Machine learning models for recalibrating nucleotide base calls

Abstract

The present disclosure describes methods, non-transitory computer readable media, and systems that can utilize machine learning models to recalibrate nucleotide base calls (e.g., variant calls) of a call generation model. For example, the disclosed system can train and utilize a call recalibration machine learning model to generate a set of predicted variant call classifications based on sequencing metrics associated with a sample nucleotide sequence. Leveraging the set of variant call classifications, the disclosed system can further update or revise nucleotide base calls (e.g., variant calls) corresponding to genomic coordinates. In effect, the disclosed system can utilize a call generation model to generate initial nucleotide base calls based on sequencing metrics for nucleotide reads of a sample sequence, and can further utilize a call recalibration machine learning model to generate classification predictions to update or recalibrate the initial nucleotide base calls from a subset of the same sequencing metrics or other sequencing metrics.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. patent application Ser. No. 17 / 384,423, filed July 23, 2021, entitled "MACHINE-LEARNING MODEL FOR RECALIBRATING NUCLEOTIDE-BASE CALLS," the contents of which are incorporated herein by reference in their entirety. [Background technology]

[0002] Despite these recent advances in sequencing and variant calling, existing sequencing data analysis software often includes variant callers that identify an excessive number of false positives and false negatives. For example, in some situations, existing software applies variant callers that erroneously identify an excessive number of variants at sites or coordinates where there are no such variants in the reference sequence. As another example, existing variant callers often erroneously identify an excessive number of non-variant subsequences in a sample sequence at sites or coordinates where the sample sequence contains an actual variant that differs from the reference base of the reference sequence. Indeed, while existing variant callers achieve a certain level of accuracy, due to their limitations, they still leave room for improvement in reducing false positives and recovering false negatives. For example, a variant call that identifies a specific single nucleotide polymorphism (SNP) in the hemoglobin beta (HBB) gene can have important implications. For example, if a variant caller identifies a SNP at rs344 on chromosome 11, the variant caller may either correctly identify the genetic cause of sickle cell anemia or may miss the cause of the disease. As a further example, a variant call that correctly or incorrectly identifies a deletion of one or more copies of the hemoglobin subunit alpha 1 (HbA1) or hemoglobin subunit alpha 2 (HbA2) genes may result in either correctly identifying the genetic cause of an inherited blood disease or missing the gene deletion entirely.

[0003] As a contributing factor to the aforementioned inaccuracies, many existing nucleotide-based sequencing platforms and sequencing data analysis software (collectively, hereinafter, existing sequencing systems) utilize only limited data sets when determining nucleotide base calls. For example, existing sequencing systems often rely exclusively on information extracted directly from the nucleotide reads of the sample sequence, such as read depth, number of mismatches, and mapping quality, to determine nucleotide base calls. Although sequence information from nucleotide reads can provide valuable insights for determining nucleotide base calls, existing systems that rely only on these data may perform poorly when it comes to accurately determining nucleotide base calls. In fact, some existing sequencing systems that rely on raw sequence data inaccurately determine SNPs, indels, or other variants in the sample sequence compared to more complex models. In fact, existing sequencing systems often identify false negative or false positive variants in the Truth Challenges of the U.S. Food and Drug Administration (FDA).

[0004] In addition to making inaccurate variant calls, some existing sequencing systems also waste computational resources inefficiently by using overly complex models. Specifically, the variant callers of some existing sequencing systems are computationally expensive and slow. In fact, some existing systems utilize variant callers with deep learning architectures or some other neural network architectures that require extensive computational resources (e.g., computation time, processing power, and memory) to train and apply. For example, some existing systems utilize deep learning architectures that require a lot of time across multiple computing devices to generate nucleotide base calls for a single sample sequence, even after training.

[0005] As a further drawback of existing sequencing systems with complex networks, many such systems utilize model architectures that make sequence data uninterpretable. More specifically, some existing deep neural networks transform and manipulate sequence data multiple times, changing it from one vector to another across various layers and neurons as a basis for generating variant calls. In many cases, the internal data of these deep neural networks is uninterpretable and cannot be utilized in any way outside the neural network architecture itself. Summary of the Invention [Means for solving the problem]

[0006] The present disclosure describes embodiments of methods, non-transitory computer readable media, and systems that can utilize machine learning models to recalibrate nucleotide base calls (e.g., variant calls) of a call generation model. For example, the disclosed system can train and utilize a call recalibration machine learning model to generate a classification prediction set (e.g., variant call classification) from sequencing metrics associated with a sample nucleotide sequence. Leveraging the classification prediction set, the disclosed system can further update or revise the nucleotide base calls for the sample sequence. In effect, the disclosed system can (i) utilize a call generation model to generate initial nucleotide base calls (e.g., initial variant calls) for genomic coordinates of a reference genome based on sequencing metrics for nucleotide reads of the sample sequence, and (ii) utilize a call recalibration machine learning model to generate classification predictions to update or recalibrate the initial nucleotide base calls from a subset of the same sequencing metrics. After recalibration, the disclosed system can output the updated or recalibrated nucleotide base calls as final nucleotide base calls (e.g., final variant calls) in a variant call file or other base call output file.

[0007] By utilizing a call recalibration machine learning model to update sequencing metrics for generating nucleotide base calls, the disclosed system can improve accuracy, efficiency, and speed over existing sequencing systems. As described further below, for example, the disclosed call recalibration machine learning model determines variant calls with better accuracy and faster computation time than more complex neural networks for variant calling. In addition, the disclosed system can improve the interpretability of factors that affect accurate variant calls by utilizing a call recalibration machine learning model that processes data in an accessible and interpretable format. Indeed, for improved interpretability of the disclosed system, in some embodiments, the disclosed system can generate and provide visualizations of various contribution measures associated with individual sequencing metrics to visually depict the respective measures of the impact of the sequencing metrics on the resulting nucleotide base calls. [Brief description of the drawings]

[0008] The detailed description refers to the drawings, which are briefly described below. [Figure 1] FIG. 1 shows a block diagram of a sequencing system including a basecaller recalibration system in accordance with one or more embodiments. [Diagram 2] 1 shows an overview of generating nucleotide base calls utilizing a base caller recalibration system according to one or more embodiments. [Diagram 3] 3A and 3B illustrate generating or determining a sequencing metric according to one or more embodiments. [Figure 3C] 1 illustrates generating or determining a sequencing metric in accordance with one or more embodiments. [Figure 4] FIG. 1 illustrates the use of a call recalibration machine learning model in accordance with one or more embodiments to generate variant call classifications and recalibrate nucleotide base calls. [Diagram 5] 1 illustrates an example process for training a call recalibration machine learning model according to one or more embodiments. [Figure 6] 1 illustrates an exemplary contribution measure interface displayed on a client device in accordance with one or more embodiments. [Figure 7A] 1 shows a graph illustrating accuracy improvement associated with a base caller recalibration system utilizing a call recalibration machine learning model in accordance with one or more embodiments. [Figure 7B] 1 shows a graph illustrating accuracy improvement associated with a base caller recalibration system utilizing a call recalibration machine learning model in accordance with one or more embodiments. [Figure 8] 1 shows a graph comparing a basecaller recalibrated system to a non-recalibrated system in accordance with one or more embodiments. [Figure 9] 1 depicts a flowchart of a set of operations for generating nucleotide base calls based on variant call classifications from a call recalibration machine learning model according to one or more embodiments. [Figure 10] 1 illustrates a block diagram of an exemplary computing device in accordance with one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present disclosure describes an embodiment of a base caller recalibration system that utilizes a call generation model and a call recalibration machine learning model to generate and recalibrate nucleotide base calls for a sample nucleotide sequence. In particular, the base caller recalibration system can utilize the call generation model to generate initial nucleotide base calls (e.g., base calls that identify variant or non-variant calls with respect to a reference genome) from sequencing metrics identified or obtained from nucleotide reads of a sample nucleotide sequence. Furthermore, the base caller recalibration system can utilize the call recalibration machine learning model to recalibrate and improve the accuracy of the initial nucleotide base calls by updating various call metrics, such as call quality, genotypes associated with the calls, and / or genotype quality associated with the genotypes. By utilizing the call recalibration machine learning model to update the metrics, the base caller recalibration system can remove false positives from the nucleotide base calls of the sample nucleotide sequence and / or recover false negatives from the nucleotide base calls of the sample nucleotide sequence.

[0010] As described above, in certain embodiments, the base caller recalibration system extracts or determines sequencing metrics from sample nucleotide sequences. For example, the base caller recalibration system determines sequencing metrics from nucleotide base calls of nucleotide reads from a sample nucleotide sequence. Indeed, in some cases, the base caller recalibration system generates or determines a nucleotide base call set from nucleotide reads captured or determined via fluorescent imaging of a sample nucleotide sequence (e.g., at a particular genomic coordinate). From the read-based nucleotide base calls, in some embodiments, the base caller recalibration system determines or extracts various sequencing metrics (e.g., various types of sequencing metrics obtained from the reads and / or from different components of the call generation model).

[0011] More specifically, in certain implementations, the base caller recalibration system determines different types of sequencing metrics associated with different sources. For example, the base caller recalibration system determines read-based sequencing metrics, including metrics derived from nucleotide reads of a sample nucleotide sequence. In addition, the base caller recalibration system determines sequencing metrics of external sources identified from one or more external databases representing genomic sequences associated with various nucleotide attributes, mapping challenges, and sequencing biases. In addition, the base caller recalibration system determines call model-generated sequencing metrics generated via a variant caller or other call generation model, such as variables internal to the base caller recalibration system that are not accessible to other systems or parties (e.g., proprietary quality scores, base context, read filtering, proprietary hypothesis scores, and other metrics). Indeed, in some cases, the base caller recalibration system determines call model-generated sequencing metrics in the form of variant calling sequencing metrics and mapping alignment sequencing metrics, each type being extracted by a different component of the call generation model.

[0012] As further noted, in certain implementations, the base caller recalibration system generates a set of predicted classifications from the sequencing metrics to correct or improve the nucleotide base call or variant call data or fields associated with the nucleotide base call. More specifically, the base caller recalibration system utilizes a call recalibration machine learning model to generate a set of three variant call classifications from the sequencing metrics that impact or reflect the accuracy of identifying a variant at a particular genomic coordinate (e.g., a genomic coordinate corresponding to a nucleotide base call of a nucleotide read from a sample nucleotide sequence). For example, the base caller recalibration system utilizes a call recalibration machine learning model to generate variant call classifications including: i) a false positive classification (e.g., the probability that a nucleotide base call is a false positive variant), ii) a genotype error classification (e.g., the probability of misidentifying a genotype of a nucleotide base call), and iii) a true positive classification (e.g., the probability that a nucleotide base call is a true positive variant). Thus, in some cases, the variant call classifications represent a variant caller intermediate score metric.

[0013] From the variant call classification, the base caller recalibration system can further modify or update (metrics of) the final nucleotide base call for a genomic coordinate (e.g., a final nucleotide base call indicative of a variant call or a non-variant call). For example, the base caller recalibration system uses the variant call classification to update data fields corresponding to call quality, genotype, and genotype quality in a digital call file (e.g., a variant call format file or other base call output file) that indicates or represents the nucleotide base call. Indeed, as mentioned above, in some embodiments, the base caller recalibration system uses a call generation model to generate or determine a final nucleotide base call from sequencing metrics for a genomic coordinate.

[0014] In addition, the base caller recalibration system can utilize the variant call classification to update the nucleotide base calls to improve accuracy. In certain implementations, the base caller recalibration system utilizes the call recalibration machine learning model to generate variant call classifications, while also utilizing the call generation model to generate nucleotide base calls based on the variant call classifications. Indeed, in some embodiments, the base caller recalibration system (i) utilizes the call generation model to generate initial nucleotide base calls, and (ii) utilizes the call recalibration machine learning model to modify data fields corresponding to the variant call file of nucleotide base calls. In some cases, the base caller recalibration system further edits the nucleotide base calls based on one or more of the data fields and generates a variant call file having edited nucleotide base calls. In contrast, in some cases, the base caller recalibration system determines the final nucleotide base calls for the genomic coordinates based on both the sequencing metrics for the call generation model and the variant call classifications from the call recalibration machine learning model, without the initial nucleotide base calls from the call generation model. For example, in some embodiments, the call generation model may output a final variant call that takes into account the variant call classification (without generating an initial variant call that is updated). In contrast, in certain cases, the call generation model may initially determine that the confidence or quality corresponding to a potential variant call does not meet a threshold for inclusion in the variant call file, but may determine to include the variant call in the variant call file (after taking into account the variant call classification that updates the base call quality metrics). As a result of implementing the call recalibration machine learning model and the call generation model in this manner, the base caller recalibration system recovers false negative calls and / or removes false positive calls that were initially made by the call generation model.

[0015] In one or more embodiments, the base caller recalibration system further determines a contribution measure associated with one or more of the sequencing metrics. In particular, the base caller recalibration system determines a measure of the impact or influence that each sequencing metric or a subset of sequencing metrics has on the final nucleotide base call. For example, some metrics may be weighted more heavily than other metrics when determining a call at one genomic coordinate versus another. Indeed, due to the accessibility and interpretability of the call generation model and the call recalibration machine learning model, the base caller recalibration system can access the internal sequencing metrics used to generate the nucleotide base calls and determine the respective contribution measures when ultimately determining which metrics are causing or driving the recalibration of the nucleotide base calls (e.g., variant calls). In some cases, the base caller recalibration system also generates and provides a visualization of the contribution measures for display on the client device.

[0016] As alluded to above, the base caller recalibration system provides several advantages, benefits, and / or improvements over existing sequencing systems, including variant callers and other sequencing data analysis software. For example, the base caller recalibration system introduces a first type of machine learning model (call recalibration machine learning model) that is uniquely trained to perform a new application. Unlike traditional variant callers that generate nucleotide base calls exclusively from raw extracted metrics, the base caller recalibration system utilizes a unique call recalibration machine learning model that generates specific variant call classifications from external and internal sequencing metrics. Indeed, in some cases, the base caller recalibration system utilizes the call recalibration machine learning model to update the nucleotide base calls generated by the call generation model from the same metrics (or a subset of the same metrics) used by the call recalibration machine learning model to generate variant call classifications.

[0017] In addition to introducing the first type of machine learning model, compared to existing sequencing systems, the genome classification system improves the accuracy of nucleotide base calls. Indeed, although some existing sequencing systems generate nucleotide base calls with some degree of accuracy, these systems nevertheless identify an excessive number of false positive variant calls (e.g., by identifying variants that are not present in the reference sequence) and / or filter out an excessive number of false negative variant calls (e.g., by identifying a genomic location as non-variant when the location actually has a variant). The base caller recalibration system improves the accuracy of the existing system by utilizing the call recalibration machine learning model to remove a large number of false positives and recover a large number of false negatives. By editing the initial nucleotide base calls or generating final nucleotide base calls based on the variant call classification from the call recalibration machine learning model, the base caller recalibration system can use the unique machine learning output to recalibrate base calls with better accuracy than existing variant callers or variant calling machine learning models. For example, the base caller recalibration system utilizes a call recalibration machine learning model to generate variant call classifications from both internal (e.g., unique and model-specific) and external sequencing metrics, which results in the recovery of previously filtered out variant nucleotide base calls and / or the removal of previously unfiltered non-variant nucleotide base calls.

[0018] At least partially contributing to the improved accuracy, the base caller recalibration system exhibits improved flexibility over existing sequencing systems. For example, as mentioned above, existing sequencing systems may utilize variant callers that rely exclusively on internal sequencing metrics for a particular base call to generate nucleotide base calls, without re-engineering or modifying such internal sequencing metrics, or analyzing external source sequencing metrics related to the genomic coordinates of the corresponding nucleotide base call. In contrast, in some embodiments, the base caller recalibration system generates and manipulates both external and internal sequencing metrics. Indeed, in some cases, the base caller recalibration system determines the sequencing metrics of the call model generation from the variant caller component and the mapping and alignment component of the call generation model by efficiently combining a Bayesian probability model with machine learning techniques. In addition, the base caller recalibration system utilizes a call recalibration machine learning model to generate updated nucleotide base calls (e.g., from variant call classifications) from one or more sequencing metrics.

[0019] In addition to improving accuracy and flexibility, in certain embodiments, the base caller recalibration system improves efficiency and speed. As noted above, some existing sequencing systems utilize computationally expensive and slow neural network architectures (e.g., deep learning architectures such as convolutional neural networks) that require many hours (e.g., 5-8 hours on multiple processors running on a server) and large amounts of computational resources even to implement and generate files with variant calls from the sequencing run. Such deep learning architectures may further require days (or weeks) to train. Conversely, the base caller recalibration system utilizes relatively lightweight and fast architectures for both the call generation model and the call recalibration machine learning model. Indeed, in contrast to the many hours across multiple processors required by conventional systems, the base caller recalibration system often requires less than 30 minutes of runtime (for both the call generation model and the call recalibration machine learning model together) on a single field programmable gate array or single processor to generate nucleotide base calls for a sample nucleotide sequence. Thus, the base caller recalibration system is much faster and less computationally expensive than many deep learning approaches to variant calling. Not only are the models of the basecaller recalibration system faster and less computationally expensive to implement than many existing deep learning-based systems, the models of the basecaller recalibration system are also much faster and less computationally expensive to train.

