Signal-to-Noise Ratio Metrics for Determining Nucleotide Base Calls and Base Call Quality

Abstract

The present disclosure describes a method, non-transitory computer readable medium, and system that can generate a signal-to-noise ratio metric for a cluster of oligonucleotides with tagged nucleotide bases and utilize the signal-to-noise ratio metric to generate a nucleotide base call and determine base call quality. For example, the disclosed system can generate a signal-to-noise ratio metric using a scaling factor and a noise level associated with an optical signal detected from a cluster of oligonucleotides. The disclosed system can utilize the signal-to-noise ratio metric to generate an intensity-value boundary for generating a nucleotide base call for the signal according to one or more base call distribution models. Additionally, the disclosed system can utilize a threshold to filter out signals detected from a cluster of oligonucleotides that have a low signal-to-noise ratio metric. The disclosed system can further utilize the signal-to-noise ratio metric to generate a quality metric for the generated nucleotide base call.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Application No. 63 / 216,401, filed June 29, 2021, which is incorporated herein by reference in its entirety. [Background technology]

[0002] In recent years, biotechnology companies and research institutions have improved the hardware and software platforms used to determine the sequence of nucleotide bases (also called "nucleobases") in nucleic acid samples. For example, some existing nucleic acid sequencing platforms determine individual nucleotide bases of a nucleic acid sequence by using traditional Sanger sequencing or by using sequencing-by-synthesis (SBS). When using SBS, existing platforms can monitor thousands, tens of thousands, or even more nucleic acid polymers that are synthesized in parallel to detect more accurate nucleotide base calls. For example, a camera in an SBS platform can capture images of illuminated fluorescent tags from nucleotide bases incorporated into such synthesized nucleic acid sequences (often separated into clusters). After capturing the images, existing SBS platforms transmit the image data to a computing device having sequencing data analysis software to determine the nucleotide base sequence of the nucleic acid polymer. The sequencing data analysis software can determine the nucleotide bases detected in a given image based on the optical signals captured in the image data. By repeatedly incorporating nucleotide bases into oligonucleotides and capturing images of the emitted light signals at various sequencing cycles, the SBS platform can determine the sequence of nucleotide bases present in a nucleic acid sample.

[0003] Despite these recent advances, existing sequencing platforms typically suffer from technical limitations that hinder the accuracy and flexibility of these platforms. In particular, inflexible intensity-value boundary models often prevent such sequencing platforms from interpreting the optical signals captured in the image data to make correct nucleotide base calls. Furthermore, flawed base call quality and filtering models tend to limit the ability of such platforms to determine the accuracy of the determined nucleotide base calls.

[0004] In fact, the intensity-value boundary models of existing sequencing platforms often result in inaccuracies when making nucleotide base calls and interpreting the optical signals emitted from the illuminated fluorescent tags of nucleotide bases to classify those nucleotide bases. For example, some existing platforms generate nucleotide base calls using decision boundaries that map intensity values ​​(e.g., wavelengths and / or brightness values) associated with optical signals to corresponding nucleotide bases. However, these platforms may use decision boundaries that are inappropriate for a given optical signal (e.g., do not accurately map intensity values ​​to nucleotide bases), resulting in inaccurate nucleotide base calls. Such inaccurate calls are often caused by the rigid application by some existing platforms of the same set of decision boundaries for all optical signals. In fact, existing sequencing platforms may use a single model (e.g., a single Gaussian mixture model) to generate the decision boundaries used for all detected optical signals. However, different optical signals may have different factors (such as different levels of signal purity) that affect the associated intensity values. By not considering these factors, existing platforms are unable to flexibly adjust the decision boundaries to the characteristics of the optical signals.

[0005] Some existing sequencing platforms attempt to avoid inaccuracies in the generation of nucleotide base calls by filtering out problematic clusters of nucleic acid polymers (e.g., by excluding the corresponding nucleotide base calls from the resulting base call data). For example, existing platforms can filter out clusters of nucleic acid polymers using a purity filter that analyzes the purity value of the corresponding optical signal. The purity value can be determined as the ratio of the distance between the intensity associated with the optical signal and the centroid of the nearest nucleotide base to the distance between that intensity and another centroid (e.g., the second nearest centroid).

[0006] Existing platforms can filter out nucleotide base calls of clusters whose purity values ​​fail to meet a threshold (e.g., multiple times within a first set of sequencing cycles), indicating that the emitted optical signals are of low quality and unreliable (e.g., the corresponding nucleotide base calls may be inaccurate). However, clusters may become more problematic as sequencing progresses. In fact, the poor quality of clusters that meet the purity filter in early sequencing cycles may surface in later sequencing cycles. By using the purity filter, many existing platforms are unable to properly identify these problematic clusters. Thus, such platforms tend to generate unreliable nucleotide base calls based on insufficient optical signals emitted from these clusters and include these nucleotide base calls in the base call data.

[0007] In addition to problems with generating accurate nucleotide base calls and filtering out nucleic acid polymers that emit unreliable optical signals, existing sequencing platforms are often inaccurate in determining the quality of a given nucleotide base call. For example, many existing platforms determine a metric such as a Phred quality score that estimates the likelihood of error in a nucleotide base call. However, the model used to determine this quality score leaves many features associated with a nucleotide base call (e.g., associated with the corresponding optical signal) unaccounted for, even when such features significantly contribute to the quality of the nucleotide base call. Thus, existing platforms often fail to accurately estimate the quality of a nucleotide base call.

[0008] Furthermore, as previously mentioned, existing platforms are unable to tune the decision boundaries used to generate nucleotide base calls to the characteristics of the optical signal. In many cases, quality estimates are intrinsically tied to the decision boundaries used to generate nucleotide base calls. Thus, using decision boundaries that cannot accurately map optical signal intensity values ​​to nucleotide bases can lead to inaccurate estimates of the quality of the resulting nucleotide base calls. Summary of the Invention [Means for solving the problem]

[0009] The present disclosure describes embodiments of methods, non-transitory computer readable media, and systems that determine signal-to-noise ratio metrics of optical signals emitted from fluorescent tags of nucleotide bases and use such signal-to-noise ratio metrics to determine more accurate and flexible base calling. For example, the disclosed system can determine separate signal-to-noise ratio metrics for different clusters of oligonucleotides to which tagged nucleotide bases are added. The disclosed system can utilize intensity values ​​associated with optical signals emitted from the clusters to determine their corresponding signal-to-noise ratio metrics. For example, the disclosed system determines the signal-to-noise ratio metrics of labeled nucleotide bases in a cluster of oligonucleotides based on a scaling factor and a noise level of the optical signal of the cluster. In some cases, the disclosed system updates the signal-to-noise ratio metrics after each sequencing cycle.

[0010] The disclosed system can utilize such signal-to-noise ratio metrics associated with clusters for various base calling applications, which are described further below. For example, the disclosed system can use such signal-to-noise ratio metrics to generate intensity-value boundaries to distinguish signals corresponding to different nucleotide bases according to a base call distribution model (e.g., a segmented Gaussian mixture model), filter out low quality clusters, and / or determine a quality score for a nucleotide base call. By utilizing such signal-to-noise ratio metrics, the disclosed system can flexibly adjust the decision boundaries between different nucleotide clouds used to determine a nucleotide base call to the characteristics of the detected optical signal, allowing for more accurate base calling. Additionally, the disclosed system can utilize the signal-to-noise ratio metrics to more accurately filter low quality wells and more accurately determine a quality score for a given nucleotide base call.

[0011] Additional features and advantages of one or more embodiments of the present disclosure are outlined in the description below.

[0012] The detailed description refers to the drawings, which are briefly described below. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 illustrates a block diagram of a sequencing system including a signal-to-noise aware base calling system according to one or more embodiments. [Diagram 2] 1 illustrates an overview of a signal-to-noise recognition base calling system that generates and utilizes a signal-to-noise ratio metric in accordance with one or more embodiments. [Diagram 3] 1 illustrates a diagram for determining a signal-to-noise ratio metric in accordance with one or more embodiments. [Figure 4] 1 illustrates a block diagram for utilizing a signal-to-noise ratio metric for distribution model segmentation in accordance with one or more embodiments. [Diagram 5] FIG. 1 illustrates a block diagram for utilizing a signal-to-noise ratio metric of a signal to filter nucleotide base calls according to one or more embodiments. [Figure 6] FIG. 1 illustrates a block diagram for generating a quality metric for a nucleotide base call according to one or more embodiments. [Figure 7] 1 illustrates a graph reflecting the results of a study on the effectiveness of a signal-to-noise recognition base calling system according to one or more embodiments. [Figure 8A] 1 illustrates graphs reflecting additional study results regarding the effectiveness of a signal-to-noise recognition base calling system according to one or more embodiments. [Figure 8B] 1 illustrates graphs reflecting additional study results regarding the effectiveness of a signal-to-noise recognition base calling system according to one or more embodiments. [Figure 9] 1 illustrates a flowchart of a series of operations for generating a quality metric for a nucleotide base call using a signal-to-noise ratio metric in accordance with one or more embodiments. [Figure 10] 1 illustrates a flowchart of a series of operations for filtering nucleotide base calls corresponding to a signal using a signal-to-noise ratio metric in accordance with one or more embodiments. [Figure 11] 1 illustrates a flowchart of a series of operations for generating intensity-value boundaries for a signal-to-noise range using a signal-to-noise ratio metric in accordance with one or more embodiments. [Figure 12] 1 illustrates a block diagram of an exemplary computing device for implementing one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] The present disclosure describes one or more embodiments of a signal-to-noise-aware base calling system that utilizes a signal-to-noise ratio metric to determine nucleotide base calls, measure the quality of nucleotide base calls, and filter out low-quality wells. In particular, in some implementations, the signal-to-noise-aware base calling system determines a signal-to-noise ratio metric for a section of a nucleotide-sample slide (e.g., a well of a patterned flow cell or a subsection of an unpatterned flow cell) that contains a cluster of oligonucleotides. For example, the signal-to-noise-aware base calling system can determine the signal-to-noise ratio metric based on a scaling factor and a noise level that corresponds to the intensity value of the optical signal emitted by the cluster.

[0015] A signal-to-noise-aware base calling system can utilize such a signal-to-noise ratio metric to determine better quality or more accurate nucleic acid base calls throughout various applications. For example, in some cases, a signal-to-noise-aware base calling system utilizes a signal-to-noise ratio metric to generate intensity-value boundaries to distinguish signals corresponding to different nucleotide bases according to one or more base call distribution models (e.g., segmented Gaussian mixture models). In some cases, a signal-to-noise-aware base calling system uses or establishes a signal-to-noise threshold and filters out nucleotide base calls associated with a section of a nucleotide-sample slide from the sequencing data if the signal-to-noise ratio metric does not meet the threshold. In some embodiments, a signal-to-noise-aware base calling system utilizes a signal-to-noise ratio metric as an input to a model (e.g., a Phred algorithm) that estimates the quality of a nucleotide base call generated for a section of a nucleotide-sample slide.

[0016] As just mentioned, in one or more embodiments, the signal-to-noise aware base calling system determines a signal-to-noise ratio metric for a section of the nucleotide-sample slide. In one or more embodiments, the signal-to-noise ratio metric is specific to that section of the nucleotide-sample slide, and the signal-to-noise aware base calling system determines other signal-to-noise ratio metrics for other sections of the nucleotide-sample slide. In one or more embodiments, the signal-to-noise aware base calling system updates the signal-to-noise ratio metric for the section of the nucleotide-sample slide with each sequencing cycle.

[0017] As alluded to above, in one or more embodiments, the signal-to-noise aware base calling system determines a signal-to-noise ratio metric for a section of the nucleotide-sample slide based on an intensity value of a signal (e.g., an optical signal) detected from the section of the nucleotide-sample slide. For example, the signal-to-noise aware base calling system can determine a scaling factor for the detected signal. In some cases, the signal-to-noise aware base calling system determines the scaling factor using a least squares algorithm based on the intensity value of the signal. The signal-to-noise aware base calling system can further determine a noise level corresponding to the detected signal. For example, in some embodiments, the signal-to-noise aware base calling system determines the noise level based on a corrected intensity value for the signal. The signal-to-noise aware base calling system can determine a signal-to-noise ratio metric based on both the scaling factor and the noise level.

[0018] As further described above, in some implementations, the signal-to-noise-aware base calling system utilizes a signal-to-noise ratio metric to generate intensity-value boundaries for distinguishing signals corresponding to different nucleotide bases. To illustrate, in certain cases, the signal-to-noise-aware base calling system generates a signal-to-noise ratio metric for a plurality of sections of a nucleotide-sample slide (e.g., based on signals detected during a sequencing cycle). The signal-to-noise-aware base calling system can determine a signal-to-noise ratio range for the determined signal-to-noise ratio metric and fit a base call distribution model to the nucleotide-sample slide sections associated with each signal-to-noise ratio range. The signal-to-noise-aware base calling system can then generate nucleotide base calls for the sections of the nucleotide-sample slide according to the base call distribution model for the signal-to-noise ratio range that encompasses the signal-to-noise ratio metric of that section of the nucleotide-sample slide.

[0019] Additionally, as described above, in one or more embodiments, the signal-to-noise-aware base calling system utilizes a signal-to-noise ratio metric of the nucleotide-sample slide section in determining whether to filter out the corresponding nucleotide base call from the nucleotide base call data (e.g., sequencing data) resulting from sequencing. Indeed, in some implementations, the signal-to-noise-aware base calling system establishes a signal-to-noise ratio threshold. Upon determining that the signal-to-noise ratio metric meets the signal-to-noise ratio threshold, the signal-to-noise-aware base calling system can determine a nucleotide base call for the nucleotide-sample slide section and include it in the nucleotide base call data. If the signal-to-noise ratio metric does not meet the signal-to-noise ratio threshold, the signal-to-noise-aware base calling system can exclude the nucleotide base call of the nucleotide-sample slide section from the nucleotide base call data.

