System and method for cluster-specific intensity correction and base call

Abstract

The disclosed technology generates a variation correction factor for each cluster to correct for inter-cluster intensity profile variations for improved base calling. An amplification factor corrects for scale variations. Channel-specific offset factors correct for shift variations along each intensity channel. The variation correction factor for a target cluster is generated based on a combination of an analysis of historical intensity data generated for the target cluster in a previous distribution determination cycle of the sequencing run and an analysis of current intensity data generated for the target cluster in the current sequencing cycle of the sequencing run. The variation correction factor is then used to correct subsequent intensity data generated for the target cluster in the next sequencing cycle of the sequencing run. The corrected subsequent intensity data is then used to base call the target cluster in the next sequencing cycle.

Description

[Technical Field]

[0001] Priority application This application claims priority to U.S. Provisional Patent Application No. 63 / 106,256, entitled "Systems and Methods for Per-Cluster Intensity Correction and Base Calling," filed on 27 October 2020.

[0002] The disclosed technology relates to apparatus and corresponding methods for automated image analysis or pattern recognition. This specification includes systems for transforming images for the purposes of (a) improving their visual quality before recognition, (b) positioning and aligning images with sensors or stored prototypes, or reducing the amount of image data by discarding irrelevant data, and (c) measuring significant characteristics of images. In particular, the disclosed technology relates to generating variation correction coefficients for correcting variations in inter-cluster intensity profiles in image data.

[0003] embedded U.S. Patent Provisional Application No. 63 / 020,449, entitled "EQUALIZATION-BASED IMAGE PROCESSING AND SPATIAL CROSSTALK ATTENUATOR" (Agent Reference Number ILLM1032-1 / IP-1991-PRV), filed on May 5, 2020. U.S. Patent Non-Provisional Application No. 15 / 936,365, filed on March 26, 2018, entitled "DETECTION APPARATUS HAVING A MICROFLUOROMETER, A FLUIDIC SYSTEM, AND A FLOW CELL LATCH CLAMP MODULE" U.S. Patent Nonprovisional Application No. 16 / 567,224, titled "FLOW CELLS AND METHODS RELATED TO SAME," filed on September 11, 2019. U.S. Patent Nonprovisional Application No. 16 / 439,635, titled "DEVICE FOR LUMINESCENT IMAGING," filed on June 12, 2019. U.S. Patent Nonprovisional Application No. 15 / 594,413, filed on May 12, 2017, entitled "INTEGRATED OPTOELECTRONIC READ HEAD AND FLUIDIC CARTRIDGE USEFUL FOR NUCLEIC ACID SEQUENCING" U.S. Patent Nonprovisional Application No. 16 / 351,193, titled "ILLUMINATION FOR FLUORESCENCE IMAGING USING OBJECTIVE LENS," filed on March 12, 2019. U.S. Patent Non-Provisional Application No. 12 / 638,770, filed on December 15, 2009, entitled "DYNAMIC AUTOFOCUS METHOD AND SYSTEM FOR ASSAY IMAGER" U.S. Patent Nonprovisional Application No. 13 / 783,043, titled "KINETIC EXCLUSION AMPLIFICATION OF NUCLEIC ACID LIBRARIES," filed on March 1, 2013. U.S. Patent Application No. 13 / 006,206, entitled "DATA PROCESSING SYSTEM AND METHODS," filed on January 13, 2011. U.S. Patent Nonprovisional Application No. 14 / 530,299, titled "IMAGE ANALYSIS USEFUL FOR PATTERNED OBJECTS," filed on October 31, 2014. U.S. Patent Nonprovisional Application No. 15 / 153,953, titled "METHODS AND SYSTEMS FOR ANALYZING IMAGE DATA," filed on December 3, 2014. U.S. Patent Nonprovisional Application No. 14 / 020,570, filed on September 6, 2013, entitled "CENTROID MARKERS FOR IMAGE ANALYSIS OF HIGH DENSITY CLUSTERS IN COMPLEX POLYNUCLEOTIDE SEQUENCING" U.S. Patent Nonprovisional Application No. 14 / 530,299, titled "IMAGE ANALYSIS USEFUL FOR PATTERNED OBJECTS," filed on October 31, 2014. U.S. Patent Nonprovisional Application No. 12 / 565,341, titled "METHOD AND SYSTEM FOR DETERMINING THE ACCURACY OF DNA BASE IDENTIFICATIONS," filed on September 23, 2009. U.S. Patent Nonprovisional Application No. 12 / 295,337, titled "SYSTEMS AND DEVICES FOR SEQUENCE BY SYNTHESIS ANALYSIS," filed on March 30, 2007. U.S. Patent Nonprovisional Application No. 12 / 020,739, titled "IMAGE DATA EFFICIENT GENETIC SEQUENCING METHOD AND SYSTEM," filed on January 28, 2008. U.S. Patent Nonprovisional Application No. 13 / 833,619, titled "BIOSENSORS FOR BIOLOGICAL OR CHEMICAL ANALYSIS AND SYSTEMS AND METHODS FOR SAME" (Agent Reference Number IP-0626-US), filed on March 15, 2013. U.S. Patent Nonprovisional Application No. 15 / 175,489 (Agent Reference Number IP-0689-US), filed on June 7, 2016, entitled "BIOSENSORS FOR BIOLOGICAL OR CHEMICAL ANALYSIS AND METHODS OF MANUFACTURING THE SAME" U.S. Non-Patent Non-Provisional Application No. 13 / 882,088 (Agent Reference Number IP-0462-US), filed on April 26, 2013, entitled "MICRODEVICES AND BIOSENSOR CARTRIDGES FOR BIOLOGICAL OR CHEMICAL ANALYSIS AND SYSTEMS AND METHODS FOR THE SAME" U.S. Patent Nonprovisional Application No. 13 / 624,200, titled "METHODS AND COMPOSITIONS FOR NUCLEIC ACID SEQUENCING" (Agent Reference Number IP-0538-US), filed on September 21, 2012. U.S. Patent Provisional Application No. 62 / 821,602, titled "TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-1 / IP-1693-PRV), filed on March 21, 2019. U.S. Patent Provisional Application No. 62 / 821,618, titled "ARTIFICIAL INTELLIGENCE-BASED GENERATION OF SEQUENCING METADATA" (Agent Reference Number ILLM1008-3 / IP-1741-PRV), filed on March 21, 2019. U.S. Patent Provisional Application No. 62 / 821,681, entitled "ARTIFICIAL INTELLIGENCE-BASED BASE CALLING" (Agent Reference Number ILLM1008-4 / IP-1744-PRV), filed on March 21, 2019. U.S. Patent Provisional Application No. 62 / 821,724, titled "ARTIFICIAL INTELLIGENCE-BASED QUALITY SCORING" (Agent Reference Number ILLM1008-7 / IP-1747-PRV), filed on March 21, 2019. U.S. Patent Provisional Application No. 62 / 821,766, entitled "ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-9 / IP-1752-PRV), filed on March 21, 2019. Dutch Patent Application No. 2023310, titled "TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-11 / IP-1693-NL), filed on June 14, 2019. Dutch Patent Application No. 2023311, titled "ARTIFICIAL INTELLIGENCE-BASED GENERATION OF SEQUENCING METADATA" (Agent Reference Number ILLM1008-12 / IP-1741-NL), filed on June 14, 2019. Dutch Patent Application No. 2023312, titled "ARTIFICIAL INTELLIGENCE-BASED BASE CALLING" (Agent Reference Number ILLM1008-13 / IP-1744-NL), filed on June 14, 2019. Dutch Patent Application No. 2023314, titled "ARTIFICIAL INTELLIGENCE-BASED QUALITY SCORING," filed on June 14, 2019 (Agent Reference Number ILLM1008-14 / IP-1747-NL), and Dutch patent application No. 2023316, titled "ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent reference number ILLM1008-15 / IP-1752-NL), filed on June 14, 2019. U.S. Patent Nonprovisional Application No. 16 / 825,987, titled "TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-16 / IP-1693-US), filed on March 20, 2020. U.S. Patent Nonprovisional Application No. 16 / 825,991, titled "TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-17 / IP-1741-US), filed on March 20, 2020. U.S. Patent Nonprovisional Application No. 16 / 826,126, entitled "ARTIFICIAL INTELLIGENCE-BASED BASE CALLING" (Agent Reference Number ILLM1008-18 / IP-1744-US), filed on March 20, 2020. U.S. Patent Nonprovisional Application No. 16 / 826,134, entitled "ARTIFICIAL INTELLIGENCE-BASED QUALITY SCORING" (Agent Reference Number ILLM1008-19 / IP-1747-US), filed on March 20, 2020. U.S. Patent Nonprovisional Application No. 16 / 826,168, entitled "ARTIFICIAL INTELLIGENCE-BASED SEQUENCING" (Agent Reference Number ILLM1008-20 / IP-1752-PRV), filed on March 21, 2020. U.S. Patent Provisional Application No. 62 / 849,091, titled "SYSTEMS AND DEVICES FOR CHARACTERIZATION AND PERFORMANCE ANALYSIS OF PIXEL-BASED SEQUENCING" (Agent Reference Number ILLM1011-1 / IP-1750-PRV), filed on May 16, 2019. U.S. Patent Provisional Application No. 62 / 849,132, entitled "BASE CALLING USING CONVOLUTIONS" (Agent Reference Number ILLM1011-2 / IP-1750-PR2), filed on May 16, 2019. U.S. Patent Provisional Application No. 62 / 849,133, titled "BASE CALLING USING COMPACT CONVOLUTIONS" (Agent Reference Number ILLM1011-3 / IP-1750-PR3), filed on May 16, 2019. U.S. Patent Provisional Application No. 62 / 979,384, titled "ARTIFICIAL INTELLIGENCE-BASED BASE CALLING OF INDEX SEQUENCES" (Agent Reference Number ILLM1015-1 / IP-1857-PRV), filed on February 20, 2020. U.S. Patent Provisional Application No. 62 / 979,414, titled "ARTIFICIAL INTELLIGENCE-BASED MANY-TO-MANY BASE CALLING" (Agent Reference Number ILLM1016-1 / IP-1858-PRV), filed on February 20, 2020. U.S. Provisional Patent Application No. 62 / 979,385 (Attorney Docket No. ILLM1017-1 / IP-1859-PRV), entitled "KNOWLEDGE DISTILLATION-BASED COMPRESSION OF ARTIFICIAL INTELLIGENCE-BASED BASE CALLER", filed on February 20, 2020, U.S. Provisional Patent Application No. 62 / 979,412 (Attorney Docket No. ILLM1020-1 / IP-1866-PRV), entitled "MULTI-CYCLE CLUSTER BASED REAL TIME ANALYSIS SYSTEM", filed on February 20, 2020, U.S. Provisional Patent Application No. 62 / 979,411 (Attorney Docket No. ILLM1029-1 / IP-1964-PRV), entitled "DATA COMPRESSION FOR ARTIFICIAL INTELLIGENCE-BASED BASE CALLING", filed on February 20, 2020, and U.S. Provisional Patent Application No. 62 / 979,399 (Attorney Docket No. ILLM1030-1 / IP-1782-PRV), entitled "SQUEEZING LAYER FOR ARTIFICIAL INTELLIGENCE-BASED BASE CALLING", filed on February 20, 2020.

BACKGROUND ART

[0004] The subject matter considered in this section should not be assumed to be prior art merely as a result of mention in this section. Similarly, problems mentioned in this section, or problems associated with the subject matter provided as background, should not be assumed to have been previously recognized in the prior art. The subject matter of this section merely represents different approaches, which may themselves also correspond to embodiments of the claimed technology.

