mixture M2 included a 1:10 mixture and a 10% target allele frequency, and sample mixture M3 included a 1:50 mixture and a 2% target allele frequency. In some cases, the researchers used different versions of Taq polymerase for polymer chain reaction (PCR), including LA Advantage (provided by Clontech Laboratories), Herculase II Fusion (HERK), and LongAmp Taq polymerase (NEB). The researchers also sequenced nucleotide reads from sample mixtures M1, M2, and M3 using two different protocols: PCR amplification before mixing and PCR after mixing. FIG. 12A to FIG. 12D In the PCR, the researchers further mapped the nucleotide read coverage at the genomic coordinates at the start and end of chromosome M. In addition, the researchers used different versions of PCR Taq polymerase and protocols to determine the false positive and false negative variant calls of SNPs and indels in sample mixtures M1, M2, and M3, such as Fig.13 As depicted in .

[0180] like Fig. 12A and Fig. 12B As shown, coverage graphs 1200a and 1200b show the coverage of nucleotide reads sequenced from sample mixture M1 using HERK that have been mapped and aligned by the segmentation-read alignment system 106 and the existing sequencing system. Fig. 12A and Fig. 12B, graphic keys 1202a and 1202b show the coverage plot lines of the split-read alignment system 106 named MapperV2 (i.e., MapperV2_All, MapperV2_60, MapperV2_20, and MapperV2_gvcf) and the coverage plot lines of the existing sequencing system named curMapper (i.e., curMapper_All, curMapper_60, curMapper_20, and curMapper_gvcf). Fig. 12A As indicated by the coverage graph 1200a and the graphical key 1202a in FIG. 1 , the segmentation-read alignment system 106 maps and aligns nucleotide reads with consistently higher coverage across the starting genomic region of chromosome M (chrM:0-100) relative to existing sequencing systems, including all mapped nucleotide reads, as shown by comparison of the plot lines of MapperV2_All and curMapper_All. Even beyond the starting genomic region of chromosome M, as shown in FIG. Fig. 12B As indicated by the coverage graph 1200b and graphic key 1202b in , relative to existing sequencing systems, the split-read alignment system 106 maps and aligns nucleotide reads with consistently higher coverage across the terminal genomic region of chromosome M (chrM:16469-16569), also including all mapped nucleotide reads, as shown by comparison of the plot lines of MapperV2_All and curMapper_All.

[0181] like Fig. 12C and Fig.12D As shown, coverage graphs 1200c and 1200d similarly show the coverage of nucleotide reads sequenced from sample mixture M1 using Clontech Taq polymerase that have been mapped and aligned by the segmentation-read alignment system 106 and the existing sequencing system. Fig. 12C and Fig.12D , graphic keys 1202c and 1202d show the coverage plot lines of the split-read alignment system 106 named MapperV2 (i.e., MapperV2_All, MapperV2_60, MapperV2_20, and MapperV2_gvcf) and the coverage plot lines of the existing sequencing system named curMapper (i.e., curMapper_All, curMapper_60, curMapper_20, and curMapper_gvcf). Fig. 12CAs indicated by the coverage graph 1200c and the graphical key 1202c in FIG. 1 , the split-read alignment system 106 maps and aligns nucleotide reads with consistently higher coverage across the starting genomic region of chromosome M (chrM:0-100) relative to existing sequencing systems, including all mapped nucleotide reads, as shown by comparison of the plot lines of MapperV2_All and curMapper_All. Even beyond the starting genomic region of chromosome M, as shown in FIG. Fig.12D As indicated by the coverage graph 1200d and graphic key 1202d in , relative to existing sequencing systems, the split-read alignment system 106 also maps and aligns nucleotide reads with consistently higher coverage across the terminal genomic region of chromosome M (chrM:16469-16569), also including all mapped nucleotide reads, as shown by comparison of the plot lines of MapperV2_All and curMapper_All.

[0182] As indicated above, Fig.13 A variant call table 1300 is depicted, which shows false positive and false negative variant calls of SNPs and indels obtained by the segmentation-read segment alignment system 106 and the existing sequencing system at the genomic region of chromosome M for sample mixtures M1, M2 and M3 using different versions of PCR Taq polymerase and different PCR protocols. On the left, as indicated by the "dataset_jama_REV7169" column, the variant call table 1300 shows false positive and false negative variant calls of SNPs and indels obtained by the existing sequencing system. On the right, as indicated by the "CGM_mapperV2" column, the variant call table 1300 shows false positive and false negative variant calls of SNPs and indels obtained by the segmentation-read segment alignment system 106. As indicated by the "Total" and "Diff" columns of the variant call table 1300, the segmentation-read segment alignment system 106 consistently determines fewer total false positive and false negative SNP and indel calls than the existing sequencing system. Although the "Diff" column of the variant call table 1300 shows that the segmentation-read alignment system 106 exhibited between one and eight fewer false positive and false negative SNP and indel calls, this reduction in false positive and false negative SNP and indel calls is significant for such a short chromosome (i.e., chromosome M, which is only 16,569 base pairs long).

[0183] In addition to improved nucleotide read coverage and improved variant calling for chromosome M, in some embodiments, the segmentation-read alignment system 106 also improves the accuracy of structural variant calling. According to one or more embodiments, Fig.14ADepicted is a table 1400a showing that the segmentation-read alignment system 106 recovers insertion calls of genes affecting acute myeloid leukemia (AML) that were missed by existing sequencing systems. According to one or more embodiments, Fig. 14B Depicted is a table 1400b showing that the split-read alignment system 106 determines more accurate duplication and translocation calls relative to existing sequencing systems.