[0020] In addition to (or instead of) generating or filtering intensity-value boundaries, in one or more embodiments, the signal-to-noise aware base calling system utilizes a signal-to-noise ratio metric of a section of the nucleotide-sample slide to estimate the quality of a nucleotide base call made for the section of the nucleotide-sample slide. For example, in some cases, the signal-to-noise aware base calling system provides the signal-to-noise ratio metric as an input to a base call quality model (e.g., the Phred algorithm). The signal-to-noise aware base calling system can utilize the base call quality model to generate a quality metric that estimates the error of the nucleotide base call based on the signal-to-noise ratio metric. In some implementations, the signal-to-noise aware base calling system provides the signal-to-noise ratio metric as one of many inputs to the base call quality model (e.g., along with a purity value).

[0021] The signal-to-noise aware base calling system provides several advantages over conventional sequencing platforms. For example, to begin with, the signal-to-noise aware base calling system introduces a new computational model for determining a signal-to-noise ratio metric of an optical signal emitted by a fluorescent tag and captured by a camera. In particular, the disclosed computational model determines a signal-to-noise ratio metric corresponding to an optical signal by decomposing the purity of the optical signal and relating it to the noise associated with the wavelength or intensity of the light emitted by the fluorescent tag. For example, as described above and below, the computational model can separate the detected optical signal into a scaling factor and a noise level and determine a signal-to-noise ratio metric based on these values. By doing so, the computational model can more accurately distinguish between the optical signal corresponding to a nucleotide base call and the noise. The human mind cannot detect the optical signal emitted from a labeled nucleotide base, much less separate the optical signal from the associated noise. Thus, by determining the signal-to-noise ratio metric, the new computational model provides functionality that was previously unavailable to sequencing platforms.

[0022] By utilizing the signal-to-noise ratio metric, the signal-to-noise-aware base calling system improves nucleotide base calling. For example, as described above, the signal-to-noise-aware base calling system fits base call distribution models used to generate nucleotide base calls to various signal-to-noise ratio ranges. These base call distribution models provide intensity-value boundaries (e.g., decision boundaries) on which the nucleotide base calls are based. Thus, the signal-to-noise-aware base calling system flexibly adjusts the intensity-value boundaries to various levels of signal purity associated with signals detected from the sections of the nucleotide-sample slide. As further demonstrated by the results described below, the signal-to-noise-aware base calling system improves the nucleotide base calling of the sections of the nucleotide-sample slide using intensity-value boundaries appropriate for those emitted signals, resulting in more accurate nucleotide base calling.

[0023] By utilizing the signal-to-noise ratio metric, the signal-to-noise aware base calling system also filters out low quality base calls of nucleotide-sample slide sections. In particular, the signal-to-noise aware base calling system more accurately identifies nucleotide-sample slide sections that are emitting insufficient signals. In fact, the signal-to-noise aware base calling system can identify nucleotide-sample slide sections that would otherwise pass the purity filters implemented by conventional sequencing platforms only to surface errors in later sequencing cycles. By improving the filtering process, the signal-to-noise aware base calling system produces more accurate and more reliable nucleotide base call data.

[0024] In addition to improved nucleotide base calling and improved filtering, the signal-to-noise aware base calling system determines nucleotide base call quality more accurately than conventional sequencing platforms. Indeed, by utilizing a signal-to-noise ratio metric, the signal-to-noise aware base calling system can more accurately estimate the quality of nucleotide base calls. For example, as described above, the signal-to-noise aware base calling system can provide a signal-to-noise ratio metric of a section of a nucleotide-sample slide as an input to a base call quality model (e.g., a Phred model). Thus, the signal-to-noise aware base calling system utilizes new and improved (and possibly additional) indicators of nucleotide base call quality compared to conventional sequencing platforms, allowing for more accurate quality estimation. Furthermore, by using intensity-value boundaries that are adjusted to the characteristics of the detected optical signal, the quality estimates tied to these intensity-value boundaries are also adjusted to the characteristics of the optical signal.

[0025] As indicated by the preceding discussion, the present disclosure utilizes various terms to describe the features and advantages of the signal-to-noise recognition base calling system. Here, further details are provided regarding the meaning of such terms. For example, as used herein, the term "nucleotide-sample slide" refers to a plate or slide that contains oligonucleotides for sequencing nucleotide segments for a sample. In particular, a nucleotide-sample slide refers to a slide that contains fluidic channels through which reagents and buffers can travel as part of sequencing. For example, in one or more embodiments, the nucleotide-sample slide includes a flow cell (e.g., a patterned or unpatterned flow cell) that includes a small fluidic channel and a short oligonucleotide complementary to an adapter sequence.

[0026] Relatedly, as used herein, the term "nucleotide-sample slide section" (or "nucleotide-sample slide section") refers to an area that is a part of a nucleotide-sample slide. In particular, a nucleotide-sample slide section can refer to a separate portion of a nucleotide-sample slide that is distinct from other portions of the nucleotide-sample slide. For example, a nucleotide sample slide section can include a well (e.g., a nanowell) of a patterned flow cell or a separate subsection (e.g., a subsection corresponding to a cluster) of a non-patterned flow cell. In some cases, a nucleotide sample slide section includes tiles or subtiles having clusters of the same or similar oligonucleotides growing in parallel.

[0027] Additionally, as used herein, the term "labeled nucleotide base" refers to a nucleotide base that has a fluorescent or light-based indicator of the classification of the nucleotide base. In particular, a labeled nucleotide base can refer to a nucleotide base that incorporates a fluorescent or light-based indicator to identify the type of base (e.g., adenine, cytosine, thymine, or guanine). For example, in one or more embodiments, the labeled nucleotide base includes a nucleotide base that has a fluorescent tag that emits a signal that identifies the base type.

[0028] Furthermore, as used herein, the term "signal" refers to a signal emitted, reflected, or otherwise transmitted from a labeled nucleotide base or a group of labeled nucleotide bases (e.g., labeled nucleotide bases added to a cluster of oligonucleotides). In particular, the signal can refer to a signal indicative of the type of base. For example, the signal can include an optical signal emitted or reflected from a fluorescent tag of a nucleotide base or a fluorescent tag of a plurality of nucleotide bases incorporated into an oligonucleotide. In some implementations, the signal-to-noise recognition base calling system triggers the signal through an external stimulus, such as a laser or other light source. In some cases, the signal-to-noise recognition base calling system triggers the signal through some internal stimulus. Furthermore, in some embodiments, the signal-to-noise recognition base calling system observes the signal using a filter that is applied when capturing an image of a nucleotide-sample slide (e.g., a section of a nucleotide-sample slide). As alluded to above, in certain cases, the signal includes a collection of signals provided by each labeled nucleotide base added to an individual oligonucleotide in a cluster of oligonucleotides.

[0029] As used herein, the term "intensity value" refers to a value that indicates a characteristic or attribute of a signal emitted, reflected, or otherwise transmitted from a labeled nucleotide base or a cluster of labeled nucleotide bases from a cluster of oligonucleotides. In particular, the intensity value can refer to a value associated with color intensity (e.g., wavelength) or light intensity (e.g., brightness). In some cases, the signal-to-noise recognition base calling system uses different filters (or intensity channels) to capture several images of a cluster of oligonucleotides having labeled nucleotide bases. Thus, the intensity value of a signal can correspond to the intensity of a signal observed through a particular filter.

[0030] Additionally, as used herein, the term "signal-to-noise ratio metric" refers to a measure of a target signal compared to the level or content of noise. In particular, the signal-to-noise ratio metric can refer to the strength of the optical signal detected from a labeled nucleotide base compared to the associated noise. For example, in some implementations, the signal-to-noise ratio metric includes the ratio of a scaling factor associated with a signal compared to the corresponding noise level. As used herein, the term "scaling factor" refers to a coefficient or value indicative of brightness. In particular, as used herein, the term scaling factor can refer to a value that accounts for the scale variation (e.g., amplitude / brightness variation) in the inter-cluster intensity profile variation (related to the difference in scale and shift from the origin of the multi-dimensional space of the intensity profiles of clusters within a cluster population). In one or more embodiments, the signal-to-noise recognition base calling system equates the determined scaling factor for the optical signal to the optical signal itself (e.g., signal purity without the addition of noise). Furthermore, as used herein, the term "noise level" refers to a value indicative of the noise associated with a signal. Indeed, in some cases, the noise level includes a value indicative of noise, including signal variations resulting from (or reflecting) the distribution in the observed population. The signal variations may result from the components or contents of the nucleotide-sample slide or the chemical or physical properties of the sequencing device, such as signal variations due to oligonucleotide length, phasing or prephasing, or the position of the cluster of oligonucleotides relative to the field of view of the camera or other sensor. In one or more embodiments, as discussed in more detail below, the signal-to-noise-aware base calling system uses one or more intensity values ​​of the signal to determine the scaling factor and the noise level. As used herein, the term "signal-to-noise ratio range" refers to the range of the signal-to-noise ratio metric. In other words, in some implementations, the signal-to-noise-aware base calling system establishes one or more signal-to-noise ratio ranges and determines whether the signal-to-noise ratio metric of the signal falls within a particular signal-to-noise ratio range.

[0031] Further, as used herein, the term "signal-to-noise ratio threshold" refers to a threshold established to filter out clusters of oligonucleotides (e.g., nucleotide base calls associated with a cluster of oligonucleotides) based on a signal-to-noise ratio metric. For example, in some implementations, a signal-to-noise recognition base calling system determines a signal-to-noise ratio threshold as a signal-to-noise ratio value that must be met (e.g., met or exceeded) by a signal from a labeled nucleotide base corresponding to a cluster of oligonucleotides in order for the nucleotide base calls of that cluster to be included in the resulting nucleotide base call data.

[0032] As used herein, the term "nucleotide base call" refers to the assignment or determination of a particular nucleotide base that is added to or incorporated into an oligonucleotide for a sequencing cycle. In particular, a nucleotide base call refers to the assignment or determination of the type of nucleotide that is incorporated into an oligonucleotide on a nucleotide-sample slide. In some cases, a nucleotide base call includes the assignment or determination of a nucleotide base to an intensity value resulting from a nucleotide added to an oligonucleotide in a section of a nucleotide-sample slide. Alternatively, a nucleotide base call includes the assignment or determination of a nucleotide base to a chromatogram peak or current change resulting from a nucleotide passing through a nanopore of a nucleotide-sample slide. By using a nucleotide base call, the sequencing system determines the sequence of a nucleic acid polymer. For example, a single nucleotide base call can include an adenine call, a cytosine call, a guanine call, or a thymine call.

[0033] Additionally, as used herein, the term "sequencing cycle" (or "cycle") refers to repeated addition or incorporation of nucleotide bases into an oligonucleotide, or repeated addition or incorporation of nucleotide bases into an oligonucleotide in parallel. In particular, a cycle can include repeated taking and analyzing one or more images with data indicative of individual nucleotide bases added or incorporated into an oligonucleotide, or into an oligonucleotide in parallel. Thus, a cycle can be repeated as part of sequencing a nucleic acid polymer. For example, in one or more embodiments, each sequencing cycle involves either a single read, in which the DNA or RNA strand is read in only one direction, or a paired-end read, in which the DNA or RNA strand is read from both ends. Furthermore, in certain cases, each sequencing cycle involves a camera taking images of a nucleotide-sample slide or multiple sections of a nucleotide-sample slide to generate image data for determining the specific nucleotide bases added or incorporated into a particular oligonucleotide. Following the image capture step, the sequencing system can remove the specific fluorescent label from the incorporated nucleotide base and perform another sequencing cycle until the nucleic acid polymer is completely sequenced. In one or more embodiments, a sequencing cycle comprises a cycle within a sequencing-by-synthesis (SBS) run.

[0034] Additionally, as used herein, the term "nucleotide base call data" refers to digital files, image data, or other digital information that indicate individual nucleotide bases or sequences of nucleotide bases of a nucleic acid polymer. In particular, nucleotide base call data can include intensity values ​​(e.g., color or light intensity values ​​of individual clusters) from an image taken by a camera of a nucleotide-sample slide, or other data that indicates individual nucleotide bases or sequences of nucleotide bases of a nucleic acid polymer. In addition to or as an alternative to intensity values, nucleotide base call data can include chromatogram peaks or current changes that indicate individual nucleic acid bases in a sequence. Additionally, in some embodiments, nucleotide base call data includes individual nucleotide base calls that identify individual nucleotide bases (e.g., A, T, C, or G). For example, nucleotide base call data can include data of nucleotide base calls in a sequence of a nucleic acid polymer, organized into a digital file, such as a Binary Base Call (BCL) file, the number of nucleotide base calls that correspond to a particular base (e.g., adenine, cytosine, thymine, or guanine). Additionally, nucleotide base call data can include error / precision information, such as a quality metric associated with each nucleotide base call. In some embodiments, the nucleotide base call data includes information from a sequencing machine that utilizes sequencing by synthesis (SBS).

[0035] As used herein, the term "quality metric" refers to a particular score or other measure that indicates the accuracy of a nucleotide base call of a sequencing cycle. In particular, a quality metric includes a value that indicates the likelihood that one or more predicted nucleotide base calls contain an error. For example, in certain implementations, a quality metric can include a Q-score (e.g., a Phred quality score) that predicts the probability of error for any given nucleotide base call within a sequencing cycle.