[0005] The present disclosure relates to analyzing image data to base call clusters during a sequencing run. One problem with analyzing image data is the variation in the intensity profiles of clusters in the population of clusters being base called. This causes a decrease in data throughput and an increase in error rate during the sequencing run.

[0006] There are many potential reasons for the variation in intensity profiles between clusters. The variation can be due to differences in cluster brightness caused by the fragment length distribution of the cluster population. The variation can be due to phase errors that occur when molecules within a cluster do not incorporate nucleotides in some sequencing cycles and lag behind other molecules, or when a molecule incorporates two or more nucleotides in a single sequencing cycle. The variation can be due to fading, i.e., the exponential decay of the signal intensity of the cluster as a function of the number of sequencing cycles due to excessive washing and laser exposure as the sequencing run progresses. The variation can be due to stunted cluster colonies, i.e., small cluster sizes that produce wells on the patterned flow cell that are empty or only partially filled. The variation can be due to overlapping cluster colonies caused by non-exclusive amplification. The variation can be due to insufficient or non-uniform illumination, for example, when a cluster is located at the edge of the flow cell. The variation can be due to impurities on the flow cell that obscure the emitted signal. The variation can be due to multi-clonal clusters, i.e., when multiple clusters are deposited in the same well.

[0007] An opportunity arises to correct the variation in intensity profiles between clusters. As a result, an improvement in base call throughput and a reduction in the base call error rate during the sequencing run can be obtained.

Brief Description of the Drawings

[0008] A patent or application file must include at least one color drawing. A copy of this patent or patent application publication containing the color drawing(s) will be provided by the Office upon request and payment of the required fees. The color drawing(s) may also be available in PAIR (patent application information retrieval) via the Supplemental Contents tab. In the drawings, similar reference letters generally refer to the same parts throughout different drawings. Furthermore, the drawings are not necessarily to scale, but rather emphasize that they illustrate the principles of the disclosed technology. In the following description, various embodiments of the disclosed technology are described with reference to the following drawings. [Figure 1] An example of inter-cluster intensity profile variation discovered and corrected by the disclosed technology is shown. [Figure 2] An example of a base call pipeline that implements the variation correction logic disclosed herein is shown. [Figure 3] This specification shows a least squares decision-maker that implements the least squares method disclosed herein. [Figure 4] This example illustrates how channel-specific distribution intensities are measured for the target cluster in the current sequencing cycle. [Figure 5] This example illustrates how channel-specific intensity errors are calculated for the target cluster in the current sequencing cycle. [Figure 6] This example illustrates how the distance from the distribution centroid to the origin is calculated for the target cluster in the current sequencing cycle. [Figure 7] Here is another example of a base call pipeline that implements variability correction logic. [Figure 8] One embodiment of the weighting function described herein is shown. [Figure 9] One embodiment is shown in which the maximum likelihood weight is directly applied to the variation correction coefficient. [Figure 10] One embodiment is shown in which an exponential decay factor is applied to the variation correction coefficient. [Figure 11] Another embodiment for determining channel-specific offset coefficients is shown. [Figure 12] We compare the execution of three approaches: a scaling-only solution, an offset-only solution (discussed in Figure 11), and the least squares method (discussed in Figure 3). [Figure 13] We compare the execution of three approaches: a scaling-only solution, an offset-only solution (discussed in Figure 11), and the least squares method (discussed in Figure 3). [Figure 14] We compare the execution of three approaches: a scaling-only solution, an offset-only solution (discussed in Figure 11), and the least squares method (discussed in Figure 3). [Figure 15] This is a computer system that may be used to implement the disclosed technology. [Modes for carrying out the invention]

[0009] The following considerations are presented to enable those skilled in the art to fabricate and use the disclosed technology and are provided in relation to specific uses and their requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and uses without departing from the spirit and scope of the disclosed technology. Accordingly, the disclosed technology is not intended to be limited to the embodiments shown, but is given the broadest scope consistent with the principles and features disclosed herein.

[0010] Introduction The disclosed technology development began with the analysis of the intensity profiles of clusters within a cluster population that are base-called during sequencing runs. The analysis revealed that the intensity profiles of clusters within a cluster population take on similar forms (e.g., trapezoidal) but differ in scale and are shifted from the origin 132 in the multidimensional space 100. This is referred to as "inter-cluster intensity profile variation." The multidimensional space 100 can be a Cartesian space, a polar space, a cylindrical space, or a spherical space.

[0011] Figure 1 shows an example of inter-cluster intensity profile variability discovered and corrected by the disclosed technique. Figure 1 shows intensity profiles 112, 122, and 132 for clusters 1, 2, and 3 in the cluster population, respectively. The intensity profile for the target cluster includes intensity values ​​that capture chemiluminescent signals generated due to nucleotide incorporation into the target cluster over multiple sequencing cycles (e.g., 150) of the sequencing run.

[0012] In the embodiment shown in Figure 1, intensity values ​​are extracted from two different color / intensity channel sequencing images generated by the sequencer in each sequencing cycle within a set of multiple sequencing cycles. Examples of sequencers include Illumina's iSeq, HiSeqX, HiSeq3000, HiSeq4000, HiSeq2500, NovaSeq6000, NextSeq550, NextSeq1000, NextSeq2000, NextSeqDx, MiSeq, and MiSeqDx.

[0013] In one embodiment, the sequencer uses sequencing by synthesis (SBS) to generate a sequenced image. SBS relies on extending a nascent strand complementary to a cluster strand having fluorescently labeled nucleotides, while tracking the release signal of each newly added nucleotide. The fluorescently labeled nucleotides have a 3' removable block that anchors a nucleotide-type fluorophore signal. SBS is performed in iterative sequencing cycles, each comprising three steps: (a) extending the emerging strand by adding fluorescently labeled nucleotides; (b) generating a sequenced image by exciting the fluorophore using one or more lasers in the sequencer's optical system and imaging through different filters of the optical system; and (c) cleaving the fluorophore and removing the 3' block in preparation for the next sequencing cycle. The acquisition and imaging cycles are repeated for a specified number of sequencing cycles to define the read length. Using this approach, each sequencing cycle matches a new position along the cluster strand.

[0014] In Figure 1,

[0015]

number

[0016] The symbols represent the intensity values ​​of cluster 1.

[0017]

number

[0018] The symbols represent the intensity values ​​of cluster 2.

[0019]

number

[0020] The symbols represent the intensity values ​​of cluster 3. The identities of the four different nucleotide types / bases A, C, T, and G are encoded as combinations of intensity values ​​in two color images, i.e., first and second intensity channels. For example, nucleic acids can be sequenced by providing a first nucleotide type (e.g., base T) detected in the first intensity channel (x-axis of multidimensional space 100), a second nucleotide type (e.g., base C) detected in the second intensity channel (y-axis of multidimensional space 100), a third nucleotide type (e.g., base A) detected in both the first and second intensity channels, and an unlabeled fourth nucleotide type (e.g., base G) that is not detected or is detected minimally in either intensity channel.

[0021] In some embodiments, the intensity profile is generated by iteratively fitting four intensity distributions (e.g., Gaussian distributions) to the intensity values ​​in the first and second intensity channels. The four intensity distributions correspond to the four bases A, C, T, and G. In the intensity profile, the intensity values ​​in the first intensity channel are plotted against the intensity values ​​in the second intensity channel (e.g., as a scatter plot), and the intensity values ​​are separated into four intensity distributions.

[0022] The intensity profile can take any shape (e.g., trapezoid, square, rectangle, rhombus, etc.). Further details on how the four intensity distributions are adapted to the intensity values ​​of the base call can be found in U.S. Patent Application Publication 2018 / 0274023(A1), the disclosure of which is incorporated herein by reference in its entirety.

[0023] In one embodiment, each intensity channel corresponds to one of several filter wavelength bands used by the optical system. In another embodiment, each intensity channel corresponds to one of several imaging events in the sequencing cycle. In yet another embodiment, each intensity channel corresponds to a combination of irradiation by a specific laser and imaging through a specific optical filter of the optical system.

[0024] It will be apparent to those skilled in the art that the disclosed technology can be similarly applied to sequencing images generated using a one-channel embodiment, a four-channel embodiment, and so on. For example, in the four-channel embodiment, four channel-specific offset coefficients are determined to compensate for shift variations in each of the four intensity channels.

[0025] Fluctuation correction logic Inter-cluster strength profile variability in the strength profiles of a large number of clusters (e.g., thousands, millions, hundreds of millions, etc.) within a cluster population leads to a decrease in base call throughput and an increase in base call error rate. To compensate for inter-cluster strength profile variability, a variability correction logic is disclosed that generates a variability correction coefficient for each cluster.

[0026] In a two-channel embodiment, the variation correction coefficient includes an amplification coefficient that takes into account the scale variation in the inter-cluster intensity profile variation, and two channel-specific offset coefficients that take into account the shift variation along the first and second intensity channels in the inter-cluster intensity profile variation, respectively. In another implementation, the shift variation is taken into account by using a common offset coefficient for different intensity channels (e.g., the first and second intensity channels).

[0027] The variability correction factor for the target cluster is generated in the current sequencing cycle of a sequencing run based on a combination of an analysis of the historical intensity statistics determined for the target cluster in the preceding sequencing cycle of the sequencing run and an analysis of the current intensity statistics determined for the target cluster in the current sequencing cycle. The variability correction factor is used to correct the next intensity reading registered for the target cluster in the next sequencing cycle of the sequencing run. The corrected next intensity reading is used to base call the target cluster in the next sequencing cycle. As a result of repeatedly applying the respective variability correction factor to each intensity profile of each cluster in successive sequencing cycles of the sequencing run, the intensity profiles become consistent and fixed at the origin 132 (for example, at the lower corner of the trapezoid).

[0028] Figure 2 shows an example of a base call pipeline 200 that implements variability correction logic.

[0029] Current sequencing cycle In the current sequencing cycle i, the sequencer generates sequencing image 202. Sequencing image 202 includes current intensity data 202 registered to multiple clusters within the cluster population, as well as current intensity data 202t registered to the target cluster in the current sequencing cycle i. The "t" in current intensity data 202t refers to the target cluster.

[0030] The current intensity data 202t is provided to the base caller 212. The base caller 212 processes the current intensity data 202t and generates the current base call 222 for the target cluster in the current sequencing cycle i. Examples of base callers 212 include Illumina's Real-Time Analysis (RTA) software, Illumina's neural network-based base caller (e.g., described in U.S. Patent Publication 2020 / 0302297(A1)), and Illumina's equalizer-based base caller (e.g., described in U.S. Provisional Patent Application 63 / 020,449).

[0031] In the current sequencing cycle i, the intensity profile of the target cluster includes the current intensity data 202t and the current historical intensity data registered in the target cluster during the sequencing cycles of the sequencing runs preceding the current sequencing cycle i, i.e., the preceding sequencing cycles 1 to i-1. The current intensity data 202t and the current historical intensity data are collectively referred to as the currently available intensity data.

[0032] In the intensity profile, the four intensity distributions correspond to the four bases A, C, T, and G. In one embodiment, the current base call 222 is performed by determining which of the four intensity distributions the current intensity data 202t belongs to. In some embodiments, this is achieved by using an expectation maximization algorithm. The expectation maximization algorithm repeatedly maximizes the likelihood of observing the mean (centroid) and distribution (covariance) that best fit the currently available intensity data.