[0184] like Fig.14A As shown, for example, table 1400a compares the insertion detection obtained by segmentation-read segment alignment system 106 and existing sequencing system in the fms-like tyrosine kinase 3 (FLT3) gene of known genomic samples from normal tissue and tumor tissue. The mutation of FLT3 gene causes a significant percentage of AML cases, wherein internal tandem duplication (ITD) represents the most common FLT3 mutation type. As indicated in table 1400a, based on improved segmentation group scoring and selecting a better improved segmentation group for variant detection, segmentation-read segment alignment system 106 (shown in "new M / A+ detection generation model" column) correctly determines the insertion detection of more than at least 50 base pairs long at genomic coordinates chr13:28034103 and chr13:28034120 for a pair of known genomic samples with FLT3-ITD mutation, but the existing sequencing system (shown only in "detection generation model" column) misdetects such insertion. As further indicated in Table 1400a, the segmentation-read alignment system 106 (shown in the "New M / A+Call Generation Model" column) also correctly determined the presence or absence of such insertions at other genomic coordinates, which were also correctly determined in the existing sequencing system (shown only in the "Call Generation Model" column). Such restored insertion calls (and the preservation of previous accurate insertion calls) obtained by the segmentation-read alignment system 106 demonstrate critical accuracy improvements and accuracy preservation of structural variant calls within genes important for cancer diagnosis.

[0185] like Fig. 14BAs shown, table 1400b compares the accuracy of somatic structural variants detected by segmentation-read alignment system 106 and existing sequencing system from sequencing data HCC1954. HCC1954 is a cell line showing epithelial breast cancer. As indicated in table 1400b, based on improved segmentation group scores and selecting better improved segmentation groups for variant detection, segmentation-read alignment system 106 (shown in "new M / A+ detection generation model" row) shows better re-detection (recall), precision and F score than existing sequencing system (shown only in "detection generation model" row) for repeated detection in HCC1954. Also as indicated in table 1400b, segmentation-read alignment system 106 (shown in "new M / A+ detection generation model" row) shows better precision and F score than existing sequencing system (shown in "detection generation model" row) for translocation detection in HCC1954. Because the re-calls, precision, and F-scores reported in Table 1400b were determined without ground-truth calls, the re-calls, precision, and F-scores of the segment-read alignment system 106 when determined with ground-truth calls.

[0186] Figures 1 to 14B , corresponding text and examples provide many different methods, systems, devices and non-transitory computer-readable media for the segmentation-read segment alignment system 106. In addition to the foregoing, one or more specific implementations may also be described according to flowcharts including actions for achieving specific results, such as Fig.15 shown. Fig.15 Can be performed with more or less actions.In addition, these actions can be performed in different orders.In addition, the actions described herein can be repeated or performed in parallel with each other or in parallel with different examples of the same or similar actions.

[0187] As mentioned, Fig.15 A flow chart illustrating a series of actions 1500 for selecting a predicted segmentation group from among candidate segmentation groups. Fig.15 The actions according to one implementation are illustrated, but alternative implementations may omit, add, reorder, and / or modify Fig.15 Any action shown. Fig.15 The actions of may be performed as part of a method. Alternatively, the non-transitory computer readable medium may include a method that, when executed by at least one processor, causes a computing device to perform Fig.15 In some further embodiments, the system includes at least one processor and a non-transitory computer-readable medium, which includes instructions that, when executed by one or more processors, cause the system to perform Fig.15In some cases, at least one processor comprises a configurable processor, and executing at least one processor comprises configuring the configurable processor.

[0188] like Fig.15 As shown, the series of acts 1500 includes an act of identifying one or more nucleotide reads 1502. In particular, act 1502 includes identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample.

[0189] Fig.15 The series of actions 1500 illustrated in 1 also includes an action 1504 of determining a candidate segmentation group. In particular, action 1504 includes determining a candidate segmentation group including fragment alignments corresponding to one or more nucleotide reads. In some specific implementations, determining a candidate segmentation group in the candidate segmentation group also includes grouping one or more fragment alignments of a single-end nucleotide read into the candidate segmentation group; or grouping one or more fragment alignments of a double-end nucleotide read from a pair of double-end nucleotide reads into the candidate segmentation group.

[0190] like Fig.15 As further illustrated, the series of actions 1500 includes an action 1506 of generating a segmentation group score. In particular, action 1506 includes generating a segmentation group score for a segmentation alignment of a candidate segmentation group with a reference genome. In some specific implementations, action 1506 also includes the following additional actions: generating a segment alignment score for each segment alignment of the candidate segmentation group with the reference genome; and generating a segmentation group score for the candidate segmentation group based on the segment alignment score. In addition, in some specific implementations, action 1506 also includes, for a candidate segmentation group in the candidate segmentation group, generating a break penalty for the relative geometry of the first segment alignment and the second segment alignment relative to the reference genome; and generating a segmentation group score for the candidate segmentation group based on the break penalty. In addition, in some specific implementations, the series of actions 1506 includes, for a candidate segmentation group in the candidate segmentation group, generating an overlap penalty for the overlap between the first segment alignment and the second segment alignment within the nucleotide read; and generating a segmentation group score for the candidate segmentation group based on the overlap penalty.