[0020] As part of the improved speed and efficiency, in some embodiments, the base caller recalibration system recalibrates nucleotide base calls on a call-by-call basis as each call is processed by the call generation model. In effect, the base caller recalibration system can generate variant call classifications for recalibrating nucleotide base calls (e.g., utilizing a call recalibration machine learning model), while also generating nucleotide base calls from the variant call classifications along with one or more sequencing metrics. In some embodiments, the base caller recalibration system utilizes a call generation model in parallel with the call recalibration machine learning model to simultaneously generate initial nucleotide base calls and variant call classifications for correcting or recalibrating the initial nucleotide base calls.

[0021] As an additional advantage over existing sequencing systems, in certain implementations, the base caller recalibration system can identify or facilitate modifications to individual metrics that affect the accuracy of nucleotide base calls. While the neural network architecture of many conventional systems does not allow any interpretation of the internal model data with latent features, the base caller recalibration system utilizes a model architecture that facilitates the interpretation of the effect of individual sequencing metrics. More specifically, in some cases, the base caller recalibration system utilizes a call generation model and a call recalibration machine learning model that allows the extraction and analysis of individual sequencing metrics used throughout the process of generating nucleotide base calls. In effect, the base caller recalibration system can determine the respective contribution measures for the sequencing metrics involved in determining a nucleotide base call at a particular genomic coordinate.

[0022] As suggested by the preceding discussion, the present disclosure utilizes various terms to describe the features and advantages of the base caller recalibration system. Further details regarding the meaning of these terms as used in the present disclosure are provided below. For example, the term "sample nucleotide sequence" or "sample sequence" as used in the present disclosure refers to a sequence of nucleotides isolated or extracted from a sample organism (or a copy of such an isolated or extracted sequence). In particular, the sample nucleotide sequence is isolated or extracted from a sample organism and includes a segment of a nucleic acid polymer composed of nitrogenous heterocyclic bases. For example, the sample nucleotide sequence can include a segment of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or other polymeric forms of nucleic acid or chimeric or hybrid forms of nucleic acid as described below. More specifically, in some cases, the sample nucleotide sequence is one found in a sample prepared or isolated by the kit and received by the sequencing device.

[0023] As further used herein, the term "nucleotide base call" (or sometimes simply "call") refers to the determination or prediction of a particular nucleotide base (or nucleotide base pair) for a genomic coordinate or oligonucleotide of a sample genome during a sequencing cycle. In particular, a nucleotide base call can refer to (i) a determination or prediction of the type of nucleotide base incorporated within an oligonucleotide on a nucleotide sample slide (e.g., a read-based nucleotide base call), or (ii) a determination or prediction of the type of nucleotide base present at a genomic coordinate or region in a sample genome, including a variant or non-variant call in a digital output file. In some cases, for a nucleotide read, a nucleotide base call includes a nucleotide base determination or prediction based on an intensity value resulting from a fluorescently tagged nucleotide added to an oligonucleotide on a nucleotide sample slide (e.g., in a well of a flow cell). Alternatively, a nucleic acid base call includes a nucleotide base determination or prediction to a chromatogram peak or current change resulting from a nucleotide passing through a nanopore of a nucleotide sample slide. In contrast, a nucleotide base call can also include an initial or final prediction of a nucleotide base in a genomic coordinate of a sample genome for a variant call file or other base call output file based on a nucleotide read corresponding to the genomic coordinate. Thus, a nucleotide base call can include a base call corresponding to a genomic coordinate and a reference genome, such as an indication of a variant or non-variant at a particular position corresponding to a reference genome. In fact, a nucleotide base call can refer to a variant call, including but not limited to a base call that is part of a single nucleotide polymorphism (SNP), an insertion or deletion (indel), or a structural variant. By using a nucleotide base call, a sequencing system determines the sequence of a nucleic acid polymer.For example, single nucleotide base calls can include adenine, cytosine, guanine, or thymine calls (abbreviated as A, C, G, T) for DNA, or uracil (instead of thymine) (abbreviated as U) for RNA.

[0024] Relatedly, as used herein, the term "nucleotide read" refers to a predicted sequence of one or more nucleotide bases (or nucleotide base pairs) from all or a portion of a sample nucleotide sequence. In particular, a nucleotide read includes a sequence of determined or predicted nucleotide base calls for a nucleotide fragment (or a group of monoclonal nucleotide fragments) from a sequencing library corresponding to a genomic sample. For example, a base caller recalibration system determines a nucleotide read by generating nucleotide base calls for nucleotide bases that have passed through a nanopore of a nucleotide sample slide, that have been determined via fluorescent tagging, or that have been determined from a well in a flow cell.

[0025] As described above, in some embodiments, the base caller recalibration system determines a sequencing metric for the nucleotide base call of the nucleotide read. As used herein, the term "sequencing metric" refers to a quantitative measurement or score that indicates the degree to which an individual nucleotide base call (or a sequence of nucleotide base calls) aligns, compares, or quantifies to a genomic coordinate or genomic region of a reference genome, to a nucleotide base call from a nucleotide read, or to an external genome sequencing or genome structure. For example, a sequencing metric includes a quantitative measurement or score that indicates the degree to which (i) an individual nucleotide base call aligns, maps, or covers a genomic coordinate or reference base of a reference genome, (ii) the degree to which a nucleotide base call compares to a reference or alternative nucleotide read in terms of mapping, mismatches, base call quality, or other raw sequencing metrics, or (iii) the degree to which a genomic coordinate or region corresponding to a nucleotide base call demonstrates mappability, repetitive base call content, DNA structure, or other generalized metrics.

[0026] In some embodiments, the base caller recalibration system determines various types of sequencing metrics from different sources, such as read-based sequencing metrics, external source sequencing metrics, and call model generation sequencing metrics. As used herein, the term "read-based sequencing metrics" refers to sequencing metrics derived from nucleotide reads of a sample nucleotide sequence. For example, read-based sequencing metrics include sequencing metrics determined by applying statistical tests to detect differences between a reference sequence and a nucleotide read. For example, read-based sequencing metrics can include comparative mapping quality distribution metrics indicating a comparison between mapping qualities, or comparative mismatch count metrics indicating a comparison between mismatch counts.

[0027] In contrast, "external source sequencing metrics" refer to sequencing metrics identified or obtained from one or more external databases. For example, external source sequencing metrics include metrics related to nucleotide mappability, replication timing, or DNA structure that are available outside the base caller recalibration system.

[0028] Further, "sequencing metrics of the call model generation" refers to internal model-specific sequencing metrics generated or extracted by the call generation model. For example, the sequencing metrics of the call model generation include variant calling sequencing metrics extracted or determined via the variant caller component of the call generation model, and mapping and alignment sequencing metrics extracted or determined via the mapping and alignment component of the call generation model. As indicated above, the sequencing metrics of the call model generation can include alignment metrics, such as deletion size metrics or mapping quality metrics, that quantify the degree to which the sample nucleic acid sequence aligns with the genomic coordinates of the exemplary nucleic acid sequence. Furthermore, the sequencing metrics of the call model generation can include depth metrics, such as forward-reverse depth metrics or normalized depth metrics, that quantify the depth of the nucleotide base calls for the sample nucleic acid sequence at the genomic coordinates of the exemplary nucleic acid sequence. The sequencing metrics of the call model generation can also include call quality metrics, such as nucleotide base call quality metrics, callability metrics, or somatic quality metrics, that quantify the quality or accuracy of the nucleotide base calls.

[0029] As used herein, the term "base call quality metric" refers to a particular score or other measure that indicates the accuracy of a nucleotide base call. In particular, a base call quality metric includes a value that indicates the likelihood that one or more predicted nucleotide base calls for a genomic coordinate will contain an error. For example, in certain implementations, a base call quality metric can include a Q score (e.g., a Phred quality score) that predicts the probability of an error for any given nucleotide base call. To illustrate, a quality score (or Q score) can indicate that the probability of an incorrect nucleotide base call at a genomic coordinate is equal to 1 in 100 for a Q20 score, 1 in 1,000 for a Q30 score, 1 in 10,000 for a Q40 score, etc.

[0030] Relatedly, as used herein, the term "re-engineered sequencing metrics" refers to sequencing metrics that have been updated, modified, augmented, improved, or re-engineered to measure or compare nucleotide base calls (e.g., nucleotide base calls or variant calls for a read) against other nucleotide base calls, standards, or references, or targeted for a particular purpose or task. For example, re-engineered sequencing metrics can include modifications to raw sequencing metrics, or combinations of raw sequencing metrics. In some embodiments, for example, the base caller recalibration system generates one or more of the read-based sequencing metrics, external source sequencing metrics, and / or call model generation sequencing metrics as re-engineered sequencing metrics. In some cases, re-engineered sequencing metrics refer to sequencing metrics that are generated by the base caller recalibration system and thus are unique or internal to the base caller recalibration system and are not available to third party systems. Exemplary re-engineered sequencing metrics include a comparative mapping quality distribution metric indicating a comparison between mapping quality distributions associated with the reference sequence and the alternative supporting nucleotide read, or a comparative base quality metric indicating a comparison between the base qualities of the reference sequence and the alternative supporting nucleotide read.

[0031] As further used herein, the term "genomic coordinate" refers to a specific location or position of a nucleotide base within a genome (e.g., the genome of an organism or a reference genome). In some cases, the genomic coordinate includes an identifier for a particular chromosome of the genome and an identifier for a nucleotide base position within the particular chromosome. For example, the genomic coordinate(s) may include a chromosome number, name, or other identifier (e.g., chr1 or chrX) and a specific position(s) such as a numbered position (e.g., chr1:1234570 or chr1:1234570-1234870) following the chromosome identifier. Furthermore, in certain implementations, the genomic coordinate refers to the source of the reference genome (e.g., mt for a mitochondrial DNA reference genome, or SARS-CoV-2 for a reference genome of the SARS-CoV-2 virus), and the nucleotide base position within the source for the reference genome (e.g., mt:16568 or SARS-CoV-2:29001). In contrast, in certain cases, genomic coordinates refer to nucleotide base positions within a reference genome, without reference to a chromosome or source (e.g., 29727).

[0032] As described above, the genome coordinates include a location within a reference genome. Such a location may be within a particular reference genome. As used herein, the term "reference genome" refers to a digital nucleic acid sequence assembled as a representative (or representatives) of genes and other gene sequences of an organism. Regardless of sequence length, in some cases, the reference genome represents an exemplary set of genes or a set of nucleic acid sequences in a digital nucleic acid sequence that have been determined by scientists as representative of a particular species of organism. For example, a linear human reference genome may be GRCh38 or other versions of the reference genome from the Genome Reference Consortium. As a further example, the reference genome may include a reference graph genome, such as Illumina DRAGEN Graph Reference Genome hg19, that includes both a linear reference genome and paths that represent nucleic acid sequences from ancestral haplotypes.

[0033] As alluded to above, the base caller recalibration system can utilize machine learning models to correct sequencing metrics and update nucleotide base calls. As used herein, the term "machine learning model" refers to a computer algorithm or collection of computer algorithms that automatically improves for a particular task through experience based on the use of data. For example, the machine learning model can utilize one or more learning techniques to improve accuracy and / or effectiveness. Exemplary machine learning models include various types of decision trees, support vector machines, Bayesian networks, or neural networks. In some cases, the call recalibration machine learning model is a series of gradient boosted decision trees (e.g., XGBoost algorithm), and in other cases, the call recalibration machine learning model is a random forest model, a multi-layer perceptron, linear regression, support vector machine, a deep table learning architecture, a deep learning transformer (e.g., a self-attention based table transformer), or a logistic regression.

[0034] In some cases, the base caller recalibration system utilizes a call recalibration machine learning model to correct or update nucleotide base calls based on sequencing metrics. As used herein, the term "call recalibration machine learning model" refers to a machine learning model that generates variant call classifications. For example, in some cases, the call recalibration machine learning model is trained to generate variant call classifications that indicate various probabilities or predictions of variant calls based on sequencing metrics. Thus, in some cases, the call recalibration machine learning model is a variant call recalibration machine learning model. In certain embodiments, the call recalibration machine learning model includes multiple sub-models or works in conjunction with another call recalibration machine learning model. For example, a first call recalibration machine learning model (e.g., an ensemble of gradient boosted trees) generates a first set of variant call classifications, and a second call recalibration machine learning model (e.g., a random forest) generates a second set of variant call classifications.

[0035] Relatedly, the term "variant call classification" refers to a predicted classification from a call recalibration machine learning model that indicates a probability, score, or other quantitative measure associated with some aspect of a nucleotide base call based on one or more sequencing metrics. In some cases, the variant call classification includes a predicted probability that the genotype for a nucleotide base call is correct. For example, in some embodiments, the call recalibration machine learning model generates one or more of the following variant call classifications: i) a false positive classification indicating the probability that the nucleotide base call is a false positive, ii) a genotype error classification indicating the probability that the genotype (e.g., an indication of a heterozygous or homozygous genotype for a variant call at a particular position) is inaccurate, and / or iii) a true positive classification indicating the probability that the nucleotide base call is a true positive. Thus, in some cases, the variant call classification represents a variant caller intermediate score metric.

[0036] As mentioned, in some embodiments, the call recalibration machine learning model can be a neural network. The term "neural network" refers to a machine learning model that can be trained and / or adjusted based on inputs to determine classification or approximate unknown functions. For example, a neural network includes a model of interconnected artificial neurons (e.g., organized into layers) that learn to communicate, approximate complex functions, and generate outputs (e.g., generated digital images) based on multiple inputs provided to the neural network. In some cases, a neural network refers to an algorithm (or set of algorithms) that implements deep learning techniques to model high-level abstractions in data. For example, a neural network can include a convolutional neural network, a recurrent neural network (e.g., LSTM), a graph neural network, a self-attention transform neural network, or a generative adversarial neural network.

[0037] As described above, the base caller recalibration system can generate variant call classifications that indicate or reflect the likelihood of identifying a variant at a genomic coordinate. As used herein, the term "variant" refers to a nucleotide base or multiple nucleotide bases that do not align with, differ from, or change from the corresponding nucleotide base (or multiple nucleotide bases) in a reference sequence or reference genome. For example, variants include SNPs, indels, or structural variants that represent nucleotide bases in a sample nucleotide sequence that differ from the nucleotide bases in the corresponding genomic coordinates of the reference sequence. Along these lines, a "variant nucleotide base call" refers to a nucleotide base call that includes a variant at a particular genomic coordinate. Conversely, a "non-variant nucleotide base call" refers to a nucleotide base call that includes a non-variant at a genomic coordinate.

[0038] As mentioned, in some embodiments, the base caller recalibration system modifies data fields corresponding to the variant call file. As used herein, the term "variant call file" refers to a digital file that indicates or represents one or more nucleotide base calls (e.g., variant calls) compared to a reference genome along with other information about the nucleotide base calls (e.g., variant calls). For example, a variant call format (VCF) file refers to a text file format that has information about variants at a particular genomic coordinate, including a meta information row, a header row, and a data row, where each data row has information about a single nucleotide base call (e.g., a single variant). As described further below, the base caller recalibration system can generate different versions of the variant call file, including a pre-filter variant call file that includes variant nucleotide base calls that pass or do not pass a quality filter for a base call quality metric, or a post-filter variant call file that includes variant nucleotide base calls that pass a quality filter but exclude variant nucleotide base calls that do not pass the quality filter. In some embodiments, the base caller recalibration system modifies data fields corresponding to metrics of nucleotide base calls associated with the variant call file, such as fields for call quality, genotype, and genotype quality. As used herein, the term "call quality," when used with respect to a data field in a variant call file, refers to a measure or index of the likelihood or probability that a variant is present at a given location. Thus, a call quality field (or QUAL field) corresponding to a VCF file may include a base call quality metric, such as a Phred-scaled quality or Q-score, that represents the probability that a genomic coordinate of a sample genome contains a variant. Similarly, "genotype quality," when used with respect to a field, refers to the likelihood or probability that a particular predicted genotype for a nucleotide base call is correct.

[0039] As described above, in some embodiments, the base caller recalibration system utilizes a call generation model to generate nucleotide base calls for genomic coordinates. As used herein, the term "call generation model" refers to a probabilistic model that generates sequencing data from nucleotide reads of a sample nucleotide sequence, including nucleotide base calls and associated metrics. Thus, in some cases, the call generation model may be a variant call generation model. For example, in some cases, the call generation model refers to a Bayesian probability model that generates variant calls based on nucleotide reads of a sample nucleotide sequence. Such models can process or analyze sequencing metrics corresponding to read pileups (e.g., multiple nucleotide reads corresponding to a single genomic coordinate), including mapping quality, base quality, and various hypotheses including extraneous reads, missing reads, joint detection, and the like. The call generation model may also include multiple components, including, but not limited to, different software applications or components for mapping and alignment, sorting, duplicate marking, calculation of read pileup depth, and variant calling. In some cases, the call generation model refers to ILLUMINA DRAGEN models for variant calling functions and mapping and alignment functions.

[0040] As mentioned above, in certain described embodiments, the base caller recalibration system generates or determines a contribution measure associated with each sequencing metric. As used herein, the term "contribution measure" refers to a measure of the effect, influence, or impact that a sequencing metric has on a base call output file (e.g., a variant call file), a given nucleotide base call in a base call output file, or (particularly) a given recalibration of a field for a given variant call. For example, the contribution measure indicates how much of a role one sequencing metric plays over a different nucleotide base call (and compared to other sequencing metrics) in determining a nucleotide base call.

[0041] The following paragraphs describe the base caller recalibration system with respect to example diagrams depicting example embodiments and implementations. For example, Figure 1 shows a schematic diagram of a system environment (or "environment") 100 in which a base caller recalibration system 106 operates according to one or more embodiments. As shown, the environment 100 includes one or more server devices 102 connected to user client devices 108 and sequencing devices 114 via a network 112. While Figure 1 illustrates one embodiment of the base caller recalibration system 106, the present disclosure below describes alternative embodiments and configurations.