[0033] By using the expectation maximization algorithm, once four intensity distributions are determined in the current sequencing cycle i, the likelihood of the current intensity data 202t belonging to each of the four intensity distributions is calculated. The highest likelihood gives the current base call 222. As an example, suppose "m" and "n" are the intensity values ​​of the current intensity data 202t in the first and second intensity channels, respectively. The expectation maximization algorithm generates four values ​​representing the likelihood of the intensity values ​​"m" and "n" belonging to each of the four intensity distributions. The maximum of the four values ​​identifies the called base.

[0034] In other embodiments, k-means clustering algorithms, k-means-like clustering algorithms, histogram-based methods, etc., may be used as base calls.

[0035] Next sequencing cycle In the next sequencing cycle i+1, the intensity correction parameter determiner 232 determines the intensity correction parameter 242 for the target cluster based on the current base call 222. In a two-channel embodiment, the intensity correction parameter 242 includes the distribution intensity in the first intensity channel, the distribution intensity in the second intensity channel, the intensity error in the first intensity channel, the intensity error in the second intensity channel, the distance from the distribution centroid to the origin, and a similarity measurement of the distribution intensity versus the intensity error.

[0036] Each of the 242 intensity correction parameters is defined as follows:

[0037] 1) The distribution intensity in the first intensity channel is the intensity value in the first intensity channel at the centroid of the base-specific intensity distribution to which the target cluster belongs in the current sequencing cycle i. Note that the base-specific intensity distribution is the basis for calling the current base call 222.

[0038] 2) The distribution intensity in the second intensity channel is the intensity value in the second intensity channel at the centroid of the base-specific intensity distribution.

[0039] 3) The intensity error in the first intensity channel is the difference between the measured intensity value of the current intensity data 202t in the first intensity channel and the distributed intensity in the first intensity channel.

[0040] 4) The intensity error in the second intensity channel is the difference between the measured intensity value of the current intensity data 202t in the second intensity channel and the distributed intensity in the second intensity channel.

[0041] 5) The distance from the distribution centroid to the origin is the Euclidean distance between the centroid of the base-specific intensity distribution and the origin 132 of the multidimensional space 100 to which the base-specific intensity distribution is fitted (e.g., by using an expectation maximization algorithm). In other embodiments, distance metrics such as the Mahalanobis distance and the minimum covariance determinant (MCD) distance, as well as their associated centroid estimates, may be used. 6) The similarity measurement between distribution intensity and intensity error is the sum of the channel-level dot products between the distribution intensity and intensity error in the first and second intensity channels.

[0042] The cumulative intensity correction parameter determiner 252 determines the cumulative intensity correction parameter 262 by accumulating the intensity correction parameter 242 together with the historical cumulative intensity correction parameter 254 from the preceding sequence determination cycle i-1. Examples of accumulation include summation and averaging.

[0043] The variation correction coefficient determination unit 272 determines the variation correction coefficient 282 based on the determined cumulative intensity correction parameter 262.

[0044] In the next sequencing cycle i+1, the sequencer generates sequencing image 294. Sequencing image 294 includes the following intensity data 294 registered to multiple clusters within the cluster population, as well as the following intensity data 294t registered to the target cluster in the next sequencing cycle i+1. The "t" in the following intensity data 294t refers to the target cluster.

[0045] The intensity corrector 292 applies the variation correction coefficient 282 to the next intensity data 294t to generate the corrected next intensity data 296t. In the corrected next intensity data 296t, "t" refers to the target cluster.

[0046] In the next sequencing cycle i+1, the intensity profile of the target cluster will include the corrected next intensity data 296t and the next historical intensity data registered in the target cluster in the sequencing cycles of the sequencing run preceding the next sequencing cycle i+1, i.e., the preceding sequencing cycles 1-i. The corrected next intensity data 296t and the next historical intensity data are collectively referred to as the next available intensity data.

[0047] The corrected next intensity data 296t is provided to the base caller 212. The base caller 212 processes the corrected next intensity data 296t and generates the next base call 298 for the target cluster in the next sequencing cycle i+1. To generate the next base call 298, the expectation maximization algorithm observes the mean (centroid) and distribution (covariates) based on the corrected next intensity data 296t to best fit the next available intensity data.

[0048] By using the expectation maximization algorithm, once the four intensity distributions are determined in the next sequencing cycle i+1, the likelihood of the corrected next intensity data 296t belonging to each of the four intensity distributions is calculated. The largest likelihood gives the next base call 298.

[0049] Note that the base call pipeline 200 is executed per cluster and runs in parallel for multiple clusters within a cluster group. Furthermore, the base call pipeline 200 is executed repeatedly for consecutive sequencing cycles of a sequencing run (for example, for 150 consecutive sequencing cycles for read 1 and another 150 consecutive sequencing cycles for read 2 in a paired-end sequencing run).

[0050] Least squares method Figure 3 shows a least squares decision-maker implementing the least squares method 300 disclosed herein. The least squares method 300 determines closed-form equations for the cumulative intensity correction parameter 262 and the variation correction coefficient 282. The least squares decision-maker 302 includes an intensity modeler 312 and a minimizer 322.

[0051] The intensity model 312 models the relationship between the measured intensity of the target cluster and the variation correction coefficient 282 according to the following equation.

[0052] y C,i =ax C,i +d i +n C,i Equation (1)

[0053] During the ceremony, 'a' is the amplification factor of the target cluster. d i This is the channel-specific offset coefficient for intensity channel i. x C,i This represents the distribution intensity in intensity channel i for the target cluster in the current sequencing cycle C. y C,i This represents the measured intensity in intensity channel i for the target cluster in the current sequencing cycle C. n C,i This is the additive noise of the intensity channel i for the target cluster in the current sequencing cycle C.

[0054] Minimizer 322 minimizes the following equation using least squares method 300.

[0055]

number

[0056] During the ceremony, `errorf` is an error function.

[0057]

number

[0058] This is the amplification factor of the target cluster.

[0059]

number

[0060] This is the channel-specific offset coefficient for intensity channel i. C represents the current sequencing cycle.

[0061] Using the chain rule, the minimizer 322 is the amplification coefficient.

[0062]

number

[0063] and channel-specific offset coefficients

[0064]

number

[0065] We calculate the two partial derivatives of the error function with respect to . To minimize the error function, the partial derivatives set equation 2 to zero.

[0066]

number

[0067] Channel-specific intensity error e c,i is defined as follows.

[0068] e C,i = y C,i - x C,i Equation (5)

[0069] Closed-form expression The first partial derivative determines the closed-form expression of the amplification factor

[0070]

Number

[0071] as follows.

[0072]

Number

[0073] Closed-form expression of the cumulative intensity correction parameter ₂₆₂

[0074]

Number

[0075] recharacterizes Equation 9 as follows.

[0076]

Number

[0077] where

[0078]

Number

[0079] Each of the 262 cumulative intensity correction parameters is defined as follows: 1) First cumulative intensity correction parameter

[0080]

number

[0081] This is the sum of the distribution intensities in the first intensity channel measured for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 2) Second cumulative intensity correction parameter

[0082]

number

[0083] This is the sum of the distribution intensities in the second intensity channel measured for the target cluster in each of the preceding sequencing cycles 1 to i-1, and in the current sequencing cycle i. 3) Third cumulative intensity correction parameter

[0084]

number

[0085] This is the sum of the intensity errors in the first intensity channel calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 4) Fourth cumulative intensity correction parameter

[0086]

number

[0087] This is the sum of the intensity errors in the second intensity channel calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 5) Fifth cumulative intensity correction parameter

[0088]

number

[0089] This is the sum of the distances from the distribution centroid to the origin calculated for each of the preceding sequencing cycles 1 to i-1, and for the current sequencing cycle i, for the target cluster. 6) Sixth cumulative intensity correction parameter

[0090]

number

[0091] This is the sum of the distribution intensity versus intensity error similarity measurements calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1, and in the current sequencing cycle i.

[0092] The second partial derivative is the offset coefficient.

[0093]

number

[0094] The closed form of the expression is determined as follows:

[0095]

number

[0096] Next, regarding each intensity channel:

[0097]

number

[0098] For the first intensity channel, i.e., i = 1:

[0099]

number

[0100] During the ceremony,

[0101]

number

[0102] This is the offset coefficient of the first intensity channel.

[0103] For the second intensity channel, i.e., i = 2:

[0104]

number

[0105] During the ceremony,

[0106]

number

[0107] This is the offset coefficient for the second intensity channel.

[0108] Substitution of equations 17 and 18 into equation 11:

[0109]

number

[0110] During the ceremony,

[0111]

number

[0112] This is the amplification factor for the target cluster.

[0113] In another implementation, to reduce the memory requirements per cluster, a common offset coefficient for different intensity channels (e.g., first and second intensity channels) is used as a constraint.

[0114]

number

[0115] By introducing this, the following is determined:

[0116]

number

[0117] It will be apparent to those skilled in the art that the least squares method 300 is performed before the sequencing run to determine the closed-form equation. Once determined, the closed-form equation is applied iteratively to the intensity values ​​generated during the sequencing run for each cluster in each sequencing cycle of the sequencing run.

[0118] Intensity correction parameters The following discussion focuses on how six intensity correction parameters—namely, the distribution intensity in the first intensity channel, the distribution intensity in the second intensity channel, the intensity error in the first intensity channel, the intensity error in the second intensity channel, the distance from the distribution centroid to the origin, and the similarity measurement of distribution intensity versus intensity error—are determined for the target cluster in the current sequencing cycle.

[0119] It will be apparent to those skilled in the art that the number of intensity correction parameters varies depending on the number of intensity channels. For example, in a 4-channel embodiment, the 4-channel specific distributed intensity and the 4-channel specific intensity error are calculated for each of the four intensity channels.

[0120] Figure 4 shows an example 400 illustrating how channel-specific distribution intensities are measured for the target cluster in the current sequencing cycle i.

[0121]

number

[0122] The symbols represent the intensity values ​​of the first and second intensity channels registered in cluster 1 during the current sequencing cycle i and the preceding sequencing cycles 1 to i-1.

[0123] In Figure 4, the four intensity distributions C402, A406, G462, and T466 are connected to form a constellation 102 of cluster 1. In Figure 4, * The symbol " " represents the measured intensity "m,n" 422 in the first and second intensity channels registered in cluster 1 in the current sequencing cycle i. Since the measured intensity "m,n" 422 is closest to the centroid 414 of intensity distribution C402, cluster 1 belongs to intensity distribution C402 and is therefore assigned to base call C in the current sequencing cycle i.

[0124] Furthermore, since cluster 1 belongs to the C intensity distribution 402, the intensity values ​​"a,b" of the centroid 414 are the distribution intensities for cluster 1 in the current sequencing cycle i. Also, "a" is the channel-specific distribution intensity of the first intensity channel, and "b" is the channel-specific distribution intensity of the second intensity channel.

[0125] Figure 5 shows an example illustrating how channel-specific intensity errors are calculated for the target cluster in the current sequencing cycle i. Intensity error in the first intensity channel

[0126]

number

[0127] 532 is calculated for cluster 1 in the current sequencing cycle i as the difference between the channel-specific measured intensity (m) in the first intensity channel and the channel-specific distributed intensity (a) in the first intensity channel.

[0128]

number

[0129] Intensity error in the second intensity channel

[0130]

number

[0131] 502 is calculated for cluster 1 in the current sequencing cycle i as the difference between the channel-specific measured intensity (n) and the channel-specific distributed intensity (b) in the second intensity channel.

[0132]

number

[0133] Figure 6 shows an example 600 illustrating how the distance from the distribution centroid to the origin is calculated for the target cluster in the current sequencing cycle i. Cluster 1 belongs to the C intensity distribution 402, and the intensity values ​​"a,b" of the centroid 414 are the distribution intensities for cluster 1 in the current sequencing cycle i.

