Equalizer-based signal correction for base calling

Abstract

20 26 20 49 72 25 J un 2 02 6 E Q U I L I Z E R - B A S E D S I G N A L C O R R E C T I O N F O R B A S E C A L L I N G 2 5 J u n 2 0 2 6 A B S T R A C T 2 0 2 6 2 0 4 9 7 2

Description

[0236] As used herein, “register”, “registering”, “registration” and like terms refer to any process to correlate signals in an image or data set from a first time point or perspective with signals in an image or data set from another time point or perspective. For example, registration can be used to align signals from a set of images to form a template. In another example, registration can be used to align signals from other images to a template. One signal may be directly or indirectly registered to another signal. For example, a signal from image “S” may be registered to image “G” directly. As another example, a signal from image “N” may be directly registered to image “G”, or alternatively, the signal from image “N” may be registered to image “S”, which has previously been registered to image “G”. Thus, the signal from image “N” is indirectly registered to image “G”.

[0237] As used herein, the term “fiducial” is intended to mean a distinguishable point of reference in or on an object. The point of reference can be, for example, a mark, second object, shape, edge, area, irregularity, channel, pit, post or the like. The point of reference can be present in an image of the object or in another data set derived from detecting the object. The point of reference can be specified by an x and / or y coordinate in a plane of the object. Alternatively or additionally, the point of reference can be specified by a z coordinate that is orthogonal to the xy plane, for example, being defined by the relative locations of the object and a detector. One or more coordinates for a point of reference can be specified relative to one or more other analytes of an object or of an image or other data set derived from the object.

[0238] As used herein, the term “optical signal” is intended to include, for example, fluorescent, luminescent, scatter, or absorption signals. Optical signals can be detected in the ultraviolet (UV) range (about 200 to 390 nm), visible (VIS) range (about 391 to 770 nm), infrared (IR) range (about 0.771 to 25 microns), or other range of the electromagnetic spectrum. Optical signals can be detected in a way that excludes all or part of one or more of these ranges.

[0239] As used herein, the term “signal level” is intended to mean an amount or quantity of detected energy or coded information that has a desired or predefined characteristic. For example, an optical signal can be quantified by one or more of intensity, wavelength, energy, frequency, power, luminance or the like. Other signals can be quantified according to characteristics such as voltage, current, electric field strength, magnetic field strength, frequency, 2026204972   25 Jun 2026 power, temperature, etc. Absence of signal is understood to be a signal level of zero or a signal level that is not meaningfully distinguished from noise.

[0240] As used herein, the term “simulate” is intended to mean creating a representation or model of a physical thing or action that predicts characteristics of the thing or action. The representation or model can in many cases be distinguishable from the thing or action. For example, the representation or model can be distinguishable from a thing with respect to one or more characteristic such as color, intensity of signals detected from all or part of the thing, size, or shape. In particular implementations, the representation or model can be idealized, exaggerated, muted, or incomplete when compared to the thing or action. Thus, in some implementations, a representation of model can be distinguishable from the thing or action that it represents, for example, with respect to at least one of the characteristics set forth above. The representation or model can be provided in a computer readable format or medium such as one or more of those set forth elsewhere herein.

[0241] As used herein, the term “specific signal” is intended to mean detected energy or coded information that is selectively observed over other energy or information such as background energy or information. For example, a specific signal can be an optical signal detected at a particular intensity, wavelength or color; an electrical signal detected at a particular frequency, power or field strength; or other signals known in the art pertaining to spectroscopy and analytical detection.

[0242] As used herein, the term “swath” is intended to mean a rectangular portion of an object. The swath can be an elongated strip that is scanned by relative movement between the object and a detector in a direction that is parallel to the longest dimension of the strip. Generally, the width of the rectangular portion or strip will be constant along its full length. Multiple swaths of an object can be parallel to each other. Multiple swaths of an object can be adjacent to each other, overlapping with each other, abutting each other, or separated from each other by an interstitial area.