[0191] In some embodiments, action 1506 further comprises generating a segmentation group score for a candidate segmentation group in the candidate segmentation group by: generating a segment alignment score, a break penalty, and an overlap penalty for the segment alignments of the candidate segmentation group; and merging the segment alignment scores and subtracting the break penalty and the overlap penalty from the merged segment alignment score. In some specific implementations, action 1006 further comprises determining the candidate segmentation group by iteratively grouping the segment alignments in the order of the outermost segment alignment to the innermost segment alignment of the nucleotide read; and generating the segmentation group score by iteratively scoring the groupings of the segment alignments in the order in which the segment alignments are grouped.

[0192] Fig.15 The series of actions 1500 illustrated in 1 also includes an action 1508 of selecting a predicted segmentation group. In particular, action 1508 includes selecting a predicted segmentation group from a candidate segmentation group based on a segmentation group score for base calling of a genomic region. In some embodiments, action 1508 includes identifying candidate segmentation group pairs of different fragment alignments including a partner of a double-end nucleotide read from a candidate segmentation group; generating a pair score for evaluating a comparison pair of a candidate segmentation group pair with a reference genome for a candidate segmentation group pair; and for each partner of a double-end nucleotide read, further selecting a predicted segmentation group based on the pair score. In addition, in some embodiments, action 1508 also includes determining the sum of the segmentation group scores of the corresponding 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 score and the pairing penalty.

[0193] In some embodiments, the series of actions 1500 includes the following additional actions: determining alternative contiguous fragment alignment scores for inner fragment alignments and outer fragment alignments of alternative contiguous sequences within a reference genome corresponding to the nucleotide reads; determining segmentation group scores for inner fragment alignments and outer fragment alignments of the primary assembly region of the reference genome; and selecting the alternative contiguous fragment alignment score as a replacement segmentation group score based on determining that the alternative contiguous fragment alignment score exceeds the segmentation group score.

[0194] Additionally, in one or more implementations, the series of acts 1500 includes an additional act of determining nucleobase calls for the genomic region based on alignment of the predicted segmentation set to a reference genome.

[0195] The series of acts 1500 may also include the following additional acts: determining that a segment alignment score for a segment alignment fails to satisfy a threshold segment alignment score; and removing the segment alignment from consideration in forming a candidate segmentation group.

[0196] The series of actions 1500 may include the following additional actions: determining that the alignment score of the candidate segmentation group fails to satisfy a minimum alignment score; and prohibiting reporting the segmentation alignment of the candidate segmentation group in the alignment file or the variant call file based on the alignment score failing to satisfy the minimum alignment score.

[0197] The methods described herein can be used in combination with a variety of nucleic acid sequencing techniques. Particularly suitable techniques are those in which nucleic acids are attached to fixed positions in an array so that their relative positions do not change and in which the array is repeatedly imaged. The specific implementation of obtaining images in different color channels (e.g., matching the different markers used to distinguish a type of core base from another type of core base) is particularly suitable. In some specific implementations, the process of determining the nucleotide sequence of a target nucleic acid (i.e., nucleic acid polymer) can be an automated process. Preferred specific implementations include sequencing by synthesis (SBS) technology.

[0198] SBS techniques generally include enzymatic extension of nascent nucleic acid chains by repeated addition of nucleotides to the template strand. In traditional SBS methods, a single nucleotide monomer can be provided to the target nucleotide in the presence of a polymerase in each delivery. However, in the methods described herein, more than one type of nucleotide monomer can be provided to the target nucleic acid in the presence of a polymerase in the delivery.

[0199] SBS can utilize nucleotide monomers with terminator parts or lack nucleotide monomers of any terminator parts.The method utilizing the nucleotide monomers lacking terminator includes for example pyrophosphate sequencing and the sequencing of nucleotides using gamma-phosphate labeling, as described in further detail below.In the method using the nucleotide monomers lacking terminator, the number of nucleotides added in each cycle is usually variable, and the number depends on the mode of template sequence and nucleotide delivery.For utilizing the SBS technology of the nucleotide monomers with terminator parts, terminator can be effectively irreversible under the sequencing conditions used, such as the situation of traditional Sanger sequencing utilizing dideoxynucleotides, or terminator can be reversible, such as the situation of the sequencing method developed by Solexa (now Illumina, Inc.).

[0200] The SBS technique can utilize nucleotide monomers with a labeling portion or nucleotide monomers lacking a labeling portion. Thus, incorporation events can be detected based on the following: properties of the label, such as fluorescence of the label; properties of the nucleotide monomer, such as molecular weight or charge; byproducts of the incorporated nucleotide, such as release of pyrophosphate; and the like. In specific implementations where two or more different nucleotides are present in the sequencing reagent, the different nucleotides may be distinguishable from each other, or alternatively the two or more different labels may be indistinguishable under the detection technology used. For example, different nucleotides present in the sequencing reagent may have different labels, and they may be distinguished using appropriate optical devices, as exemplified by the sequencing method developed by Solexa (now Illumina, Inc.).

[0201] Preferred embodiments include pyrophosphate sequencing techniques. Pyrophosphate sequencing detects the release of inorganic pyrophosphate (PPi) when a specific nucleotide is incorporated into a nascent chain (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M., and Nyren, P. (1996), "Real-time DNA sequencing using detection of pyrophosphate release.", Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001), "Pyrosequencing sheds light on DNA sequencing.", Genome Res. 11(1), 3-11; Ronaghi, M., Uhlen, M., and Nyren, P. (1998), "A sequencing method based on real-time pyrophosphate.", Science 281 (5375), 363; U.S. Pat. No. 6,210,891; U.S. Pat. No. 6,258,568 and U.S. Pat. No. 6,274,320, the disclosures of which are incorporated herein by reference in their entirety). In pyrophosphate sequencing, the released PPi can be detected by being immediately converted into ATP by adenosine triphosphate (ATP) sulfurylase, and the level of ATP generated is 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 of the array. The image can be obtained after the array is treated with a specific nucleotide type (e.g., A, T, C or G). The images obtained after adding each nucleotide type will differ in which features in the array are detected. These differences in the image reflect the different sequence contents of the features on the array. However, the relative position of each feature will remain unchanged in the image. The images can be stored, processed and analyzed using the methods described herein. For example, images obtained after treating the array with each different nucleotide type can be processed in the same manner as exemplified herein for images obtained from different detection channels for a reversible terminator-based sequencing method.