[0042] 1, server device 102, client device 108, and sequencing device 114 may communicate with each other via network 112. Network 112 may include any suitable network over which computing devices may communicate. An exemplary network is described in further detail below in connection with FIG. 10.

[0043] As illustrated by FIG. 1, the sequencing device 114 includes a device for sequencing nucleic acid polymers. In some embodiments, the sequencing device 114 analyzes nucleic acid segments or oligonucleotides extracted from a sample to generate nucleotide reads or other data using computer-implemented methods and systems (described herein) either directly or indirectly on the sequencing device 114. More specifically, the sequencing device 114 receives and analyzes nucleic acid sequences extracted from a sample in a nucleotide sample slide (e.g., a flow cell). In one or more embodiments, the sequencing device 114 utilizes SBS to sequence the nucleic acid polymer into nucleotide reads. In some embodiments, the sequencing device 114, in addition to or as an alternative to communicating via the network 112, bypasses the network 112 and communicates directly with the client device 108.

[0044] As further illustrated by FIG. 1, the server device 102 may generate, receive, analyze, store, and transmit digital data, such as data for determining nucleotide base calls or for sequencing a nucleic acid polymer. As illustrated in FIG. 1, the sequencing device 114 may transmit the call data from the sequencing device 114 (and the server device 102 may receive the call data). The server device 102 may also communicate with the client device 108. In particular, the server device 102 may transmit to the client device 108 data including variant call files or other information indicative of nucleotide base calls, sequencing metrics, error data, or other metrics associated with the nucleotide base calls, such as call quality, genotypes, and genotype quality.

[0045] In some embodiments, server device 102 comprises a distributed collection of servers, where server device 102 includes several server devices distributed across network 112 and located at the same or different physical locations, and server device 102 may include a content server, an application server, a communication server, a web hosting server, or another type of server.

[0046] As further shown in FIG. 1 , the server device 102 can include a sequencing system 104. Generally, the sequencing system 104 analyzes call data, such as sequencing data received from the sequencing device 114, to determine a nucleotide-based sequence for the nucleic acid polymer. For example, the sequencing system 104 can receive raw data from the sequencing device 114 and determine a nucleotide-based sequence for the nucleic acid segment. In some embodiments, the sequencing system 104 determines a nucleotide-based sequence in a DNA and / or RNA segment or oligonucleotide. In addition to processing and determining a sequence for the nucleic acid polymer, the sequencing system 104 also generates a variant call file indicating one or more nucleotide base calls for one or more genomic coordinates.

[0047] As just described and as shown in FIG. 1, the base caller recalibration system 106 analyzes call data, such as sequencing metrics from the sequencer 114, to determine nucleotide base calls for sample nucleic acid sequences. The base caller recalibration system 106 includes a call generation model and a call recalibration machine learning model. In some embodiments, the base caller recalibration system 106 determines sequencing metrics for the sample nucleotide sequences. Based on data derived or prepared from the sequencing metrics, the base caller recalibration system 106 trains and applies the call generation model to determine nucleotide base calls for the sample sequences corresponding to the genomic coordinates. The base caller recalibration system 106 further utilizes the call recalibration machine learning model to generate a variant call classification set and update or correct the nucleotide base calls based on various probabilities, such as false positive probability, genotype error probability, and / or true positive probability. Based on such data, for example, the base caller recalibration system 106 can update data fields corresponding to the variant call file to update the nucleotide base calls to improve accuracy.

[0048] As further illustrated and shown in FIG. 1, the client device 108 can generate, store, receive, and transmit digital data. In particular, the client device 108 can receive sequencing metrics from the sequencing device 114. Additionally, the client device 108 can communicate with the server device 102 to receive variant call files that include nucleotide base calls and / or other metrics, such as call quality, genotype index, and genotype quality. Thus, the client device 108 can present or display information about the nucleotide base calls in a graphical user interface to a user associated with the client device 108. For example, the client device 108 can present a contribution measure interface that includes a visualization or depiction of various contribution measures associated with or resulting from individual sequencing metrics for a particular nucleotide base call.

[0049] The client devices 108 illustrated in FIG. 1 can include various types of client devices. For example, in some embodiments, the client devices 108 include non-mobile devices, such as desktop computers or servers, or other types of client devices. In yet other embodiments, the client devices 108 include mobile devices, such as laptops, tablets, cell phones, or smartphones. Further details regarding the client devices 108 are described below with respect to FIG. 10.

[0050] 1, the 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) that is stored and executed on the client device 108. The sequencing application 110 can include instructions that (when executed) cause the client device 108 to receive data from the base caller recalibration system 106 and present data from a variant call file for display on the client device 108. Additionally, the sequencing application 110 can instruct the client device 108 to display a visualization of the contribution measures for the sequencing metrics of the nucleotide base calls.

[0051] As further illustrated in FIG. 1 , the base caller recalibration system 106 may be located on the client device 108 or on the sequencing device 114 as part of the sequencing application 110. Thus, in some embodiments, the base caller recalibration system 106 is implemented (e.g., fully or partially located) on the client device 108. In yet other embodiments, the base caller recalibration system 106 is implemented by one or more other components of the environment 100, such as the sequencing device 114. In particular, the base caller recalibration system 106 may be implemented in a variety of different ways across the server device 102, the network 112, the client device 108, and the sequencing device 114. For example, the base caller recalibration system 106 may be downloaded from the server device 102 to the client device 108 and / or the sequencing device 114, with all or a portion of the functionality of the base caller recalibration system 106 being executed on the respective devices in the environment 100.

[0052] As further illustrated in FIG. 1 , the environment 100 includes a database 116. The database 116 can store information such as variant call files, sample nucleotide sequences, nucleotide reads, nucleotide base calls, and sequencing metrics. In some embodiments, the server device 102, the client device 108, and / or the sequencing device 114 communicate with the database 116 (e.g., via the network 112) to store and / or access information such as variant call files, sample nucleotide sequences, nucleotide reads, nucleotide base calls, and sequencing metrics. In some cases, the database 116 also stores one or more models, such as a call recalibration machine learning model and / or a call generation model.

[0053] 1 illustrates components of environment 100 communicating over network 112, in certain implementations, components of environment 100 may also communicate directly with one another, bypassing the network. For example, as previously discussed, in some implementations, client device 108 may communicate directly with sequencing device 114. Additionally, in some embodiments, client device 108 communicates directly with base caller recalibration system 106. Furthermore, base caller recalibration system 106 may access one or more databases housed on or accessed by server device 102 or elsewhere in environment 100.

[0054] As indicated above, the base caller recalibration system 106 can determine nucleotide base calls based on one or more variant call classifications. In particular, the base caller recalibration system 106 can utilize a call recalibration machine learning model to determine variant call classifications from sequencing metrics, and can determine or update various metrics associated with nucleotide base calls from the generated variant call classifications. Figure 2 illustrates an exemplary sequence of operations that the base caller recalibration system 106 performs to determine nucleotide base calls based on variant call classifications, according to one or more embodiments.

[0055] As illustrated in FIG. 2, the base caller recalibration system 106 performs operation 202 to determine sequencing metrics. In particular, the base caller recalibration system 106 determines sequencing metrics, such as read-based sequencing metrics, external source sequencing metrics, and call model generation sequencing metrics. For example, the base caller recalibration system 106 determines sequencing metrics indicative of various attributes or data regarding various nucleotide base calls of nucleotide reads from a sample nucleotide sequence. Further details regarding determining various types of sequencing metrics are provided below with reference to FIGS. 3A-3C.

[0056] As further illustrated in FIG. 2, the base caller recalibration system 106 performs operation 204 to generate variant call classifications. More specifically, the base caller recalibration system 106 utilizes a call recalibration machine learning model to generate (or update or refine) variant call classifications from sequencing metrics. In particular, the base caller recalibration system 106 utilizes a call recalibration machine learning model to process or analyze one or more sequencing metrics and generate a classification set (e.g., predicted probabilities associated with genotypes). For example, the base caller recalibration system 106 utilizes a call recalibration machine learning model to generate a variant call classification set that indicates a particular probability associated with a genotype for a corresponding nucleotide base call based on the sequencing metrics.

[0057] In some embodiments, the base caller recalibration system 106 utilizes a call recalibration machine learning model (represented as "FP" in FIG. 2) to generate a false positive classification. For example, the base caller recalibration system 106 generates a false positive classification indicating the probability that a nucleotide base call (e.g., a genotype call) is a false positive variant, or that a nucleotide base call indicates a variant that is not actually present in the sample nucleotide sequence relative to the reference nucleotide sequence. The base caller recalibration system 106 generates the false positive classification from one or more sequencing metrics that are considered together by the call recalibration machine learning model.

[0058] In certain implementations, the base caller recalibration system 106 also (or alternatively) generates a genotype error classification (represented in FIG. 2 as “Genotype Error”). More specifically, the base caller recalibration system 106 utilizes a call recalibration machine learning model to determine the probability that a genotype associated with a nucleotide base call is incorrect. For example, the base caller recalibration system 106 determines the probability that a het / hom error exists for a nucleotide base call, where the nucleotide base call may indicate a heterozygous genotype for a nucleotide base at a genomic coordinate or region in the sample genome, where the nucleotide base of the sample genome is actually homozygous at the corresponding genomic coordinate or region. Conversely, the base caller recalibration system 106 determines the probability of determining that a genotype for a nucleotide base call is homozygous when the corresponding nucleotide base is heterozygous.

[0059] In one or more embodiments, the base caller recalibration system 106 also (or alternatively) generates a true positive classification (represented as "TP" in FIG. 2). In particular, the base caller recalibration system 106 utilizes a call recalibration machine learning model to determine the probability that a nucleotide base call (e.g., a genotype call) is a true positive variant, or that the nucleotide base call indicates a true variant in the sample nucleotide sequence and that the variant is actually present in the reference nucleotide sequence at the corresponding genomic coordinate. Further details regarding generating variant call classifications are provided below with reference to subsequent figures.

[0060] As further illustrated in FIG. 2, the base caller recalibration system 106 also performs operation 206 for determining nucleotide base calls based on the variant call classification. More specifically, the base caller recalibration system 106 determines nucleotide base calls for a sample nucleotide sequence at a genomic coordinate within a reference genome. To determine or generate a nucleotide base call, in some embodiments, the base caller recalibration system 106 utilizes a call generation model to determine an initial nucleotide base call and edits or updates the particular initial nucleotide base call based on the variant call classification generated by the call recalibration machine learning model.

[0061] In particular, the base caller recalibration system 106 utilizes a call generation model to process or analyze sequencing metrics (e.g., one or more of the same sequencing metrics used to generate the variant call classification in operation 204) and determine nucleotide base calls from the sequencing metrics. For example, the base caller recalibration system 106 applies a number of Bayesian probability models or algorithms to derive various probabilities for different nucleotide bases, quality metrics, mapping metrics, joint metrics, and other data occurring within the sample nucleotide sequence for inclusion within the variant call file. From the probability models, the base caller recalibration system 106 determines a final nucleotide base call (e.g., a call indicating a difference or identity to a reference base from a reference genome) that indicates a predicted nucleotide base of the sample genome at the corresponding genomic coordinate.

[0062] 2, in certain implementations, the base caller recalibration system 106 utilizes the initial variant call classifications (e.g., as determined via operation 204) to generate, recalibrate, determine, modify, or augment nucleotide base calls. In particular, the base caller recalibration system 106 utilizes the probabilities associated with the three variant call classifications to determine or update certain metrics associated with the nucleotide base calls. For example, the base caller recalibration system 106 modifies data fields corresponding to the variant call file for metrics such as call quality, genotypes, and genotype quality.

[0063] In some cases, the base caller recalibration system 106 extrapolates from the variant call classification to determine metrics corresponding to the variant call file, such as call quality, genotype, and genotype quality associated with the nucleotide base call. Indeed, by utilizing the genotype error classification, the base caller recalibration system 106 can repair certain errors in or associated with the initial nucleotide base call. For example, if the base caller recalibration system 106 determines a high false positive probability for a nucleotide base call, the base caller recalibration system 106 applies a call recalibration machine learning model to act as a variant filter to modify (e.g., reduce) the call quality associated with the nucleotide base call. As another example, the base caller recalibration system 106 utilizes the genotype error probability to modify the genotype and / or genotype quality of the nucleotide base call if the system previously filters out or double penalizes het / hom errors (e.g., if the system generates an incorrect nucleotide base call that would in turn miss a correct nucleotide base call).

[0064] In certain embodiments, the base caller recalibration system 106 considers a single variant call classification to revise the data fields for a nucleotide base call (e.g., call quality, genotype, or genotype quality). In other embodiments, the base caller recalibration system 106 considers multiple variant call classifications at once (e.g., in a weighted combination) to revise or update one or more data fields for call quality, genotype, and / or genotype quality. Further details regarding the generation and revision of nucleotide base calls are provided below with reference to subsequent figures.

[0065] In one or more implementations, the base caller recalibration system 106 generates variant call classifications (e.g., via operation 204) during or during the process of determining nucleotide base calls. For example, the base caller recalibration system 106 simultaneously implements the call recalibration machine learning model and the call generation model to generate nucleotide base calls and variant call classifications for correcting the nucleotide base calls. The base caller recalibration system 106 further corrects data fields corresponding to the variant call files of the nucleotide base calls to generate final nucleotide base calls (e.g., in the pre-filter or post-filter variant call files). In effect, the base caller recalibration system 106 generates final (e.g., recalibrated) nucleotide base calls from the variant call classifications and sequencing metrics processed by the call generation model (e.g., one or more of the same sequencing metrics used to generate the variant call classifications). As described above, this simultaneous or parallel operation provides the base caller recalibration system 106 with improved computational efficiency and increased speed by recalibrating nucleotide base calls as they are initially generated (rather than performing one operation before the other).

[0066] In one or more implementations, the base caller recalibration system 106 determines a nucleotide base call as a part of a SNP, deletion, insertion, or structural variation. For example, the base caller recalibration system 106 determines that a nucleotide base call represents a SNP at a genomic coordinate (e.g., chr1:151863125) by identifying a G in the sample nucleotide sequence where an A is present in the reference sequence. As another example, the base caller recalibration system 106 determines that a nucleotide base call around one or more genomic coordinates (e.g., chr1:49263256) represents a deletion by identifying a single G in the sample nucleotide sequence where GTAAC is present in the reference sequence.

[0067] As a further example, the base caller recalibration system 106 determines that a sequence of nucleotide base calls represents an insertion at a genomic coordinate (e.g., chr1:7602080) by identifying a sequence of TTTCC in the sample nucleotide sequence where a T is present in the reference sequence. Indeed, in some cases, the insertion comprises a sequence of nucleotide base calls that replaces a single reference base in the genomic coordinate of the reference sequence.

[0068] As mentioned above, in certain described embodiments, the base caller recalibration system 106 determines or extracts sequencing metrics for nucleotide base calls. In particular, the base caller recalibration system 106 determines sequencing metrics, such as read-based sequencing metrics, external source sequencing metrics, and call model generation sequencing metrics, from calls corresponding to nucleotide reads from a sample nucleotide sequence. Figures 3A-3C illustrate determining sequencing metrics according to one or more embodiments. Specifically, Figure 3A illustrates determining read-based sequencing metrics, Figure 3B illustrates determining call model generation sequencing metrics, and Figure 3C illustrates determining external source sequencing metrics.

[0069] As illustrated in FIG. 3A, the base caller recalibration system 106 accesses, retrieves, obtains, determines, or generates nucleotide reads 302. In particular, the base caller recalibration system 106 utilizes a sequencer 114 to determine nucleotide reads 302, including nucleotide base calls for regions from a sample nucleotide sequence (e.g., a sample genome). For example, the base caller recalibration system 106 utilizes sequencing-by-synthesis (SBS) and / or Sanger sequencing techniques to generate nucleotide reads 302 and determine nucleotide base calls for oligonucleotide clusters from wells in a flow cell and / or via fluorescent tagging. More specifically, the base caller recalibration system 106 utilizes cluster generation and SBS chemistry to sequence millions or billions of clusters in a flow cell. During the SBS chemistry, for each cluster, the base caller recalibration system 106 stores the nucleotide base calls from the nucleotide reads 302 for each cycle of sequencing via real-time analysis (RTA) software.

[0070] As further illustrated in FIG. 3A, in some embodiments, the base caller recalibration system 106 performs read processing and mapping 304. For example, the base caller recalibration system 106 utilizes RTA software to store base call data in the form of individual base call data files (or BCLs). In some cases, the base caller recalibration system 106 further converts the BCL files into sequence data 308 (e.g., via a BCL to FASTQ conversion), as illustrated in FIG. 3B. As illustrated in FIG. 3A, the base caller recalibration system 106 generates a multiple read coverage (e.g., a read pileup) that includes multiple nucleotide reads 302 or nucleotide base calls that correspond to a single genomic coordinate.

[0071] In particular, in certain embodiments, the base caller recalibration system 106 aligns nucleotide reads to a reference genome or receives information about read alignment. Specifically, the base caller recalibration system 106 determines (or receives information indicating) which nucleotide bases of a given read align with which genomic coordinates of a reference sequence. Different reads have different lengths and contain different nucleotide bases. Thus, in some cases, the base caller recalibration system 106 analyzes each nucleotide of each read to determine (or receives information indicating) where the read "fits" with respect to the reference sequence, e.g., where a base in the read aligns with a base in the reference. In some cases, the base caller recalibration system 106 aligns many reads at a single genomic coordinate, thus resulting in read pile-up.