[0243] As used herein, the term “variance” is intended to mean a difference between that which is expected and that which is observed or a difference between two or more observations. For example, variance can be the discrepancy between an expected value and a measured value. Variance can be represented using statistical functions such as standard deviation, the square of standard deviation, coefficient of variation or the like.

[0244] As used herein, the term “xy coordinates” is intended to mean information that specifies location, size, shape, and / or orientation in an xy plane. The information can be, for example, numerical coordinates in a Cartesian system. The coordinates can be provided relative to one or both of the x and y axes or can be provided relative to another location in the xy plane. 2026204972   25 Jun 2026 For example, coordinates of a analyte of an object can specify the location of the analyte relative to location of a fiducial or other analyte of the object.

[0245] As used herein, the term “xy plane” is intended to mean a 2 dimensional area defined by straight line axes x and y. When used in reference to a detector and an object observed by the detector, the area can be further specified as being orthogonal to the direction of observation between the detector and object being detected.

[0246] As used herein, the term “z coordinate” is intended to mean information that specifies the location of a point, line or area along an axes that is orthogonal to an xy plane. In particular implementations, the z axis is orthogonal to an area of an object that is observed by a detector. For example, the direction of focus for an optical system may be specified along the z axis.

[0247] In some implementations, acquired signal data is transformed using an affine transformation. In some such implementations, template generation makes use of the fact that the affine transforms between color channels are consistent between runs. Because of this consistency, a set of default offsets can be used when determining the coordinates of the analytes in a specimen. For example, a default offsets file can contain the relative transformation (shift, scale, skew) for the different channels relative to one channel, such as the A channel. In other implementations, however, the offsets between color channels drift during a run and / or between runs, making offset-driven template generation difficult. In such implementations, the methods and systems provided herein can utilize offset-less template generation, which is described further below.

[0248] In some aspects of the above implementations, the system can comprise a flow cell. In some aspects, the flow cell comprises lanes, or other configurations, of tiles, wherein at least some of the tiles comprise one or more arrays of analytes. In some aspects, the analytes comprise a plurality of molecules such as nucleic acids. In certain aspects, the flow cell is configured to deliver a labeled nucleotide base to an array of nucleic acids, thereby extending a primer hybridized to a nucleic acid within a analyte so as to produce a signal corresponding to a analyte comprising the nucleic acid. In preferred implementations, the nucleic acids within a analyte are identical or substantially identical to each other.

[0249] In some of the systems for image analysis described herein, each image in the set of images includes color signals, wherein a different color corresponds to a different nucleotide base. In some aspects, each image of the set of images comprises signals having a single color selected from at least four different colors. In some aspects, each image in the set of images comprises signals having a single color selected from four different colors. In some of the systems described herein, nucleic acids can be sequenced by providing four different labeled nucleotide bases to the array of molecules so as to produce four different images, each image 2026204972   25 Jun 2026 comprising signals having a single color, wherein the signal color is different for each of the four different images, thereby producing a cycle of four color images that corresponds to the four possible nucleotides present at a particular position in the nucleic acid. In certain aspects, the system comprises a flow cell that is configured to deliver additional labeled nucleotide bases to the array of molecules, thereby producing a plurality of cycles of color images.

[0250] In preferred implementations, the methods provided herein can include determining whether a processor is actively acquiring data or whether the processor is in a low activity state. Acquiring and storing large numbers of high-quality images typically requires massive amounts of storage capacity. Additionally, once acquired and stored, the analysis of image data can become resource intensive and can interfere with processing capacity of other functions, such as ongoing acquisition and storage of additional image data. Accordingly, as used herein, the term low activity state refers to the processing capacity of a processor at a given time. In some implementations, a low activity state occurs when a processor is not acquiring and / or storing data. In some implementations, a low activity state occurs when some data acquisition and / or storage is taking place, but additional processing capacity remains such that image analysis can occur at the same time without interfering with other functions.