[0202] In another exemplary type of SBS, cycle sequencing is accomplished by stepwise addition of reversible terminator nucleotides comprising, for example, cleavable or photobleachable dye labels, as described in, for example, WO 04 / 018497 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated herein by reference. The method is 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, in which not only termination can be reversed but also the fluorescent label can be cleaved, facilitates efficient cyclic reversible termination (CRT) sequencing. Polymerases can also be co-engineered to efficiently incorporate and extend from these modified nucleotides.

[0203] Preferably, in the specific implementation of sequencing based on reversible terminators, the label does not substantially inhibit extension under SBS reaction conditions. However, the detection label can be removable, such as removed by cleavage or degradation. Images can be captured after the label is incorporated into the arrayed nucleic acid feature. In a specific implementation, each cycle involves four different nucleotide types being delivered to the array simultaneously, and each nucleotide type has spectrally different labels. Four images can then be obtained, each image using a detection channel that is selective to one of the four different labels. Alternatively, different nucleotide types can be added sequentially, and the image of the array can be obtained between each addition step. In such specific implementations, each image will show the nucleic acid feature that has been incorporated with a specific type of nucleotide. Due to the different sequence content of each feature, different feature portions are present or absent in different images. However, the relative position of the feature portion will remain unchanged in the image. The images obtained by such reversible terminator-SBS methods can be stored, processed and analyzed as described herein. After the image capture step, the label can be removed and the reversible terminator portion can be removed for subsequent cycles of nucleotide addition and detection. Removing labels after they have been detected in a particular cycle and before subsequent cycles can provide the advantage of reducing background signal and crosstalk between cycles. Examples of useful labeling and removal methods are set forth below.

[0204] In a specific implementation, some or all of the nucleotide monomers may include a reversible terminator. In such implementations, the reversible terminator / cleavable fluorophore may include a fluorophore connected to a ribose moiety via a 3' ester bond (Metzker, Genome Res. 15: 1767-1776 (2005), which is incorporated herein by reference). Other methods have separated terminator chemistry from the cleavage of a fluorescent label (Ruparel et al., Proc Natl Acad Sci USA 102: 5932-7 (2005), which is incorporated herein by reference in its entirety). Ruparel et al. describe the development of reversible terminators that use small 3' allyl groups to block extension, 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 a cleavable linker. Another method of reversible termination is to use natural termination, which occurs after a bulky dye is placed on a dNTP. The presence of a charged bulky dye on a dNTP can act as an efficient terminator by steric hindrance and / or electrostatic hindrance. Unless the dye is removed, the presence of an incorporation event prevents further incorporation. The cracking of the dye removes the fluorophore and effectively reverses termination. The example of modified nucleotides is also described in U.S. Patent No. 7,427,673 and U.S. Patent No. 7,057,026, the disclosure of which is incorporated herein by reference in its entirety.

[0205] Additional exemplary SBS systems and methods that may be utilized with the methods and systems described herein are described in U.S. Patent Application Publication No. 2007 / 0166705, U.S. Patent Application Publication No. 2006 / 0188901, U.S. 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 entireties.

[0206] Some specific implementations can use less than four different marks to use the detection of four different nucleotides.For example, the method and system described in the material of the U.S. Patent Application Publication No. 2013 / 0079232 that can be incorporated can be used to perform SBS.As a first example, a pair of nucleotide types can be detected at the same wavelength, but based on the intensity difference of a member in the pair relative to another member, or based on a member in the pair causing the signal of another member of the pair detected to be compared with the change (for example, by chemical modification, photochemical modification or physical modification) that the signal of ... The three exemplary configurations described above are not considered mutually exclusive and can be used in various combinations. An exemplary 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 channel and the second channel (e.g., dTTP with at least one label detected in both channels when excited by the first excitation wavelength and / or the second excitation wavelength), and a fourth nucleotide type lacking a label detected or minimally detected in either channel (e.g., dGTP without a label).

[0207] In addition, as described in the materials of incorporated U.S. Patent Application Publication No. 2013 / 0079232, a single channel can be used to obtain sequencing data. In such so-called single dye sequencing methods, a first nucleotide type is marked, but the mark is removed after the first image is generated, and a second nucleotide type is marked only after the first image is generated. A third nucleotide type retains its mark in both the first image and the second image, and a fourth nucleotide type remains unmarked in both images.

[0208] Some specific implementations can utilize sequencing technology while connecting. Such technology utilizes DNA ligase to incorporate oligonucleotides and identify the incorporation of such oligonucleotides. Oligonucleotides generally have different labels related to the identity of specific nucleotides in the sequence of oligonucleotide hybridization. As with other SBS methods, images can be obtained after the array of nucleic acid feature parts is treated with labeled sequencing reagents. Each image will show the nucleic acid feature parts of the labeling of a specific type. Due to the different sequence content of each feature part, different feature parts are present or absent in different images, but the relative position of the feature parts will remain unchanged in the image. The images obtained by the sequencing method based on connection 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 No. 6,969,488, U.S. Patent No. 6,172,218 and U.S. Patent No. 6,306,597, and the disclosures of these patents are incorporated herein by reference in their entirety.