[0134] The distance from the centroid of the distribution to the origin is calculated for cluster 1 in the current sequencing cycle i as the Euclidean distance (d) 652 between the centroid 414 and the origin 132 "x,y".

[0135]

number

[0136] The similarity measurement of distribution intensity versus intensity error is calculated for cluster 1 in the current sequencing cycle i as the sum of channel-level dot products between channel-specific distribution intensity and channel-specific intensity error.

[0137]

number

[0138] During the ceremony, The dot product operator is represented by the '·' symbol.

[0139] Base Call Pipeline Figure 7 shows another example of a base call pipeline 700 that implements a variation correction logic. Assume the current sequence determination cycle i is the 25th sequence determination cycle of the sequence determination run, i.e., i=25. The preceding sequence determination cycle i-1 is the 24th sequence determination cycle of the sequence determination run, i.e., i-1=24. The next sequence determination cycle i+1 is the 26th sequence determination cycle of the sequence determination run, i.e., i+1=26. The subsequent sequence determination cycle i+2 is the 27th sequence determination cycle of the sequence determination run, i.e., i+2=27.

[0140] Preceding sequencing cycle In each of the 1st to 24th sequencing cycles, each set of cumulative intensity correction parameters is determined from each set of intensity correction parameters. The preceding cumulative intensity correction parameters 702 for the target cluster are the sum of the intensity correction parameter units of the 24 sets of intensity correction parameters. In the 2-channel embodiment, each of the 24 sets of intensity correction parameters includes six intensity correction parameters: the distribution intensity in the first intensity channel, the distribution intensity in the second intensity channel, the intensity error in the first intensity channel, the intensity error in the second intensity channel, the distance from the distribution centroid to the origin, and the similarity of the distribution intensity versus intensity error. Each of the 24 sets of cumulative intensity correction parameters includes six cumulative intensity correction parameters

[0141]

number

[0142] Includes.

[0143] The preceding cumulative intensity correction parameter 702 is metadata (or statistics) about the underlying preceding intensity values ​​and the preceding intensity correction parameters from which they are calculated. As a result, the preceding cumulative intensity correction parameter 702 has a much smaller memory footprint compared to the underlying preceding intensity values ​​and preceding intensity correction parameters. The preceding cumulative intensity correction parameter 702 is cached in memory during the sequencing run and accumulated together with the current intensity correction parameter 732 for the target cluster to generate the current cumulative intensity correction parameter 742 for the target cluster, as shown by triangle 734.

[0144] In one implementation, the preceding cumulative intensity correction parameters 702 are stored in a quantized fixed-bit width format. For example, one or two bytes may be used to store each preceding cumulative intensity correction parameter in the preceding cumulative intensity correction parameter 702.

[0145] Current sequencing cycle The current measured intensity 712 of the target cluster includes the intensity values ​​registered in the target cluster during the 25th sequencing cycle. Based on the current measured intensity 712, the current base call 722 is invoked for the target cluster during the 25th sequencing cycle (for example, by using an expected value maximization algorithm).

[0146] Next sequencing cycle Based on the current base call 722, the current intensity correction parameter 732 is determined for the target cluster. The current cumulative intensity correction parameter 742 is calculated for the target cluster by accumulating the preceding cumulative intensity correction parameter 702 together with the current intensity correction parameter 732, as shown by triangle 734. One example of accumulation is the sum. In the sum embodiment, the current cumulative intensity correction parameter 742 is calculated by the sum of the preceding cumulative intensity correction parameter 702 and the current intensity correction parameter 732 (as shown in intermediate terms 1-6 above).

[0147] Another example of accumulation is averaging.

[0148]

number

[0149] In the formula, C is the index of the current sequencing cycle i. That is, in the example discussed herein, C = 25.

[0150] Based on the intermediate terms 1.2 to 6.2, each of the cumulative intensity correction parameters is defined as follows: 1) First cumulative intensity correction parameter

[0151]

number

[0152] This is the average of the distribution intensity in the first intensity channel measured for the target cluster in each of the preceding sequencing cycles 1 to i-1, and in the current sequencing cycle i. 2) Second cumulative intensity correction parameter

[0153]

number

[0154] This is the average of the distribution intensities in the second intensity channel measured for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 3) Third cumulative intensity correction parameter

[0155]

number

[0156] This is the average of the intensity errors in the first intensity channel calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 4) Fourth cumulative intensity correction parameter

[0157]

number

[0158] This is the average of the intensity errors in the second intensity channel calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i. 5) Fifth cumulative intensity correction parameter

[0159]

number

[0160] This is the average of the distances from the distribution centroid to the origin calculated for each of the preceding sequencing cycles 1 to i-1, and for the current sequencing cycle i, for the target cluster. 6) Sixth cumulative intensity correction parameter

[0161]

number

[0162] This is the average of the distribution intensity versus intensity error similarity measurements calculated for the target cluster in each of the preceding sequencing cycles 1 to i-1 and in the current sequencing cycle i.

[0163] Compact expression In one embodiment, the preceding cumulative intensity correction parameters 702 are stored in a compact representation (e.g., a summed representation or an averaged representation). In the averaging embodiment, the preceding cumulative intensity correction parameters 702 are stored in their averaged representations, and the pre-averaging representation is retrieved by first multiplying them by the number of sequence determination cycles in which they are accumulated. That is, in the embodiments discussed herein, 24 is the multiplier.

[0164] Next, the result of the multiplication, i.e., the pre-averaging representation, is summed with the current intensity correction parameter 732 for each intensity correction parameter. Then, the sum is divided by the index C (C=25) of the current sequence determination cycle i to determine the current cumulative intensity correction parameter 742.

[0165]

number

[0166] Let x be the first cumulative intensity correction parameter for the 24th sequencing cycle. 25 C,1 However, this represents the distribution intensity in the first intensity channel for the 25th sequencing cycle.

[0167]

number

[0168] However, suppose that this is the first cumulative intensity correction parameter for the 25th sequencing cycle, and is used to correct the intensity measured for the 26th sequencing cycle. In this case, it would be as follows:

[0169]

number

[0170]

number

[0171] Let x be the second cumulative intensity correction parameter for the 24th sequencing cycle. 25 C,2 However, this represents the distribution intensity in the second intensity channel for the 25th sequencing cycle.

[0172]

number

[0173] However, suppose that this is the second cumulative intensity correction parameter for the 25th sequencing cycle, and is used to correct the intensity measured for the 26th sequencing cycle. In this case, it would be as follows:

[0174]

number

[0175]

number

[0176] However, this is the third cumulative intensity correction parameter for the 24th sequencing cycle. 25 C,1 However, this represents the intensity error in the first intensity channel for the 25th sequencing cycle.

[0177]

number

[0178] is the third cumulative intensity correction parameter for the 25th sequencing cycle and is used to correct the intensity measured for the 26th sequencing cycle. In this case, it is as follows.

[0179]

Number

[0180]

Number

[0181] [[ID=I19]] is the fourth cumulative intensity correction parameter for the 24th sequencing cycle. 25 C,2 is the intensity error in the second intensity channel for the 25th sequencing cycle.

[0182]

Number

[0183] is the fourth cumulative intensity correction parameter for the 25th sequencing cycle and is used to correct the intensity measured for the 26th sequencing cycle. In this case, it is as follows.

[0184]

Number

[0185]

Number

[0186] is the fifth cumulative intensity correction parameter for the s24th sequencing cycle.

[0187]

Number

[0188] is the distance from the centroid of the distribution for the 25th array determination cycle to the origin.

[0189]

Number

[0190] is the 5th cumulative intensity correction parameter for the 25th array determination cycle and is used to correct the intensity measured for the 26th array determination cycle. In this case, it is as follows.

[0191]

Number

[0192]

Number

[0193] is the 6th cumulative intensity correction parameter for the 24th array determination cycle.

[0194]

Number

[0195] is the similarity measurement of distribution intensity vs. intensity error for the 25th array determination cycle.

[0196]

Number

[0197] is the 6th cumulative intensity correction parameter for the 25th array determination cycle and is used to correct the intensity measured for the 26th array determination cycle. In this case, it is as follows.

[0198]

number

[0199] The current cumulative intensity correction parameter 742 is metadata (or statistics) about the current measured intensity 712 and the current intensity correction parameter 732. As a result, the current cumulative intensity correction parameter 742 has a much smaller memory footprint compared to the current measured intensity 712 and the current intensity correction parameter 732. The current cumulative intensity correction parameter 742 is cached in memory during the sequencing run and accumulated along with the next intensity correction parameter 794 for the target cluster to generate the next cumulative intensity correction parameter 796 for the target cluster, as indicated by triangle 784.

[0200] In one implementation, the current cumulative intensity correction parameter 742 is stored in a quantized fixed-bit width format. For example, one or two bytes may be used to store each preceding cumulative intensity correction parameter in the current cumulative intensity correction parameter 742.

[0201] The current cumulative intensity correction parameter 742 is used to determine the current amplification factor 752 for the target cluster. This involves performing a closed-form equation in equation 23, depending on the current cumulative intensity correction parameter 742.

[0202] The current cumulative intensity correction parameter 742 and the current amplification factor 752 are used to determine the current channel-specific offset factor 762 for the target cluster. This involves performing closed-form equations in equations 17 and 18, depending on the current cumulative intensity correction parameter 742 and the amplification factor 752.

[0203] The next measured intensity 772, measured for the target cluster in the 26th sequencing cycle, is corrected using the current amplification factor 752 and the current channel-specific offset factor 762. In one embodiment, the correction includes subtracting the current channel-specific offset factor 762 on a per-channel basis from the next measured intensity 772 to generate the next shift intensity, and dividing the next shift intensity by the current amplification factor 752 to generate the next corrected measured intensity 782 for the target cluster.

[0204] Next, the following base call 792 is invoked for the target cluster in the 26th sequence determination cycle, using the following corrected measured intensity 782. This is achieved by providing the following corrected measured intensity 782 as input to the base caller 212 (for example, by using an expectation maximization algorithm).

[0205] Subsequent sequencing cycle The controller (not shown) repeats the base call pipeline 700 for consecutive sequencing cycles of the sequencing run, as illustrated by operations 794, 796, 798, and 799. For example, for the 27th sequencing cycle, the current cumulative intensity correction parameter 742 functions as the preceding cumulative intensity correction parameter 702, as shown by triangle 784. Note that the base call pipeline 700 is executed cluster by cluster and runs in parallel for multiple clusters within a cluster population.

[0206] Weighted least squares method Figure 8 shows one embodiment of the weighting function 800 described herein. Since the least squares method 300 may require several sequencing cycles to converge, the weighting function 800 is used to attenuate the variation correction coefficient in the initial sequencing cycle of a sequencing run and to amplify the variation correction coefficient in subsequent sequencing cycles of a sequencing run.

[0207] The weighting function 800 operates as follows: First, the initial amplification coefficient 802 and the initial offset coefficients 822 and 832 are initialized. In one embodiment, the initial amplification coefficient 802 is initialized in the first sequencing cycle of the sequencing run with a predetermined value (e.g., "1"), and the initial offset coefficients 822 and 832 are initialized in the first sequencing cycle with a predetermined value (e.g., "0"). The weighting function 800 combines the initial amplification coefficient 802 with the amplification coefficient 806 (determined by least squares method 300) (e.g., summed), and combines the initial first and second offset coefficients 822 and 832 with the first and second offset coefficients 826 and 836 (determined by least squares method 300), so that the amplification coefficient 806 and the first and second offset coefficients 826 and 836 are attenuated in the initial sequencing cycle and amplified in subsequent sequencing cycles.