[0036] As used herein, the term "base calling quality model" refers to a computer model or algorithm that generates a quality metric for a nucleotide base call. For example, a base calling quality model can refer to a computer algorithm that analyzes the characteristics of a signal and / or corresponding clusters or labeled nucleotide bases and generates a quality metric for a nucleotide base call based on the analysis. By way of example, in some implementations, a base calling quality model includes a computer algorithm that generates a Phred quality score.

[0037] Additionally, as used herein, the term "intensity-value boundary" refers to a decision boundary used in generating a nucleotide base call for a signal. In particular, an intensity-value boundary can refer to a decision boundary that classifies a nucleotide base (e.g., as A, T, C, or G) based on one or more intensity values ​​of a signal. Illustratively, an intensity-value boundary can define or otherwise indicate the boundary of a nucleotide cloud corresponding to each of the nucleotide bases. In some implementations, an intensity-value boundary does not mark the limit at which a signal is classified as a nucleotide base, but rather marks a point at which a signal may be classified as a nucleotide base with a particular level of accuracy.

[0038] As used herein, the term "base call distribution model" refers to a computer model or algorithm that generates intensity-value boundaries. For example, in some implementations, the base call distribution model includes, but is not limited to, a Gaussian distribution model, a uniform distribution model, a Bernoulli distribution model, a binomial distribution model, or a Poisson distribution model. As used herein, the term "centroid" refers to the center of a nucleotide cloud defined or otherwise indicated by one or more intensity-value boundaries. Furthermore, as used herein, the term "centroid intensity value" refers to an intensity value associated with a centroid. In particular, the centroid intensity value indicates an intensity value corresponding to the center of a nucleotide cloud.

[0039] The following paragraphs describe the signal-to-noise aware base calling system with reference to exemplary diagrams depicting exemplary embodiments and implementations. For example, FIG. 1 shows a schematic diagram of a system environment (or "environment") 100 in which a signal-to-noise aware base calling system 106 operates according to one or more embodiments. As shown, the environment 100 includes one or more server devices 102 connected to a sequencing device 110 and a user client device 114 via a network 108. While FIG. 1 illustrates an embodiment of the signal-to-noise aware base calling system 106, the present disclosure describes alternative embodiments and configurations below.

[0040] 1, server device 102, sequencing device 110, and user client device 114 are connected via network 108. Thus, each of the components of environment 100 can communicate via network 108. Network 108 includes any suitable network with which computing devices can communicate. An exemplary network is described in further detail below with respect to FIG. 12.

[0041] As illustrated by FIG. 1, the sequencing device 110 includes a device for sequencing a nucleic acid polymer. In some embodiments, the sequencing device 110 utilizes computer-implemented methods and systems, either directly or indirectly on the sequencing device 110, to analyze nucleic acid segments or oligonucleotides extracted from a sample to generate data. More specifically, the sequencing device 110 receives and analyzes nucleic acid sequence segments extracted from a sample in a nucleotide-sample slide (e.g., a flow cell). In one or more embodiments, the sequencing device 110 utilizes SBS to sequence the nucleic acid polymer. In some embodiments, the sequencing device 110, in addition to or as an alternative to communicating via the network 108, bypasses the network 108 and communicates directly with the server device 102 and / or the user client device 114.

[0042] As just mentioned and illustrated in FIG. 1 , the signal-to-noise aware base calling system 106 can generate, or at least contribute to, the generation of nucleotide base call data 112. In particular, in some embodiments, the signal-to-noise aware base calling system 106 utilizes a signal-to-noise ratio metric to generate the nucleotide base call data 112. By way of example, in some cases, the signal-to-noise aware base calling system 106 determines a signal-to-noise ratio metric for sections of the nucleotide-sample slide (e.g., of signals detected from those sections) during each sequencing cycle. The signal-to-noise aware base calling system 106 can utilize the signal-to-noise ratio metric for each section to generate nucleotide base calls corresponding to signals detected from that section. The signal-to-noise aware base calling system 106 can also utilize the signal-to-noise ratio metric to exclude a section from the base calling process and / or exclude nucleotide base calls generated for that section from the nucleotide base call data 112. Additionally, the signal-to-noise aware base calling system 106 can utilize the signal-to-noise ratio metric determined for a section of the nucleotide-sample slide to generate a quality metric corresponding to the nucleotide base calls made for signals detected from the section. In some instances, the signal-to-noise aware base calling system 106 contributes additional information to the nucleotide base call data 112 (e.g., the signal-to-noise ratio metric itself, a signal-to-noise ratio threshold used for filtering, an average quality metric, etc.).

[0043] As further illustrated by Figure 1, the server device 102 can generate, receive, analyze, store, and transmit digital data, such as data related to nucleotide base calling or sequencing of a nucleic acid polymer. As shown in Figure 1, the sequencing device 110 can transmit (and the server device 102 can receive) nucleotide base call data 112 from the sequencing device 110. The server device 102 can also communicate with a user client device 114. In particular, the server device 102 can transmit nucleic acid base sequences, error data, and other information to the user client device 114.

[0044] In some embodiments, server device 102 comprises a distributed collection of servers, where server device 102 includes several server devices distributed across network 108 and located at the same or different physical locations. Additionally, server device 102 may include a content server, an application server, a communications server, a web hosting server, or another type of server.

[0045] As further shown in FIG. 1, the server device 102 can include a sequencing system 104. Generally, the sequencing system 104 analyzes nucleotide base call data 112 received from the sequencing device 110 to determine a nucleotide base sequence of a nucleic acid polymer, such as a nucleotide base sequence of a sample genome. For example, the sequencing system 104 can receive raw data from the sequencing device 110 and determine the nucleobase sequence of a nucleic acid segment. In some embodiments, the sequencing system 104 determines the sequence of nucleobases in a DNA and / or RNA segment or oligonucleotide. In some cases, as described above, the sequencing system 104 receives pre-processed data that includes nucleotide base calls, error / accuracy information in the form of quality metrics, and / or data regarding filtered (e.g., excluded) clusters. Thus, in some implementations, the sequencing system 104 organizes data from the nucleotide base call data 112 into a useful user-readable format.

[0046] 1, the signal-to-noise aware base calling system 106 may be located on the server device 102, as part of the sequencing device 110 and / or the sequencing system 104. Thus, in some embodiments, the signal-to-noise aware base calling system 106 is implemented by (e.g., located entirely or partially on) the server device 102. In yet other embodiments, the signal-to-noise aware base calling system 106 is implemented by one or more other components of the environment 100, such as the sequencing device 110. Notably, the signal-to-noise aware base calling system 106 can be implemented in a variety of different ways across the server device 102, the network 108, and the sequencing device 110.

[0047] As further illustrated and shown in FIG. 1, the user client device 114 can generate, store, receive, and transmit digital data. In particular, the user client device 114 can receive sequencing data from the server device 102 or the sequencing device 110. Additionally, the user client device 114 can communicate with the server device 102 to receive nucleic acid base sequences, as well as reports of irregularities in sequencing cycles. Thus, the user client device 114 can present sequencing data and notification of nucleic acid base calls to a user associated with the user client device 114 in a graphical user interface. In some cases, the user client device 114 can further present intensity-value boundaries, nucleotide base call data, and other information related to the calculation and use of signal-to-noise ratio metrics for display.

[0048] The user client devices 114 illustrated in FIG. 1 can include various types of client devices. For example, in some embodiments, the user client devices 114 include non-mobile devices, such as desktop computers or servers, or other types of client devices. In yet other embodiments, the user client devices 114 include mobile devices, such as laptops, tablets, cell phones, or smartphones. Additional details regarding the user client devices 114 are described below with respect to FIG. 12.

[0049] As further illustrated in FIG. 1 , the user client device 114 includes a sequencing application 116. The sequencing application 116 can be a web application or a native application (e.g., a mobile application, a desktop application) stored and executed on the user client device 114. The sequencing application 116 can receive data from the signal-to-noise aware base calling system 106 and can present the sequencing data for display on the user client device 114. Additionally, the sequencing application 116 can provide notifications regarding intensity-value boundaries, filtered nucleotide base calls, etc. In some implementations, the signal-to-noise aware base calling system 106 is located on the user client device 114 as part of the sequencing application 116.

[0050] 1 illustrates components of environment 100 communicating over network 108, in certain implementations, components of environment 100 may also communicate directly with one another, bypassing network 108. For example, and as previously discussed, in some implementations, server device 102 communicates directly with sequencing devices 110 and / or user client devices 114. Additionally, signal-to-noise aware base calling system 106 may access one or more databases housed or accessed by server device 102 or elsewhere in environment 100.

[0051] As previously described, the signal-to-noise aware base calling system 106 generates a signal-to-noise ratio metric for a section of a nucleotide-sample slide. In particular, the signal-to-noise aware base calling system 106 generates a signal-to-noise ratio metric for signals detected from labeled nucleotide bases located at or within the section. The signal-to-noise aware base calling system 106 can utilize the signal-to-noise ratio metric to provide various nucleotide base calling features. Figure 2 illustrates an overview of a signal-to-noise aware base calling system 106 that generates and utilizes a signal-to-noise ratio metric in accordance with one or more embodiments.

[0052] As shown in FIG. 2, the signal-to-noise recognition base calling system 106 utilizes a nucleotide-sample slide 202 for sequencing. The nucleotide-sample slide 202 can include oligonucleotides that accept or incorporate labeled nucleotide bases. In particular, the nucleotide-sample slide 202 can include clusters of oligonucleotides within each section (e.g., well). When stimulated, the labeled nucleotide bases can emit a signal having a characteristic that is associated with the type of nucleotide base.

[0053] As further shown in FIG. 2, the signal-to-noise-aware base calling system 106 captures an image 204 of at least one section of the nucleotide-sample slide 202. In particular, the signal-to-noise-aware base calling system 106 captures the image 204 as the labeled nucleotide bases in the section of the nucleotide-sample slide 202 emit signals. As shown, in one or more embodiments, the signal-to-noise-aware base calling system 106 captures multiple images. For example, the signal-to-noise-aware base calling system 106 can capture multiple images using various image filters. To illustrate, in some embodiments, the signal-to-noise-aware base calling system 106 utilizes a two-channel implementation and captures two images of the section of the nucleotide-sample slide 202. In particular, the signal-to-noise-aware base calling system 106 captures a first image using a first image filter and a second image using a second image filter. The first image and the second image can capture the intensity of the emitted signal corresponding to the image filter used. In some cases, the signal-to-noise aware base calling system 106 utilizes a four-channel implementation and captures four different images of a section of the nucleotide-sample slide 202. As with the two-channel implementation, the signal-to-noise aware base calling system 106 can capture each image for the four-channel implementation using a different image filter. Each image can capture the intensity of the emitted signal based on the image filter used for that image. Thus, in some cases, each of the four images shows an emitted signal having a different intensity.

[0054] 2, the images 204 depict signals 206 emitted from labeled nucleotide bases located within a section of the nucleotide-sample slide 202. As previously described, the signals 206 can be indicative of the types of nucleotide bases added to the oligonucleotides within the section of the nucleotide-sample slide 202. For example, as discussed in more detail below, the signals 206 can have one or more corresponding intensity values ​​indicative of the types of nucleotide bases. To illustrate, in some implementations, each of the images 204 captures at least one intensity value corresponding to the signals 206.

[0055] The signal 206 may have some associated noise. In particular, the signal 206 may have an associated noise level that affects the purity of the signal 206. Thus, as illustrated by FIG. 2, the signal-to-noise aware base calling system 106 may generate a signal-to-noise ratio metric 208 for the signal 206. For example, the signal-to-noise aware base calling system 106 may determine a scaling factor corresponding to the signal 206. In one or more embodiments, the signal-to-noise aware base calling system 106 may equate the determined scaling factor to the signal 206. Furthermore, the signal-to-noise aware base calling system 106 may determine a noise level corresponding to the signal 206. Thus, the signal-to-noise aware base calling system 106 may utilize the scaling factor and the noise level to generate the signal-to-noise ratio metric 208 for the signal 206.

[0056] The signal-to-noise-aware base calling system 106 can utilize a signal-to-noise ratio metric 208 to provide various base calling characteristics. For example, as shown in FIG. 2, the signal-to-noise-aware base calling system 106 can utilize a signal-to-noise ratio metric 208 for distribution model segmentation 210. In particular, the signal-to-noise-aware base calling system 106 can utilize the signal-to-noise ratio metric 208 to segment a base call distribution model, such as a Gaussian mixture model, into separate base call distribution models. In some implementations, the signal-to-noise-aware base calling system 106 segments the base call distribution model by fitting a separate base call distribution model to each of a plurality of signal-to-noise ratio ranges. Indeed, as discussed further below, the signal-to-noise-aware base calling system 106 can determine signal-to-noise ratio metrics (including the signal-to-noise ratio metric 208) of multiple signals detected from multiple sections of the nucleotide-sample slide 202. The signal-to-noise-aware base calling system 106 further determines a plurality of signal-to-noise ratio ranges for the plurality of signal-to-noise ratio metrics, such that the signal-to-noise-aware base calling system 106 can fit a base call distribution to each of the signal-to-noise ratio ranges.

[0057] The signal-to-noise-aware base calling system 106 can further utilize a base call distribution model for a particular signal-to-noise ratio range to generate nucleotide base calls for signals having a signal-to-noise ratio metric that falls within that range. Thus, the signal-to-noise-aware base calling system 106 can utilize the signal-to-noise ratio metric 208 to generate nucleotide base calls for the signal 206 via distribution model segmentation 210.