[0209] Some implementations can 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 a solid-state nanopore microscope", Nat. Mater., 2: 611-615 (2003), the disclosures of which are incorporated herein by reference in their entirety). In such implementations, the target nucleic acid passes through a nanopore. The nanopore can be a synthetic pore or a biological membrane protein, such as α-hemolysin. As the target nucleic acid passes through the nanopore, each base pair can be identified by measuring the fluctuations in the conductivity of the pore. (U.S. Pat. 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.", Nanomed., 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 disclosures of which are incorporated herein by reference in their entirety). The data obtained from nanopore sequencing can be stored, processed and analyzed as described herein. In particular, according to the exemplary processing of optical images and other images described herein, the data can be processed like an image.

[0210] Some embodiments may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation may be detected by fluorescence resonance energy transfer (FRET) interactions between a polymerase carrying a fluorophore and a γ-phosphate labeled nucleotide, as described, for example, in U.S. Pat. No. 7,329,492 and U.S. Pat. No. 7,211,414 (each of which is incorporated herein by reference), or may be detected with a zero-mode waveguide, as described, for example, in U.S. Pat. No. 7,315,019 (which is incorporated herein by reference), and may be detected using fluorescent nucleotide analogs and engineered polymerases, as described, for example, in U.S. Pat. No. 7,405,281 and U.S. Patent Application Publication No. 2008 / 0108082 (each of which is incorporated herein by reference). Illumination can be limited to a volume of the order of magnitude surrounding the surface-tethered polymerase, so that the incorporation of fluorescently labeled nucleotides can be observed under low background (Levene, MJ et al., "Zero-mode waveguides for single-molecule analysis at high concentrations.", Science 299, 682-686 (2003); Lundquist, PM et al., "Parallel confocal detection of single molecules in real time.", Opt. Lett. 33, 1026-1028 (2008); Korlach, J. et al., "Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nano structures.", Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures of which are incorporated herein by reference in their entirety). Images obtained by such methods can be stored, processed, and analyzed as described herein.

[0211] Some SBS implementations include detection of the proton released when nucleotides are incorporated into extension products. For example, sequencing based on the detection of releasing protons can use the commercially available electrical detectors and related technologies from Ion Torrent company (Guilford, CT, it is a Life Technologies subsidiary) or sequencing methods and systems described in US 2009 / 0026082A1, US2009 / 0127589 A1, US2010 / 0137143 A1 or US 2010 / 0282617A1, each of which is incorporated herein by reference. The method for amplifying target nucleic acids using kinetic exclusion set forth herein can be easily applied to substrates for detecting protons. More specifically, the method set forth herein can be used to produce amplicon clone colonies for detecting protons.

[0212] The above-mentioned SBS method can be advantageously carried out in a variety of formats so that a plurality of different target nucleic acids are manipulated simultaneously. In a specific implementation, different target nucleic acids can be processed in a common reaction vessel or on the surface of a specific substrate. This allows to conveniently deliver sequencing reagents, remove unreacted reagents and detect incorporation events in a variety of ways. In the specific implementation of the target nucleic acid using surface binding, the target nucleic acid can be an array format. In the array format, the target nucleic acid can be attached to the surface in a spatially distinguishable manner. The target nucleic acid can be attached by direct covalent attachment, attached to beads or other particles or attached to a polymerase or other molecules attached to the surface to combine. The array can include a single copy of the target nucleic acid at each site (also referred to as a feature portion), or multiple copies with the same sequence can be present at each site or feature portion. Multiple copies can be produced by an amplification method (such as bridge amplification or emulsion PCR as described in further detail below).

[0213] The methods described herein may use arrays having features at any of a variety of densities, including, for example, at least about 10 features / cm 2 , 100 features / cm 2 , 500 features / cm 2 , 1,000 features / cm 2 , 5,000 features / cm 2 , 10,000 features / cm 2 , 50,000 features / cm 2 , 100,000 features / cm 2 , 1,000,000 features / cm 2 , 5,000,000 features / cm 2 or higher.

[0214] The advantage of the method described herein is that they provide rapid and effective detection of multiple target nucleic acids in parallel. Therefore, the present disclosure provides an integrated system that can use technology known in the art (such as those illustrated above) to prepare and detect nucleic acids. Therefore, the integrated system of the present disclosure can include amplification reagents and / or sequencing reagents that can be delivered to one or more fixed DNA fragments. The system includes components such as pumps, valves, reservoirs, fluid pipelines, etc. The circulation cell can be configured for and / or for detecting target nucleic acids in the integrated system. Exemplary circulation cells are described in, for example, US 2010 / 0111768A1 and U.S. Serial No. 13 / 273,666, each of which is incorporated herein by reference. As illustrated for the circulation cell, one or more fluid components of the integrated system can be used for amplification method and detection method. Taking nucleic acid sequencing as an example, one or more fluid components of the integrated system can be used for the amplification method described herein and for delivering sequencing reagents in sequencing methods (such as those illustrated above). Alternatively, the integrated system can include a separate fluid system to perform amplification method and perform detection method. Examples of integrated sequencing systems that can generate amplified nucleic acids and also determine nucleic acid sequences include, but are not limited to, MiSeq TM The ELISA platform (Illumina, Inc., San Diego, CA) and the apparatus described in U.S. Ser. No. 13 / 273,666, which is incorporated herein by reference.