[0208] In one embodiment, the weighting function 800 applies (for example, multiplies) the initial minimum weight (inimin weight) 804 to the initial amplification coefficient 802 and the initial first and second offset coefficients 822 and 832, and the maximum weight (lsqmax weight) 808 by least squares to the amplification coefficient 806 and the first and second offset coefficients 826 and 836, resulting in the following:

[0209]

number

[0210] During the ceremony, c is the index of the current sequencing cycle. p is a number between 2 and 7.

[0211] The first sequence determination cycle, i.e., when c=1 and when p=2, is given by equation

[0212]

number

[0213] This is equal to "-1". Then, between "0" and "-1", the lsqmax weight 808 selects the maximum value of the two, i.e., 0.

[0214]

number

[0215] This is equal to "2". Then, between "1" and "2", the inimin weight 804 selects the minimum of the two values, i.e., 1.

[0216] Next, the lsqmax weight 808 is multiplied by 0, along with the amplification factor 806 and the first and second offset factors 826 and 836. The inimin weight 804 is multiplied by 1, along with the initial amplification factor 802 and the initial first and second offset factors 822 and 832. The results of the two multiplications are summed to produce the weighted amplification factor 810 and the weighted first and second offset factors 820 and 830.

[0217] As the sequencing run progresses and the value of index "c" increments, the values ​​of the lsqmax weight 808 and the inimin weight 804 also change and are applied accordingly. As a result, the amplification coefficient 806 and the first and second offset coefficients 826 and 836 (learned from least squares 300) are progressively amplified in each successive sequencing cycle.

[0218] The weighting function 800 generates weighted amplification coefficients 810 and weighted first and second offset coefficients 820 and 830, which are used to correct the measured intensity for the target cluster in the next sequencing cycle i+1, and to generate the corrected measured intensity for base calling the target cluster in the next sequencing cycle i+1.

[0219]

number

[0220] During the ceremony, W is the weight.

[0221] Maximum Likelihood Method Figure 9 shows one embodiment in which the maximum likelihood weights 906, 908, and 910 are directly applied to the variation correction coefficients. The maximum likelihood weights 906, 908, and 910 are generated by applying the maximum likelihood method 900 to the probability distribution 902 of historical values ​​observed for the variation correction coefficients in previous sequencing runs. Figure 9 also shows the cumulative intensity correction parameter 904.

[0222] The maximum likelihood weights 906, 908, and 910 are functions of the current sequence determination cycle, as represented by index "C". The maximum likelihood weights 906, 908, and 910 vary per sequence determination cycle, depending on index C. The maximum likelihood weights 906, 908, and 910 are also functions of additive noise, as represented by the letter "n". The sigma term "σ" is the respective variation correction coefficient, i.e., the variance (σ). 2 This represents the range of variation in the historical value observed for ). In some embodiments, the sigma term for additive noise can be estimated using maximum likelihood 900 or user-specified. The amplification coefficients, channel-specific offset coefficients, and sigma terms for additive noise are determined per sequencing run and remain fixed for all sequencing cycles of the sequencing run. The sigma terms incorporate prior knowledge about the uncertainty observed in the variation correction coefficients.

[0223] Some example values ​​for the sigma term are as follows:

[0224] 'ml_chanest_sigma_a', 0.15 'ml_chanest_sigma_d1', 0.1 'ml_chanest_sigma_d2', 0.02 'ml_chanest_sigma_n', 0.14

[0225] In one embodiment, the center / initial / mean value of the probability distribution of the amplification coefficient is set to "1", and the center / initial / mean value of the probability distribution of the channel-specific offset coefficient is set to "0".

[0226] Smaller values ​​of the sigma terms in the amplification coefficients and channel-specific offset coefficients at maximum likelihood weights 906, 908, and 910 indicate lower variability in their respective historical values. This results in higher values ​​for maximum likelihood weights 906, 908, and 910. This then leads to weighted amplification coefficient 920 channels, which are weighted with a preference for a central value of 1, and weighted channel-specific offset coefficients 930 and 940, which are weighted with a preference for a central value of 0, especially in the early sequencing cycles.

[0227] Conversely, larger values ​​of the sigma term in the amplification coefficients and channel-specific offset coefficients at maximum likelihood weights 906, 908, and 910 indicate high variability in their respective historical values. This results in lower values ​​for maximum likelihood weights 906, 908, and 910. This then leads to weighted amplification coefficients 920, which are weighted preferentially to the output of least squares 300 (e.g., equation 23), and weighted channel-specific offset coefficients 930 and 940, which are weighted preferentially to the output of least squares 300 (e.g., equations 17 and 18), especially in later sequence cycles.

[0228] The maximum likelihood weights 906, 908, and 910 are directly incorporated into the calculation of the weighted amplification coefficient 920 and the weighted channel-specific offset coefficients 930 and 940, respectively.

[0229] Exponential decay factor method Figure 10 shows one embodiment of applying an exponential decay factor to a variation correction coefficient. The exponential decay logic 1000 is based on the so-called "tau" and "stats.cycle". The term "stats.cycle" refers to the current sequence determination cycle.

[0230] tau is set to a predetermined value depending on the degree of time dispersion observed in the intensity correction parameter. If the intensity correction parameter is time-invariant, tau can be set to infinity. If the intensity correction parameter changes rapidly over time, tau can be set to a small value. In one embodiment, tau is set to 32.

[0231] Assume that tau is 32. Then, according to statements 1002, 1004, and 1006, the attenuation factor is "1" for sequence cycles 1 to 31, which does not result in attenuation of the cumulative intensity correction parameter. For sequence determination cycles 32 and above, based on statement 1008, the attenuation factor is 31 for 32. The exponential attenuation characteristic stems from the fact that, as shown below in statements 1010, 1012, 1014, 1016, 1018, and 1020, each of the cumulative intensity correction parameters is multiplied by the attenuation factor in each consecutive sequence determination cycle.

[0232] Sequence determination cycle 32, decay factor

[0233]

number

[0234] in the case of

[0235]

number

[0236] Sequence determination cycle 33, decay factor

[0237]

number

[0238] in the case of

[0239]

number

[0240] In Figure 10, the cumulative intensity correction parameter is accumulated using a summation operation. In the embodiment of exponential decay factor averaging, the cumulative intensity correction parameter is accumulated using an average operation. In the embodiment of exponential decay factor averaging, the divisor "C" in intermediate terms 1.2 to 6.2 remains fixed after tau number of sequencing cycles of the sequencing run, i.e., after the 32nd sequencing cycle of the sequencing run.

[0241] In some embodiments, a combination of weighted least squares (Figure 8), maximum likelihood (Figure 9), and exponential decay factor (Figure 10) is used to generate weighted variation correction coefficients for each sequencing cycle of the sequencing run.

[0242] Channel-specific offset coefficient Figure 11 shows another embodiment for determining channel-specific offset coefficients. In the two-channel embodiment, the first channel-specific offset coefficient ("Δx") for the first intensity channel and for the target cluster in the current sequencing cycle is calculated as the difference between the measured intensity ("p") in the first intensity channel for the target cluster in the current sequencing cycle and the intensity value ("u") in the first intensity channel at the centroid 1104 of the base-specific intensity distribution A1102 to which the target cluster belongs in the current sequencing cycle (as determined, for example, by an expectation maximization algorithm).

[0243] In the two-channel embodiment, the second channel-specific offset coefficient ("Δy") for the second intensity channel and for the target cluster in the current sequencing cycle is calculated as the difference between the measured intensity ("q") in the second intensity channel for the target cluster in the current sequencing cycle and the intensity value ("v") in the second intensity channel at the centroid 1104 of the base-specific intensity distribution A1102 to which the target cluster belongs in the current sequencing cycle.

[0244] In one embodiment, a first channel-specific offset coefficient ("Δx") and a second channel-specific offset coefficient ("Δy") are determined in each sequencing cycle of the sequencing run. In some embodiments, after a configurable number of sequencing cycles (e.g., 10 or 20 sequencing cycles), the first channel-specific offset coefficient ("Δx") and the second channel-specific offset coefficient ("Δy") are initialized to a predetermined value (e.g., "0").

[0245] In the rolling average embodiment, the average is calculated for the first offset channel-specific coefficient ("Δx") and the second channel-specific offset coefficient ("Δy") after a configurable number of sequencing cycles. The average is then used as a substitute for the first offset channel-specific coefficient ("Δx") and the second channel-specific offset coefficient ("Δy") until the next average is calculated for the next set of a configurable number of sequencing cycles.

[0246] In some implementations, the first channel-specific offset coefficient ("Δx") and the second channel-specific offset coefficient ("Δy") are calculated only when the target cluster belongs to the A, C, and T base-specific intensity distribution, and not when the target cluster belongs to the G (dark) base-specific intensity distribution. In embodiments where the sequencing run includes paired-end reads, the first channel-specific offset coefficient ("Δx") and the second channel-specific offset coefficient ("Δy") are initialized for the second read with the values ​​available at the end of the first read, and then updated in each set of any number of sequencing cycles.

[0247] Execution result Figures 12, 13, and 14 compare the execution of three approaches: a scaling-only solution, an offset-only solution (discussed in Figure 11), and the least squares 300 method. The three approaches are applied to intensity data generated using Illumina's Real-Time Analysis (RTA) software across 20 datasets from Illumina's NextSeq2000 sequencer.

[0248] Figure 12 compares the percentage of clusters passing the RTA's 2-channel chastity filter for each of the following approaches: scaling only (blue), offset only (orange), and least squares (gray). The comparison is performed across 20 datasets (shown as the x-axis). All three approaches achieve >65% clusters passing the 2-channel chastity filter, 16 out of 20 (80%) record a pass rate higher than 75%, and 8 out of 20 (or 20%) record a pass rate of >80%. Note that the least squares 300 approach performs best.

[0249] Figure 13 compares the error rates of scaling-only (blue), offset-only (orange), and least squares (gray) methods for low-variability samples. The comparison is performed across 20 low-variability datasets (shown as the x-axis) spiked with known phage genomes (PhiX). Seventeen of the 20 datasets (17 / 20 or 68%) achieved an error rate of <35%, with the majority enjoying an error rate of less than 25%. Note that the least squares 300 method yielded the best results.

[0250] Figure 14 compares the percentage of sequencing determination data with a quality score exceeding Q30 (i.e., base call error < 10^(-30 / 10) or 0.1%) for each of the following approaches: scaling-only (blue), offset-only (orange), and least squares (gray). The comparison is performed across the same 20 datasets (shown as the x-axis). While all three approaches achieve high Q30 quality scores (e.g., over 80%), 16 out of 20 (80%) recorded a pass rate higher than 75%, and 8 out of 20 (or 20%) recorded a pass rate of >80%. Note that least squares 300 outperforms the other approaches by >2 percentage points.

[0251] Computer system Figure 15 shows a computer system 1500 that can be used to implement the disclosed technology. The computer system 1500 includes at least one central processing unit (CPU) 1572 that communicates with a number of peripheral devices via a bus subsystem 1555. These peripheral devices may include, for example, a storage subsystem 1510 including a memory device and file storage subsystem 1536, a user interface input device 1538, a user interface output device 1576, and a network interface subsystem 1574. The input and output devices enable user interaction with the computer system 1500. The network interface subsystem 1574 provides an interface to an external network, including interfaces to corresponding interface devices in other computer systems.

[0252] In one embodiment, the least squares decision-maker 302 is linked to the memory subsystem 1510 and the user interface input device 1538 in a communicative manner.