[0058] 2, the signal-to-noise aware base calling system 106 can utilize a signal-to-noise ratio metric 208 for signal-to-noise filtering 212. In particular, the signal-to-noise aware base calling system 106 can establish a signal-to-noise ratio threshold and exclude a signal 206 (e.g., a nucleotide-corresponding section of the sample slide 202) from the nucleotide base call data if the signal-to-noise ratio metric 208 does not meet the signal-to-noise ratio threshold.

[0059] 2, the signal-to-noise aware base calling system 106 can utilize a signal-to-noise ratio metric 208 to determine a quality metric 214 of a nucleotide base call made for the signal 206. For example, the signal-to-noise aware base calling system 106 can utilize a base call quality model to determine the quality metric 214 based on the signal-to-noise ratio metric 208.

[0060] While much of the discussion above (as well as the discussion below) focuses on determining signal-to-noise ratio metrics for sections of a nucleotide-sample slide, it should be understood that the signal-to-noise-aware base calling system 106 may determine signal-to-noise ratio metrics for each of multiple sections of a nucleotide-sample slide in parallel. For example, in one or more embodiments, the signal-to-noise-aware base calling system 106 detects signals from each section of a nucleotide-sample slide (e.g., each well or each section corresponding to a cluster) and determines a signal-to-noise ratio metric for each detected signal. Thus, the signal-to-noise-aware base calling system 106 may utilize various signal-to-noise ratio metrics in determining nucleotide base calls via a segmented base call distribution model, signal-to-noise filtering, and determining quality metrics for the nucleotide base calls made.

[0061] As previously discussed, in one or more embodiments, the signal-to-noise recognition base calling system 106 determines a signal-to-noise ratio metric of the signals detected from the labeled nucleotide bases in a section of the nucleotide-sample slide. Figure 3 illustrates a diagram for determining a signal-to-noise ratio metric according to one or more embodiments.

[0062] 3, the signal-to-noise aware base calling system 106 captures an image 304 of at least one section of a nucleotide-sample slide 302. For example, a camera for the sequencing device 110 (and associated with the signal-to-noise aware base calling system 106) captures an image 304 of tiles within the nucleotide-sample slide 302. Each tile includes multiple nanowells that make up a cluster or multiple subsections that make up a cluster. As further shown, the image 304 depicts a signal 306 emitted from at least one section of the nucleotide-sample slide 302 (e.g., from labeled nucleotide bases within the wells or subsections that correspond to the clusters).

[0063] 3, the signal-to-noise aware base calling system 106 determines a scaling factor 310 corresponding to the signal 306. In particular, the signal-to-noise aware base calling system 106 utilizes a least squares model 308 to determine the scaling factor 310. In one or more embodiments, the signal-to-noise aware base calling system 106 utilizes the least squares model 308 to determine a variation correction factor corresponding to the signal 306. In one or more embodiments, such as when a two-channel implementation is used, the variation correction factor includes a scaling factor 310 that accounts for scale variations in the inter-cluster intensity profile, and two offset factors (also referred to as channel-specific offset factors) that account for shift variations along the first and second intensity channels, respectively, in the inter-cluster intensity profile variation.

[0064] The signal-to-noise aware base calling system 106 can determine the variation correction factor by utilizing a least squares model 308 to determine a relationship between the measured intensities of the labeled nucleotide bases (e.g., the measured intensities corresponding to the signal 306) and the variation correction factor. The signal-to-noise aware base calling system 106 can further determine an error function based on the relationship between the measured intensities and the variation correction factor. The signal-to-noise aware base calling system 106 can determine the scaling factor 310 by generating a partial derivative of the error function with respect to the scaling factor. In particular, in some implementations, 106 utilizes the least squares model 308 to determine two partial derivatives of the error function: one with respect to the scaling factor 310 and the other with respect to a channel-specific offset factor. Indeed, in some implementations, the signal-to-noise cognitive base calling system 106 utilizes a least squares model 308 to determine the scaling factor 310, as described in U.S. patent application Ser. No. 63 / 106,256, filed Oct. 27, 2020, and entitled "SYSTEMS AND METHODS FOR PRE-CLUSTER INTENSITY CORRECTION AND BASE CALLING," which is incorporated herein by reference in its entirety.

[0065] As further shown in FIG. 3, the signal-to-noise aware base calling system 106 determines a noise level 312 corresponding to the signal 306. In particular, as shown, the signal-to-noise aware base calling system 106 can determine the noise level 312 using a corrected intensity value for a section of the nucleotide-sample slide 302 (e.g., for the signal 306). In one or more embodiments, the term "corrected intensity value" refers to an intensity value corresponding to a signal emitted from a section of the nucleotide-sample slide that has been adjusted based on one or more characteristics of the signal. By way of example, in one or more embodiments, the corrected intensity value includes an intensity value corrected to account for an offset and a scaling factor corresponding to the intensity value. Once the correction is made, in some cases, the corrected intensity value is closer to the center of gravity of the nucleotide cloud than the corresponding initially measured intensity value of the signal. For example, in a two-channel implementation, the signal-to-noise aware base calling system 106 can determine a pair of corrected intensity values ​​(e.g., one for each intensity channel) such that the pair is closer to the center of gravity of the nucleotide cloud than the corresponding pair of initially measured intensity values ​​of the signal. In one or more embodiments, the signal to noise recognition base calling system 106 determines the corrected intensity value using:

[0066]

number

[0067] In function (1),

[0068]

number

[0069] FIG. 3 provides a visualization of the corrected intensity values ​​via graph 314. Axes 316a-316b of graph 314 represent intensity values ​​for each intensity channel in a two-channel implementation. Graph 314 maps nucleotide clouds 318a-318d to intensity values ​​with respective intensity-value boundaries. As shown in FIG. 3, the originally measured intensity value for signal 306 corresponds to point 320 within nucleotide cloud 318d. Furthermore, the corrected intensity value corresponds to point 322. As further shown, point 322 corresponding to the corrected intensity value is closer to the centroid 324 of nucleotide cloud 318d.

[0070] In one or more embodiments, the signal-to-noise aware base calling system 106 determines the noise level 312 by determining the distance between the corrected intensity value and the centroid intensity value of the nucleotide cloud, such as the nearest nucleotide cloud or the nearest centroid. For example, in one or more embodiments, the signal-to-noise aware base calling system 106 determines the noise level 312 as follows: where B X and B. Y Represents the center of gravity strength value.

[0071]

number

[0072] In one or more embodiments, the signal-to-noise aware base calling system 106 further determines the noise level 312 using the noise level determined for the same section of the nucleotide-sample slide 302 determined for one or more previous sequencing cycles. Indeed, in some implementations, the signal-to-noise aware base calling system 106 stores the noise level determined for the section of the nucleotide-sample slide 302 after each sequencing cycle. In one or more embodiments, the signal-to-noise aware base calling system 106 averages the stored noise levels for the previous sequencing cycles and utilizes the averaged noise level in determining the noise level 312 for the current sequencing cycle (e.g., by adding the averaged noise level to the noise level determined using function 2, by averaging the averaged noise level with the noise level determined using function 2, etc.). In some implementations, the signal-to-noise aware base calling system 106 utilizes a weighted average of the noise levels for the previous sequencing cycles. For example, the signal-to-noise aware base calling system 106 can assign weights to noise levels determined for previous sequencing cycles based on recency. Illustratively, the signal-to-noise aware base calling system 106 can assign relatively higher weights to noise levels determined for more recent sequencing cycles.

[0073] In some implementations, the signal-to-noise aware base calling system 106 utilizes noise levels for a set number of previous sequencing cycles in determining the noise level for the current sequencing cycle. For example, the signal-to-noise aware base calling system 106 can determine a set number of previous sequencing cycles to utilize based on user input. In some cases, the signal-to-noise aware base calling system 106 utilizes noise levels for all previous sequencing cycles (e.g., all noise levels within the same read or across multiple reads).

[0074] While the above paragraph describes using a previous noise level associated with a section of a nucleotide-sample slide to determine the noise level for that section for the current sequencing cycle, in some cases, the signal-to-noise recognition base calling system 106 utilizes previous noise levels associated with all sections of the nucleotide-sample slide.

[0075] 3, the signal-to-noise aware base calling system 106 utilizes a scaling factor 310 and a noise level 312 to determine a signal-to-noise ratio metric 326 for the signal 306. For example, the signal-to-noise aware base calling system 106 may utilize a ratio of the scaling factor 310 to the noise level 312 to determine the signal-to-noise ratio metric 326. Indeed, in one or more embodiments, the signal-to-noise aware base calling system 106 equates the scaling factor 310 to the signal 306 (e.g., treats the scaling factor 310 as the signal 306) for purposes of determining the signal-to-noise ratio metric 326.

[0076] In one or more embodiments, the signal-to-noise aware base calling system 106 takes phasing or prephasing into account when determining a signal-to-noise ratio metric of the signal. As used herein, the term "phasing" refers to an effect or situation in which sequencing for one molecule lags behind other molecules by at least one base in a particular cycle. Conversely, as used herein, the term "prephasing" refers to an effect or situation in which sequencing for one molecule jumps ahead of other molecules by at least one base in a particular cycle. In one or more embodiments, to correct for the effect of phasing or prephasing, the signal-to-noise aware base calling system 106 can detect a signal having intensity values ​​for base incorporation in each cycle and correct the intensity values ​​by (i) subtracting the intensity value of the immediately preceding cycle from the intensity value of the current cycle, and (ii) subtracting the intensity value of the immediately following cycle from the intensity value of the current cycle. Indeed, in one or more embodiments, the signal-to-noise recognition base calling system 106 corrects for the effects of fading or pre-phasing as described in U.S. Patent No. 10,689,696, issued June 23, 2020, and entitled "Methods and Systems for Analyzing Image Data," which is incorporated herein by reference in its entirety.

[0077] As previously discussed, in one or more embodiments, the signal-to-noise aware base calling system 106 utilizes a signal-to-noise ratio metric corresponding to signals detected from multiple sections of a nucleotide-sample slide for distribution model segmentation. Figure 4 illustrates a block diagram for utilizing a signal-to-noise ratio metric for distribution model segmentation according to one or more embodiments.

[0078] As shown in Figure 4, the signal-to-noise aware base calling system 106 determines signal-to-noise ratio metrics 402a-402d. In particular, the signal-to-noise aware base calling system 106 determines signal-to-noise ratio metrics for multiple sections of a nucleotide-sample slide based on signals detected from those sections during a sequencing cycle. The signal-to-noise aware base calling system 106 can determine the signal-to-noise ratio metrics as described above with reference to Figure 3.

[0079] As further shown in FIG. 4, the signal-to-noise-aware base calling system 106 separates the signal-to-noise ratio metrics 402a-402d into different groups. For example, the signal-to-noise-aware base calling system 106 can utilize signal-to-noise ratio ranges to separate the signal-to-noise ratio metrics 402a-402d. Indeed, in one or more embodiments, the signal-to-noise-aware base calling system 106 establishes multiple signal-to-noise ratio ranges. The signal-to-noise-aware base calling system 106 can establish the signal-to-noise ratio ranges based on user input, using fixed ranges, or based on the signal-to-noise ratio metrics determined for the current sequencing cycle (e.g., establish a first range covering the lowest set of signal-to-noise ratio metrics, establish a second range covering the second lowest set of signal-to-noise ratio metrics, etc.). Although FIG. 4 illustrates a particular number of signal-to-noise ratio ranges, the signal-to-noise-aware base calling system 106 can establish a variety of signal-to-noise ratio ranges.

[0080] In one or more embodiments, each of the signal-to-noise ratio metrics 402a-402d corresponds to a different signal-to-noise ratio range. For example, the signal-to-noise ratio metric 402a can correspond to a first signal-to-noise ratio range (e.g., 9.00-9.99), the signal-to-noise ratio metric 402b can correspond to a second signal-to-noise ratio range (e.g., 10.00-10.99), the signal-to-noise ratio metric 402c can correspond to a third signal-to-noise ratio range (e.g., 11.00-11.99), and the signal-to-noise ratio metric 402d can correspond to a fourth signal-to-noise ratio range (e.g., 12.00-12.99). The signal-to-noise recognition base calling system 106 can associate the detected signal from each section of the nucleotide-sample slide with the signal-to-noise ratio range within which the signal's corresponding signal-to-noise ratio metric falls. 4, the signal-to-noise recognition base calling system 106 establishes a set of intensity values ​​404a-404d based on the signal-to-noise ratio ranges. For example, the set of intensity values ​​404a includes intensity values ​​for signals associated with the signal-to-noise ratio metric 402a (e.g., associated with a first signal-to-noise ratio range that includes the signal-to-noise ratio metric 402a).

[0081] As further shown, the signal-to-noise aware base calling system 106 generates intensity-value boundaries for the nucleotide-signal from the section of the sample slide. For example, Figure 4 shows graphs 406a-406d having a set of intensity-value boundaries (e.g., intensity-value boundary 408) corresponding to each possible nucleotide base (e.g., A, T, C, or G).

[0082] In one or more embodiments, the signal-to-noise aware base calling system 106 generates a set of intensity-value boundaries according to one or more base call distribution models. For example, the signal-to-noise aware base calling system 106 can generate a first set of intensity-value boundaries (e.g., those shown in graph 406a) according to a first base call distribution model, a second set of intensity-value boundaries (e.g., those shown in graph 406b) according to a second base call distribution model, etc.

[0083] 4, the signal-to-noise aware base calling system 106 can utilize a base call distribution model 410 to generate intensity-value boundaries. In some cases, the base call distribution model 410 includes a single base call distribution model, although the signal-to-noise aware base calling system 106 can utilize multiple base call distribution models (e.g., a separate base call distribution model for each signal-to-noise ratio range) in some implementations. Additionally, the base call distribution model 410 can include a Gaussian distribution model in one or more embodiments, although other base call distribution models can also be utilized.