[0072] In certain embodiments, the base caller recalibration system 106 performs additional statistical tests to determine or detect differences between the metrics associated with the reference nucleotide sequence and the metrics associated with the alternative supporting nucleotide reads. Through these statistical tests, the base caller recalibration system 106 re-operates the raw sequencing metrics to determine the read-based sequencing metrics 306. In some cases, the base caller recalibration system 106 determines or extracts raw sequencing metrics including one or more of: (i) an alignment metric to quantify the alignment of the sample nucleotide sequence with the genomic coordinates of the exemplary nucleotide sequence (e.g., a nucleotide sequence from a reference genome or an ancestral haplotype); (ii) a depth metric to quantify the depth of the nucleotide base call for the sample nucleotide sequence at the genomic coordinates of the exemplary nucleotide sequence; or (iii) a call quality metric to quantify the quality of the nucleotide base call for the sample nucleotide sequence at the genomic coordinates of the exemplary nucleotide sequence. For example, the base caller recalibration system 106 determines a mapping quality metric (e.g., the MAPQ metric shown in FIG. 3A), a soft clipping metric, or other alignment metric that measures the alignment of the sample sequence with the reference genome. As another example, the base caller recalibration system 106 determines a forward-reverse depth metric (or other such depth metric) or a callability metric (or other such call quality metric) for variant nucleotide base calls.

[0073] As just mentioned, in some embodiments, the base caller recalibration system 106 re-operates raw sequencing metrics to generate read-based sequencing metrics 306 that are more useful for comparing metrics associated with a reference nucleotide sequence with metrics associated with various supporting alternative nucleotide reads. For example, the base caller recalibration system 106 determines various metrics for a sample sequence with respect to a reference sequence, and further determines various metrics for a sample sequence with respect to an alternative supporting sequence. In addition, the base caller recalibration system 106 performs a comparative analysis between the metrics associated with the reference sequence and the metrics associated with the alternative supporting reads.

[0074] For example, the base caller recalibration system 106 compares how nucleotide bases of a sample nucleotide sequence (e.g., a sample genome) map to a reference sequence with how the nucleotide bases map to various alternative supporting reads. In some cases, the base caller recalibration system 106 determines a mapping quality associated with the reference sequence to compare with a mapping quality associated with the alternative supporting reads. For example, the base caller recalibration system 106 determines a mapping quality statistic that reflects differences in the distribution of reads supporting the reference sequence versus reads supporting alternative alleles.

[0075] In these or other cases, the base caller recalibration system 106 determines mismatch counts between the sample sequence and the reference sequence, and between the reference sequence and the alternative support read. The base caller recalibration system 106 further compares the mismatch counts to determine a comparative mismatch count metric. Furthermore, the base caller recalibration system 106 determines a soft clipping metric for the sample sequence relative to the reference sequence, and further determines a soft clipping metric for the alternative support read. The base caller recalibration system 106 also compares the soft clipping metric between the reference sequence and the alternative support read to generate a comparative soft clipping metric. Furthermore, the base caller recalibration system 106 compares base call quality metrics for the reference sequence and the alternative support read, and / or compares the query position of the sample sequence relative to the reference sequence with the query position for the alternative support read.

[0076] As further illustrated in FIG. 3A , the base caller recalibration system 106 utilizes comparisons and / or other statistical tests to generate read-based sequencing metrics 306 including: i) a comparative mapping quality distribution metric indicative of a mapping quality distribution comparing the mapping quality for the reference sequence to the mapping quality for the alternate supporting read; ii) a comparative secondary mapping alignment metric indicative of a comparison of the secondary mapping of bases in the reference sequence to the secondary mapping of bases in the alternate supporting read; iii) a comparative mismatch count metric indicative of a comparison of mismatched nucleotide bases for the reference sequence to the mismatched bases for the alternate supporting read; iv) a comparative soft clipping metric indicative of a comparison of the soft clipping metric for the reference sequence to the soft clipping metric for the alternate supporting read; v) a read depth of the nucleotide reads 302 and 1 vi) one or more comparative base quality metrics indicative of a comparison between a base quality for the reference sequence and a base quality for the alternative supporting reads (e.g., overall base quality, initial base quality, and later base quality in the nucleotide reads 302); vii) a comparative query position metric indicative of a comparison between a query position for the reference sequence and a query position for the alternative supporting reads; viii) one or more context information metrics indicative of homopolymer and periodicity of the nucleotide base calls; ix) a strand bias metric indicative of a strand bias associated with one or more of the nucleotide reads 302; and x) a read direction bias metric indicative of a read direction bias associated with the nucleotide reads 302. In some cases, the base caller recalibration system 106 generates or re-engineers additional or alternative read-based sequencing metrics as part of the read-based sequencing metrics 306.

[0077] In addition to the read-based sequencing metrics 306, as illustrated in FIG. 3B, the base caller recalibration system 106 generates call model-generated sequencing metrics 312. In particular, the base caller recalibration system 106 utilizes the call generation model 310 to generate the call model-generated sequencing metrics from the sequence data 308. For example, the base caller recalibration system 106 extracts or determines the sequence data 308 based on the read processing and mapping 304 described with respect to FIG. 3A. In some cases, the base caller recalibration system 106 generates the sequence data 308 as part of one or more digital files, such as BCL and FASTQ files.

[0078] To generate such files, in some embodiments, the sequencing device 114 (or base caller recalibration system 106) utilizes cluster generation and SBS chemistry to sequence millions or billions of clusters in a flow cell. During the SBS chemistry, for each cluster, the sequencing device 114 (or base caller recalibration system 106) stores nucleotide base calls from the nucleotide reads 302 for each cycle of sequencing via real-time analysis (RTA) software. The sequencing device 114 (or base caller recalibration system 106) further stores the base call data in the form of individual base call data files (or BCLs) utilizing the RTA software. In some cases, the sequencing device 114 (or base caller recalibration system 106) further converts the BCL files into sequence data 308 (e.g., via a BCL to FASTQ conversion). For example, the sequencer 114 (or base caller recalibration system 106) generates a FASTQ file from the nucleotide reads 302, where the FASTQ file includes sequence data 308.

[0079] In some cases, the base caller recalibration system 106 generates sequence data 308 for each cluster that passes the initial quality filter from the sample sequences. For example, the base caller recalibration system 106 generates an entry for each cluster, with each entry including four rows (or four items of sequence data): i) a sequence identifier with information about the sequencing run and the cluster, ii) the nucleotide base calls that make up the sequence (e.g., a sequence of A, C, T, G, and / or N calls), iii) a separator (e.g., a "+" symbol), and iv) a base call quality metric indicating the probability of accuracy for the nucleotide base call (Phred+33 coded).

[0080] As further illustrated in FIG. 3B , the base caller recalibration system 106 implements, utilizes, or applies a call generation model 310 to process or analyze the sequence data 308. Indeed, in some embodiments, the base caller recalibration system 106 utilizes the call generation model 310 to re-manipulate raw sequencing metrics (e.g., raw sequencing metrics in the sequence data 308) to generate call model-generated sequencing metrics 312. In particular, the call generation model 310 includes a mapping and alignment component for mapping and aligning nucleotide base calls from the sequence data 308. In addition, the call generation model 310 includes a variant calling component for generating nucleotide base calls (e.g., reference base calls, such as variant calls or non-variant calls) from the sequence data 308. In some cases, the base caller recalibration system 106 extracts the call model-generated sequencing metrics 312 that have been generated utilizing the mapping and variant calling components of the call generation model 310.

[0081] To illustrate examples of call model-generated sequencing metrics 312, in some cases, the base caller recalibration system 106 may generate a call model-generated sequencing metric 312 that includes: i) a base call quality metric (e.g., a DRAGEN QUAL score) indicative of a quality score for the nucleotide base calls generated via the call generation model 310; ii) a call model-generated foreign read detection metric (e.g., a foreign read detection (FRD) score) indicative of the probability that one or more of the nucleotide reads 302 in the pileup may be a foreign read (e.g., their true location is elsewhere in the reference sequence); iii) a call model-generated base quality dropoff metric (e.g., a base quality dropoff (BQD) score) indicative of the probability of a base quality dropoff based on one or more of strand bias, error location in the thread, or low average base quality across a subset of the nucleotide reads 302; iv) average read depth; v) indel statistics (e.g., polymerase chain reaction or "PCR" curves); and / or vi) a hidden Markov model (HMM) indicative of a base quality dropoff metric (e.g., a base quality dropoff (BQD) score ... The base caller recalibration system 106 generates variant calling metrics 312, including one or more of: (a) a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct; (b) a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct; (c) a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct; (d) a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct; (e.g., a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct; and (e.g., a sequence matching metric that indicates the probability that a secondary nucleotide base call is correct.

[0082] As noted above, in some cases, the base caller recalibration system 106 determines the FRD score according to the method described in U.S. Patent Application No. 16 / 280,022 to Eric Jon Ojard, entitled System and Method for Correlated Error Event Mitigation for Variant Calling, which is incorporated herein by reference in its entirety. In certain implementations, the base caller recalibration system 106 also (or alternatively) determines the BQD score, FRD score, HMM statistics, and / or other variant calling metrics according to the methods described in U.S. Patent Application Nos. 17 / 165,828, 15 / 643,381, and 14 / 811,836, which are incorporated herein by reference in their entireties.

[0083] As illustrated in FIG. 3B, the sequencing metrics 312 of the call model generation include, but are not limited to, variant calling metrics extracted via the variant calling component of the call generation model 310. In addition to or as an alternative to the above-mentioned examples of sequencing metrics 312 for generating a call model, in some cases, the base caller recalibration system 106 may be configured to generate a base call model based on a number of metrics, such as: i) the number of samples in a population; ii) the number of reads, variants (e.g., SNPs, indels, and MNPs) processed to generate nucleotide base calls; iii) the number of biallelic sites (e.g., genomic coordinates containing two observed alleles); iv) the number of multiallelic sites (e.g., the number of sites in a variant call file that contain three or more observed alleles); v) the number of SNPs; vi) the number of different types of indels (e.g., homozygous insertions, heterozygous insertions, and heterozygous deletions); vii) the total number of heterozygous indels (e.g., insertions + deletions, insertions + SNPs, or deletions + SNPs); viii) the number of de novo SNPs (e.g., de novo SNPs that meet a threshold level); x) the number of SNPs in the first chromosome divided by the number of SNPs in the second chromosome; xii) the number of SNP transitions; xiii) the number of SNP transversions; xiv) the number of heterozygous variants; xv) the number of homozygous variants; xvi) the ratio between the number of heterozygous variants and the number of homozygous variants; xvii) the number of variants detected in the dbSNP reference file; and / or xviii) the total number of variants minus the number detected in the dbSNP file.

[0084] Additionally, the sequencing metrics for call model generation 312 can include mapping and alignment sequencing metrics extracted via the mapping and alignment component of the call generation model 310. For example, the base pair caller recalibration system 106 can generate a sequence metric for each of the following: i) the number of total input reads, ii) the number of duplicate mark reads, iii) the number of mate reads with duplicate marks removed, iv) the number of unique reads, v) the number of reads with mate sequences, vi) the number of reads without mate sequences, vii) an index of reads that fail a quality check, viii) an index of mapped reads, ix) the number of unique and mapped reads, x) the number of unmapped reads, xi) the number of singleton reads, xi) the number of unique reads, ii) the number of unique reads, iii) the number of unmapped reads, xi ... xii) number of paired reads; xiii) number of properly paired reads (e.g., when both reads of a pair are mapped and fall within an acceptable range of each other based on the estimated insert length distribution); xiv) number of mismatched reads (e.g., number of reads that are not properly paired); xv) number of paired reads that are mapped to different chromosomes; xvi) number of paired reads that are mapped to different chromosomes and have a mapping quality metric of 10 or higher; xvii) indels R1 and R2 Generate or extract (e.g., via metric re-operation) mapping and alignment metrics including one or more of: the percentage of reads in R1 and R2; xviii) the percentage of soft-clipped bases in R1 and R2; xix) the number of mismatched bases in indels R1 and R2; xx) the number of bases (e.g., total and / or R1 or R2) with a base quality of at least 30; xxi) the number of alignments (e.g., total alignments, secondary alignments, and / or supplemental alignments); xxii) estimated read length; and xxiii) estimated sample contamination.

[0085] 3C, as illustrated therein, the base caller recalibration system 106 generates, extracts, or determines externally sourced sequencing metrics 316. In particular, the base caller recalibration system 106 determines the externally sourced sequencing metrics 316 from one or more databases external to the base caller recalibration system 106, such as a sequencing information database 314 (e.g., database 116). For example, the base caller recalibration system 106 accesses sequencing metrics that are general or generally applicable to nucleotide sequencing. Additionally, the base caller recalibration system 106 accesses or determines sequencing information for a particular reference sequence (e.g., stored in the sequencing information database 314). In some cases, the base caller recalibration system 106 determines external source sequencing metrics 316 including: i) a mappability metric indicating the ease or difficulty of mapping a particular nucleotide sequence (or a particular nucleotide read or nucleotide base call); ii) a guanine-cytosine content metric indicating the count (or dropout or average) of guanine-cytosine content in a reference nucleotide sequence (e.g., a reference genome); iii) a replication timing metric indicating the time required to replicate a particular number of nucleotides from a reference sequence; iv) one or more DNA structure metrics indicating the DNA structure of the reference sequence (e.g., a reference genome); v) a conservation metric indicating a measure of sequence conservation across multiple species (e.g., a measure of change relative to the average); and / or others.

[0086] As mentioned above, in certain described embodiments, the base caller recalibration system 106 utilizes a call recalibration machine learning model in conjunction with a call generation model to generate nucleotide base calls. In particular, the base caller recalibration system 106 utilizes the call recalibration machine learning model to modify data fields corresponding to the variant call file that represents the nucleotide base calls. Figure 4 illustrates generating nucleotide base calls by modifying the variant call file using a call recalibration machine learning model and a call generation model according to one or more embodiments.

[0087] As illustrated in FIG. 4, the base caller recalibration system 106 accesses a sequencing information database 402 (e.g., sequencing information database 314), a reference sequence 403, and sequence data 404 (e.g., sequence data 308) extrapolated from one or more nucleotide reads. In practice, the base caller recalibration system 106 performs sequencing metric extraction 410 to extract or re-engineer sequencing metrics as described above in connection with FIGS. 3A-3C. For example, the base caller recalibration system 106 generates read-based sequencing metrics, external source sequencing metrics, and call model generated sequencing metrics. In some cases, the base caller recalibration system 106 utilizes a mapping and alignment component 406 of a call generation model 420 (e.g., call generation model 310) to determine mapping and alignment sequencing metrics as described above. In addition, the base caller recalibration system 106 utilizes a variant caller component 408 of the call generation model 420 to generate variant calling metrics as described above. Additionally, the base caller recalibration system 106 determines read-based sequencing metrics and external source sequencing metrics (eg, from the sequencing information database 402 and / or the reference sequences 403).

[0088] As further illustrated in FIG. 4, the base caller recalibration system 106 generates variant call classifications 414. More specifically, the base caller recalibration system 106 utilizes a call recalibration machine learning model 412 to generate the variant call classifications 414 from the sequencing metrics. For example, the call recalibration machine learning model 412 generates the variant call classifications 414 including a false positive classification, a genotype error classification, and a true positive classification. Specifically, the false positive classification indicates the probability that the nucleotide base call (e.g., variant call) is a false positive. Conversely, the true positive classification indicates the probability that the nucleotide base call (e.g., variant call) is a true positive. Furthermore, the genotype error classification indicates the probability of an error associated with a genotype for the nucleotide base call (e.g., variant call).

[0089] In some cases, the call recalibration machine learning model 412 is an ensemble of gradient boosted trees that process sequencing metrics to generate variant call classifications 414. For example, the call recalibration machine learning model 412 includes a series of weak learners, such as non-linear decision trees, trained in logistic regression to generate variant call classifications 414. In some cases, the call recalibration machine learning model 412 includes metrics within the various trees that define how the call recalibration machine learning model 412 processes sequencing metrics to generate variant call classifications 414. Further details regarding training of the call recalibration machine learning model 412 are provided below with reference to FIG. 5.

[0090] In certain embodiments, the call recalibration machine learning model 412 is a different type of machine learning model, such as a neural network, a support vector machine, or a random forest. For example, if the call recalibration machine learning model 412 is a neural network, the call recalibration machine learning model 412 includes one or more layers, each having neurons that make up the layer for processing sequencing metrics. In some cases, the call recalibration machine learning model 412 generates variant call classifications 414 by extracting latent vectors from the sequencing metrics, passing the latent vectors from layer to layer (or neuron to neuron), and manipulating the vectors until it generates variant call classifications 414 (e.g., as a set of three distinct classifications) utilizing an output layer (e.g., one or more fully connected layers).

[0091] As alluded to above, in some embodiments, the base caller recalibration system 106 can utilize multiple call recalibration machine learning models together. For example, the base caller recalibration system 106 utilizes the call recalibration machine learning model 412 to generate a first set of variant call classifications and further utilizes a second call recalibration machine learning model (e.g., having the same or a different architecture) to generate a second set of variant call classifications. For example, the base caller recalibration system 106 utilizes two (or more) different call recalibration machine learning models in parallel, each trained with a different random seed (e.g., for different biases to process the data differently) to result in different variant call classifications from the same sequencing metric.