[0253] The user interface input device 1538 may include pointing devices such as keyboards, mice, trackballs, touchpads, or graphics tablets, scanners, touchscreens integrated into displays, audio input devices such as voice recognition systems and microphones, and other types of input devices. In general, the use of the term “input device” is intended to include all possible types of devices and methods for inputting information into the computer system 1500.

[0254] The user interface output device 1576 may include a display subsystem, a printer, a fax machine, or a non-visual display such as an audio output device. The display subsystem may include a flat panel device such as an LED display, a cathode ray tube (CRT), or a liquid crystal display (LCD), a projection device, or any other mechanism for creating a visible image. The display subsystem may also provide a non-visual display such as an audio output device. In general, the use of the term “output device” is intended to include all possible types of devices and methods for outputting information from the computer system 1500 to a user or another machine or computer system.

[0255] The storage subsystem 1510 stores programming and data constructs that provide some or all of the functions of the modules and methods described herein. These software modules are generally executed by the processor 1578.

[0256] The processor 1578 may be a graphics processing unit (GPU), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), and / or a coarse-grained reconfigurable architecture (CGRA). The processor 1578 can be hosted on deep learning cloud platforms such as Google Cloud Platform®, Xilinx®, and Cirrascale®. Examples of processor 1578 include Google's Tensor Processing Unit (TPU) (trademark), rackmount solutions such as the GX4 Rackmount Series (trademark) and GX15 Rackmount Series (trademark), Qualcomm's Zeroth Platform (trademark) with NVIDIA DGX-1 (trademark), Microsoft's Stratix V FPGA (trademark), Graphcore's Intelligent Processor Unit (IPU) (trademark), Snapdragon processors (trademark), NVIDIA's Volta (trademark), NVIDIA's DRIVE PX (trademark), NVIDIA's JETSON TX1 / TX2 MODULE (trademark), Intel's Nirvana (trademark), Movidius VPU (trademark), Fujitsu DPI (trademark), ARM's DynamicIQ (trademark), IBM TrueNorth (trademark), Lambda GPU Server with Testa V100s (trademark), and others.

[0257] The memory subsystem 1522 used in the storage subsystem 1510 may include a number of memories, including a main random access memory (RAM) 1532 for storing instructions and data during program execution, and a read-only memory (ROM) 1534 for storing fixed instructions. The file storage subsystem 1536 can provide persistent storage for program and data files, which may include a hard disk drive, a floppy disk drive with associated removable media, a CD-ROM drive, an optical drive, or a removable media cartridge. Modules implementing the functionality of a particular embodiment may be stored by the file storage subsystem 1536 within the storage subsystem 1510 or in other machines accessible by the processor.

[0258] The bus subsystem 1555 provides a mechanism for various components and subsystems of the computer system 1500 to communicate with each other as intended. Although the bus subsystem 1555 is schematically shown as a single bus, alternative embodiments of the bus subsystem may use multiple buses.

[0259] The computer system 1500 itself can be of various types, including personal computers, portable computers, workstations, computer terminals, network computers, televisions, mainframes, server farms, a widely distributed set of loosely networked computers, or any other data processing systems or user devices. Because computers and networks are constantly changing in nature, the description of the computer system 1500 shown in Figure 15 is intended only as a specific example for the purpose of illustrating preferred embodiments of the invention. Many other configurations of the computer system 1500 may have more or fewer components than the computer system shown in Figure 15.

[0260] Each processor or module may include an algorithm (e.g., instructions stored on a tangible and / or non-temporary computer-readable storage medium) or sub-algorithm for executing a specific process. While the fluctuation corrector 232 is conceptually exemplified as a collection of modules, it may be implemented using any combination of dedicated hardware boards, DSPs, processors, etc. Alternatively, the fluctuation corrector 232 may be implemented using a single processor or a ready-made PC with multiple processors, with the functional operation distributed among the processors. Further options include implementing the modules described below using a hybrid configuration in which certain modular functions are performed using dedicated hardware, while the remaining modular functions are performed using a ready-made PC, etc. Modules may also be implemented as software modules within a processing unit.

[0261] Various processes and steps of the methods described herein (for example, Figure 9) may be performed using a computer. The computer may include a processor that is part of a detection device, networked with a detection device used to acquire data to be processed by the computer, or separate from the detection device. In some embodiments, information (e.g., image data) may be transmitted directly or over a computer network between components of the system disclosed herein. A local area network (LAN) or wide area network (WAN) may be an enterprise computing network, including access to the Internet, to which computers and computing devices, including the system, are connected. In one embodiment, the LAN conforms to the Transmission Control Protocol / Internet Protocol (TCP / IP) industry standard. In some cases, information (e.g., image data) may be input to the system disclosed herein via an input device (e.g., a disk drive, compact disk player, USB port, etc.). In some cases, information may be received by loading information from a storage device, such as a disk or flash drive.

[0262] The processor used to execute the algorithms or other processes described herein may include a microprocessor. The microprocessor may be any conventional general-purpose single-chip or multi-chip microprocessor, such as an Intel Pentium® processor. A particularly useful computer may utilize an Intel Ivybridge dual-12 core processor and an LSI RAID controller, with 128GB of RAM and a 2TB solid-state disk drive. Furthermore, the processor may include any conventional dedicated processor, such as a digital signal processor or a graphics processor. The processor typically has conventional address lines, conventional data lines, and one or more conventional control lines.

[0263] Embodiments disclosed herein may be implemented as methods (e.g., Figures 2 and 7), apparatus, systems, or articles using standard programming or engineering techniques for generating software, firmware, hardware, or any combination thereof. As used herein, the term “article” means code or logic implemented in hardware such as optical storage devices or computer-readable media, and in volatile or non-volatile memory devices. Such hardware may include, but is not limited to, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), complex programmable logic devices (CPLDs), programmable logic arrays (PLAs), microprocessors, or other similar processing devices. In certain embodiments, the information or algorithms described herein reside in a non-transient storage medium.

[0264] In certain embodiments, the computer implementation methods described herein (e.g., those discussed in Figure 11) can be performed in real time while multiple images of an object are being acquired. Such real-time analysis is particularly useful in nucleic acid sequencing applications where nucleic acid sequences are subjected to repeated cycles of fluid and detection processes. While it may often be beneficial to perform the methods described herein in real time or in the background for the analysis of sequencing data, it may also be beneficial to perform the methods described herein while other data acquisition or analysis algorithms are in process. Examples of real-time analysis methods that can be used in this method are commercially available from Illumina, Inc. (San Diego, Calif) and / or used in the MiSeq and HiSeq sequencing instruments described in U.S. Patent Application Publication 2012 / 0020537(A1), which is incorporated herein by reference.

[0265] In this application, the terms “cluster,” “well,” “sample,” “specimen,” and “fluorescent sample” are used interchangeably, as each well contains the corresponding cluster / sample / specimen / fluorescent sample. As defined herein, “sample” and its derivatives are used in the broadest sense and include any sample, culture, etc., suspected to contain a target. In some embodiments, a sample includes DNA, RNA, PNA, LNA, chimeric or hybrid nucleic acids. A sample may 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, e.g., genomic DNA, fresh-frozen or formalin-fixed paraffin-embedded nucleic acid sample. A sample may also originate 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 tumor and normal tissue samples (fitted), 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 the nucleic acid material may include nucleic acids obtained from newborns, such as those typically used in newborn screening.

[0266] Nucleic acid samples may include high molecular weight substances such as genomic DNA (gDNA). Samples may include low molecular weight substances such as nucleic acid molecules obtained from FFPE or stored DNA samples. In another embodiment, the low molecular weight substance includes enzymatically or mechanically fragmented DNA. Samples may include cell-free circulating DNA. In some embodiments, samples may include nucleic acid molecules obtained from biopsies, tumors, scrapes, swabs, blood, mucus, urine, plasma, semen, hair, laser-captured microanatomy, surgical excisions, and other clinical or laboratory-obtained samples. In some embodiments, samples may be epidemiological, agricultural, forensic, or pathogenic samples. In some embodiments, samples may include nucleic acid molecules obtained from animals such as humans or mammalian sources. In another embodiment, samples may include nucleic acid molecules obtained from non-mammalian sources such as plants, bacteria, viruses, or fungi. In some embodiments, the source of nucleic acid molecules may be stored or extinct samples or species.

[0267] Furthermore, 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 acids obtained from a crime scene, nucleic acids obtained from a missing persons DNA database, nucleic acids obtained from a laboratory associated with a forensic investigation, or forensic samples obtained by law enforcement agencies, one or more military forces or such personnel. The nucleic acid sample may be crude DNA comprising a purified sample or lysate derived, for example, from an oral swab, paper, cloth, or other substrate that can be impregnated with saliva, blood, or other bodily fluids. In some embodiments, the nucleic acid sample may include a small amount or fragmented portion of DNA, such as genomic DNA. In some embodiments, the target sequence may be present in one or more bodily fluids, including, but not limited to, blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequence may be obtained from hair, skin, tissue samples, autopsies, or the remains of victims. In some embodiments, nucleic acids containing one or more target sequences may be obtained from deceased animals or humans. In some embodiments, the target sequence may include nucleic acids obtained from non-human sources such as microorganisms, plant cells, or entomological sources. In some embodiments, the target sequence or amplified target sequence is intended for human identification. In some embodiments, the disclosure generally relates to a method for identifying features of forensic specimens. 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 specimen or human identification specimen containing at least one target sequence may be amplified using one or more of the target-specific primers disclosed herein, or using the primer criteria outlined herein.

[0268] The disclosed technology generates a variation correction coefficient for correcting variations in the inter-cluster intensity profile of image data. The disclosed technology can be implemented as a system, method, or product. One or more features of the embodiments can be combined with the base embodiments. Non-exclusive embodiments are taught to be combinable. One or more features of the embodiments can be combined with other embodiments. This disclosure periodically informs users of these options. The omission of some embodiments from the repetitive enumeration of these options should not be construed as limiting the combinations taught in the preceding sections. These descriptions are incorporated herein by reference to each of the following embodiments.

[0269] Other embodiments of the methods described in this section may include a non-temporary computer-readable storage medium that stores instructions that can be executed by a processor to perform any of the methods described above. Yet another embodiment of the methods described in this section may include a system that includes memory and one or more processors that can operate to execute instructions stored in memory to perform any of the methods described above.

[0270] In another embodiment, variation correction is performed for non-intensity data, such as pH changes induced by the release of hydrogen ions during molecular elongation. The pH change is detected and converted into a voltage change proportional to the number of incorporated bases (e.g., in the case of Ion Torrent).

[0271] In yet another embodiment, non-intensity data is constructed from nanopore detection, which uses a biosensor to measure the disruption of current as the analyte passes through or near the openings of nanopores, while simultaneously determining base identity. For example, Oxford Nanopore Technologies (ONT) sequencing is based on the concept of passing a single strand of DNA (or RNA) through a membrane via nanopores and applying a potential difference across the membrane. The nucleotides present in the pores affect the electrical resistance of the pores, and therefore, current measurements over time can indicate the sequence of DNA bases passing through the pores. This current signal (due to its appearance when plotted as "crushing") is raw data collected by an ONT sequencer. These measurements are stored as 16-bit integer data acquisition (DAC) values ​​taken at a frequency of 4 kHz (e.g.). Using a DNA strand rate of ~450 base pairs per second, this gives, on average, about 9 raw observations per base. This signal is then processed to identify the disruption of the opening signal corresponding to individual readings. The extension of these raw signals is a process that involves base calling and converting DAC values ​​into DNA base sequences. In some embodiments, non-intensity data includes normalized or scaled DAC values.