[0084] Although not shown in Figure 4, the signal-to-noise aware base calling system 106 can utilize one of the sets of intensity-value boundaries to generate a nucleotide base call for the signal. In particular, the signal-to-noise aware base calling system 106 can utilize a set of intensity-value boundaries that correspond to a signal-to-noise ratio range associated with the signal to generate a nucleotide base call (i.e., according to a base call distribution model that corresponds to the signal-to-noise ratio range). In one or more embodiments, the signal-to-noise aware base calling system 106 further generates a nucleotide base call utilizing the intensity value determined for the signal.

[0085] To illustrate, upon determining that a signal had a corresponding signal-to-noise ratio metric that falls within a first signal-to-noise ratio range (e.g., 9.00-9.99), the signal-to-noise aware base calling system 106 can generate a nucleotide base call using the set of intensity-value boundaries (e.g., those shown in graph 406a) generated for the first signal-to-noise ratio range. The signal-to-noise aware base calling system 106 can further determine how the set of intensity values ​​for the signal relate to the set of intensity-value boundaries and generate a nucleotide base call accordingly. For example, upon determining that the set of intensity values ​​for the signal falls within a decision boundary for a particular nucleotide base, the signal-to-noise aware base calling system 106 can generate a nucleotide base call indicating that the signal is associated with that nucleotide base. Based on determining that the set of intensity values ​​for the signal is outside the decision boundaries of all nucleotide bases, the signal-to-noise aware base calling system 106 can generate a nucleotide base call for the signal based on the proximity of each nucleotide base to the decision boundaries and / or based on the proximity of each nucleotide base to the center of gravity of the nucleotide cloud corresponding to each nucleotide base.

[0086] Because the signal-to-noise-aware base calling system 106 generates nucleotide base calls for a signal according to a base call distribution model that corresponds to a signal-to-noise ratio range associated with the signal, the signal-to-noise-aware base calling system 106 can potentially generate different nucleotide base calls for signals having similar intensity values. By way of example, in one or more embodiments, the signal-to-noise-aware base calling system 106 generates a first set of intensity-value boundaries corresponding to different nucleotide bases according to a first base call distribution model for a first signal-to-noise ratio range. The signal-to-noise-aware base calling system 106 further generates a second set of intensity-value boundaries corresponding to different nucleotide bases according to a second base call distribution model for a second signal-to-noise ratio range, the second set of intensity-value boundaries being different from the first set of intensity-value boundaries.

[0087] Furthermore, the signal-to-noise-aware base calling system 106 can detect a first signal corresponding to a first signal-to-noise ratio metric within a first signal-to-noise ratio range and having a set of intensity values ​​outside a first set of intensity-value boundaries and outside a second set of intensity-value boundaries, and detect a second signal corresponding to a second signal-to-noise ratio metric within a second signal-to-noise ratio range and having a set of intensity values ​​(e.g., the same set of intensity values ​​as the first signal). Thus, the signal-to-noise-aware base calling system 106 can generate a first nucleotide base call for the first signal based on a first set of intensity-value boundaries for a first base call distribution model, and generate a second nucleotide base call for the second signal based on a second set of intensity-value boundaries for a second base call distribution model. In fact, even if two signals have the same set of intensity values, the signal-to-noise-aware base calling system 106 can generate different nucleotide base calls utilizing two different base call distribution models.

[0088] By generating intensity-value boundaries for various signal-to-noise ratio ranges, the signal-to-noise-aware base calling system 106 operates more flexibly compared to conventional sequencing platforms. Indeed, the signal-to-noise-aware base calling system 106 adjusts the intensity-value boundaries to suit the characteristics, such as the signal-to-noise ratio metric, of the detected signals, providing more flexibility than conventional platforms that tend to utilize the same set of decision boundaries for all signals regardless of their characteristics. By adjusting the intensity-value boundaries as described, the signal-to-noise-aware base calling system 106 operates more accurately than conventional sequencing platforms. In particular, the signal-to-noise-aware base calling system 106 generates nucleotide base calls for signals using intensity-value boundaries that are more appropriate for those signals because the intensity-value boundaries correspond more closely to the characteristics of the signals.

[0089] Furthermore, by generating different intensity-value boundaries for different signal-to-noise ratio ranges, the signal-to-noise aware base calling system 106 more accurately determines the quality of the nucleotide base calls made for the detected signals. Indeed, as seen in FIG. 4, the graphs 406a-406d each include a set of dashed contour lines. The contour lines can represent different quality metrics (e.g., Q-scores) corresponding to the nucleotide base calls. For example, the contour line located closest to a given intensity-boundary value can correspond to a quality metric indicating a relatively high confidence (e.g., low probability of error) in the accuracy of the nucleotide base call associated with the intensity-value boundary, while the contour lines further away correspond to a quality metric indicating a relatively low confidence. Thus, the contour lines associated with the intensity-value boundary indicate that intensity values ​​farther away from the intensity-value boundary correspond to a lower confidence when a nucleotide base call corresponding to the intensity-value boundary is assigned.

[0090] As further seen in FIG. 4, the set of dashed contour lines associated with the intensity-value boundaries varies among graphs 406a-406d (e.g., the contour lines become closer to each other as the signal-to-noise ratio range of the graph includes higher signal-to-noise ratio metrics). Thus, like the generation of the nucleotide base calls themselves, graphs 406a-406d illustrate that the determination of the quality of the nucleotide base calls is also tailored to the characteristics of the corresponding signals. Thus, generating nucleotide base calls using distinct intensity-value boundaries can result in a more accurate determination of the quality of those nucleotide base calls, which will be discussed in further greater detail below with reference to FIG. 6.

[0091] 4 illustrates the generation of intensity-value boundaries and corresponding nucleotide base calls in a two-channel implementation in which two intensity channels are used. However, it should be noted that the signal-to-noise aware base calling system 106 can operate similarly in a four-channel implementation in which four intensity channels are used. For example, in some implementations, the base call distribution model utilized to generate the intensity-value boundaries is configured to generate intensity-value boundaries according to the four intensity channels.

[0092] As further described above, in one or more embodiments, the signal-to-noise aware base calling system 106 utilizes a signal-to-noise ratio metric associated with a section of a nucleotide-sample slide to filter out one or more nucleotide base calls made for that section from the nucleotide base call data. Figure 5 illustrates a block diagram of a signal-to-noise aware base calling system 106 that utilizes a signal-to-noise ratio metric of a signal to filter nucleotide base calls in accordance with one or more embodiments.

[0093] As shown in FIG. 5, the signal-to-noise-aware base calling system 106 performs an operation of comparing 502 the signal-to-noise ratio metric determined for the signal to a signal-to-noise ratio threshold. Indeed, in one or more embodiments, the signal-to-noise-aware base calling system 106 establishes a signal-to-noise ratio threshold used to filter nucleotide base calls. The signal-to-noise-aware base calling system 106 can establish the signal-to-noise ratio threshold based on user input or utilize a pre-defined signal-to-noise ratio threshold. In some implementations, the signal-to-noise-aware base calling system 106 establishes the signal-to-noise ratio threshold based on historical data. For example, the signal-to-noise-aware base calling system 106 can analyze previous sequencing data to determine which signal-to-noise ratio metrics are typically associated with nucleotide base calls that fall below a desired quality metric. Thus, the signal-to-noise-aware base calling system 106 can establish a signal-to-noise ratio threshold that is high enough to filter out signals having such undesirable signal-to-noise ratio metrics. In some cases, the signal-to-noise aware base calling system 106 adjusts the signal-to-noise ratio threshold with each sequencing cycle or series of sequencing cycles, however, in some cases, the signal-to-noise aware base calling system 106 utilizes a constant signal-to-noise ratio threshold throughout all sequencing cycles.

[0094] 5, upon determining that the signal-to-noise ratio metric does not meet (e.g., is less than) the signal-to-noise ratio threshold, the signal-to-noise-aware base calling system 106 performs an operation 504 of excluding a nucleotide base call corresponding to the signal from the nucleotide base call data. In particular, in some implementations, upon determining that the signal-to-noise ratio metric corresponding to a signal does not meet the signal-to-noise ratio threshold, the signal-to-noise-aware base calling system 106 determines that the signal is of low quality and that the corresponding nucleotide base call (if made) is unreliable. Thus, the signal-to-noise-aware base calling system 106 excludes the nucleotide base call from the nucleotide base call data.

[0095] In some implementations, the signal-to-noise aware base calling system 106 further excludes from the nucleotide base call data one or more subsequent nucleotide base calls generated for one or more subsequent signals detected from the same section of the nucleotide-sample slide. In other words, the signal-to-noise aware base calling system 106 can exclude all nucleotide base calls generated for that section of the nucleotide-sample slide during subsequent sequencing cycles. As described above, the signal-to-noise aware base calling system 106 can therefore exclude (or not continue to determine nucleotide base calls for) all nucleotide base calls for a cluster of oligonucleotides corresponding to a well of a patterned nucleotide-sample slide, or a subsection of a non-patterned nucleotide-sample slide of the cluster. In some implementations, the signal-to-noise aware base calling system 106 also excludes from the nucleotide base call data one or more previous nucleotide base calls generated from that section of the nucleotide-sample slide.

[0096] Indeed, in one or more embodiments, upon determining that the signal-to-noise ratio metric determined for a signal does not meet the signal-to-noise ratio threshold, the signal-to-noise-aware base calling system 106 completely filters out and removes the corresponding section of the nucleotide-sample slide. In other words, the signal-to-noise-aware base calling system 106 determines that the corresponding section of the nucleotide-sample slide is of low quality and unreliable based on not meeting the signal-to-noise ratio threshold. Thus, upon determining that the signal-to-noise ratio threshold is not met, the signal-to-noise-aware base calling system 106 can remove the section of the nucleotide-sample slide from subsequent sequencing cycles (e.g., the signal-to-noise-aware base calling system 106 does not analyze the section in future cycles).

[0097] 5, upon determining that the signal-to-noise ratio metric does not meet (e.g., is equal to or greater than) the signal-to-noise ratio threshold, the signal-to-noise aware base calling system 106 performs operation 506 of including a nucleotide base call corresponding to the signal in the nucleotide base call data. For example, the signal-to-noise aware base calling system 106 can generate a nucleotide base call for the signal and add the nucleotide base call to the nucleotide base call data.

[0098] In one or more embodiments, the signal-to-noise aware base calling system 106 compares the signal-to-noise ratio metric determined for a section of the nucleotide-sample slide to a signal-to-noise ratio threshold for each sequencing cycle. Thus, the signal-to-noise aware base calling system 106 can determine to exclude nucleotide base calls made for that section of the nucleotide-sample slide from the nucleotide base call data in any sequencing cycle.

[0099] By using a signal-to-noise ratio metric to filter out certain nucleotide base calls (or their corresponding sections across a nucleotide-sample slide), the signal-to-noise-aware base calling system 106 operates more accurately than conventional sequencing platforms. In fact, the signal-to-noise-aware base calling system 106 can more accurately identify low-quality nucleotide base calls (or low-quality sections of a nucleotide-sample slide) compared to conventional platforms that often rely exclusively on purity-based filtering. In fact, as discussed above, filtering based on purity values ​​may fail to identify issues that are dormant in early sequencing cycles but may emerge as sequencing progresses. Thus, conventional platforms that rely exclusively on purity values ​​for filtering tend to include erroneous nucleotide base calls in the resulting nucleotide base call data. However, by utilizing a signal-to-noise ratio metric for filtering, the signal-to-noise-aware base calling system 106 can more accurately identify low-quality nucleotide base calls and filter them out of the nucleotide base call data, providing more accurate sequencing results.

[0100] As discussed above, in one or more embodiments, the signal-to-noise aware base calling system 106 utilizes a signal-to-noise ratio metric to determine a quality metric that estimates the error of a nucleotide base call made for a signal. Figure 6 illustrates a block diagram for generating a quality metric for a nucleotide base call according to one or more embodiments.

[0101] 6, the signal-to-noise aware base calling system 106 determines a signal-to-noise ratio metric 602 corresponding to a signal captured with an image 604 (or multiple images). As further shown, the signal-to-noise aware base calling system 106 generates a nucleotide base call 610 for the signal. For example, the signal-to-noise aware base calling system 106 can utilize the signal-to-noise ratio metric 602 to generate the nucleotide base call 610 according to a base call distribution model, as described above with reference to FIG.

[0102] 6, the signal-to-noise aware base calling system 106 generates a quality metric 612 of the nucleotide base call 610 to estimate an error in the nucleotide base call 610. In particular, the signal-to-noise aware base calling system 106 utilizes a base call quality model 606 to generate the quality metric 612. In one or more embodiments, the base call quality model 606 accepts one or more dimensions (e.g., inputs) related to the characteristics of the signal and / or the characteristics of the corresponding section of the nucleotide-sample slide and generates a quality metric based on these dimensions. Thus, the signal-to-noise aware base calling system 106 can provide the signal-to-noise ratio metric 602 as one of the inputs to the base call quality model 606.

[0103] 6 and as previously described, the base calling quality model 606 can include a Phred algorithm (as illustrated by graph 608). Thus, the signal-to-noise aware base calling system 106 can utilize the signal-to-noise ratio metric 602 as one of the inputs to the Phred algorithm. Additionally, the signal-to-noise aware base calling system 106 can utilize the Phred algorithm to generate a Q-score (i.e., a Phred quality score) that estimates the accuracy of the nucleotide base call 610. In other words, the quality metric 612 can include a Q-score generated by the Phred algorithm.