[0251] As used herein, “identifying a conflict” refers to identifying a situation where multiple processes compete for resources. In some such implementations, one process is given priority over another process. In some implementations, a conflict may relate to the need to give priority for allocation of time, processing capacity, storage capacity or any other resource for which priority is given. Thus, in some implementations, where processing time or capacity is to be distributed between two processes such as either analyzing a data set and acquiring and / or storing the data set, a conflict between the two processes exists and can be resolved by giving priority to one of the processes.

[0252] Also provided herein are systems for performing image analysis. The systems can include a processor; a storage capacity; and a program for image analysis, the program comprising instructions for processing a first data set for storage and the second data set for analysis, wherein the processing comprises acquiring and / or storing the first data set on the storage device and analyzing the second data set when the processor is not acquiring the first data set. In certain aspects, the program includes instructions for identifying at least one instance of a conflict between acquiring and / or storing the first data set and analyzing the second data set; and resolving the conflict in favor of acquiring and / or storing image data such that acquiring and / or storing the first data set is given priority. In certain aspects, the first data set comprises image files obtained from an optical imaging device. In certain aspects, the system further 2026204972   25 Jun 2026 comprises an optical imaging device. In some aspects, the optical imaging device comprises a light source and a detection device.

[0253] As used herein, the term “program” refers to instructions or commands to perform a task or process. The term “program” can be used interchangeably with the term module. In certain implementations, a program can be a compilation of various instructions executed under the same set of commands. In other implementations, a program can refer to a discrete batch or file.

[0254] Set forth below are some of the surprising effects of utilizing the methods and systems for performing image analysis set forth herein. In some sequencing implementations, an important measure of a sequencing system's utility is its overall efficiency. For example, the amount of mappable data produced per day and the total cost of installing and running the instrument are important aspects of an economical sequencing solution. To reduce the time to generate mappable data and to increase the efficiency of the system, real-time base calling can be enabled on an instrument computer and can run in parallel with sequencing chemistry and imaging. This allows much of the data processing and analysis to be completed before the sequencing chemistry finishes. Additionally, it can reduce the storage required for intermediate data and limit the amount of data that needs to travel across the network.

[0255] While sequence output has increased, the data per run transferred from the systems provided herein to the network and to secondary analysis processing hardware has substantially decreased. By transforming data on the instrument computer (acquiring computer), network loads are dramatically reduced. Without these on-instrument, off-network data reduction techniques, the image output of a fleet of DNA sequencing instruments would cripple most networks.

[0256] The widespread adoption of the high-throughput DNA sequencing instruments has been driven in part by ease of use, support for a range of applications, and suitability for virtually any lab environment. The highly efficient algorithms presented herein allow significant analysis functionality to be added to a simple workstation that can control sequencing instruments. This reduction in the requirements for computational hardware has several practical benefits that will become even more important as sequencing output levels continue to increase. For example, by performing image analysis and base calling on a simple tower, heat production, laboratory footprint, and power consumption are kept to a minimum. In contrast, other commercial sequencing technologies have recently ramped up their computing infrastructure for primary analysis, with up to five times more processing power, leading to commensurate increases in heat output and power consumption. Thus, in some implementations, the computational 2026204972   25 Jun 2026 efficiency of the methods and systems provided herein enables customers to increase their sequencing throughput while keeping server hardware expenses to a minimum.

[0257] Accordingly, in some implementations, the methods and / or systems presented herein act as a state machine, keeping track of the individual state of each specimen, and when it detects that a specimen is ready to advance to the next state, it does the appropriate processing and advances the specimen to that state. A more detailed example of how the state machine monitors a file system to determine when a specimen is ready to advance to the next state according to a preferred implementation is set forth in Example 1 below.

[0258] In preferred implementations, the methods and systems provided herein are multithreaded and can work with a configurable number of threads. Thus, for example in the context of nucleic acid sequencing, the methods and systems provided herein are capable of working in the background during a live sequencing run for real-time analysis, or it can be run using a preexisting set of image data for off-line analysis. In certain preferred implementations, the methods and systems handle multi-threading by giving each thread its own subset of specimen for which it is responsible. This minimizes the possibility of thread contention.