[0092] In some embodiments, the base caller recalibration system 106 further generates a combined set of variant call classifications from the different variant call classifications generated via the different call recalibration machine learning models. In some cases, the base caller recalibration system 106 generates variant call classifications (e.g., variant call classifications 414) from the first and second variant call classification sets generated from the first and second call recalibration machine learning models, respectively. For example, the base caller recalibration system 106 determines an average or weighted combination of the first and second variant call classification sets to generate a combined variant call classification for recalibrating nucleotide base calls. In some embodiments, the base caller recalibration system 106 determines an average of each variant call classification across each call recalibration machine learning model and renormalizes the average variant call classification. In other embodiments, the base caller recalibration system 106 learns linear weights and adapts the weights to minimize the overall error or loss for the variant call classification. In yet other embodiments, the base caller recalibration system 106 weights the variant call classification for each call recalibration machine learning model based on the inverse of the average error across the models.

[0093] In one or more implementations, the basecaller recalibration system 106 further utilizes a meta-model following the call recalibration machine learning models. For example, the basecaller recalibration system 106 utilizes a classification combiner machine learning model to combine the variant call classifications generated from each call recalibration machine learning model, such as by selecting weights to apply to the variant call classifications generated by each call recalibration machine learning model. Indeed, in some cases, the basecaller recalibration system 106 trains the classification combiner machine learning model to determine, select, or predict the respective weights for the call recalibration machine learning models that result in the highest accuracy or the lowest loss.

[0094] When generating the variant call classification 414, in some embodiments, the base caller recalibration system 106 generates the variant call classification by utilizing statistics to summarize the mapping quality distributions (e.g., comparative mapping quality distribution metrics) of the reference and alternative supporting reads. For example, the base caller recalibration system 106 can determine and utilize the average of the MAPQ for the reads supporting the alternative allele as the variant call classification. In these or other embodiments, the call recalibration machine learning model 412 learns from the data that if the MAPQ of the alternative allele is low and the depth metric is high relative to the other MAPQs and depth metrics in the distribution, the resulting nucleotide base call is more likely to be a false positive variant. In fact, as the probability of a false positive variant increases, the MAPQ metric may decrease.

[0095] As a further example of utilizing the call recalibration machine learning model 412 to generate variant call classifications 414, in some cases, the base caller recalibration system 106 compares a mapping quality (e.g., MAPQ) associated with a nucleotide read (e.g., from a sequencing metric) to a mapping quality threshold. For example, the base caller recalibration system 106 utilizes a mapping quality threshold, such as a threshold difference between the best alignment score and the next best alignment score. Upon determining that the mapping quality does not meet the threshold, the base caller recalibration system 106 adjusts one or more of the variant call classifications 414 accordingly. For example, the base caller recalibration system 106 increases the probability of a genotype error and / or a false positive error based on whether the mapping quality meets a corresponding threshold.

[0096] In addition to (or as an alternative to) the method of generating variant call classifications 414 just described, the base caller recalibration system 106 can (i) utilize an accumulation of statistical analyses across complex functions (depending on the architecture of the call recalibration machine learning model 412) to determine how to best fit the data (e.g., based on relationships between various metrics) or (ii) compare other metrics, such as read depth, base quality, or others associated with nucleotide base calls (e.g., from sequencing metrics), to corresponding thresholds. The base caller recalibration system 106 further generates variant call classifications 414 accordingly. For example, in some embodiments, the base caller recalibration system 106 trains the call recalibration machine learning model 412 to minimize losses generated from several (different types of) sequencing metrics to determine weights and biases that best fit (e.g., result in reduced or minimized losses) the data to generate variant call classifications 414. As another example, upon determining that the read depth does not meet a read depth threshold (e.g., a maximum read depth corresponding to a particular genomic coordinate or across all genomic coordinates generally), the base caller recalibration system 106 increases the genotype error probability and / or increases or decreases the false positive probability and true positive probability for the corresponding nucleotide base call.

[0097] In addition to generating variant call classifications 414, as further illustrated in FIG. 4, the base caller recalibration system 106 performs data field generation 416. More specifically, the base caller recalibration system 106 utilizes the variant caller component 408 of the call generation model 420 to generate data fields for the nucleotide base calls corresponding to the variant call file and modifies or maintains values ​​of such data fields based on the variant call classifications 414. For example, the base caller recalibration system 106 modifies various metrics, such as quality metrics, mapping metrics, or other metrics associated with the nucleotide base calls. In certain embodiments, the nucleotide base calls are represented or defined by a variant call file 418 that includes metrics corresponding to data fields, such as call quality metrics corresponding to the call quality fields, genotype metrics corresponding to the genotype fields, and genotype quality metrics corresponding to the genotype quality fields.

[0098] In certain embodiments, the base caller recalibration system 106 utilizes the variant caller component 408 in conjunction with the variant call classification 414 to generate (data fields for) nucleotide base calls. For example, the base caller recalibration system 106 utilizes the variant caller component 408 to generate data fields for various metrics of the nucleotide base calls, such as the nucleotides included in the call, the call quality (QUAL), the genotype (GT), and the genotype quality (GQ).

[0099] In addition to generating nucleotide base calls via the call generation model 420, the base caller recalibration system 106 also recalibrates or corrects nucleotide base calls via the variant call classifications 414 from the call recalibration machine learning model 412. In one or more implementations, the base caller recalibration system 106 corrects the nucleotide base calls by correcting or recalibrating data fields for one or more of the metrics associated with the nucleotide base calls (e.g., as included in the variant call file 418). For example, the base caller recalibration system 106 determines updated values ​​for metrics such as call quality, genotypes, and genotype quality from the variant call classifications 414. In effect, the base caller recalibration system 106 combines or compares the variant call classifications 414 to recalibrate the corresponding metrics of the nucleotide base calls included in the variant call file 418.

[0100] To update or recalibrate the call quality metrics associated with the nucleotide base calls, the base caller recalibration system 106 determines how each of the variant call classifications 414 affects the base call quality metrics and adjusts the base call quality metrics accordingly. For example, the base caller recalibration system 106 determines that a high probability for a genotype error results in a lower overall genotype quality and possibly a different overall call quality. As another example, the base caller recalibration system 106 determines that a high probability for a false positive variant results in a lower overall call quality. As yet another example, the base caller recalibration system 106 determines that a high probability for a true positive variant results in a higher overall (variant) call quality. As a further example, if the base caller recalibration system 106 determines a high probability for a genotype error (e.g., higher than the other two variant call classifications of the variant call classifications 414), the base caller recalibration system 106 determines that the nucleotide base call is most likely a true variant with an incorrect genotype. Thus, the base caller recalibration system 106 updates the genotypes along with the genotype quality and call quality associated with the nucleotide base calls.

[0101] In one or more implementations, the base caller recalibration system 106 generates a combination (e.g., a weighted combination or average) of variant call classifications 414 to recalibrate the call quality metrics. In particular, the base caller recalibration system 106 weights the false positive classification, the genotype error classification, and the true positive classification according to their respective impact on the (variant) call quality. In some cases, the base caller recalibration system 106 weights each variant call classification equally, while in other cases, the base caller recalibration system 106 determines different weights for each variant call classification. In any case, the base caller recalibration system 106 determines a weighted combination or weighted average of the variant call classifications 414 to recalibrate (increase or decrease) the call quality metrics for a nucleotide base call (e.g., an initial variant call).

[0102] To update or recalibrate the genotypic metrics associated with the nucleotide base calls (e.g., in the GT field of the variant call file 418), the base caller recalibration system 106 utilizes one or more of the variant call classifications 414. For example, the base caller recalibration system 106 compares three variant call classifications 414 (e.g., a false positive classification, a genotypic error classification, and a true positive classification) to determine which of the variant call classifications 414 has the highest probability. In some cases, the base caller recalibration system 106 utilizes the variant call classification with the highest probability to recalibrate the genotypic metrics (e.g., from 0 corresponding to the reference base to 1 corresponding to the first alternative support read). For example, if the base caller recalibration system 106 determines the highest probability for a false positive classification, the base caller recalibration system 106 recalibrates the genotypic metrics accordingly. As another example, if the base caller recalibration system 106 determines the highest probability for a true positive classification, the base caller recalibration system 106 recalibrates (or refrains from recalibrating) the genotype metrics.

[0103] In other embodiments, the base caller recalibration system 106 utilizes only the genotype error probability to modify the genotype metric. For example, if the base caller recalibration system 106 determines a high genotype error probability, the base caller recalibration system 106 recalibrates the genotype metric to indicate a different genotype for the nucleotide base call.

[0104] To update or recalibrate a genotype quality metric associated with a nucleotide base call (e.g., in the GQ field of the variant call file 418), the base caller recalibration system 106 utilizes one or more of the variant call classifications 414. More specifically, the base caller recalibration system 106 determines how each of the variant call classifications 414 affects the genotype quality metric and recalibrates the genotype quality metric accordingly (e.g., by increasing or decreasing the quality score between 0-10 or 0-100, or some other scale). For example, the base caller recalibration system 106 determines that a higher genotype error probability indicates (generally) a lower genotype quality metric, and the base caller recalibration system 106 reduces the metric accordingly.

[0105] In some cases, the base caller recalibration system 106 determines a combination (e.g., a weighted combination or weighted average) of the variant call classifications 414 to modify the genotype quality metric. For example, the base caller recalibration system 106 determines the combined effect of the variant call classifications 414 on the genotype quality metric. As another example, the base caller recalibration system 106 determines the individual impact that each variant call classification has on the genotype quality metric and weights each variant call classification accordingly. The base caller recalibration system 106 further recalibrates the genotype quality metric by increasing or decreasing its value based on the indicated probability associated with each of the variant call classifications 414.

[0106] As described, the base caller recalibration system 106 generates variant call classifications 414 and nucleotide base calls from the same set of sequencing metrics (or a subset of sequencing metrics shared between the call recalibration machine learning model 412 and the call generation model 420). In effect, the base caller recalibration system 106 utilizes the call recalibration machine learning model 412 to generate variant call classifications 414 from sequencing metrics while also generating nucleotide base calls for the sample sequences. In effect, the base caller recalibration system 106 can operate the call recalibration machine learning model 412 in parallel with the call generation model 420 to generate metrics for nucleotide base calls and variant call classifications 414 to recalibrate the generated metrics.

[0107] As further illustrated in FIG. 4, the base caller recalibration system 106 generates a variant call file 418. In particular, the base caller recalibration system 106 generates the variant call file 418 that represents or defines nucleotide base calls from sequencing metrics corresponding to genomic coordinates. As shown, the variant call file 418 includes various call metrics, such as a call quality metric (QUAL), a genotype metric (GT), and a genotype quality metric (GQ). To generate the variant call file 418, the base caller recalibration system 106 utilizes a call generation model 420 to generate metrics for the nucleotide base calls and utilizes variant call classifications 414 from a call recalibration machine learning model 412 to recalibrate the nucleotide base calls, as described.

[0108] In one or more implementations, the base caller recalibration system 106 updates or otherwise modifies data fields for the variant call file 418 according to a particular algorithm. After modifying such data fields, the base caller recalibration system 106 can generate the variant call file 418 (e.g., a post-filter variant call file) to include metrics that reflect the updated data fields for QUAL, GT, and GQ. For example, in some cases, the base caller recalibration system 106 updates the QUAL field for each variant based on the probability of a false positive variant (e.g., a false positive classification). As noted above, in some cases, QUAL indicates the probability of a certain variant (or other nucleotide base call) being present at a given position, as measured on the PHRED scale.

[0109] In addition, if the base caller recalibration system 106 determines that the highest probability among the three variant call classifications as the variant call classification 414 is a genotype error classification (e.g., the probability of a het / hom error), the base caller recalibration system 106 updates the GQ field while preserving or maintaining the GT field. Specifically, in some embodiments, the base caller recalibration system 106 updates the GQ field based on the true positive classification (e.g., the probability of a true genotype).

[0110] Additionally, if the base caller recalibration system 106 determines that the highest probability among the variant call classifications 414 is a true positive classification, then in some cases the base caller recalibration system 106 updates both the GQ and GT fields. Specifically, the base caller recalibration system 106 updates the GQ field based on the genotype error classification, and also updates the GT field to switch the genotype depending on whether the existing GT is 0 / X or X / X (X is a non-zero value).

[0111] If the base caller recalibration system 106 determines that neither the true positive classification nor the genotype error classification has the highest probability among the variant call classifications 414, in some embodiments, the base caller recalibration system 106 updates the GQ field. In other words, if the base caller recalibration system 106 determines that the false positive classification has the highest probability, the base caller recalibration system 106 updates the GQ field. In particular, the base caller recalibration system 106 updates the GQ field based on the probability indicated by the true positive classification.

[0112] As alluded to above, in some embodiments, the base caller recalibration system 106 increases or decreases a base call quality metric (e.g., Q-score) for a nucleotide base call. Based on the variant call classification 414, for example, the base caller recalibration system 106 increases a base call quality metric for a nucleotide base call that would not have previously passed the quality filter and determines that the increased base call quality metric now passes the quality filter. In some such cases, the base caller recalibration system 106 includes the nucleotide base call having such an increased base call quality metric (that passes the quality filter) in the post-filter variant call file. In contrast, in other cases, the base caller recalibration system 106 decreases a base call quality metric for a nucleotide base call that would have previously passed the quality filter and determines that the decreased base call quality metric now does not pass the quality filter. In some such cases, the base caller recalibration system 106 excludes nucleotide base calls with reduced base call quality metrics (that do not pass the quality filter) from the post-filter variant call file, but includes such nucleotide base calls with reduced base call quality metrics in the pre-filter variant call file.

[0113] For example, the base caller recalibration system 106 can remove false positive variant calls and recover false negative variant calls by modifying the corresponding base call quality metric. To remove false positives, in some cases, the base caller recalibration system 106 reduces the base call quality metric of a nucleotide base call that initially passed the quality filter based on the variant call classification 414 from the call recalibration machine learning model 412. Based on determining that the reduced base call quality metric falls below a threshold metric (e.g., a Q-score of 3.0 or 10.0), the base caller recalibration system 106 determines that the nucleotide base call no longer passes the quality filter. Thus, the base caller recalibration system 106 filters out or removes a false positive nucleotide base call that initially passed the filter by modifying its base call quality metric.

[0114] In addition to removing false positives based on changes to the base call quality metrics, the base caller recalibration system 106 can remove false positive variant calls based on changes to the genotype. To remove false positives, in some cases, the base caller recalibration system 106 changes the genotype of an initial nucleotide base call that indicates a different nucleotide base from the reference base (e.g., GT=1 or 2) to the genotype of an updated nucleotide base call that indicates the same nucleotide base as the reference base (e.g., GT=0) based on the variant call classification 414 from the call recalibration machine learning model 412. Based on the genotype being the same as the reference base, the base caller recalibration system 106 does not identify the nucleotide base call as a variant, and in some cases excludes the data for the nucleotide base call from the variant call file.

[0115] To recover the false negatives, the base caller recalibration system 106 increases the base call quality metric of the nucleotide base call that did not initially pass the quality filter based on the variant call classification 414 from the call recalibration machine learning model 412. Based on determining that the increased base call quality metric exceeds a threshold metric, the base caller recalibration system 106 determines that the nucleotide base call passes the quality filter. Thus, the base caller recalibration system 106 recovers the false negative nucleotide base call that was initially filtered out by modifying its base call quality metric.

[0116] In addition to recovering false negatives based on changes to the base call quality metrics, the base caller recalibration system 106 can recover false negative variant calls based on changes to the genotype. To recover false negatives, in some cases, the base caller recalibration system 106 changes the genotype of an initial nucleotide base call that indicates the same nucleotide base as the reference base (e.g., GT=0) to a different genotype of an updated nucleotide base call that indicates a different nucleotide base from the reference base (e.g., GT=1 or 2) based on the variant call classification 414 from the call recalibration machine learning model 412. Based on the different genotype of the updated nucleotide base call and the passed base call quality metrics, the base caller recalibration system 106 identifies the nucleotide base call as a variant and includes the nucleotide base call in the variant call file.

[0117] Indeed, in some implementations, the base caller recalibration system 106 utilizes the call generation model 420 and the call recalibration machine learning model 412 to operate in a particular order. For example, the base caller recalibration system 106 generates a FASTQ file by converting a BCL file to a FASTQ. In addition, the base caller recalibration system 106 (then) utilizes the mapping and alignment component 406 of the call generation model 420 to map and align nucleotide bases from the sample nucleotide sequence. In some cases, the base caller recalibration system 106 maps and aligns the nucleotide bases of the sample sequence relative to a reference sequence (e.g., a reference genome) and / or various alternative supporting reads.

[0118] As described herein, after mapping and alignment, the base caller recalibration system 106 then utilizes the variant caller component 408 of the call generation model 420 to generate initial nucleotide base calls for the sample sequence corresponding to the particular genomic coordinates based on various sequencing metrics. Thereafter, or simultaneously, the base caller recalibration system 106 also applies a call recalibration machine learning model 412 to generate variant call classifications 414 from the sequencing metrics extracted through the mapping and alignment, variant calling, and / or from other sources as described above. Based on the variant call classifications 414, the base caller recalibration system 106 recalibrates the nucleotide base calls (e.g., by modifying various data fields that correspond to particular metrics of the nucleotide base calls, such as QUAL, GT, and GQ).

[0119] In some cases, the base caller recalibration system 106 further applies a quality filter to the nucleotide base calls to determine whether the nucleotide base calls pass the quality filter (e.g., a Q20 or other Q score hard pass filter). The base caller recalibration system 106 then identifies a subset of the nucleotide base calls that represent variants from the reference base and that pass the quality filter. The base caller recalibration system 106 further generates a modified or updated variant call file (e.g., variant call file 418) that includes the subset of the nucleotide base calls and recalibrated metrics for the subset of the nucleotide base calls, such as an updated QUAL metric, an updated GT metric, and / or an updated GQ metric.