[0215] The above sequencing system sequences the nucleic acid polymers present in the sample received by the sequencing device. As defined herein, "sample" and its derivatives are used in their broadest sense, including any specimens, cultures, etc. suspected of containing a target. In some specific implementations, the sample includes DNA, RNA, PNA, LNA, chimeric or hybridized nucleic acids. The sample may include any biological, clinical, surgical, agricultural, atmospheric or aquatic animal and plant specimens containing one or more nucleic acids. The term also includes any isolated nucleic acid sample, such as genomic DNA, fresh frozen or formalin-fixed paraffin-embedded nucleic acid specimens. It is also envisioned that the sample may come from: a single individual, a collection of nucleic acid samples from genetically related members, a nucleic acid sample from a genetically unrelated member, a nucleic acid sample (matched therewith) from a single individual (such as a tumor sample and a normal tissue sample), 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 there is contaminating bacterial DNA in a sample containing plant or animal DNA. In some specific implementations, the source of nucleic acid material may include nucleic acids obtained from newborns, such as nucleic acids commonly used for newborn screening.

[0216] 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 specific implementation, low molecular weight substances include DNA of enzymatic fragmentation or mechanical fragmentation. Samples may include cell-free circulating DNA. In some specific implementations, the sample may include nucleic acid molecules obtained from biopsy tissue, tumor, scrapings, swabs, blood, mucus, urine, blood plasma, semen, hair, laser capture microdissection, surgical resection and other clinical or laboratory samples. In some specific implementations, the sample may be an epidemiological sample, an agricultural sample, a forensic sample or a pathogenic sample. In some specific implementations, the sample may include nucleic acid molecules obtained from animals such as humans or mammalian sources. In another specific implementation, the sample may include nucleic acid molecules obtained from non-mammalian sources (such as plants, bacteria, viruses or fungi). In some specific implementations, the source of nucleic acid molecules may be archived or extinct samples or species.

[0217] In addition, the methods and compositions disclosed herein can be used to amplify nucleic acid samples with low-quality nucleic acid molecules, such as degraded and / or fragmented genomic DNA from forensic samples. In a specific implementation, forensic samples may include nucleic acids obtained from crime scenes, nucleic acids obtained from missing persons DNA databases, nucleic acids obtained from laboratories associated with forensic investigations, or include forensic samples obtained by law enforcement agencies, one or more military services, or any such personnel. Nucleic acid samples can be purified samples or lysates containing crude DNA, such as from oral swabs, paper, fabrics, or other substrates that can be impregnated with saliva, blood, or other body fluids. Therefore, in some specific implementations, nucleic acid samples may include a small amount of DNA such as genomic DNA, or a fragmented portion of DNA. In some specific implementations, the target sequence may be present in one or more body fluids, wherein the body fluids include but are not limited to blood, sputum, plasma, semen, urine, and serum. In some specific implementations, the target sequence can be obtained from the victim's hair, skin, tissue samples, autopsy, or remains. In some specific implementations, nucleic acids including one or more target sequences can be obtained from dead animals or humans. In some implementations, the target sequence may include a nucleic acid obtained from non-human DNA (such as microbial, plant, or insect DNA). In some implementations, the target sequence or amplified target sequence is related to the purpose of human identification. In some implementations, the disclosure as a whole relates to methods for identifying characteristics of forensic samples. In some implementations, the disclosure as a whole relates to human identification methods using one or more target-specific primers disclosed herein or one or more target-specific primers designed using the primer design criteria outlined herein. In one implementation, a forensic sample or human identification sample comprising at least one target sequence can be amplified using any one or more target-specific primers disclosed herein or using the primer standards outlined herein.

[0218] The components of the segmentation-read alignment system 106 may include software, hardware, or both. For example, the components of the segmentation-read alignment 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., the user client device 108). When executed by one or more processors, the computer-executable instructions of the segmentation-read alignment system 106 may cause the computing device to perform the bubble detection method described herein. Alternatively, the components of the segmentation-read alignment system 106 may include hardware, such as a dedicated processing device for performing a certain function or group of functions. Additionally or in an alternative, the components of the segmentation-read alignment system 106 may include a combination of computer-executable instructions and hardware.

[0219] In addition, the components of the segmentation-read alignment system 106 that perform the functions described herein with respect to the segmentation-read alignment system 106 may be implemented, for example, as part of a stand-alone application, a module of an application, a plug-in of an application, one or more library functions that can be detected by other applications, and / or a cloud computing model. Therefore, the components of the segmentation-read alignment system 106 may be implemented as part of a stand-alone application on a personal computing device or a mobile device. Additionally or alternatively, the components of the 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 United States and / or other countries.

[0220] As discussed in more detail below, specific implementations of the present disclosure may include or utilize a special-purpose or general-purpose computer including computer hardware (such as, for example, one or more processors and system memory). Specific implementations within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. Specifically, one or more of the processes described herein may be at least partially implemented 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). In general, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., a memory, etc.) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.

[0221] Computer-readable media can be any available media that can be accessed by a general or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Therefore, by way of example and not limitation, specific implementations of the present disclosure may include at least two distinct types of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media.

[0222] Non-transitory computer-readable storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid-state drives (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 media that can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general or special purpose computer.

[0223] "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 or wireless), the computer appropriately regards the connection as a transmission medium. The transmission medium may include a network and / or data link, which can be used to carry the desired program code means in the form of computer executable instructions or data structures, and which can be accessed by general or special computers. The above combinations should also be included in the scope of computer-readable media.