[0259] A method of the present disclosure can include a step of obtaining a target image of an object using a detection apparatus, wherein the image includes a repeating pattern of analytes on the object. Detection apparatus that are capable of high resolution imaging of surfaces are particularly useful. In particular implementations, the detection apparatus will have sufficient resolution to distinguish analytes at the densities, pitches, and / or analyte sizes set forth herein. Particularly useful are detection apparatus capable of obtaining images or image data from surfaces. Example detectors are those that are configured to maintain an object and detector in a static relationship while obtaining an area image. Scanning apparatus can also be used. For example, an apparatus that obtains sequential area images (e.g., so called ‘step and shoot’ detectors) can be used. Also useful are devices that continually scan a point or line over the surface of an object to accumulate data to construct an image of the surface. Point scanning detectors can be configured to scan a point (i.e., a small detection area) over the surface of an object via a raster motion in the x-y plane of the surface. Line scanning detectors can be configured to scan a line along the y dimension of the surface of an object, the longest dimension of the line occurring along the x dimension. It will be understood that the detection device, object or both can be moved to achieve scanning detection. Detection apparatus that are particularly useful, for example in nucleic acid sequencing applications, are described in US Pat App. Pub. Nos. 2012 / 0270305 A1; 2013 / 0023422 A1; and 2013 / 0260372 A1; and U.S. Pat. Nos. 5,528,050; 5,719,391; 8,158,926 and 8,241,573, each of which is incorporated herein by reference. 2026204972   25 Jun 2026

[0260] The implementations disclosed herein may be implemented as a method, apparatus, system or article of manufacture using programming or engineering techniques to produce software, firmware, hardware, or any combination thereof. The term “article of manufacture” as used herein refers to code or logic implemented in hardware or computer readable media such as optical storage devices, and volatile or non-volatile memory devices. Such hardware may include, but is not limited to, field programmable gate arrays (FPGAs), coarse grained reconfigurable architectures (CGRAs), application-specific integrated circuits (ASICs), complex programmable logic devices (CPLDs), programmable logic arrays (PLAs), microprocessors, or other similar processing devices. In particular implementations, information or algorithms set forth herein are present in non-transient storage media.

[0261] In particular implementations, a computer implemented method set forth herein can occur in real time while multiple images of an object are being obtained. Such real time analysis is particularly useful for nucleic acid sequencing applications wherein an array of nucleic acids is subjected to repeated cycles of fluidic and detection steps. Analysis of the sequencing data can often be computationally intensive such that it can be beneficial to perform the methods set forth herein in real time or in the background while other data acquisition or analysis algorithms are in process. Example real time analysis methods that can be used with the present methods are those used for the MiSeq and HiSeq sequencing devices commercially available from Illumina, Inc. (San Diego, Calif.) and / or described in US Pat. App. Pub. No. 2012 / 0020537 A1, which is incorporated herein by reference.

[0262] An example data analysis system, formed by one or more programmed computers, with programming being stored on one or more machine readable media with code executed to carry out one or more steps of methods described herein. In one implementation, for example, the system includes an interface designed to permit networking of the system to one or more detection systems (e.g., optical imaging systems) that are configured to acquire data from target objects. The interface may receive and condition data, where appropriate. In particular implementations the detection system will output digital image data, for example, image data that is representative of individual picture elements or pixels that, together, form an image of an array or other object. A processor processes the received detection data in accordance with a one or more routines defined by processing code. The processing code may be stored in various types of memory circuitry.

[0263] In accordance with the presently contemplated implementations, the processing code executed on the detection data includes a data analysis routine designed to analyze the detection data to determine the locations and metadata of individual analytes visible or encoded in the data, as well as locations at which no analyte is detected (i.e., where there is no analyte, or where no 2026204972   25 Jun 2026 meaningful signal was detected from an existing analyte). In particular implementations, analyte locations in an array will typically appear brighter than non-analyte locations due to the presence of fluorescing dyes attached to the imaged analytes. It will be understood that the analytes need not appear brighter than their surrounding area, for example, when a target for the probe at the analyte is not present in an array being detected. The color at which individual analytes appear may be a function of the dye employed as well as of the wavelength of the light used by the imaging system for imaging purposes. Analytes to which targets are not bound or that are otherwise devoid of a particular label can be identified according to other characteristics, such as their expected location in the microarray.