[0120] As mentioned above, in certain embodiments, the base caller recalibration system 106 trains or tunes a call recalibration machine learning model (e.g., the call recalibration machine learning model 412). In particular, the base caller recalibration system 106 utilizes an iterative training process to adapt the call recalibration machine learning model by adjusting or adding decision trees or learning parameters that result in accurate variant call classifications (e.g., the variant call classifications 414). Figure 5 illustrates training a call recalibration machine learning model according to one or more embodiments.

[0121] As illustrated in FIG. 5, the base caller recalibration system 106 accesses sample sequencing metrics 504 from a database 502 (e.g., database 116). For example, the base caller recalibration system 106 accesses sample sequencing metrics including sample read-based metrics, sample external source sequencing metrics, and sample call model generation sequencing metrics. In some cases, the sample sequencing metrics 504 have corresponding ground truth variant call files 516 associated with them, which indicate the actual nucleotide base calls and their various metrics resulting from the sample sequencing metrics 504. For example, the base caller recalibration system 106 utilizes the sample sequencing metrics 504 and a ground truth variant call file from a training dataset from the Food and Drug Administration, referred to as the PrecisionFDA dataset. In some cases, the sample sequencing metrics 504 include a subset of the sample sequencing metrics for each nucleotide base call in the ground truth variant call file. The ground truth variant call file can have ground truth variant calls corresponding to each subset of sample sequencing metrics (e.g., genotype metrics in a genotype field).

[0122] As further illustrated in FIG. 5, the base caller recalibration system 106 generates predicted variant call classifications 508 based on the sample sequencing metrics 504. Specifically, the base caller recalibration system 106 utilizes a call recalibration machine learning model 506 (e.g., call recalibration machine learning model 412) to generate the predicted variant call classifications 508. Indeed, in some embodiments, the call recalibration machine learning model 506 generates a set of three predicted variant call classifications as the predicted variant call classifications 508, including a predicted false positive classification, a predicted genotype error classification, and a predicted true positive classification. Thus, the predicted variant call classifications 508 can take the form of any of the variant call classifications described above.

[0123] Based on the predicted variant call classification 508, the base caller recalibration system 106 determines nucleotide base calls and generates a modified variant call file 510 including the nucleotide base calls and corresponding fields. As indicated above, the base caller recalibration system 106 can (i) utilize a call generation model to generate initial nucleotide base calls and (ii) utilize a call recalibration machine learning model 506 to modify data fields corresponding to the variant call file for the nucleotide base calls. Such modified or recalibrated values ​​are output in the modified variant call file 510, for example, by the call generation model. For example, the base caller recalibration system 106 determines recalibration values ​​for certain metrics in the modified variant call file 510, including a call quality metric (QUAL), a genotype metric (GT), and a genotype quality metric (GQ).

[0124] As further illustrated in FIG. 5, the base caller recalibration system 106 performs a comparison 512. Specifically, the base caller recalibration system 106 performs a comparison 512 between (i) the variant nucleotide base calls and / or data fields in the corrected variant call file 510 and (ii) the variant nucleotide base calls and / or data fields in the ground truth variant call file 516. In some embodiments, the base caller recalibration system 106 utilizes a loss function 514 to compare (e.g., determine a measure of error or loss between) the variant nucleotide base calls and / or data fields from the two variant call files. For example, if the call recalibration machine learning model 506 is an ensemble of gradient boosted trees, the base caller recalibration system 106 utilizes a mean squared error loss function (e.g., for regression) and / or a logarithmic loss function (e.g., for classification) as the loss function 514.

[0125] In contrast, in embodiments in which the call recalibration machine learning model 506 is a neural network, the base caller recalibration system 106 may utilize a cross-entropy loss function, an L1 loss function, or a mean squared error loss function as the loss function 514. For example, the base caller recalibration system 106 utilizes the loss function 514 to determine the differences between variant nucleotide base calls and / or data fields from the corrected variant call file 510 and the ground truth variant call file 516.

[0126] 5, the basecaller recalibration system 106 performs model fitting 518. In particular, the basecaller recalibration system 106 adapts the call recalibration machine learning model 506 based on the comparison 512. For example, the basecaller recalibration system 106 makes modifications or adjustments to the call recalibration machine learning model 506 to reduce a loss measure from a loss function 514 for subsequent training iterations.

[0127] In the case of gradient boosted trees, for example, the base caller recalibration system 106 trains the call recalibration machine learning model 506 on the gradient of the error determined by the loss function 514. For example, the base caller recalibration system 106 solves a (e.g., infinite-dimensional) convex optimization problem while regularizing the objective function to avoid over-fitting. In some implementations, the base caller recalibration system 106 scales the gradient to emphasize correction for under-represented classes (e.g., when there are significantly more true positives than false positives).

[0128] In some embodiments, the basecaller recalibration system 106 adds a new weak learner (e.g., a new boosted tree) to the call recalibration machine learning model 506 at each successive training iteration as part of solving the optimization problem. For example, the basecaller recalibration system 106 finds a feature (e.g., a sequencing metric) that minimizes the loss from the loss function 514 and adds that feature to the tree of the current iteration or starts building a new tree with that feature.

[0129] In addition to or as an alternative to gradient boosted decision trees, the base caller recalibration system 106 trains a logistic regression to learn parameters for generating one or more variant call classifications, such as true positive classifications. To avoid over-fitting, the base caller recalibration system 106 further regularizes based on hyperparameters such as learning rate, stochastic gradient boosting, number of trees, tree depth, complexity penalization, and L1 / L2 regularization.

[0130] In embodiments where the call recalibrated machine learning model 506 is a neural network, the basecaller recalibration system 106 performs model fitting 518 by modifying the internal parameters (e.g., weights) of the call recalibrated machine learning model 506 to reduce a loss measure for the loss function 514. In effect, the basecaller recalibration system 106 modifies how the call recalibrated machine learning model 506 analyzes and passes data between layers and neurons by modifying the internal network parameters. Thus, over multiple iterations, the basecaller recalibration system 106 improves the accuracy of the call recalibrated machine learning model 506.

[0131] Indeed, in some cases, the base caller recalibration system 106 repeats the training process illustrated in FIG. 5 multiple times. For example, the base caller recalibration system 106 repeats the iterative training by selecting a new set of sequencing metrics for each nucleotide base call along with the corresponding ground truth nucleotide base call in the corresponding ground truth variant call file. The base caller recalibration system 106 further generates a new set of predicted variant call classifications for each iteration along with the new modified variant call file. As described above, the base caller recalibration system 106 also compares the variant nucleotide base calls and / or data fields from the modified variant call file in each iteration to the corresponding variant-nucleotide base calls and / or data fields from the corresponding ground truth variant call file, and further performs model fitting 518. The base caller recalibration system 106 repeats this process until the call recalibration machine learning model 506 generates predicted variant call classifications that result in variant calls that meet a threshold measure of loss.

[0132] As mentioned above, in certain described embodiments, the base caller recalibration system 106 generates and provides contribution measures associated with the sequencing metrics. In particular, the base caller recalibration system 106 determines each contribution measure that indicates how influential an individual sequencing metric is in determining a particular nucleotide base call. Figure 6 shows an example visualization of the contribution measures for sequencing metrics associated with nucleotide base calls, according to one or more embodiments.

[0133] As illustrated in FIG. 6, the client device 108 displays a contribution measure interface 602 that includes individual depictions of the contribution measures associated with corresponding sequencing metrics. In effect, the base caller recalibration system 106 determines the contribution measures of sequencing metrics based on how influential or influential the sequencing metrics are on the final nucleotide base call. Unlike many conventional systems that utilize deep learning architectures, the structure of the call generation model used by the base caller recalibration system 106 facilitates the determination of such a contribution measure for each metric.

[0134] For example, the base caller recalibration system 106 determines the contribution measure by determining a Shapley Additive Explanation (SHAP) value for each of the sequencing metrics for a nucleotide base call. Specifically, the base caller recalibration system 106 determines the SHAP value by determining the impact of the sequencing metric compared to the results of a baseline value (e.g., the baseline value of the sequencing metric). As illustrated in FIG. 6, the base caller recalibration system 106 determines the contribution measure for several enumerated sequencing metrics, where the thicker (e.g., bulbier) portion of the graph for each sequencing metric indicates (roughly) its contribution measure.

[0135] 6, the basecaller recalibration system 106 can similarly rank the sequencing metrics according to the contribution measure. For example, the basecaller recalibration system 106 determines that the contribution for the mapq_p metric is the highest among those displayed in the contribution-measure interface 602, followed by the qual metric, the gt0 metric, etc. down the list.

[0136] As mentioned above, in certain described embodiments, the base caller recalibration system 106 improves accuracy over conventional systems. In particular, the base caller recalibration system 106 reduces false positive variant nucleotide base calls and false negative variant nucleotide base calls compared to conventional systems. In fact, by utilizing a call recalibration machine learning model to recalibrate nucleotide base calls, the base caller recalibration system 106 further improves over previous versions of the call generation model that did not utilize a call recalibration machine learning model (but still outperformed other systems). Figures 7A-7B show graphs depicting the improvement of the base caller recalibration system 106 according to one or more embodiments. Specifically, Figure 7A shows the improvement for nucleotide base calls of SNPs, and Figure 7B shows the improvement for non-SNPs (e.g., indels).

[0137] As illustrated in FIG. 7A, graph 702 includes receiver operating characteristic (ROC) curves showing the reduction in SNP false positives for the base caller recalibrated system 106 compared to a system utilizing a call generation model (e.g., call generation model 420) without recalibration via variant call classification with a call recalibration machine learning model. Graph 702 depicts two different ROC curve portions that represent sensitivity to detected false positive variants, where sensitivity represents the number of correctly determined true positive variant calls divided by the sum of true positive variant calls and false positive variant calls. In particular, graph 702 depicts the ROC curve for the base caller recalibrated system 106 utilizing a call recalibration machine learning model, i.e., the "Recalibrated Model." The "Recalibrated Model" refers to the call recalibration machine learning model that was tested using the PrecisionFDA HG002 high confidence truth set. Additionally, graph 702 depicts the sensitivity ROC curve for the call generation model without the call recalibration machine learning model, i.e., the "Non-Recalibrated Model." To zoom in on the top of each ROC curve, the y-axis of sensitivity starts at about 0.985. At the end of the ROC curve for the "Recalibrated Model," the sensitivity is about 0.998.

[0138] As shown, the basecaller recalibration system 106 with the call recalibration machine learning model shows approximately a 65% improvement in false positives at some sensitivity (e.g., as shown by the left shift of the curve for the recalibrated model compared to the curve for the non-recalibrated model at the same y-axis value). As another example, as shown in table 704, at their best measurement point, the basecaller recalibration system 106 produces only 4,073 false positives, while the non-recalibrated system produces 6,972 false positives.

[0139] As further depicted in table 704, the base caller recalibration system 106 also exhibits other improvements. For example, the base caller recalibration system 106 produces fewer false negatives and fewer genotype errors (e.g., het / hom errors) than a non-recalibrated system. The base caller recalibration system 106 also improves recall, precision, and F-measure over a non-recalibrated system, while minimally increasing overall computation time. In particular, by adding the call recalibration machine learning model to the call generation model, the call recalibration machine learning model adds approximately 1-5 minutes to the generation of a variant call file from the sequencing run (e.g., approximately 21-25 minutes).

[0140] As illustrated in FIG. 7B, graph 706 includes ROC curves showing the reduction in non-SNP (i.e., indel) false positives for the basecaller recalibrated system 106 compared to a non-recalibrated version of the call generation model. Similar to the discussion above, graph 706 depicts a basecaller recalibrated system 106 utilizing a call recalibrated machine learning model ("recalibrated model") compared to the "non-recalibrated model." To zoom in on the top of each ROC curve, the y-axis of sensitivity starts near 0.98.

[0141] As shown by graph 706, the base caller recalibrated system 106 improves non-SNP false positives at the same sensitivity (e.g., as indicated by larger dots). As shown in table 708, for example, at their best measurement point, the base caller recalibrated system 106 produces only 998 false positives at the illustrated sensitivity, while the non-recalibrated system produces 1,342 false positives.

[0142] As further depicted by table 708, the base caller recalibrated system 106 also exhibits other improvements over conventional systems. For example, the base caller recalibrated system 106 produces fewer genotype errors (e.g., het / hom errors) than non-recalibrated systems. The base caller recalibrated system 106 also improves accuracy and F-measures for non-SNPs compared to non-recalibrated systems.

[0143] In addition to the improved accuracy shown by the ROC curves of Figures 7A and 7B, Figure 8 shows a graph 802 that demonstrates the improved accuracy by the basecaller recalibration system 106 compared to a non-recalibrated system. Specifically, graph 802 depicts the false positive (FP) and false negative (FN) variant calls for the basecaller recalibration system 106 versus those of a non-recalibrated system (e.g., generated for both standard hash tables (HT) and graph hash tables) for both SNPs and indels. As shown, the basecaller recalibration system 106 significantly reduces the overall FP+FN values ​​in the best F1 score measurements for standard and graph hash tables for both SNPs and indels. In fact, the basecaller recalibration system 106 reduces the number of false positives and the number of false negatives by utilizing a call recalibration machine learning model.

[0144] Referring now to FIG. 9, this figure illustrates a flowchart of a set of operations 900 for generating nucleotide base calls based on variant call classifications from a call recalibration machine learning model according to one or more embodiments. FIG. 9 illustrates operations according to one embodiment, however alternative embodiments may omit, add, rearrange, and / or modify any of the operations depicted in FIG. 9. The operations of FIG. 9 may be performed as part of a method. Alternatively, a non-transitory computer-readable storage medium may include instructions that, when executed by one or more processors, cause a computing device to perform the operations depicted in FIG. 9. In a further embodiment, a system comprises at least one processor and a non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the system to perform the operations of FIG. 9.

[0145] As shown in FIG. 9 , operation 900 includes operation 902 of determining sequencing metrics for a nucleotide base call. In particular, operation 902 involves determining sequencing metrics for a nucleotide base call of a nucleotide read corresponding to a genomic coordinate of a sample nucleotide sequence. For example, operation 902 involves determining one or more of read-based sequencing metrics, call model-generated sequencing metrics, or external source sequencing metrics. In some cases, determining the call model-generated sequencing metrics involves determining variant calling sequencing metrics and mapping and alignment sequencing metrics from a call generation model. In certain embodiments, operation 902 involves determining re-engineered sequencing metrics derived from other sequencing metrics for a nucleotide base call. Operation 902 may also include determining one or more of read-based sequencing metrics including metrics derived from nucleotide reads of the sample nucleotide sequence, call model-generated sequencing metrics generated via a call generation model, or external source sequencing metrics identified from one or more external databases.

[0146] Additionally, the series of operations 900 includes an operation 904 of generating a variant call classification based on the sequencing metrics. In particular, operation 904 involves utilizing a call recalibration machine learning model and generating one or more variant call classifications indicative of the accuracy of identifying a variant at a genomic coordinate based on the sequencing metrics. In some embodiments, the series of operations 900 includes an operation of modifying one or more data fields corresponding to the variant call file for one or more of call quality, genotype, or genotype quality based on the one or more variant call classifications. In some cases, operation 904 involves generating one or more of a false positive classification, a genotype error classification, or a true positive classification. Operation 904 can involve utilizing a call recalibration machine learning model to generate one or more classifications corresponding to a genotype for the final nucleotide base call for the genomic coordinate. In some cases, the call recalibration machine learning model includes one or more of a neural network, a deep learning transformer, a gradient boosted decision tree, a random forest model, a linear regression, a support vector machine, or a logistic regression.

[0147] In certain embodiments, operation 904 involves utilizing a call recalibration machine learning model to generate one or more variant call classifications based on variant calling sequencing metrics and mapping and alignment sequencing metrics. Operation 904 can include utilizing a machine learning classifier to generate one or more of a false positive probability that the variant call or nucleotide base call is a false positive, a genotype error probability that the variant call or nucleotide base call is an incorrect genotype, or a true positive probability that the variant call or nucleotide base call is a true positive. Operation 904 can also include utilizing a machine learning classifier to generate one or more of a false positive probability that the final nucleotide base call is a false positive, a genotype error probability that the genotype for the final nucleotide base call is an incorrect genotype, or a true positive probability that the final nucleotide base call is a true positive.

[0148] As further illustrated in FIG. 9 , the series of operations 900 includes an operation 906 of determining a final nucleotide base call based on the variant call classification. In particular, operation 906 involves determining a final nucleotide base call for the genomic coordinate based on one or more variant call classifications. For example, operation 906 includes determining an initial nucleotide base call for the genomic coordinate utilizing a call generation model based on one or more sequencing metrics and revising the variant call file and one or more data fields corresponding to the initial nucleotide base call based on one or more variant call classifications from a call recalibration machine learning model. In some cases, operation 906 involves generating a variant call for the genomic coordinate based on the one or more variant call classifications. In some embodiments, operation 906 involves determining a final nucleotide base call as part of a variant call that includes a single nucleotide polymorphism, deletion, insertion, or structural variation corresponding to the genomic coordinate.

[0149] Indeed, operation 906 can involve determining a final nucleotide base call for the genomic coordinate by changing the genotype of the initial nucleotide base call to an updated genotype of the updated nucleotide base call. The series of operations 900 can also include operations of updating a base call quality metric for the variant call based on the one or more variant call classifications, determining that the base call quality metric for the variant call passes a quality filter, and generating a variant call file that includes the variant calls based on the base call quality metric that passes the quality filter.