[0104] In some cases, the signal-to-noise aware base calling system 106 utilizes a quality metric determined for the nucleotide base call corresponding to the signal to map the nucleotide base call to the reference genome. In particular, the signal-to-noise aware base calling system 106 can map the oligonucleotides located in the section of the nucleotide-sample slide that emits the signal to the reference genome. Thus, in one or more embodiments, the signal-to-noise aware base calling system 106 detects the signal by detecting a signal from a labeled nucleotide base incorporated into the growing oligonucleotide at a genomic location that is later determined in alignment with the reference genome. Additionally, the signal-to-noise aware base calling system 106 generates a signal-to-noise ratio metric of the nucleotide base call at the genomic location that corresponds to the signal. Furthermore, the signal-to-noise aware base calling system 106 can determine a quality metric of the nucleotide base call and utilize the quality metric to map the nucleotide base call to the reference genome.

[0105] As indicated above, in some implementations, the signal-to-noise-aware base calling system 106 utilizes a value in addition to the signal-to-noise ratio metric to determine a quality metric of a nucleotide base call. For example, in some cases, the signal-to-noise-aware base calling system 106 utilizes a purity value corresponding to the signal in addition to the signal-to-noise ratio metric. To illustrate, in some cases, the signal-to-noise-aware base calling system 106 determines a purity value of a signal (e.g., of a corresponding section of a nucleotide-sample slide) based on the distance between the intensity value for the signal and the intensity value of the closest centroid, and the distance between the intensity value for the signal and the intensity value of at least one additional centroid. In some cases, the signal-to-noise-aware base calling system 106 utilizes the second closest centroid as an additional centroid. Thus, the signal-to-noise-aware base calling system 106 can utilize a base call quality model to generate a quality metric based on the signal-to-noise ratio metric and the purity value.

[0106] By utilizing a signal-to-noise ratio metric corresponding to a signal to generate a quality metric for a nucleotide base call corresponding to a signal, the signal-to-noise aware base calling system 106 can more accurately estimate the quality of a nucleotide base call as compared to conventional sequencing platforms. In fact, by incorporating a signal-to-noise ratio metric into the analysis, the signal-to-noise aware base calling system 106 utilizes an additional indicator of quality. Thus, the signal-to-noise aware base calling system 106 utilizes more information to make a quality determination than conventional sequencing platforms.

[0107] As described above, the signal-to-noise aware base calling system 106 provides improved filtering of low quality sections of a nucleotide-sample slide. In particular, the signal-to-noise aware base calling system 106 more accurately identifies low quality sections and excludes them from corresponding nucleotide base calls being generated or included in the nucleotide base call data. Thus, the signal-to-noise aware base calling system 106 provides more accurate sequencing results compared to conventional sequencing platforms that may not be able to identify problematic portions of a nucleotide-sample slide.

[0108] The researchers conducted a study to determine the nucleotide base call error rate of a section of a nucleotide-sample slide associated with various signal-to-noise ratio metrics. In particular, the researchers analyzed the nucleotide base call error rate over a series of sequencing cycles. Figure 7 illustrates a graph showing the nucleotide base call error rate of one or more sections of a nucleotide-sample slide having various signal-to-noise ratio metrics according to one or more embodiments.

[0109] As shown by the graph of FIG. 7, tested sections of one or more nucleotide-sample slides associated with lower signal-to-noise ratio metrics (e.g., SNR=4, SNR=5, etc.) exhibit high error rates for nucleotide-base calls. In comparison, sections associated with higher signal-to-noise ratio metrics (e.g., SNR=15, SNR=14, etc.) are associated with relatively low error rates for nucleotide base calls. Thus, by excluding nucleotide base calls associated with sections having lower signal-to-noise ratio metrics from the nucleotide base call data, the signal-to-noise aware base calling system 106 prevents the inclusion of high error data in the nucleotide base call data. Thus, the signal-to-noise aware base calling system 106 provides more accurate and reliable base calls to the nucleotide base call data.

[0110] Researchers conducted additional studies to compare the effectiveness of various embodiments of the signal-to-noise aware base calling system 106. Figures 8A-8B illustrate graphs reflecting the results of a study on the effectiveness of the signal-to-noise aware base calling system 106, according to one or more embodiments.

[0111] In particular, the graphs of Figures 8A-8B compare the performance of an embodiment of a signal-to-noise aware base calling system 106 to a baseline nucleotide base calling system (labeled "RTA3"). The graphs further compare the performance of an embodiment of a signal-to-noise aware base calling system 106 that utilizes a purity filter and does not use distribution model segmentation (labeled "LS, no SNR, purity filter"). The graphs show the performance of another embodiment of a signal-to-noise aware base calling system 106 that uses a purity filter along with distribution model segmentation (labeled "LS, with SNR, purity filter"). Additionally, the graphs show the performance of yet another embodiment of a signal-to-noise aware base calling system 106 that uses a filter that utilizes distribution model segmentation and a signal-to-noise ratio threshold (labeled "LS, with SNR, SNR filter").

[0112] The graph in FIG. 8A shows the nucleotide base calling error rate associated with each model tested based on the percentage of nucleotide-sample slide sections (e.g., wells) analyzed. For example, the percentage of sections analyzed can be based on the percentage of sections that pass a filter (e.g., a purity filter or a filter based on a signal-to-noise ratio threshold) implemented by the model tested and align with a reference (e.g., a reference genome). As shown in FIG. 8A, the implementation of a signal-to-noise ratio metric results in a lower nucleotide base calling error rate. More specifically, the use of a distribution model segmentation and a signal-to-noise ratio threshold provides the lowest nucleotide base calling error rate among all compared models. As a further note, the graph in FIG. 8A shows that adjusting the threshold used to filter out nucleotide-sample slide sections has the opposite effect on the error rate (i.e., moving to the right on the x-axis corresponds to a lower threshold and thus a higher percentage of sections passing the filter, causing a higher error rate).

[0113] The graph in FIG. 8B compares the performance of the models over a series of sequencing cycles. As shown, the error rate associated with each model increases as the model progresses through a series of sequencing cycles. However, the embodiment of the signal-to-noise-aware base calling system 106 provides the lowest error rate. Furthermore, as described above with reference to the graph in FIG. 8A, the use of distribution model segmentation and a signal-to-noise ratio threshold by the signal-to-noise-aware base calling system 106 provides the lowest nucleotide base calling error rate among all compared models. Thus, as shown by both FIG. 8A and FIG. 8B, the implementation of the signal-to-noise ratio metric provides improved accuracy when generating nucleotide base calls.

[0114] 1-8B, corresponding text, and examples provide several different methods, systems, devices, and non-transitory computer-readable media of a signal-to-noise recognition base calling system 106. In addition to the above, one or more embodiments may also be described in terms of flow charts that include operations for achieving a particular result, as illustrated in FIGS. 9-11. FIGS. 9-11 may be performed with more or fewer operations. Furthermore, operations may be performed in different orders. Additionally, operations described herein may be repeated or performed in parallel with each other, or with different occurrences of the same or similar operations.

[0115] FIG. 9 illustrates a flowchart of a series of operations 900 for generating a quality metric for a nucleotide base call using a signal-to-noise ratio metric, according to one or more embodiments. Although FIG. 9 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations depicted in FIG. 9. In some implementations, the operations of FIG. 9 are performed as part of a method. In some cases, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause a computing device to perform the operations of FIG. 9. In some implementations, a system performs the operations of FIG. 9. For example, in one or more cases, a system includes at least one processor and a non-transitory computer-readable medium that includes instructions, the instructions, when executed by the at least one processor, cause the system to perform the operations of FIG. 9.

[0116] The series of operations 900 includes an operation 902 for detecting a signal from a labeled nucleotide base within a section of a nucleotide-sample slide. For example, operation 902 can include detecting a signal from a labeled nucleotide base within a well of a patterned flow cell or a subsection of an unpatterned flow cell.

[0117] Additionally, the series of operations 900 includes an operation 904 of determining a scaling factor and a noise level corresponding to the signal. For example, operation 904 can include determining a scaling factor and a noise level corresponding to the signal based on the intensity values ​​for the signal for the section of the nucleotide-sample slide.

[0118] In one or more embodiments, the signal-to-noise aware base calling system 106 determines the noise level corresponding to the signal based on the intensity value for the signal for the section of the nucleotide-sample slide by determining a corrected intensity value for the signal for the section of the nucleotide-sample slide and determining a noise level corresponding to the signal based on the corrected intensity value for the signal. In some cases, the signal-to-noise aware base calling system 106 determines the corrected intensity value for the signal by determining a corrected intensity value for the signal for the section of the nucleotide-sample slide based on the intensity value for the signal, a scaling factor corresponding to the signal, and a correction offset factor corresponding to the signal. In some cases, the signal-to-noise aware base calling system 106 determines the noise level corresponding to the signal based on the corrected intensity value for the signal by determining a centroid intensity value of the nucleotide base call corresponding to the signal and determining a distance between the centroid intensity value and the corrected intensity value for the signal.

[0119] In one or more embodiments, the signal-to-noise aware base calling system 106 determines an average noise level of one or more previous sequencing cycles for a section of the nucleotide-sample slide. Thus, the signal-to-noise aware base calling system 106 can determine a noise level corresponding to a signal by determining a noise level of a current sequencing cycle based on an average noise level of one or more previous sequencing cycles for a section of the nucleotide-sample slide.

[0120] In some implementations, the signal-to-noise aware base calling system 106 determines a noise level corresponding to the signal by determining a plurality of noise levels of a plurality of previous sequencing cycles for a section of the nucleotide-sample slide, determining a weighted average noise level of the plurality of previous sequencing cycles by applying weights to the plurality of noise levels based on the recency of the sequencing cycle, and determining a noise level of a current sequencing cycle based on the weighted average noise level of the plurality of previous sequencing cycles for the section of the nucleotide-sample slide.

[0121] In some implementations, the signal-to-noise recognition base calling system 106 determines a relationship between the measured intensities of the labeled nucleotide bases for a section of the nucleotide-sample slide and a variation correction factor including a scaling factor, determines an error function based on the relationship between the measured intensities and the variation correction factor, and determines the scaling factor by taking a partial derivative of the error function with respect to the scaling factor, thereby determining a scaling factor corresponding to the signal based on the intensity values ​​for the signal.

[0122] The series of operations 900 further includes an operation 906 of generating a signal-to-noise ratio metric based on the scaling factor and the noise level. For example, operation 906 can include generating a signal-to-noise ratio metric for the section of the nucleotide-sample slide based on the scaling factor and the noise level. In one or more embodiments, the signal-to-noise recognition base calling system 106 generates a signal-to-noise ratio metric for the section of the nucleotide sample slide by generating a signal-to-noise ratio metric for a well of a patterned flow cell or a subsection of an unpatterned flow cell.

[0123] The set of operations 900 further includes an operation 908 of generating a quality metric based on the signal-to-noise ratio metric. In particular, operation 908 can include utilizing the base call quality model to generate a quality metric that estimates an error in a nucleotide base call corresponding to the signal based on the signal-to-noise ratio metric. In some implementations, the signal-to-noise aware base calling system 106 generates a quality metric that estimates an error in a nucleotide base call corresponding to the signal based on the signal-to-noise ratio metric by generating a Phred quality score that estimates an accuracy of a nucleotide base call corresponding to the signal based on the signal-to-noise ratio metric.

[0124] In some implementations, the signal-to-noise aware base calling system 106 further determines a purity value for the section of the nucleotide-sample slide based on the distance between the intensity value for the signal and the intensity value of the nearest centroid, and the distance between the intensity value for the signal and the intensity value of at least one additional centroid. Thus, the signal-to-noise aware base calling system 106 can utilize a base call quality model to generate a quality metric based on a signal-to-noise ratio metric and a purity value.

[0125] FIG. 10 illustrates a flowchart of a series of operations 1000 for filtering nucleotide base calls corresponding to a signal using a signal-to-noise ratio metric, according to one or more embodiments. Although FIG. 10 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in FIG. 10. In some implementations, the operations of FIG. 10 are performed as part of a method. In some cases, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause a computing device to perform the operations of FIG. 10. In some implementations, a system performs the operations of FIG. 10. For example, in one or more cases, a system includes at least one processor and a non-transitory computer-readable medium including instructions, the instructions, when executed by the at least one processor, cause the system to perform the operations of FIG. 10.

[0126] The series of operations 1000 includes an operation 1002 of detecting a signal from a labeled nucleotide base within a section of a nucleotide-sample slide. For example, operation 1002 includes detecting a signal from a labeled nucleotide base within a well of a patterned flow cell or a subsection of an unpatterned flow cell. In some cases, the signal-to-noise recognition base calling system 106 detects the signal by detecting a signal from a labeled nucleotide base incorporated into a growing oligonucleotide at a genomic location that is later determined in an alignment with a reference genome.

[0127] The series of operations 1000 also includes an operation 1004 of determining a scaling factor and a noise level for the signal. For example, operation 1004 can include determining a scaling factor and a noise level corresponding to the signal based on the intensity values ​​for the signal for the section of the nucleotide-sample slide.

[0128] In one or more embodiments, the signal-to-noise aware base calling system 106 determines an average noise level of one or more previous sequencing cycles for a section of the nucleotide-sample slide. Thus, the signal-to-noise aware base calling system 106 can determine a noise level corresponding to a signal by determining a noise level of a current sequencing cycle based on an average noise level of one or more previous sequencing cycles for a section of the nucleotide-sample slide.