[0264] Once the data analysis routine has located individual analytes in the data, a value assignment may be carried out. In general, the value assignment will assign a digital value to each analyte based upon characteristics of the data represented by detector components (e.g., pixels) at the corresponding location. That is, for example when imaging data is processed, the value assignment routine may be designed to recognize that a specific color or wavelength of light was detected at a specific location, as indicated by a group or cluster of pixels at the location. In a typical DNA imaging application, for example, the four common nucleotides will be represented by four separate and distinguishable colors. Each color, then, may be assigned a value corresponding to that nucleotide.

[0265] As used herein, the terms “module”, “system,” or “system controller” may include a hardware and / or software system and circuitry that operates to perform one or more functions. For example, a module, system, or system controller may include a computer processor, controller, or other logic-based device that performs operations based on instructions stored on a tangible and non-transitory computer readable storage medium, such as a computer memory. Alternatively, a module, system, or system controller may include a hard-wired device that performs operations based on hard-wired logic and circuitry. The module, system, or system controller shown in the attached figures may represent the hardware and circuitry that operates based on software or hardwired instructions, the software that directs hardware to perform the operations, or a combination thereof. The module, system, or system controller can include or represent hardware circuits or circuitry that include and / or are connected with one or more processors, such as one or computer microprocessors.

[0266] As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are examples only, and are thus not limiting as to the types of memory usable for storage of a computer program. 2026204972   25 Jun 2026

[0267] In the molecular biology field, one of the processes for nucleic acid sequencing in use is sequencing-by-synthesis. The technique can be applied to massively parallel sequencing projects. For example, by using an automated platform, it is possible to carry out hundreds of thousands of sequencing reactions simultaneously. Thus, one of the implementations of the present invention relates to instruments and methods for acquiring, storing, and analyzing image data generated during nucleic acid sequencing.

[0268] Enormous gains in the amount of data that can be acquired and stored make streamlined image analysis methods even more beneficial. For example, the image analysis methods described herein permit both designers and end users to make efficient use of existing computer hardware. Accordingly, presented herein are methods and systems which reduce the computational burden of processing data in the face of rapidly increasing data output. For example, in the field of DNA sequencing, yields have scaled 15-fold over the course of a recent year, and can now reach hundreds of gigabases in a single run of a DNA sequencing device. If computational infrastructure requirements grew proportionately, large genome-scale experiments would remain out of reach to most researchers. Thus, the generation of more raw sequence data will increase the need for secondary analysis and data storage, making optimization of data transport and storage extremely valuable. Some implementations of the methods and systems presented herein can reduce the time, hardware, networking, and laboratory infrastructure requirements needed to produce usable sequence data.

[0269] The present disclosure describes various methods and systems for carrying out the methods. Examples of some of the methods are described as a series of steps. However, it should be understood that implementations are not limited to the particular steps and / or order of steps described herein. Steps may be omitted, steps may be modified, and / or other steps may be added. Moreover, steps described herein may be combined, steps may be performed simultaneously, steps may be performed concurrently, steps may be split into multiple sub-steps, steps may be performed in a different order, or steps (or a series of steps) may be re-performed in an iterative fashion. In addition, although different methods are set forth herein, it should be understood that the different methods (or steps of the different methods) may be combined in other implementations.

[0270] In some implementations, a processing unit, processor, module, or computing system that is “configured to” perform a task or operation may be understood as being particularly structured to perform the task or operation (e.g., having one or more programs or instructions stored thereon or used in conjunction therewith tailored or intended to perform the task or operation, and / or having an arrangement of processing circuitry tailored or intended to perform the task or operation). For the purposes of clarity and the avoidance of doubt, a general purpose 2026204972   25 Jun 2026 computer (which may become “configured to” perform the task or operation if appropriately programmed) is not “configured to” perform a task or operation unless or until specifically programmed or structurally modified to perform the task or operation.