[0150] In some embodiments, the series of operations 900 includes increasing a base call quality metric for a nucleotide base call based on one or more variant call classifications. In these or other embodiments, the series of operations 900 includes determining that the increased base call quality metric passes a quality filter and generating a variant call file including a final nucleotide base call based on the increased base call quality metric, or alternatively, updating the variant call file to include a final nucleotide base call based on the increased base call quality metric. In certain cases, the series of operations 900 includes decreasing a base call quality metric for the final nucleotide base call based on one or more variant call classifications, determining that the reduced base call quality metric does not pass a quality filter, and generating a post-filter variant call file that excludes the final nucleotide base call based on the reduced base call quality metric that does not pass the quality filter, or alternatively, updating the variant call file to exclude the final nucleotide base call based on the reduced base call quality metric.

[0151] In one or more implementations, the series of operations 900 includes determining a contribution measure of the sequencing metrics indicative of a respective measure of the influence of the sequencing metric on a variant call, a nucleotide base call, or a final nucleotide base call, and providing a visualization of the contribution measure corresponding to one or more of the sequencing metrics for display on a client device.

[0152] The methods described herein can be used in conjunction with various nucleic acid sequencing techniques. Particularly applicable techniques are those in which the nucleic acids are attached to fixed positions in an array such that their relative positions do not change, and the array is repeatedly imaged. For example, embodiments in which images are obtained in different color channels that correspond to different labels used to distinguish one nucleotide base type from another nucleotide base type are particularly applicable. In some embodiments, the process of determining the nucleotide sequence of the target nucleic acid can be an automated process. A preferred embodiment includes sequencing-by-synthesis (SBS) techniques.

[0153] SBS technology generally involves the enzymatic extension of nascent nucleic acid strand by repeated addition of nucleotide to template strand.In the conventional method of SBS, a single nucleotide monomer can be provided to target nucleic acid in the presence of polymerase in each delivery.However, in the method described herein, multiple types of nucleotide monomers can be provided to target nucleic acid in the presence of polymerase during delivery.

[0154] SBS can utilize nucleotide monomers with terminator moieties or nucleotide monomers that lack any terminator moiety. Methods that utilize nucleotide monomers that lack terminators include, for example, pyrosequencing and sequencing using γ-phosphate-labeled nucleotides, as described in more detail below. In methods that use nucleotide monomers that do not contain terminators, the number of nucleotides added in each cycle is generally variable and depends on the template sequence and the mode of nucleotide delivery. In SBS techniques that utilize nucleotide monomers with terminator moieties, the terminator can be effectively irreversible under the sequencing conditions used, as in the case of conventional Sanger sequencing that utilizes dideoxynucleotides, or the terminator can be reversible, as in the case of the sequencing method developed by Solexa (now Illumina).

[0155] SBS techniques can use nucleotide monomers with or without a label moiety. Thus, incorporation events can be detected based on the properties of the label, such as the fluorescence of the label, the properties of the nucleotide monomer, such as the molecular weight or charge, the by-products of incorporation of the nucleotide, such as the release of pyrophosphate, and the like. In embodiments in which two or more different nucleotides are present in the sequencing reagent, the different nucleotides can be distinguishable from each other, or alternatively, the two or more different labels can be distinguishable under the detection technique used. For example, the different nucleotides present in the sequencing reagent can have different labels, which can be distinguished using appropriate optical systems, as exemplified by the sequencing method developed by Solexa (now Illumina).

[0156] A preferred embodiment includes the technique of pyrosequencing, which detects the release of inorganic pyrophosphate (PPi) when a specific nucleotide is incorporated into a nascent strand (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996) "Real-time DNA sequencing using detection of pyrophosphate release." Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001) "Pyrosequencing sheds light on DNA sequencing." Genome Res. 11(1), 3-11; Ronaghi, M., Uhlen, M. and Nyren, P. (1998) "A sequencing method based on real-time pyrophosphate." Science 281(5375),363, U.S. 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 entireties. In pyrosequencing, the released PPi can be detected by its immediate conversion to adenosine triphosphate (ATP) by ATP sulfurase, and the level of ATP generated is detected via luciferase-generated photons. The nucleic acids to be sequenced can be attached to features in an array, and the array can be imaged to capture chemiluminescent signals generated by incorporation of nucleotides into the features of the array. Images can be obtained after treatment of the array with a particular nucleotide type (e.g., A, T, C, or G). Images obtained after addition of each nucleotide type differ with respect to which features in the array are detected. These differences in the images reflect the different sequence content of the features on the array. However, the relative position of each feature remains unchanged in the image. The images can be stored, processed, and analyzed using the methods described herein.For example, images obtained after treating the array with each different nucleotide type can be processed in the same manner as exemplified herein for images obtained from different detection channels for reversible terminator-based sequencing methods.

[0157] In another exemplary type of SBS, cycle sequencing is accomplished by stepwise addition of reversible terminator nucleotides containing cleavable or photobleachable dye labels, for example as described in WO 04 / 018497 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated by reference. This approach has been commercialized by Solexa (now Illumina Inc.) and is also described in WO 91 / 06678 and WO 07 / 123,744, each of which is incorporated by reference herein. The availability of fluorescently labeled terminators, both of which can be reversed and from which the fluorescent labels are cleaved, facilitates efficient cyclic reversible termination (CRT) sequencing. Polymerases can also be co-engineered to efficiently incorporate and extend from these modified nucleotides.

[0158] Preferably, in reversible terminator-based sequencing embodiments, the label does not substantially inhibit extension under SBS reaction conditions. However, the detection label may be removable, for example, by cleavage or degradation. Images can be taken after incorporation of the label into the arrayed nucleic acid features. In certain embodiments, each cycle involves simultaneous delivery of four different nucleotide types to the array, each nucleotide type having a spectrally distinct label. Four images can then be obtained, each using a detection channel selective for one of the four different labels. Alternatively, different nucleotide types can be added sequentially, and images of the array can be obtained during each addition step. In such embodiments, each image shows nucleic acid features that incorporate a particular type of nucleotide. Different features are present or absent in different images, since the sequence content of each feature is different. However, the relative positions of the features remain unchanged within the images. Images obtained from such reversible terminator-SBS methods can be stored, processed, and analyzed as described herein. Following the image taking step, the label can be removed, and the reversible terminator moiety can be removed for subsequent cycles of nucleotide addition and detection. Removal of the label after detection in a particular cycle and before the subsequent cycle has the advantage of reducing background signal and crosstalk between cycles. Examples of useful labeling and removal methods are described below.

[0159] In certain embodiments, some or all of the nucleotide monomers can include reversible terminators. In such embodiments, the reversible terminator / cleavable fluorophore can include a fluorophore attached to the ribose moiety via a 3' ester bond (Metzker, Genome Res. 15:1767-1776 (2005), which is incorporated herein by reference). Other approaches separate the terminator chemistry from the cleavage of the fluorescent label (Ruparel et al., Proc Natl Acad Sci USA 102:5932-7 (2005), which is incorporated herein by reference in its entirety). Ruparel et al. describe the development of reversible terminators that use a small amount of 3' allyl group to block extension, but can be easily deblocked by brief treatment with a palladium catalyst. The fluorophore was attached to the group via a photocleavable linker that can be easily cleaved by 30 seconds of exposure to long wavelength UV light. Thus, either disulfide reduction or photocleavage can be used as a cleavable linker. Another approach to reversible termination is the use of a natural terminus followed by placement of a bulky dye on the dNTP. The presence of a charged bulky dye on the dNTP can act as an effective terminator through steric and / or electrostatic hindrance. The presence of one incorporation event prevents further binding unless the dye is removed. Cleavage of the dye removes the fluorophore, effectively reversing the terminus. Examples of modified nucleotides are also described in U.S. Pat. Nos. 7,427,673 and 7,057,026, the disclosures of which are incorporated herein by reference in their entirety.

[0160] Additional exemplary SBS systems and methods that may be utilized with the methods and systems described herein are described in U.S. Patent Application Publication No. 2007 / 0166705, U.S. Patent Application Publication No. 2006 / 0188901, U.S. Pat. No. 7,057,026, U.S. Patent Application Publication No. 2006 / 0240439, U.S. Patent Application Publication No. 2006 / 0281109, WO 05 / 065814, U.S. Patent Application Publication No. 2005 / 0100900, WO 06 / 064199, WO 07 / 010,251, U.S. Patent Application Publication No. 2012 / 0270305, and U.S. Patent Application Publication No. 2013 / 0260372, the disclosures of which are incorporated herein by reference in their entireties.

[0161] Some embodiments may utilize detection of four different nucleotides using fewer than four different labels. For example, SBS may be performed using the methods and systems described in incorporated document US Patent Application Publication No. 2013 / 0079232. As a first example, pairs of nucleotide types may be detected at the same wavelength but may be distinguished based on differences in intensity for one member of the pair, or based on a change to one member of the pair (e.g., via making a chemical, photochemical, or physical modification) that results in the appearance or disappearance of a distinct signal compared to the signal detected for the other member of the pair. As a second example, three of the four different nucleotide types may be detected under certain conditions, while the fourth nucleotide type has no detectable label under those conditions or is minimally detected under those conditions (e.g., minimal detection due to background fluorescence, etc.). Incorporation of the first three nucleotide types into a nucleic acid may be determined based on the presence of their corresponding signals, and incorporation of the fourth nucleotide type into a nucleic acid may be determined based on the absence or minimal detection of any signal. As a third example, one nucleotide type can include a label that is detected in two different channels, while the other nucleotide type is detected in no more than one of the channels. The three exemplary configurations above are not considered mutually exclusive and can be used in various combinations.An exemplary embodiment combining all three examples is a fluorescence-based SBS method that uses a first nucleotide type that is detected in a first channel (e.g., dATP having a label that is detected in the first channel when excited by a first excitation wavelength), a second nucleotide type that is detected in a second channel (e.g., dCTP having a label that is detected in the second channel when excited by a second excitation wavelength), a third nucleotide type that is detected in both the first and second channels (e.g., dTTP having at least one label that is detected in both channels when excited by the first and / or second excitation wavelengths), and a fourth nucleotide type that is not detected in any channel or that is minimally devoid of a label (e.g., unlabeled dGTP).

[0162] Furthermore, as described in incorporated document U.S. Patent Application Publication No. 2013 / 0079232, sequencing data can be obtained using a single channel. In such so-called one-dye sequencing methods, a first nucleotide type is labeled but the label is removed after the first image is generated, and a second nucleotide type is labeled only after the first image is generated. A third nucleotide type retains its label in both the first and second images, and a fourth nucleotide type remains unlabeled in both images.

[0163] Some embodiments may utilize sequencing by ligation techniques. Such techniques utilize DNA ligase to incorporate oligonucleotides and identify the incorporation of such oligonucleotides. The oligonucleotides typically have different labels that correlate with the identity of a particular nucleotide in the sequence to which the oligonucleotide hybridizes. As with other SBS methods, images can be obtained after treating an array of nucleic acid sequences with labeled sequencing reagents. Each image shows nucleic acid features that incorporate a particular type of label. Because the sequence content of each feature is different, different features are present or absent in different images, but the relative positions of the features remain unchanged within the images. Images obtained from ligation-based sequencing methods can be stored, processed, and analyzed as described herein. Exemplary SBS systems and methods that may be utilized with the methods and systems described herein are described in U.S. Pat. No. 6,969,488, U.S. Pat. No. 6,172,218, and U.S. Pat. No. 6,306,597, the disclosures of which are incorporated herein by reference in their entireties.

[0164] Some embodiments may utilize nanopore sequencing (Deamer, DW & Akeson, M. "Nanopores and nucleic acids: prospects for ultrarapid sequencing." Trends Biotechnol. 18, 147-151 (2000); Deamer, D. and D. Branton, "Characterization of nucleic acids by nanopore analysis." Acc. Chem. Res. 35:817-825 (2002); Li, J., M. Gershow, D. Stein, E. Brandin, and JA Golovchenko, "DNA molecules and configurations in a solid-state nanopore microscope." Nat. Mater. 2:611-615 (2003), the disclosures of which are incorporated herein by reference in their entireties). In such embodiments, the target nucleic acid passes through the nanopore. The nanopore may be a synthetic pore or a biological membrane protein, such as α-hemolysin. As the target nucleic acid passes through the nanopore, each base pair can be identified by measuring the fluctuations in the electrical conductance of the pore. (U.S. Pat. No. 7,001,792; Soni, GV & Meller, "A. Progress toward ultrafast DNA sequencing using solid-state nanopores." Clin. Chem. 53, 1996-2001 (2007); Healy, K. "Nanopore-based single-molecule DNA analysis." Nanomed. 2, 459-481 (2007); Cockroft, SL, Chu, J., Amorin, M. & Ghadiri, MR "A single-molecule nanopore device detects DNA polymerase activity with single-nucleotide resolution." J. Am Chem. Soc. 130, 818-820 (2008), the disclosures of which are incorporated herein by reference in their entireties).Data obtained from nanopore sequencing can be stored, processed, and analyzed as described herein. In particular, the data can be processed as images according to the exemplary processing of optical and other images described herein.

[0165] Some embodiments may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation may be detected via fluorescence resonance energy transfer (FRET) interactions between fluorophore-containing polymerases and γ-phosphate-labeled nucleotides, for example, as described in U.S. Pat. No. 7,329,492 and U.S. Pat. No. 7,211,414, each of which is incorporated herein by reference, or nucleotide incorporation may be detected using zero-mode waveguides, for example, as described in U.S. Pat. No. 7,315,019, each of which is incorporated herein by reference, and fluorescent nucleotide analogs and engineered polymerases, for example, as described in U.S. Pat. No. 7,405,281 and U.S. Patent Application Publication No. 2008 / 0108082, each of which is incorporated herein by reference. Illumination can be restricted to a zeptoliter-scale volume around the surface-tethered polymerase so that incorporation of fluorescently labeled nucleotides can be observed with low background (Levene, MJ et al. "Zero-mode waveguides for single-molecule analysis at high concentrations." Science, 299, 682-686 (2003); Lundquist, PM et al. "Parallel confocal detection of single molecules in real time." Opt. Lett. 33, 1026-1028 (2008); Korlach, J. et al. "Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nano structures." Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures of which are incorporated herein by reference in their entireties).Images resulting from such methods can be stored, processed, and analyzed as described herein.

[0166] Some SBS embodiments include detection of protons released upon incorporation of a nucleotide into an extension product. For example, sequencing based on detection of released protons can be performed using electrical detectors and related technology available from Ion Torrent (Guilford, CT, a subsidiary of Life Technologies), or the sequencing methods and systems described in U.S. Patent Application Publication Nos. 2009 / 0026082(A1), 2009 / 0127589(A1), 2010 / 0137143(A1), or 2010 / 0282617(A1), each of which is incorporated herein by reference. The methods described herein for amplifying target nucleic acids using kinetic exclusion can be easily adapted to substrates used to detect protons. More specifically, the methods described herein can be used to generate clonal populations of amplicons used to detect protons.

[0167] The SBS method described above can be advantageously performed in a multiplex format, such that multiple different target nucleic acids are manipulated simultaneously. In certain embodiments, the different target nucleic acids can be processed in a common reaction vessel or on the surface of a particular substrate. This allows for convenient delivery of sequencing reagents, removal of unreacted reagents, and detection of incorporation events in a multiplexed manner. In embodiments using surface-bound target nucleic acids, the target nucleic acids can be in an array format. In an array format, the target nucleic acids can typically be bound to a surface in a spatially distinguishable manner. The target nucleic acids can be bound by direct covalent attachment, attachment to beads or other particles, or binding to a polymerase or other molecule attached to the surface. The array can include a single copy of the target nucleic acid at each site (also referred to as a feature), or multiple copies with the same sequence can be present at each site or feature. The multiple copies can be generated by amplification methods such as bridge amplification or emulsion PCR, which are described in more detail below.

[0168] The methods described herein can use arrays having any of a variety of densities of features, including, for example, at least about 10 features / cm2, 100 features / cm2, 500 features / cm2, 1,000 features / cm2, 5,000 features / cm2, 10,000 features / cm2, 50,000 features / cm2, 100,000 features / cm2, 1,000,000 features / cm2, 5,000,000 features / cm2, or more.

[0169] An advantage of the methods described herein is that they provide rapid and efficient detection of multiple target nucleic acids in parallel. Thus, the present disclosure provides an integrated system that can prepare and detect nucleic acids using techniques known in the art, such as those exemplified above. Thus, the integrated system of the present disclosure can include fluidic components that can deliver amplification and / or sequencing reagents to one or more immobilized DNA fragments, the system including components such as pumps, valves, reservoirs, fluid lines, etc. A flow cell can be configured and / or used in the integrated system for detecting target nucleic acids. Exemplary flow cells are described, for example, in U.S. Patent Application Publication No. 2010 / 0111768(A1) and U.S. Patent Application No. 13 / 273,666, each of which is incorporated herein by reference. As exemplified for the flow cell, one or more of the fluidic components of the integrated system can be used for amplification and detection methods. Taking the nucleic acid sequencing embodiment as an example, one or more of the fluidic components of the integrated system can be used for delivery of sequencing reagents in the amplification methods described herein and in the sequencing methods as exemplified above. Alternatively, an integrated system may include separate fluidic systems for performing the amplification method and for performing the detection method. Examples of integrated sequencing systems capable of producing amplified nucleic acids and sequencing the nucleic acids include, but are not limited to, the MiSeq™ platform (Illumina Inc., San Diego, Calif.) and the devices described in U.S. Patent Application No. 13 / 273,666, which is incorporated herein by reference.