[0129] Additionally, the series of operations 1000 includes an operation 1006 of generating a signal-to-noise ratio metric based on the scaling factor and the noise level. For example, operation 1006 can include generating a signal-to-noise ratio metric of the section of the nucleotide-sample slide based on the scaling factor and the noise level. In some cases, the signal-to-noise-aware base calling system 106 generates a signal-to-noise ratio metric by equating the scaling factor to the signal to determine a ratio of the scaling factor to the noise level. In some cases, the signal-to-noise-aware base calling system 106 generates a signal-to-noise ratio metric of a nucleotide base call at a genomic location corresponding to the signal.

[0130] Further, the series of operations 1000 includes an operation 1008 of filtering nucleotide base calls corresponding to the signals based on a signal-to-noise ratio metric. For example, operation 1008 can include causing nucleotide base calls corresponding to the signals to be included in or excluded from the nucleotide base call data based on comparing the signal-to-noise ratio metric to a signal-to-noise ratio threshold. In some implementations, the signal-to-noise aware base calling system 106 excludes nucleotide base calls corresponding to signals for wells of a patterned flow cell or subsections of an unpatterned flow cell.

[0131] In some implementations, the signal-to-noise aware base calling system 106 excludes subsequent nucleotide base calls corresponding to subsequent signals detected from subsequent labeled nucleotide bases added to clusters of oligonucleotides within a section of the nucleotide-sample slide based on determining that the signal-to-noise ratio metric is below a signal-to-noise ratio threshold.

[0132] FIG. 11 illustrates a flowchart of a series of operations 1100 for generating intensity-value boundaries of a signal-to-noise range using a signal-to-noise ratio metric, according to one or more embodiments. Although FIG. 11 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in FIG. 11. In some implementations, the operations of FIG. 11 are performed as part of a method. In some cases, a non-transitory computer-readable medium stores instructions that, when executed by at least one processor, cause a computing device to perform the operations of FIG. 11. In some implementations, a system performs the operations of FIG. 11. For example, in one or more cases, a system includes at least one processor and a non-transitory computer-readable medium including instructions, the instructions, when executed by the at least one processor, cause the system to perform the operations of FIG. 11.

[0133] The series of operations 1100 includes an operation 1102 of detecting a signal from a labeled nucleotide base within a section of a nucleotide-sample slide. For example, operation 1102 can include detecting a signal from a labeled nucleotide base within a well of a patterned flow cell or a subsection of an unpatterned flow cell.

[0134] The series of operations 1100 also includes an operation 1104 for generating a signal-to-noise ratio metric for the signal. For example, operation 1104 can include generating a signal-to-noise ratio metric for at least one nucleotide-sample slide section based on the signal and a noise level corresponding to the signal.

[0135] The series of operations 1100 further includes an operation 1106 of determining a signal-to-noise ratio range for the signal-to-noise ratio metric. Indeed, the signal-to-noise recognition base calling system 106 may determine multiple signal-to-noise ratio ranges.

[0136] The set of operations further includes operation 1108 of generating intensity-value boundaries for the signal-to-noise ratio ranges. For example, operation 1108 can include generating, for each signal-to-noise ratio range in the signal-to-noise ratio range, intensity-value boundaries for distinguishing signals corresponding to different nucleotide bases according to one or more base call distribution models. In one or more embodiments, generating intensity-value boundaries for distinguishing signals corresponding to different nucleotide bases according to one or more base call distribution models includes generating, for each signal-to-noise ratio range in the signal-to-noise ratio range, intensity-value boundaries according to one or more Gaussian distribution models.

[0137] In some cases, the signal-to-noise aware base calling system 106 detects signals from a subset of labeled nucleotide bases from a cluster of oligonucleotides within a section of the nucleotide-sample slide, generates a signal-to-noise ratio metric within a signal-to-noise ratio range for the section of the nucleotide-sample slide based on the signals, and determines a nucleotide base call corresponding to the signal based on a set of intensity-value boundaries of the intensity-value boundaries corresponding to the signal-to-noise ratio range. Further, the signal-to-noise aware base calling system 106 can detect additional signals from additional subsets of labeled nucleotide bases from additional clusters of oligonucleotides within additional sections of the nucleotide-sample slide, generates additional signal-to-noise ratio metrics within additional signal-to-noise ratio ranges for the additional sections of the nucleotide-sample slide based on the additional signals (the additional signal-to-noise ratio ranges are different from the signal-to-noise ratio ranges), and determines additional nucleotide base calls corresponding to the additional signals based on an additional set of intensity-value boundaries of the intensity-value boundaries corresponding to the additional signal-to-noise ratio ranges.

[0138] In one or more embodiments, generating intensity-value boundaries for distinguishing signals corresponding to different nucleotide bases according to one or more base call distribution models, for each signal-to-noise ratio range in the signal-to-noise ratio range, includes generating, for a first signal-to-noise ratio range, a first set of intensity-value boundaries corresponding to the different nucleotide bases according to a first base call distribution model, and generating, for a second signal-to-noise ratio range, a second set of intensity-value boundaries corresponding to the different nucleotide bases according to a second base call distribution model, wherein the second set of intensity-value boundaries is different from the first set of intensity-value boundaries.

[0139] In some cases, the signal-to-noise aware base calling system 106 detects a first signal corresponding to a first signal-to-noise ratio metric within a first signal-to-noise ratio range and having a set of intensity values ​​outside a first set of intensity-value boundaries and outside a second set of intensity-value boundaries, detects a second signal corresponding to a second signal-to-noise ratio metric within a second signal-to-noise ratio range and having a set of intensity values, generates a first nucleotide base call for the first signal based on the first set of intensity-value boundaries of the first base call distribution model, and generates a second nucleotide base call for the second signal based on the second set of intensity-value boundaries of the second base call distribution model.

[0140] 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 within 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 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.

[0141] 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.

[0142] 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).

[0143] 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).

[0144] Preferred embodiments include the technique of pyrosequencing, which detects the release of inorganic pyrophosphate (PPi) when a specific nucleotide is incorporated into the nascent strand (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996) "Real-time DNA sequencing using detection of pyrophosphate release." Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001) "Pyrosequencing sheds light on DNA sequencing." Genome Res. 11(1), 3-11; Ronaghi, M., Uhlen, M. and Nyren, P. (1998) "Real-time inorganic pyrophosphate-based sequencing." Science 281(5375),363, U.S. Patent Nos. 6,210,891, 6,258,568, and 6,274,320, the disclosures of which are incorporated herein by reference in their entirety). 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., 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.

[0145] 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.

[0146] 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.

[0147] In certain embodiments, some or all of the nucleotide monomers can include reversible terminators. In such embodiments, the reversible terminator / cleavable fluorophore can include a fluorophore attached to the ribose moiety via a 3' ester bond (Metzker, Genome Res. 15:1767-1776 (2005), which is incorporated herein by reference). Other approaches separate the terminator chemistry from the cleavage of the fluorescent label (Ruparel et al., Proc Natl Acad Sci USA 102:5932-7 (2005), which is incorporated herein by reference in its entirety). Ruparel et al. describe the development of reversible terminators that use a small amount of 3' allyl group to block extension, but can be easily deblocked by brief treatment with a palladium catalyst. The fluorophore was attached to the group via a photocleavable linker that can be easily cleaved by 30 seconds of exposure to long wavelength UV light. Thus, either disulfide reduction or photocleavage can be used as a cleavable linker. Another approach to reversible termination is the use of a natural terminus followed by placement of a bulky dye on the dNTP. The presence of a charged bulky dye on the dNTP can act as an effective terminator through steric and / or electrostatic hindrance. The presence of one incorporation event prevents further binding unless the dye is removed. Cleavage of the dye removes the fluor, effectively reversing the terminus. Examples of modified nucleotides are also described in U.S. Pat. No. 7,427,673 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated herein by reference in their entireties.

[0148] 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.

[0149] Some embodiments may utilize detection of four different nucleotides using fewer than four different labels. For example, SBS may be performed using the methods and systems described in incorporated document US Patent Application Publication No. 2013 / 0079232. As a first example, pairs of nucleotide types may be detected at the same wavelength but may be distinguished based on differences in intensity for one member of the pair, or based on a change to one member of the pair (e.g., via making a chemical modification, photochemical modification, or physical modification) that results in the appearance or disappearance of a distinct signal compared to the signal detected for the other member of the pair. As a second example, three of the four different nucleotide types may be detected under certain conditions, while the fourth nucleotide type may have no detectable label under those conditions or may be minimally detected under those conditions (e.g., minimal detection due to background fluorescence, etc.). Incorporation of the first three nucleotide types into a nucleic acid may be determined based on the presence of their corresponding signals, and incorporation of the fourth nucleotide type into a nucleic acid may be determined based on the absence or minimal detection of any signal. As a third example, one nucleotide type can include a label that is detected in two different channels, while the other nucleotide type is detected in no more than one of the channels. The three exemplary configurations above are not considered mutually exclusive and can be used in various combinations.An exemplary embodiment combining all three examples is a fluorescence-based SBS method that uses a first nucleotide type that is detected in a first channel (e.g., dATP having a label that is detected in the first channel when excited by a first excitation wavelength), a second nucleotide type that is detected in a second channel (e.g., dCTP having a label that is detected in the second channel when excited by a second excitation wavelength), a third nucleotide type that is detected in both the first and second channels (e.g., dTTP having at least one label that is detected in both channels when excited by the first and / or second excitation wavelengths), and a fourth nucleotide type that is not detected in any channel or that is minimally devoid of a label (e.g., unlabeled dGTP).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Some embodiments may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation may be detected via fluorescence resonance energy transfer (FRET) interactions between fluorophore-containing polymerases and γ-phosphate-labeled nucleotides, for example, as described in U.S. Pat. No. 7,329,492 and U.S. Pat. No. 7,211,414, each of which is incorporated herein by reference, or nucleotide incorporation may be detected using zero-mode waveguides, for example, as described in U.S. Pat. No. 7,315,019, each of which is incorporated herein by reference, and fluorescent nucleotide analogs and engineered polymerases, for example, as described in U.S. Pat. No. 7,405,281 and U.S. Patent Application Publication No. 2008 / 0108082, each of which is incorporated herein by reference. Illumination can be restricted to a zeptoliter-scale volume around the surface-tethered polymerase so that incorporation of fluorescently labeled nucleotides can be observed with low background (Levene, MJ et al. "Zero-mode waveguides for single-molecule analysis at high concentrations." Science, 299, 682-686 (2003); Lundquist, PM et al. "Parallel confocal detection of single molecules in real time." Opt. Lett. 33, 1026-1028 (2008); Korlach, J. et al. "Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nano structures." Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures of which are incorporated herein by reference in their entireties). Images obtained from such methods can be stored, processed, and analyzed as described herein.

[0154] 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.

[0155] The SBS method described above can be advantageously carried out in a multiplex format, so 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.

[0156] The methods described herein can be used to detect, for example, at least about 10 features / cm 2, 100 features / cm 2 , 500 features / cm 2 , 1,000 features / cm 2 , 5,000 features / cm 2 , 10,000 features / cm 2 , 50,000 features / cm 2 , 100,000 features / cm 2 , 1,000,000 features / cm 2 , 5,000,000 features / cm 2 Arrays having any of a variety of densities of features, including 100, 150, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 3600, 3700, 4000, 4200, 4600, 5000, 6000, 7000, 8000, 9000, 10000, 110000, 120000, 130000, 140000

[0157] An advantage of the methods described herein is that they provide rapid and efficient detection of multiple target nucleic acids in parallel. Thus, the present disclosure provides an integrated system that can prepare and detect nucleic acids using techniques known in the art, such as those exemplified above. Thus, the integrated system of the present disclosure can include fluidic components that can deliver amplification and / or sequencing reagents to one or more immobilized DNA fragments, the system including components such as pumps, valves, reservoirs, fluid lines, etc. A flow cell can be configured and / or used in the integrated system for detecting target nucleic acids. Exemplary flow cells are described, for example, in U.S. Patent No. 2010 / 0111768(A1) and U.S. Patent Application No. 13 / 273,666, each of which is incorporated herein by reference. As exemplified for the flow cell, one or more of the fluidic components of the integrated system can be used for amplification and detection methods. Taking the nucleic acid sequencing embodiment as an example, one or more of the fluidic components of the integrated system can be used for delivery of sequencing reagents in the amplification methods described herein and in the sequencing methods as exemplified above. Alternatively, an integrated system 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.

[0158] 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.

[0159] The nucleic acid sample can include high molecular weight material such as genomic DNA (gDNA). The sample can include low molecular weight material such as nucleic acid molecules obtained from FFPE or archived DNA samples. In another embodiment, the low molecular weight material includes enzymatically or mechanically fragmented DNA. The sample can include cell-free circulating DNA. In some embodiments, the sample can include nucleic acid molecules obtained from biopsies, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microdissection, surgical resection, and other clinical or laboratory obtained samples. In some embodiments, the sample can be an epidemiological, agricultural, forensic, or pathogenic sample. In some embodiments, the sample can include nucleic acid molecules obtained from animals, such as human or mammalian sources. In another embodiment, the sample can include nucleic acid molecules obtained from non-mammalian sources, such as plants, bacteria, viruses, or fungi. In some embodiments, the source of the nucleic acid molecule can be an archived or extinct sample or species.