[0272] The disclosed technology, or one or more embodiments of its elements, can be implemented in the form of a computer product including a non-temporary computer-readable storage medium having computer-usable program code for performing the indicated method steps. Furthermore, the disclosed technology, or one or more embodiments of its elements, can be implemented in the form of a device including memory and at least one processor coupled to the memory and operating to perform the exemplary method steps. Furthermore, in another aspect, the disclosed technology, or one or more embodiments of its elements, can be implemented in the form of means for performing one or more of the method steps described herein, the means may include (i) a hardware module, (ii) a software module running on one or more hardware processors, or (iii) a combination of hardware and software modules, where any of (i) to (iii) implements a particular technology described herein, and the software module is stored in a computer-readable storage medium (or a plurality of such media).

[0273] This application uses the terms "cumulative intensity correction parameter(s)" and "intermediate term(s)" interchangeably.

[0274] This application uses the terms "pure intensity" and "distributed intensity" interchangeably.

[0275] This application uses the terms "strength profile" and "constellation" interchangeably.

[0276] This application uses the terms "variability correction coefficient (multiple possible)" and "intensity correction coefficient (multiple possible)" interchangeably.

[0277] This application uses the terms "amplification factor" and "scale factor" interchangeably.

[0278] This application uses the terms "offset coefficient(s)" and "offset(s)" interchangeably.

[0279] This application uses the terms "target cluster" and "specific cluster" interchangeably.

[0280] This application uses the terms “next,” “subsequent,” and “successive” interchangeably.

[0281] This application uses the terms "nucleotide" and "base" interchangeably.

[0282] This application uses the terms "cumulative intensity correction parameter determination system" and "accumulator" interchangeably.

[0283] This application uses the terms "cumulative intensity correction parameter determination system" and "accumulator" interchangeably.

[0284] item 1. A computer implementation method for making base calls to a target cluster, wherein the method is: Regarding the target cluster, The current channel-specific intensity registered in the current sequencing cycle of the sequencing run is read from the base-specific intensity distribution in which the target cluster is base-called in the current sequencing cycle, and Reading the current channel-specific distribution intensity from the centroid of the base-specific intensity distribution, Based on the current channel-specific intensity and current channel-specific distribution intensity, determine the current set of intensity correction parameters for the current sequencing cycle, and The current set of cumulative intensity correction parameters for the current sequencing cycle is determined by accumulating the current set of intensity correction parameters and the preceding set of cumulative intensity correction parameters for the preceding sequencing cycle of the sequencing run. Based on the current set of cumulative intensity correction parameters, determine the current amplification coefficient and the current channel-specific offset coefficient for the current sequencing cycle, Using the current amplification coefficient and the current channel-specific offset coefficient, the next channel-specific intensity registered in the next sequencing cycle of the sequencing run is corrected, and the corrected next channel-specific intensity is generated for the next sequencing cycle. A computer implementation method comprising: base-calling the target cluster in the next sequencing cycle based on the corrected next channel-specific intensity.

[0285] 2. The computer implementation method described in item 1, wherein the set of current intensity correction parameters includes the current channel-specific distribution intensity, the current channel-specific intensity error, the distance from the current distribution centroid to the origin, and a similarity measurement of the current distribution intensity versus intensity error.

[0286] 3. The current channel-specific intensity error is the difference between the current channel-specific intensity and the current channel-specific distribution intensity, as described in the computer implementation method in item 2.

[0287] 4. The distance from the current distribution centroid to the origin is the Euclidean distance between the centroid and the origin of the multidimensional space containing the base-specific intensity distribution, as described in the computer implementation method in item 2.

[0288] 5. The computer implementation method described in item 4, wherein the multidimensional space is at least one of Cartesian space, polar space, cylindrical space, and spherical space.

[0289] 6. The computer implementation method described in item 2, where the similarity measurement of current distribution intensity versus intensity error is the sum of the channel-unit dot products between the current channel-specific distribution intensity and the current channel-specific intensity error.

[0290] 7. The computer implementation method described in item 1, wherein the current set of cumulative intensity correction parameters is the sum of the current intensity correction parameters in the current set of intensity correction parameters and the preceding cumulative intensity correction parameters in the preceding set of cumulative intensity correction parameters, in units of intensity correction parameters.

[0291] 8. The current set of cumulative intensity correction parameters is the average of the current intensity correction parameter and the preceding cumulative intensity correction parameter in units of intensity correction parameter, as described in item 7, computer implementation method.

[0292] 9. The computer implementation method described in item 1, wherein the preceding set of cumulative intensity correction parameters and the current set of cumulative intensity correction parameters are stored in a quantized fixed-bit width format.

[0293] 10. The current channel-specific offset coefficients are configured to be identical, as per the computer implementation method described in item 1.

[0294] 11. The computer implementation method described in item 10, wherein the current cumulative intensity correction parameter in the set of current cumulative intensity correction parameters includes a first common current cumulative intensity correction parameter for the current channel-specific distribution intensity and a second common current cumulative intensity correction parameter for the current channel-specific intensity error.

[0295] 12. The computer implementation method described in item 1, wherein the current channel-specific offset coefficient is subtracted channel by channel from the next channel-specific intensity to generate the next channel-specific shift intensity, and the next channel-specific shift intensity is divided by the current amplification coefficient to generate the corrected next channel-specific intensity.

[0296] 13. A computer implementation of item 1, further comprising using a weighting function to combine the initial amplification coefficient with the current amplification coefficient and the initial channel-specific offset coefficient with the current channel-specific offset coefficient to generate a weighted current amplification coefficient and a weighted current channel-specific offset coefficient for the current sequencing cycle.

[0297] 14. The weighting function is the minimum weight (w min ) is applied to the initial amplification coefficient and the initial channel-specific offset coefficient, and the maximum weight (wmax Apply ) to the current amplification coefficient and the current channel-specific offset coefficient, w min =(1-w max The computer implementation method described in item 13.

[0298] 15. Maximum weight (w max ) is defined as (cp) / c, where c is the index of the current sequence determination cycle and p is a number between 2 and 7, as described in the computer implementation method in item 14.

[0299] 16. A computer implementation method of item 15, further comprising correcting the next channel-specific intensity using a weighted current amplification factor and a weighted current channel-specific offset factor, and generating a corrected next channel-specific intensity.

[0300] 17. Using the maximum likelihood method, generate the current maximum likelihood weights for the current amplification coefficient and the current channel-specific offset coefficient for the current sequencing cycle, Applying the current maximum likelihood weights to the current amplification coefficient and the current channel-specific offset coefficient, respectively, generates the current amplification coefficient and the current channel-specific offset coefficient with maximum likelihood weights for the current sequencing cycle. A computer implementation method according to item 1, further comprising correcting the next channel-specific intensity using the current amplification coefficient and the current channel-specific offset coefficient, which are weighted to the maximum likelihood, and generating the corrected next channel-specific intensity.

[0301] 18. Applying a damping factor to the current intensity correction parameter generates a damped current intensity correction parameter for the current sequencing cycle, The computer implementation method of item 1 further comprises determining the current cumulative intensity correction parameter by accumulating the attenuated current intensity correction parameter and the preceding cumulative intensity correction parameter in units of intensity correction parameter.

[0302] 19. The computer implementation method described in item 18, wherein the decay factor is fixed for a certain number of sequencing cycles of a sequencing run and is then decayed exponentially based on decay logic.

[0303] 20. The attenuation logic is 1 - 1 / tau, where tau is a predefined number, as described in item 19 for computer implementation.

[0304] 21. A computer implementation of item 1, further comprising repeatedly reading, reading, determining, determining, determining, using, and base calling for a target cluster in a series of sequencing cycles of a sequencing run.

[0305] 22. A computer implementation of the method described in item 1, further comprising performing in parallel read, read, determine, determine, determine, use, and base call operations on multiple clusters.

[0306] 23. The closed-form equations for the current set of intensity correction parameters, the current set of cumulative intensity correction parameters, the current amplification coefficient, and the current channel-specific offset coefficient are determined using the least squares method, as described in the computer implementation method of item 1.

[0307] 24. The current channel-specific intensity is calculated using the computer implementation method described in item 1, corresponding to each intensity channel.

[0308] 25. The current channel-specific offset coefficient is the difference in channel units between the current channel-specific intensity and the current channel-specific distribution intensity, as described in the computer implementation method in item 1.

[0309] 26. A non-temporary computer-readable storage medium in which computer program instructions are stored for base calling a target cluster, wherein the instructions, when executed on a processor, Regarding the target cluster, The current channel-specific intensity registered in the current sequencing cycle of the sequencing run is read from the base-specific intensity distribution in which the target cluster is base-called in the current sequencing cycle, and Reading the current channel-specific distribution intensity from the centroid of the base-specific intensity distribution, Based on the current channel-specific intensity and current channel-specific distribution intensity, determine the current set of intensity correction parameters for the current sequencing cycle, and The current set of cumulative intensity correction parameters for the current sequencing cycle is determined by accumulating the current set of intensity correction parameters and the preceding set of cumulative intensity correction parameters for the preceding sequencing cycle of the sequencing run. Based on the current set of cumulative intensity correction parameters, determine the current amplification coefficient and the current channel-specific offset coefficient for the current sequencing cycle, Using the current amplification coefficient and the current channel-specific offset coefficient, the next channel-specific intensity registered in the next sequencing cycle of the sequencing run is corrected, and the corrected next channel-specific intensity is generated for the next sequencing cycle. A non-temporary computer-readable storage medium that implements a method including base calling the target cluster in the next sequencing cycle based on the corrected next channel-specific intensity.

[0310] 27. A non-temporary computer-readable storage medium as described in item 26, which ultimately implements each of the items that are subordinate to item 1.

[0311] 28. A system comprising one or more processors coupled to memory, wherein the memory is loaded with computer instructions for base calling a target cluster, and when the instructions are executed on the processor, Regarding the target cluster, The current channel-specific intensity registered in the current sequencing cycle of the sequencing run is read from the base-specific intensity distribution in which the target cluster is base-called in the current sequencing cycle, and Reading the current channel-specific distribution intensity from the centroid of the base-specific intensity distribution, Based on the current channel-specific intensity and current channel-specific distribution intensity, determine the current set of intensity correction parameters for the current sequencing cycle, and The current set of cumulative intensity correction parameters for the current sequencing cycle is determined by accumulating the current set of intensity correction parameters and the preceding set of cumulative intensity correction parameters for the preceding sequencing cycle of the sequencing run. Based on the current set of cumulative intensity correction parameters, determine the current amplification coefficient and the current channel-specific offset coefficient for the current sequencing cycle, Using the current amplification coefficient and the current channel-specific offset coefficient, the next channel-specific intensity registered in the next sequencing cycle of the sequencing run is corrected, and the corrected next channel-specific intensity is generated for the next sequencing cycle. The system implements actions including base calling the target cluster in the next sequencing cycle based on the corrected next channel-specific intensity.

[0312] 29. The system described in item 28, which ultimately implements each item that is subordinate to item 1.

[0313] 30. A computer implementation method for making base calls to a target cluster, the method being: Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Based on current intensity data and historical intensity data, determine the scale correction coefficient and the channel-specific shift correction coefficient. The process involves correcting the following intensity data using a scale correction coefficient and a channel-specific shift correction coefficient, and generating the corrected following intensity data. The following intensity data is for the next sequencing cycle of the sequencing run, and A computer implementation method comprising: base calling the target cluster in the next sequencing cycle based on the corrected subsequent intensity data.