[0224] Furthermore, upon reaching various computer system components, program code means in the form of computer executable instructions or data structures may be automatically transferred from a transmission medium to a non-transitory computer readable storage medium (device) (or vice versa). For example, a computer executable instruction or data structure received over a network or data link may be buffered in RAM within a network interface module (e.g., NIC), and then ultimately transferred to the computer system RAM and / or to a less volatile computer storage medium (device) at the computer system. Thus, it should be understood that a non-transitory computer readable storage medium (device) may be included in a computer system component that also (or even primarily) utilizes a transmission medium.

[0225] Computer executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a certain function or group of functions when executed at a processor. In some specific implementations, computer executable instructions are executed on a general-purpose computer to turn a general-purpose computer into a special-purpose computer that implements the elements of the present disclosure. Computer executable instructions can be, for example, binary numbers, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in a language specific to structural features and / or method actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or actions. On the contrary, the described features and actions are disclosed as exemplary forms of implementing the claims.

[0226] Those skilled in the art will appreciate that the present disclosure can be practiced in a network computing environment 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, mainframe computers, mobile phones, PDAs, tablet computers, pagers, routers, switches, etc. The present disclosure can also be practiced in a distributed system environment, where both local and remote computer systems linked by a network (by a hardwired data link, a wireless data link, or a combination of hardwired and wireless data links) perform tasks. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0227] Specific implementations of the present disclosure may also be implemented in a cloud computing environment. In this specification, "cloud computing" is defined as a model for implementing on-demand network access to a shared pool of configurable computing resources. For example, cloud computing may be employed in the marketplace to provide ubiquitous and convenient on-demand access to a shared pool of configurable computing resources. A shared pool of configurable computing resources may be quickly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly.

[0228] The cloud computing model may consist of various characteristics, such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, metered services, etc. The cloud computing model may also exhibit various service models, such as, for example, software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). The cloud computing model may also be deployed using different deployment models, such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this specification and in the claims, a "cloud computing environment" is an environment in which cloud computing is employed.

[0229] Fig.16 1600 is a block diagram of a computing device 1600 that can be configured to perform one or more of the above processes. It will be understood that one or more computing devices such as computing device 1600 can implement the segmentation-read segment alignment system 106 and the sequencing system 104. Fig.16 As shown, computing device 1600 may include a processor 1602, memory 1604, storage device 1606, I / O interface 1608, and communication interface 1610 that may be communicatively coupled via communication infrastructure 1612. In some implementations, computing device 1600 may include Fig.16 Those shown may have fewer or more components. The following paragraphs describe in more detail Fig.16 Components of computing device 1600 are shown.

[0230] In one or more specific implementations, the processor 1602 includes hardware for executing instructions such as those that constitute a computer program. As an example and not by way of limitation, in order to execute instructions for dynamically modifying a workflow, the processor 1602 may retrieve (or fetch) instructions from an internal register, an internal cache, a memory 1604, or a storage device 1606, and decode and execute them. The memory 1604 may be a volatile or non-volatile memory for storing data, metadata, and programs executed by the processor. The storage device 1606 includes a storage device for storing data or instructions for performing the methods described herein, such as a hard disk, a flash disk drive, or another digital storage device.

[0231] The I / O interface 1608 allows a user to provide input to the computing device 1600, receive output from the computing device, and otherwise transfer data to and receive data from the computing device. The I / O interface 1608 may include a mouse, a keypad or keyboard, a touch screen, a camera, an optical scanner, a network interface, a modem, other known I / O devices, or a combination of such I / O interfaces. The I / O interface 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 display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In some specific implementations, the I / O interface 1608 is configured to provide graphics data to the display for presentation to the user. The graphics data may represent one or more graphical user interfaces and / or any other graphical content that may serve a specific specific implementation.

[0232] 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. As an example and not by way of limitation, the communication interface 1610 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network such as WI-FI.

[0233] Additionally, the communication interface 1610 can facilitate communication with various types of wired or wireless networks. The communication interface 1610 can also facilitate communication using various communication protocols. The communication infrastructure 1612 can also include hardware, software, or both that couple the components of the computing device 1600 to each other. For example, the communication interface 1610 can use one or more networks and / or protocols to enable multiple computing devices connected through a particular infrastructure to communicate with each other to perform one or more aspects of the processes described herein. For illustration, the sequencing process can allow multiple devices (e.g., client devices, sequencing devices, and server devices) to exchange information such as sequencing data and error notifications.

[0234] In the foregoing description, the present disclosure has been described with reference to specific exemplary implementations thereof. Various implementations and aspects of the present disclosure are described with reference to the details discussed herein, and the accompanying drawings illustrate various implementations. The above description and drawings are illustrative of the present disclosure and should not be construed as limiting the present disclosure. Many specific details are described to provide a thorough understanding of various implementations of the present disclosure.

[0235] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The specific implementations are to be considered in all respects as being merely exemplary and not restrictive. For example, the methods described herein may be performed with fewer or more steps / actions, or the steps / actions may be performed in different orders. 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 the application is indicated by the appended claims rather than the foregoing description. All changes within the equivalent meaning and scope of the claims will be included within their scope.

Claims

1. A computer-implemented method comprising: identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; determining a candidate segmentation group comprising a fragment alignment corresponding to the one or more nucleotide reads; generating a segmentation group score for a segmentation alignment of the candidate segmentation group with a reference genome; as well as Based on the segmentation group scores, a predicted segmentation group is selected from the candidate segmentation groups for use in nucleobase calling of the genomic region.