[0160] Additionally, the methods and compositions disclosed herein may be useful for amplifying nucleic acid samples having low quality nucleic acid molecules, such as degraded and / or fragmented genomic DNA from forensic samples. In one embodiment, the forensic sample may include nucleic acid obtained from a crime scene, from a missing persons DNA database, from a laboratory associated with a forensic investigation, or may include forensic samples obtained by a law enforcement agency, one or more military services, or members thereof. The nucleic acid sample may be crude DNA, including purified samples or lysates, for example, from buccal swabs, paper, cloth, or other substrates that may be impregnated with saliva, blood, or other bodily fluids. Thus, in some embodiments, the nucleic acid sample may include small amounts of DNA, such as genomic DNA, or fragmented portions of DNA. In some embodiments, the target sequence may be present in one or more bodily fluids, including, but not limited to, blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequence may be obtained from hair, skin, tissue samples, autopsies, or remains of victims. In some embodiments, the nucleic acid including one or more target sequences may be obtained from deceased animals or humans. 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 disclosure generally relates to a method for identifying features of a forensic sample. In some embodiments, the 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 can be amplified using any one or more of the target specific primers disclosed herein or using the primer criteria outlined herein.

[0161] The components of the signal-to-noise aware base calling system 106 may include software, hardware, or both. For example, the components of the signal-to-noise aware base calling 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. When executed by one or more processors, the computer-executable instructions of the signal-to-noise aware base calling system 106 may cause the computing device to perform the bubble detection method described herein. Alternatively, the components of the signal-to-noise aware base calling 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 signal-to-noise aware base calling system 106 may include a combination of computer-executable instructions and hardware.

[0162] Furthermore, the components of the signal-to-noise aware base calling system 106 that perform the functions described herein with respect to the signal-to-noise aware base calling 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 signal-to-noise aware base calling 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 signal-to-noise aware base calling 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Those skilled in the art will appreciate that the present disclosure may be implemented in a networked computing environment having many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, tablets, pagers, routers, switches, etc. The present disclosure may also be implemented in a distributed system environment where both local and remote computer systems perform tasks that are linked through a network (either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links). In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0170] 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.

[0171] 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.

[0172] FIG. 12 illustrates a block diagram of a computing device 1200 that may be configured to perform one or more of the processes described above. It will be understood that one or more computing devices, such as the computing device 1200, may implement the signal-to-noise aware base calling system 106 and the sequencing system 104. As illustrated by FIG. 12, the computing device 1200 may include a processor 1202, a memory 1204, a storage device 1206, an I / O interface 1208, and a communication interface 1210, which may be communicatively coupled by a communication infrastructure 1212. In certain embodiments, the computing device 1200 may include fewer or more components than those illustrated in FIG. 12. The following paragraphs describe the components of the computing device 1200 illustrated in FIG. 12 in more detail.

[0173] In one or more embodiments, the processor 1202 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 1202 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 1204, or storage device 1206, decode them, and execute them. The memory 1204 may be a volatile or non-volatile memory used to store data, metadata, and programs for execution by the processor. The storage device 1206 includes a storage device, such as a hard disk, a flash disk drive, or other digital storage device, for storing data or instructions for performing the methods described herein.

[0174] The I / O interface 1208 allows a user to provide input to, receive output from, and otherwise transfer data to and receive data from the computing device 1200. The I / O interface 1208 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 1208 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 1208 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.

[0175] Communications interface 1210 may include hardware, software, or both. In any case, communications interface 1210 may provide one or more interfaces for communications (e.g., packet-based communications, etc.) between computing device 1200 and one or more other computing devices or networks. By way of example and not limitation, communications interface 1210 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.

[0176] Additionally, the communication interface 1210 can facilitate communication with various types of wired or wireless networks. The communication interface 1210 can also facilitate communication using various communication protocols. The communication infrastructure 1212 can also include hardware, software, or both that couple the components of the computing device 1200 to one another. For example, the communication interface 1210 can enable multiple computing devices connected by a particular infrastructure to communicate with one another to perform one or more aspects of the processes described herein using one or more networks and / or protocols. To illustrate, a sequencing process can enable multiple devices (e.g., a client device, a sequencing device, and a server device) to exchange information such as sequencing data and error notifications.

[0177] 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.

[0178] 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. Additionally, the steps / actions described herein may be repeated or performed in parallel with each other, or with different occurrences of the same or similar actions. 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]

[0179] 100 Environment 102 Server equipment 104 Sequencing System 106 Signal-to-Noise Recognition Base Calling System 108 Network 110 Sequencing device 112 Nucleotide base call data 114 User client device 116 Sequencing Applications 202 Nucleotides - Sample Slide 204 images 206 Signal 208 Signal to Noise Ratio Metric 210 Distribution Model Segmentation 212 Signal to Noise Filtering 214 Quality Metrics 302 Nucleotides - Sample Slide 304 images 306 Signal 308 Least Squares Model 310 Scaling Factor 312 Noise Level 314 Graph 316a~316b axis 318a-318d Nucleotide Cloud 320 points 322 points 324 Center of gravity 326 Signal to Noise Ratio Metric 402a-402d Signal-to-Noise Ratio Metrics 404a~404d Intensity value Graph 406a~406d 408 Intensity-Value Boundary 410 Base call distribution model 602 Signal to Noise Ratio Metric 604 images 606 Base Call Quality Model 608 Graph 610 Nucleotide Base Call 612 Quality Metrics 1200 Computing Devices 1202 Processor 1204 Memory 1206 Storage device 1208 I / O Interface 1210 Communication Interface 1212 Communications Infrastructure

Claims

1. A system comprising: at least one processor; and a non-transitory computer-readable medium containing instructions that, when executed by the at least one processor, cause the system to: detect signals from labeled nucleotide bases within a section of a nucleotide-sample slide; for the section of the nucleotide-sample slide, determine a scaling factor and a noise level corresponding to the signals based on intensity values for the signals; generate a signal-to-noise ratio metric for the section of the nucleotide-sample slide based on the scaling factor and the noise level; generate a quality metric that estimates an error in a nucleotide base call corresponding to the signals based on the signal-to-noise ratio metric using a base call quality model.

2. When executed by the at least one processor, the system further comprises instructions that cause the system to determine the noise level corresponding to the signals for the section of the nucleotide-sample slide based on the intensity values for the signals by: determining corrected intensity values for the signals for the section of the nucleotide-sample slide; and determining the noise level corresponding to the signals based on the corrected intensity values for the signals. The system according to claim 1.

3. When executed by the at least one processor, the system further comprises instructions that cause the system to determine the corrected intensity values for the signals for the section of the nucleotide-sample slide based on the intensity values for the signals, the scaling factor corresponding to the signals, and a correction offset factor corresponding to the signals. The system according to claim 2.

4. When executed by the at least one processor, the system further comprises instructions that cause the system to determine the noise level corresponding to the signals based on the corrected intensity values for the signals by: determining a centroid intensity value for the nucleotide base call corresponding to the signals; The system according to claim 2, further comprising instructions for causing determination by determining a distance between the center-of-gravity intensity value and the corrected intensity value for the signal. **Claim 5** When executed by the at least one processor, cause the system to determine an average noise level for one or more previous sequencing cycles for the section of the nucleotide-sample slide, The system according to claim 1, further comprising instructions for causing determination of the noise level for the current sequencing cycle for the section of the nucleotide-sample slide based on the average noise level for the one or more previous sequencing cycles, thereby determining the noise level corresponding to the signal. **Claim 6** When executed by the at least one processor, cause the system to determine, for the section of the nucleotide-sample slide, a scaling factor corresponding to the signal based on the intensity value for the signal, determining a relationship between the measured intensity for the labeled nucleotide base and a variation correction factor including the scaling factor, determining an error function based on the relationship between the measured intensity and the variation correction factor, The system according to claim 1, further comprising instructions for causing determination of the scaling factor by generating a partial derivative function of the error function with respect to the scaling factor. **Claim 7** The system according to claim 1, further comprising instructions for causing generation of a signal-to-noise ratio metric for the section of the nucleotide-sample slide by generating a signal-to-noise ratio metric for a well of a patterned flow cell or a subsection of an unpatterned flow cell when executed by the at least one processor. **Claim 8** The system according to claim 1, further comprising instructions for causing generation of a quality metric for estimating an error in the nucleotide base call corresponding to the signal based on the signal-to-noise ratio metric by generating a Phred quality score for estimating the accuracy of the nucleotide base call corresponding to the signal based on the signal-to-noise ratio metric when executed by the at least one processor. **Claim 9** When executed by the at least one processor, cause the system to determine a purity value for the section of the nucleotide-sample slide based on a distance between the intensity value for the signal and the intensity value of the centroid closest to it, and a distance between the intensity value for the signal and the intensity values for at least one additional centroid; The system according to claim 1, further comprising instructions that, when executed by the at least one processor, cause the system to generate the quality metric based on the signal-to-noise ratio metric and the purity value using the base call quality model. **Claim 10** When executed by the at least one processor, cause the system to determine, for the section of the nucleotide-sample slide, a plurality of noise levels for a plurality of previous sequencing cycles; determine a weighted average noise level for the plurality of previous sequencing cycles by applying weighted values to the plurality of noise levels based on the currency of the sequencing cycle; The system according to any one of claims 1 to 9, further comprising instructions that, for the section of the nucleotide-sample slide, determine the noise level for the current sequencing cycle based on the weighted average noise level for the plurality of previous sequencing cycles, thereby determining the noise level corresponding to the signal. **Claim 11** A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to detect a signal from labeled nucleotide bases within a section of a nucleotide-sample slide; determine, for the section of the nucleotide-sample slide, a scaling factor and a noise level corresponding to the signal based on the intensity value for the signal; generate a signal-to-noise ratio metric for the section of the nucleotide-sample slide based on the scaling factor and the noise level; include or exclude from nucleotide base call data a nucleotide base call corresponding to the signal based on comparing the signal-to-noise ratio metric to a signal-to-noise ratio threshold. **Claim 12** When executed by the at least one processor, the computing device further includes an instruction to exclude a subsequent nucleotide base call corresponding to a subsequent signal detected from a subsequent labeled nucleotide base added to a cluster of oligonucleotides in the section of the nucleotide-sample slide, based on a determination that the signal-to-noise ratio metric is lower than the signal-to-noise ratio threshold. The non-transitory computer-readable medium according to claim 11.

13. When executed by the at least one processor, the computing device further includes an instruction to generate the signal-to-noise ratio metric by making the scaling factor equal to the signal in order to determine the ratio of the scaling factor to the noise level. The non-transitory computer-readable medium according to claim 11.

14. When executed by the at least one processor, the computing device detects the signal by detecting the signal from the labeled nucleotide base incorporated into the growing oligonucleotide at the genomic position that will be determined later in the alignment with the reference genome, The non-transitory computer-readable medium according to any one of claims 11 to 13, further including an instruction to generate the signal-to-noise ratio metric of the nucleotide base call at the genomic position corresponding to the signal.

15. A method comprising: detecting a signal from a labeled nucleotide base in a section of at least one nucleotide-sample slide; generating a signal-to-noise ratio metric for the section of the at least one nucleotide-sample slide based on the signal and the noise level corresponding to the signal; determining a signal-to-noise ratio range for the signal-to-noise ratio metric; generating an intensity-value boundary for distinguishing signals corresponding to different nucleotide bases according to one or more base call distribution models for each signal-to-noise ratio range of the signal-to-noise ratio range.

16. For each signal-to-noise ratio range of the signal-to-noise ratio range, generating the intensity-value boundary for distinguishing the signals corresponding to the different nucleotide bases according to the one or more base call distribution models For a first signal-to-noise ratio range, generating a first set of intensity-value boundaries corresponding to the different nucleotide bases according to a first base call distribution model; For a second signal-to-noise ratio range, generating a second set of intensity-value boundaries corresponding to the different nucleotide bases according to a second base call distribution model, wherein the second set of intensity-value boundaries is different from the first set of intensity-value boundaries, the method according to claim 15, comprising: generating.

17. Detecting a first signal corresponding to a first signal-to-noise ratio metric within the first signal-to-noise ratio range and having a set of intensity values outside the first set of intensity-value boundaries and outside the second set of intensity-value boundaries; Detecting a second signal corresponding to a second signal-to-noise ratio metric within the second signal-to-noise ratio range and having the set of intensity values; Generating a first nucleotide base call for the first signal based on the first set of intensity-value boundaries for the first base call distribution model; Generating a second nucleotide base call for the second signal based on the second set of intensity-value boundaries for the second base call distribution model, the method according to claim 16, further comprising:

18. Detecting a signal from a subset of labeled nucleotide bases from a cluster of oligonucleotides in a section of a nucleotide-sample slide; Generating a signal-to-noise ratio metric within the signal-to-noise ratio range for the section of the nucleotide-sample slide based on the signal; Determining a nucleotide base call corresponding to the signal based on a set of intensity-value boundaries among the intensity-value boundaries corresponding to the signal-to-noise ratio range, the method according to claim 15, further comprising:

19. Detecting an additional signal from an additional subset of labeled nucleotide bases from an additional cluster of oligonucleotides in an additional section of the nucleotide-sample slide; Generating, based on the additional signal, an additional signal-to-noise ratio metric within an additional signal-to-noise ratio range for the additional section of the nucleotide-sample slide, wherein the additional signal-to-noise ratio range is different from the signal-to-noise ratio range, and Determining an additional nucleotide base call corresponding to the additional signal based on an additional set of intensity-value boundaries of the intensity-value boundaries corresponding to the additional signal-to-noise ratio range, the method of claim 18, further comprising. **Claim 20** Generating the intensity-value boundaries for distinguishing the signals corresponding to the different nucleotide bases according to the one or more base call distribution models includes generating the intensity-value boundaries according to one or more Gaussian distribution models for each signal-to-noise ratio range of the signal-to-noise ratio ranges, the method according to any one of claims 15 to 19.

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