[0314] 31. The computer implementation method described in item 30, which ultimately implements each item that is subordinate to item 1.

[0315] 32. A non-temporary computer-readable storage medium in which computer program instructions are stored for base calling a target cluster, wherein the instructions, when executed on a processor, Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Based on current intensity data and historical intensity data, determine the scale correction coefficient and the channel-specific shift correction coefficient. The process involves correcting the following intensity data using a scale correction coefficient and a channel-specific shift correction coefficient, and generating the corrected following intensity data. The following intensity data is for the next sequencing cycle of the sequencing run, and A non-temporary computer-readable storage medium that implements a method including base calling the target cluster in the next sequencing cycle based on the corrected next intensity data.

[0316] 33. A non-temporary computer-readable storage medium as described in item 32, which ultimately implements each of the items that are subordinate to item 1.

[0317] 34. A system comprising one or more processors coupled to memory, wherein the memory is loaded with computer instructions for base calling a target cluster, and when the instructions are executed on the processor, Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Based on current intensity data and historical intensity data, determine the scale correction coefficient and the channel-specific shift correction coefficient. The process involves correcting the following intensity data using a scale correction coefficient and a channel-specific shift correction coefficient, and generating the corrected following intensity data. The following intensity data is for the next sequencing cycle of the sequencing run, and A system that implements actions including base calling the target cluster in the next sequencing cycle based on the corrected intensity data.

[0318] 35. The system described in item 34, which ultimately implements each item that is subordinate to item 1.

[0319] 36. A computer implementation method for making base calls to a target cluster, the method being: Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Using the current intensity data and historical intensity data, correct the next intensity data and generate the corrected next intensity data, The following intensity data is for the next sequencing cycle of the sequencing run, and A computer implementation method comprising: base calling the target cluster in the next sequencing cycle based on the corrected subsequent intensity data.

[0320] 37. A computer implementation method as described in item 36, which ultimately implements each item that is subordinate to item 1.

[0321] 38. A non-temporary computer-readable storage medium in which computer program instructions are stored for base calling a target cluster, wherein the instructions, when executed on a processor, Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Using the current intensity data and historical intensity data, correct the next intensity data and generate the corrected next intensity data, The following intensity data is for the next sequencing cycle of the sequencing run, and A non-temporary computer-readable storage medium that implements a method including base calling the target cluster in the next sequencing cycle based on the corrected next intensity data.

[0322] 39. A non-temporary computer-readable storage medium as described in item 38, which ultimately implements each of the items that are subordinate to item 1.

[0323] 40. A system comprising one or more processors coupled to memory, wherein the memory is loaded with computer instructions for base calling a target cluster, and when the instructions are executed on the processor, Regarding the target cluster, This involves accessing current intensity data and historical intensity data, The current intensity data is for the current sequencing cycle of the sequencing run. The historical intensity data is for one or more preceding sequencing cycles of the sequencing run, and Using the current intensity data and historical intensity data, correct the next intensity data and generate the corrected next intensity data, The following intensity data is for the next sequencing cycle of the sequencing run, and A system that implements actions including base calling the target cluster in the next sequencing cycle based on the corrected intensity data.

[0324] 41. The system described in item 40, which ultimately implements each item that is subordinate to item 1.

[0325] The present invention is disclosed with reference to the preferred embodiments and examples described above, but it should be understood that these embodiments are intended to be illustrative rather than restrictive. Those skilled in the art will readily be able to modify and combine the invention, and such modifications and combinations will be considered to fall within the spirit of the invention and the scope of the following claims. [Explanation of symbols]

[0326] 1 cluster 2 clusters 3 clusters 302 Least Squares Determinant 312 Strength Modeler 322 Minimizer 1500 Computer Systems 1510 Memory subsystem 1522 Memory subsystem 1532 RAM 1534 ROM 1536 File Storage Subsystem 1538 User Interface Input Devices 1555 Bus Subsystem 1572 CPU 1574 Network Interface Subsystem 1576 User Interface Output Device 1578 processors

Claims

1. A sequencing device, A system comprising at least one processor coupled to memory, wherein computer instructions are loaded into the memory, and the computer instructions are executed by the at least one processor, With respect to the target cluster, the sequencing instrument accesses the current intensity registered from the target cluster and other clusters for the current sequencing cycle, For the target cluster, the current set of cumulative intensity correction parameters for the current sequencing cycle is determined by accumulating the intensity errors measured in the current sequencing cycle and one or more preceding sequencing cycles. For the target cluster, the current amplification coefficient is determined based on the current set of cumulative intensity correction parameters, taking into account the relative intensity variation of the target cluster to other clusters. Based on the current amplification coefficient, the next intensity to be registered for the next sequencing cycle is corrected, Using the sequencing device, determine the base call to the target cluster in the next sequencing cycle based on the corrected next intensity. Perform an action that includes system.

2. When executed by at least one of the aforementioned processors, For the aforementioned target cluster, accessing the current distribution intensity from the base-specific intensity distribution, The set of current cumulative intensity correction parameters for the current sequencing cycle is determined further based on the current distribution intensity. Further includes computer instructions that perform operations including, The system according to claim 1.

3. The current intensity includes the current channel-specific intensity registered for the current sequencing cycle. The current distribution intensity includes the current channel-specific distribution intensity from the base-specific intensity distribution. The system according to claim 2.

4. Determining the current amplification coefficient that takes into account the intensity error includes determining a coefficient that scales the current intensity for the target cluster relative to the intensity of the other clusters on the flow cell. The system according to claim 1.

5. The current amplification coefficient scales the current intensity for the target cluster relative to the channel-specific or cluster-specific intensity of the other clusters on the flow cell. The system according to claim 1.

6. When executed by at least one of the aforementioned processors, The initial amplification coefficient is combined with the current amplification coefficient to generate the modified current amplification coefficient, Based on the modified current amplification coefficient, the next intensity registered for the next sequencing cycle is corrected. Further includes computer instructions that perform actions including The system according to claim 1.

7. When executed by at least one of the aforementioned processors, By using a weighting function, a first weight is applied to the initial amplification coefficient, Using the aforementioned weighting function, a second weight is applied to the current amplification coefficient, To generate a weighted current amplification coefficient based on the first weighted initial amplification coefficient and the second weighted current amplification coefficient, The computer instruction further includes an operation that involves combining the initial amplification coefficient with the current amplification coefficient to generate the modified current amplification coefficient. The system according to claim 6.

8. When executed by at least one of the aforementioned processors, Using a weighting function, the initial amplification coefficient is assigned a minimum weight (w min ) Applying Using the aforementioned weighting function, the initial amplification coefficient is given the maximum weight (w max ) to apply, A weighted current amplification coefficient is generated based on the initial amplification coefficient weighted by the minimum weight and the current amplification coefficient weighted by the maximum weight. The computer instruction further includes an operation that involves combining the initial amplification coefficient with the current amplification coefficient to generate the modified current amplification coefficient. The system according to claim 6.

9. When executed by at least one of the aforementioned processors, For the current sequencing cycle, an offset coefficient is determined to offset the cluster-specific intensity based on the current set of cumulative intensity correction parameters, For the next sequence determination cycle, the next intensity is corrected based on the current amplification coefficient and the offset coefficient. Further includes computer instructions that perform actions including The system according to claim 1.

10. The computer instruction, when executed by at least one processor, further includes an operation that includes correcting the next intensity for the next sequencing cycle by correcting the next intensity for inter-cluster intensity variations between intensity values ​​from different clusters for each base. The system according to claim 1.

11. A non-temporary computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed by an array determination device and at least one processor, are directed to the system. With respect to the target cluster, the sequencing instrument accesses the current intensity registered from the target cluster and other clusters for the current sequencing cycle, For the target cluster, the current set of cumulative intensity correction parameters for the current sequencing cycle is determined by accumulating the intensity errors measured in the current sequencing cycle and one or more preceding sequencing cycles. With respect to the target cluster, the current amplification coefficient is determined based on the set of current cumulative intensity correction parameters, taking into account the relative intensity variation of the target cluster with respect to other clusters. Based on the current amplification coefficient, the next intensity to be registered for the next sequencing cycle is corrected, Using the sequencing device, determine the base call to the target cluster in the next sequencing cycle based on the corrected next intensity, Make them do it Non-temporary computer-readable storage medium.

12. When executed by the at least one processor, the system will For the aforementioned target cluster, accessing the current distribution intensity from the base-specific intensity distribution, The set of current cumulative intensity correction parameters for the current sequencing cycle is determined based on the current distribution intensity, Further store the computer instructions that will perform the action. The non-temporary computer-readable storage medium according to claim 11.

13. A further storage of a computer instruction, which, when executed by at least one processor, causes the system to determine the current amplification coefficient that takes into account intensity fluctuations by determining a coefficient that scales the current intensity for the target cluster relative to the intensity of the other clusters on the flow cell. The non-temporary computer-readable storage medium according to claim 11.

14. The current amplification coefficient scales the current intensity for the target cluster relative to the channel-specific or cluster-specific intensity of the other clusters on the flow cell. The non-temporary computer-readable storage medium according to claim 11.

15. When executed by the at least one processor, the system will The initial amplification coefficient is combined with the current amplification coefficient to generate the modified current amplification coefficient, Based on the modified current amplification coefficient, the next intensity registered for the next sequencing cycle is corrected, Further store the computer instructions that will perform the action. The non-temporary computer-readable storage medium according to claim 11.

16. When executed by the at least one processor, the system will By using a weighting function, a first weight is applied to the initial amplification coefficient, Using the aforementioned weighting function, a second weight is applied to the current amplification coefficient, To generate a weighted current amplification coefficient based on the first weighted initial amplification coefficient and the second weighted current amplification coefficient, The initial amplification coefficient is combined with the current amplification coefficient to generate the modified current amplification coefficient. Further store the computer instructions that will perform the action. The non-temporary computer-readable storage medium according to claim 15.

17. When executed by the at least one processor, the system will Using a weighting function, the initial amplification coefficient is assigned a minimum weight (w min ) Applying Using the aforementioned weighting function, the initial amplification coefficient is given the maximum weight (w max ) to apply, Based on the initial amplification coefficient weighted by the minimum weight and the current amplification coefficient weighted by the maximum weight, a weighted current amplification coefficient is generated. By combining the initial amplification coefficient with the current amplification coefficient, the modified current amplification coefficient is generated. Further store the computer instructions that will perform the action. The non-temporary computer-readable storage medium according to claim 15.

18. A method implemented in a computer, With respect to the target cluster, the sequencing instrument accesses the current intensity registered from the target cluster and other clusters for the current sequencing cycle, The steps include determining a set of current cumulative intensity correction parameters for the current sequencing cycle for the target cluster by accumulating the intensity errors measured in the current sequencing cycle and one or more preceding sequencing cycles, For the target cluster, the step of determining a current amplification coefficient that takes into account the relative intensity variation of the target cluster with respect to other clusters, based on the set of current cumulative intensity correction parameters, The steps include correcting the next intensity to be registered for the next sequencing cycle based on the current amplification coefficient, The step of using the sequencing device to determine the base call to the target cluster in the next sequencing cycle based on the corrected next intensity. method.

19. For the current sequencing cycle, the steps include determining an offset coefficient to offset the cluster-specific intensity based on the current set of cumulative intensity correction parameters, The next sequencing cycle further comprises the step of correcting the next intensity based on the current amplification coefficient and the offset coefficient. The method according to claim 18.

20. The step of correcting the next intensity further comprises correcting the next intensity for the next sequencing cycle by correcting the next intensity for inter-cluster intensity variations between different clusters. The method according to claim 18.

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