[0170] The sequencing system described above sequences the nucleic acid polymers present in the sample received by the sequencing device. As defined herein, "sample" and its derivatives are used in the broadest sense and include any sample, culture, etc. suspected of containing a target. In some embodiments, the sample includes DNA, RNA, PNA, LNA, chimeric or hybrid forms of nucleic acid. A sample can include any biological, clinical, surgical, agricultural, air or water sample containing one or more nucleic acids. The term also includes any isolated nucleic acid sample, such as genomic DNA, fresh frozen or formalin-fixed paraffin-embedded nucleic acid samples. It is also envisioned that the sample can be derived from a single individual, a collection of nucleic acid samples from genetically related members, nucleic acid samples from genetically unrelated members, nucleic acid samples from a single individual such as a tumor sample and a normal tissue sample (matched), or a sample from a single source containing two different forms of genetic material such as maternal and fetal DNA obtained from a maternal subject, or the presence of contaminating bacterial DNA in a sample containing plant or animal DNA. In some embodiments, the source of nucleic acid material can include nucleic acid obtained from a newborn, for example, as typically used for newborn screening.

[0171] The nucleic acid sample may include high molecular weight material such as genomic DNA (gDNA). The sample may include low molecular weight material such as nucleic acid molecules obtained from FFPE or archived DNA samples. In another embodiment, the low molecular weight material includes enzymatically or mechanically fragmented DNA. The sample may include cell-free circulating DNA. In some embodiments, the sample may include nucleic acid molecules obtained from biopsies, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microdissection, surgical resection, and other clinical or laboratory obtained samples. In some embodiments, the sample may be an epidemiological, agricultural, forensic, or pathogenic sample. In some embodiments, the sample may include nucleic acid molecules obtained from animals, such as human or mammalian sources. In another embodiment, the sample may include nucleic acid molecules obtained from non-mammalian sources, such as plants, bacteria, viruses, or fungi. In some embodiments, the source of the nucleic acid molecule may be an archived or extinct sample or species.

[0172] Additionally, the methods and compositions disclosed herein may be useful for amplifying nucleic acid samples having low quality nucleic acid molecules, such as degraded and / or fragmented genomic DNA from forensic samples. In an embodiment, the forensic sample may include nucleic acid obtained from a crime scene, from a missing persons DNA database, from a laboratory associated with a forensic investigation, or may include forensic samples obtained by a law enforcement agency, one or more military services, or members thereof. The nucleic acid sample may be crude DNA, including purified samples or lysates, for example, from buccal swabs, paper, cloth, or other substrates that may be impregnated with saliva, blood, or other bodily fluids. Thus, in some embodiments, the nucleic acid sample may include small amounts of DNA or fragmented portions of DNA, such as genomic DNA. In some embodiments, the target sequence may be present in one or more bodily fluids, including, but not limited to, blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequence may be obtained from hair, skin, tissue samples, autopsies, or remains of a victim. In some embodiments, the nucleic acid including one or more target sequences may be obtained from a deceased animal or human. In some embodiments, the target sequence may comprise nucleic acid obtained from non-human DNA, such as microbial, plant or entomological DNA. In some embodiments, the target sequence or the amplified target sequence is for human identification. In some embodiments, the present disclosure generally relates to a method for identifying features of a forensic sample. In some embodiments, the present disclosure generally relates to a human identification method using one or more target specific primers disclosed herein or one or more target specific primers designed using the primer design criteria outlined herein. In one embodiment, a forensic sample or human identification sample comprising at least one target sequence may be amplified using any one or more of the target specific primers disclosed herein or using the primer criteria outlined herein.

[0173] The components of the base caller recalibration system 106 may include software, hardware, or both. For example, the components of the base caller recalibration 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., client device 108). When executed by one or more processors, the computer-executable instructions of the base caller recalibration system 106 may cause the computing device to perform the bubble detection methods described herein. Alternatively, the components of the base caller recalibration system 106 may include hardware, such as a dedicated processing device for performing a particular function or group of functions. Additionally or alternatively, the components of the base caller recalibration system 106 may include a combination of computer-executable instructions and hardware.

[0174] Furthermore, the components of the base caller recalibration system 106 that perform the functions described herein with respect to the base caller recalibration system 106 may be implemented, for example, as part of a stand-alone application, as a module of an application, as a plug-in of an application, as a library function(s) that can be called by other applications, and / or as a cloud computing model. Thus, the components of the base caller recalibration 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 base caller recalibration 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.

[0175] Embodiments of the present disclosure may include or utilize special purpose or general purpose computers including, for example, computer hardware such as one or more processors and system memory, as discussed in more detail below. Embodiments within the scope of the present disclosure also include physical and other computer readable media for carrying or storing computer executable instructions and / or data structures. In particular, one or more of the processes described herein may be embodied in a non-transitory computer readable medium and implemented at least in part as instructions executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer readable medium (e.g., a memory, etc.) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.

[0176] A computer-readable medium may be any available medium that can be accessed by a general-purpose or special-purpose computer system. A computer-readable medium that stores computer-executable instructions is a non-transitory computer-readable storage medium (device). A computer-readable medium that carries computer-executable instructions is a transmission medium. Thus, by way of example and not limitation, embodiments of the present disclosure may include at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage medium (device) and transmission media.

[0177] Non-transitory computer-readable storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid state drives (SSD) (e.g., based on RAM), flash memory, phase-change memory (PCM), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer.

[0178] A "network" is defined as one or more data links that enable the transport of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless), the computer properly recognizes the connection as a transmission medium. A transmission medium may include a network and / or data links that may be used to carry desired program code means in the form of computer-executable instructions or data structures and that may be accessed by a general-purpose or special-purpose computer. Combinations of the above should also be included within the scope of computer-readable media.

[0179] 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 the transmission medium to the non-transitory computer readable storage medium (device) (or vice versa). For example, computer executable instructions or data structures received over a network or data link may be buffered in a RAM in a network interface module (e.g., a NIC) and then eventually transferred to the computer system RAM and / or to a less volatile computer storage medium (device) in the computer system. It should therefore be understood that the non-transitory computer readable storage medium (device) may be included in computer system components that also (or even primarily) utilize a transmission medium.

[0180] Computer-executable instructions include, for example, instructions and data that, when executed by a processor, cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to transform the general-purpose computer into a special-purpose computer that implements elements of the present disclosure. Computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and / or methodological operations, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or operations described above. Rather, the described features and operations are disclosed as example forms of implementing the claims.

[0181] Those skilled in the art will appreciate that the present disclosure may be implemented in a networked computing environment having many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, tablets, pagers, routers, switches, etc. The present disclosure may also be implemented in a distributed system environment in which both local and remote computer systems, linked through a network (either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links), perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0182] Embodiments of the present disclosure may also be implemented in a cloud computing environment. In this specification, "cloud computing" is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing may be used in the market to provide ubiquitous and convenient on-demand access to a shared pool of configurable computing resources. The shared pool of configurable computing resources may be quickly configured through virtualization, exposed with low management effort or service provider interaction, and then scaled accordingly.

[0183] The cloud computing model may consist of various characteristics such as, for example, on-demand self-service, wide area network access, resource pooling, rapid elasticity, measured service, etc. The cloud computing model may also expose various service models such as, for example, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The cloud computing model may also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this specification and claims, a "cloud computing environment" is an environment in which cloud computing is employed.

[0184] FIG. 10 illustrates a block diagram of a computing device 1000 that may be configured to perform one or more of the processes described above. It will be appreciated that one or more computing devices, such as the computing device 1000, may implement the base caller recalibration system 106 and the sequencing system 104. As illustrated by FIG. 10, the computing device 1000 may include a processor 1002, a memory 1004, a storage device 1006, an I / O interface 1008, and a communication interface 1010, which may be communicatively coupled by a communication infrastructure 1012. In certain embodiments, the computing device 1000 may include fewer or more components than those illustrated in FIG. 10. The following paragraphs will describe in more detail the components of the computing device 1000 illustrated in FIG. 10.

[0185] In one or more embodiments, the processor 1002 includes hardware for executing instructions, such as those that make up a computer program. By way of example and not limitation, to execute instructions for dynamically modifying a workflow, the processor 1002 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 1004, or a storage device 1006, decode them, and execute them. The memory 1004 may be a volatile or non-volatile memory used to store data, metadata, and programs for execution by the processor. The storage device 1006 includes storage, such as a hard disk, flash disk drive, or other digital storage device, for storing data or instructions for performing the methods described herein.

[0186] The I / O interface 1008 allows a user to provide input to, receive output from, and otherwise transfer data to and receive data from the computing device 1000. The I / O interface 1008 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 1008 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In a particular embodiment, the I / O interface 1008 is configured to provide graphical data to a display for presentation to a user. The graphical data may represent one or more graphical user interfaces and / or any other graphical content that may be useful in a particular implementation.

[0187] Communications interface 1010 may include hardware, software, or both. In any case, communications interface 1010 may provide one or more interfaces for communications (e.g., packet-based communications, etc.) between computing device 1000 and one or more other computing devices or networks. By way of example and not limitation, communications interface 1010 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.

[0188] Further, the communication interface 1010 may facilitate communication with various types of wired or wireless networks. The communication interface 1010 may also facilitate communication using various communication protocols. The communication infrastructure 1012 may also include hardware, software, or both that couples components of the computing device 1000 to one another. For example, the communication interface 1010 may enable multiple computing devices connected by a particular infrastructure to communicate with one another using one or more networks and / or protocols to perform one or more aspects of the processes described herein. To illustrate, a sequencing process may enable multiple devices (e.g., a client device, a sequencing device, and a server device) to exchange information such as sequencing data and error notifications.

[0189] In the foregoing specification, the present disclosure has been described with reference to certain exemplary embodiments thereof. Various embodiments and aspects of the present disclosure are described with reference to the details discussed herein, and the accompanying drawings illustrate various embodiments. The above description and drawings are illustrative of the present disclosure and should not be construed as limiting the present disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure.

[0190] The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects as illustrative only and not restrictive. For example, the methods described herein may be performed with fewer or more steps / actions, or the steps / actions may be performed in a different order. Further, the steps / actions described herein may be repeated or performed in parallel with each other, or with different occurrences of the same or similar operations. The scope of the present application is therefore indicated by the appended claims, rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope. [Explanation of symbols]

[0191] 100 Environment 102 Server device 102 Proc Natl Acad Sci USA 104 Sequencing System 106 base pair Kohler recalibration system 108 Client Device 110 Sequencing Applications 112 Network 114 Sequencing Instrument 116 Databases 302 nucleotide reads 304 Mapping 306 Sequencing Metrics 308 Sequence Data 310 Call Generation Model 312 Sequencing Metrics 314 Sequencing Information Database 316 Sequencing Metrics 402 Sequencing Information Database 403 Reference Sequence 404 Sequence Data 406 Alignment Component 408 Variant Caller Component 410 Sequencing Metric Extraction 412 Call Recalibration Machine Learning Model 414 Variant Call Classification 416 Data Field Generation 418 variant call file 420 Call Generation Model 502 Database 504 Sample Sequencing Metrics 506 Call Recalibration Machine Learning Model 508 Predictive variant calling classification 510 variant call file 512 comparison 514 Loss Function 516 ground truth variant call files 518 Model Fitting 602 Contribution Scale Interface 702 Graph 706 Graphs 802 Graph 1000 Computing Devices 1002 Processor 1004 Memory 1006 Storage device 1008 I / O Interface 1010 Communication Interface 1012 Communications Infrastructure

Claims

1. A system comprising: at least one processor; and when executed by the at least one processor, causing the system to: determine a sequencing metric for nucleotide base calls of nucleotide reads corresponding to genomic coordinates of a sample nucleotide sequence; generate one or more variant call classifications indicative of an accuracy of identifying variants at the genomic coordinates, using a call recalibration machine learning model and based on the sequencing metric; determine a final nucleotide base call for the genomic coordinates based on the one or more variant call classifications; a non-transitory computer-readable medium comprising instructions for causing the above actions.

2. when executed by the at least one processor, causing the system to: increase a base call quality metric for the final nucleotide base call based on the one or more variant call classifications; determine that the increased base call quality metric passes a quality filter; generate a variant call file including the final nucleotide base call based on the increased base call quality metric; the system according to claim 1, further comprising instructions for causing the above actions.

3. when executed by the at least one processor, causing the system to: decrease a base call quality metric for the final nucleotide base call based on the one or more variant call classifications; determine that the decreased base call quality metric does not pass a quality filter; generate a post-filter variant call file excluding the final nucleotide base call based on the decreased base call quality metric not passing the quality filter; the system according to claim 1, further comprising instructions for causing the above actions.

4. when executed by the at least one processor, causing the system to: determine an initial nucleotide base call for the genomic coordinates using a call generation model based on one or more sequencing metrics; Based on the one or more variant call classifications from the call recalibration machine learning model, modifying one or more data fields corresponding to the variant call file and the initial nucleotide base call, thereby determining the final nucleotide base call for the genomic coordinates, and further including an instruction to cause the system to perform the above, the system according to claim 1.

5. When executed by the at least one processor, the system further includes an instruction to cause the system to modify one or more data fields corresponding to a variant call file for one or more of call quality, genotype, or genotype quality based on the one or more variant call classifications, the system according to claim 1.

6. When executed by the at least one processor, the system further includes an instruction to cause the system to determine the sequencing metric for the nucleotide base call by determining one or more of a read-based sequencing metric, a sequencing metric for call model generation, or a sequencing metric from an external source, the system according to claim 1.

7. When executed by the at least one processor, the system further includes an instruction to cause the system to determine the sequencing metric for call model generation by determining a variant caller sequencing metric and a mapping and alignment sequencing metric from a call generation model, the system according to claim 6.

8. When executed by the at least one processor, the system further includes an instruction to cause the system to determine the sequencing metric for the nucleotide base call by determining a re-operated sequencing metric derived from other sequencing metrics for the nucleotide base call, the system according to claim 1.

9. When executed by the at least one processor, the system further includes an instruction to cause the system to generate one or more of false positive classifications, genotype error classifications, or true positive classifications to generate the one or more variant call classifications, the system according to claim 1.

10. When executed by the at least one processor, the system Determining a contribution measure for the sequencing metric, which indicates each measure of the influence of the sequencing metric on the final nucleotide base call; Further comprising instructions to cause the system of claim 1 to provide visualization of the contribution measure corresponding to one or more of the sequencing metrics for display on a client device.

11. When executed by at least one processor, cause a computing device to Determine a sequencing metric for a nucleotide base call of a nucleotide read corresponding to a genomic coordinate of a sample nucleotide sequence; Generate one or more variant call classifications indicative of the accuracy of identifying variants at the genomic coordinates, using a call recalibration machine learning model and based on the sequencing metric; Determine a final nucleotide base call for the genomic coordinates based on the one or more variant call classifications. A non-transitory computer-readable medium comprising instructions.

12. The non-transitory computer-readable medium of claim 11, further comprising instructions to cause the computing device to determine the final nucleotide base call as part of a variant call including a single nucleotide polymorphism, deletion, or insertion corresponding to the genomic coordinate when executed by the at least one processor.

13. The non-transitory computer-readable medium of claim 11, further comprising instructions to cause the computing device to determine the final nucleotide base call for the genomic coordinates by changing the genotype of the initial nucleotide base call to the updated genotype of the updated nucleotide base call when executed by the at least one processor.

14. The non-transitory computer-readable medium of claim 11, further comprising instructions to cause the computing device to generate the one or more variant call classifications by generating one or more classifications corresponding to the genotype of the final nucleotide base call for the genomic coordinates using the call recalibration machine learning model when executed by the at least one processor.

15. When executed by the at least one processor, cause the computing device to determine the determination metric by determining a variant caller array determination metric and a mapping and alignment determination metric from a call generation model; and generate the one or more variant call classifications based on the variant caller array determination metric and the mapping and alignment determination metric using the call recalibration machine learning model. The non-transitory computer-readable medium of claim 11 further comprises an instruction for causing the above operations. **Claim 16** A computer-implemented method comprising: determining a determination metric for nucleotide base calls of nucleotide reads corresponding to genomic coordinates of a sample nucleotide sequence; generating one or more variant call classifications indicative of an accuracy of identifying variants at the genomic coordinates based on the determination metric and using a call recalibration machine learning model; and determining a variant call for the genomic coordinates based on the one or more variant call classifications. **Claim 17** updating a base call quality metric for the variant call based on the one or more variant call classifications; determining that the base call quality metric for the variant call passes a quality filter; and generating a variant call file including the variant call based on the base call quality metric passing the quality filter. The computer-implemented method of claim 16 further comprises the above operations. **Claim 18** generating the one or more variant call classifications includes using a machine learning classifier to generate one or more of a false positive probability that the variant call is a false positive, a genotype error probability that the genotype of the variant call is incorrect, or a true positive probability that the variant call is a true positive. The computer-implemented method of claim 16 includes the above operations. **Claim 19** Determining the alignment determination metric includes determining one or more of a read-based alignment determination metric that includes a metric derived from the nucleotide reads of the sample nucleotide sequence, an alignment determination metric of call model generation generated via a call generation model, or an alignment determination metric of an external source identified from one or more external databases. The computer-implemented method according to claim 16.

20. The computer-implemented method according to claim 16, further comprising determining a contribution measure for the alignment determination metric, wherein the alignment determination metric indicates each measure of the influence exerted on the nucleotide base call for the genomic coordinates.

21. The computer-implemented method according to claim 16, wherein the call recalibration machine learning model includes one or more of a gradient boost decision tree, a random forest, a linear regression, or a logistic regression.

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