2. The computer-implemented method of claim 1 , further comprising determining a candidate segmentation group in the candidate segmentation groups by: grouping one or more fragment alignments of the single-end nucleotide reads into the candidate segmentation groups; or One or more fragment alignments of the paired-end nucleotide reads from a pair of paired-end nucleotide reads are grouped into the candidate segmentation group.

3. The computer-implemented method of claim 1 , further comprising: Generating segment alignment scores for alignments of candidate segmentation groups with each segment of the reference genome; as well as A segmentation group score for the candidate segmentation group is generated based on the segment alignment score.

4. The computer-implemented method of claim 1 , further comprising: For a candidate segmentation group in the candidate segmentation group, generating a break penalty for a relative geometry of a first segment alignment and a second segment alignment with respect to the reference genome; as well as A segmentation group score for the candidate segmentation group is generated based on the breakage penalty.

5. The computer-implemented method of claim 1 , further comprising: For a candidate segmentation group in the candidate segmentation group, generating an overlap penalty for an overlap between a first segment alignment and a second segment alignment within a nucleotide read; as well as A segmentation group score for the candidate segmentation group is generated based on the overlap penalty.

6. The computer-implemented method of claim 1 , further comprising generating a segmentation group score for a candidate segmentation group in the candidate segmentation groups by: generating a segment alignment score, a break penalty, and an overlap penalty for segment alignment of the candidate segmentation group; and The fragment alignment scores are combined and the break penalty and the overlap penalty are subtracted from the combined fragment alignment score.

7. The computer-implemented method of claim 1 , further comprising: Determining the candidate segmentation groups by iteratively grouping the segment alignments in order from the outermost segment alignment to the innermost segment alignment of the nucleotide reads; as well as The segmentation group scores are generated by iteratively scoring the groups of individual segment alignments in the order in which they are grouped.

8. The computer-implemented method of claim 1 , further comprising: identifying candidate segmentation group pairs comprising different segment alignments of paired-end nucleotide read partners from the candidate segmentation groups; generating, for the candidate segmentation group pair, a pair score for evaluating an alignment of the candidate segmentation group pair with the reference genome; as well as For each partner of the paired-end nucleotide reads, the predicted segmentation group is further selected based on the pair score.

9. The computer-implemented method of claim 8, further comprising: determining a sum of the segmentation group scores for the corresponding candidate segmentation group pairs; generating a pairing penalty based on an estimated insert size between the innermost segment alignments of the candidate segmentation pair; as well as The pair score of the candidate segment group pair is generated based on a sum of the segment group score and the pairing penalty score.

10. The computer-implemented method of claim 8, further comprising: determining alternative contiguous segment alignment scores for inner segment alignments and outer segment alignments of alternative contiguous sequences within the reference genome corresponding to the nucleotide reads; determining segmentation group scores for the inner segment alignment and the outer segment alignment to the primary assembled region of the reference genome; and Based on determining that the alternative contiguous segment alignment score exceeds the segmentation group score, the alternative contiguous segment alignment score is selected as a replacement segmentation group score.

11. A system, comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; determining a candidate segmentation group comprising a fragment alignment corresponding to the one or more nucleotide reads; generating a segmentation group score for a segmentation alignment of the candidate segmentation group with a reference genome; as well as Based on the segmentation group scores, a predicted segmentation group is selected from the candidate segmentation groups for use in nucleobase calling of the genomic region.

12. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to determine a nucleobase call for the genomic region based on an alignment of the predicted segmentation group to the reference genome.

13. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determining that a fragment alignment score of the fragment alignment fails to satisfy a threshold fragment alignment score; and The segment alignment is removed from consideration in forming the candidate segmentation set.

14. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determining that the alignment score of the candidate segmentation group fails to meet a minimum alignment score; and Based on the alignment score failing to satisfy the minimum alignment score, reporting of the segmentation alignment of the candidate segmentation group in an alignment file or a variant call file is prohibited.

15. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to generate a segmentation group score for a candidate segmentation group in the candidate segmentation groups by: generating a segment alignment score, a break penalty, and an overlap penalty for the segment alignment of the candidate segmentation group; and The fragment alignment scores are combined and the break penalty and the overlap penalty are subtracted from the combined fragment alignment score.

16. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: Determining the candidate segmentation groups by iteratively grouping the segment alignments in order from the outermost segment alignment to the innermost segment alignment of the nucleotide reads; and The segmentation group scores are generated by iteratively scoring the groups of individual segment alignments in the order in which they are grouped.

17. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a computing device to: identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; determining a candidate segmentation group comprising a fragment alignment corresponding to the one or more nucleotide reads; generating a segmentation group score for the segmentation alignment of the candidate segmentation group with a reference genome; and Based on the segmentation group scores, a predicted segmentation group is selected from the candidate segmentation groups for use in nucleobase calling of the genomic region.

18. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to determine a candidate segmentation group in the candidate segmentation groups by: grouping one or more fragment alignments of the single-end nucleotide reads into the candidate segmentation groups; or One or more fragment alignments of the paired-end nucleotide reads from a pair of paired-end nucleotide reads are grouped into the candidate segmentation group.

19. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to: Generating segment alignment scores for alignments of candidate segment groups with each segment of the reference genome; and A segmentation group score for the candidate segmentation group is generated based on the segment alignment score.

20. The non-transitory computer readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to: For a candidate segmentation group in the candidate segmentation group, generating a break penalty for a relative geometry of a first segment alignment and a second segment alignment with respect to the reference genome; and A segmentation group score for the candidate segmentation group is generated based on the breakage penalty.

21. The non-transitory computer-readable medium of claim 17, wherein the at least one processor comprises a configurable processor.

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