[0271] Moreover, the operations of the methods described herein can be sufficiently complex such that the operations cannot be mentally performed by an average human being or a person of ordinary skill in the art within a commercially reasonable time period. For example, the methods may rely on relatively complex computations such that such a person cannot complete the methods within a commercially reasonable time.

[0272] Throughout this application various publications, patents or patent applications have been referenced. The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.

[0273] The term “comprising” is intended herein to be open-ended, including not only the recited elements, but further encompassing any additional elements.

[0274] As used herein, the term “each”, when used in reference to a collection of items, is intended to identify an individual item in the collection but does not necessarily refer to every item in the collection. Exceptions can occur if explicit disclosure or context clearly dictates otherwise.

[0275] Although the invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the invention.

[0276] The modules in this application can be implemented in hardware or software, and need not be divided up in precisely the same blocks as shown in the figures. Some can also be implemented on different processors or computers, or spread among a number of different processors or computers. In addition, it will be appreciated that some of the modules can be combined, operated in parallel or in a different sequence than that shown in the figures without affecting the functions achieved. Also as used herein, the term “module” can include “submodules”, which themselves can be considered herein to constitute modules. The blocks in the figures designated as modules can also be thought of as flowchart steps in a method.

[0277] As used herein, the “identification” of an item of information does not necessarily require the direct specification of that item of information. Information can be “identified” in a field by simply referring to the actual information through one or more layers of indirection, or by identifying one or more items of different information which are together sufficient to determine the actual item of information. In addition, the term “specify” is used herein to mean the same as “identify”. 2026204972   25 Jun 2026

[0278] As used herein, a given signal, event or value is “in dependence upon” a predecessor signal, event or value of the predecessor signal, event or value influenced by the given signal, event or value. If there is an intervening processing element, step or time period, the given signal, event or value can still be “in dependence upon” the predecessor signal, event or value. If the intervening processing element or step combines more than one signal, event or value, the signal output of the processing element or step is considered “in dependence upon” each of the signal, event or value inputs. If the given signal, event or value is the same as the predecessor signal, event or value, this is merely a degenerate case in which the given signal, event or value is still considered to be “in dependence upon” or “dependent on” or “based on” the predecessor signal, event or value. “Responsiveness” of a given signal, event or value upon another signal, event or value is defined similarly.

[0279] As used herein, “concurrently” or “in parallel” does not require exact simultaneity. It is sufficient if the evaluation of one of the individuals begins before the evaluation of another of the individuals completes. Computer System

[0280] Figure 17 is a computer system 1700 that can be used to implement the technology disclosed. Computer system 1700 includes at least one central processing unit (CPU) 1772 that communicates with a number of peripheral devices via bus subsystem 1755. These peripheral devices can include a storage subsystem 1710 including, for example, memory devices and a file storage subsystem 1736, user interface input devices 1738, user interface output devices 1776, and a network interface subsystem 1774. The input and output devices allow user interaction with computer system 1700. Network interface subsystem 1774 provides an interface to outside networks, including an interface to corresponding interface devices in other computer systems.

[0281] In one implementation, the equalizer base caller 104 is communicably linked to the storage subsystem 1710 and the user interface input devices 1738.

[0282] User interface input devices 1738 can include a keyboard; pointing devices such as a mouse, trackball, touchpad, or graphics tablet; a scanner; a touch screen incorporated into the display; audio input devices such as voice recognition systems and microphones; and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information into computer system 1700.

[0283] User interface output devices 1776 can include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem can include an LED display, a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image. The display 2026204972   25 Jun 2026 subsystem can also provide a non-visual display such as audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computer system 1700 to the user or to another machine or computer system.

[0284] Storage subsystem 1710 stores programming and data constructs that provide the functionality of some or all of the modules and methods described herein. These software modules are generally executed by processors 1778.

[0285] Processors 1778 can be graphics processing units (GPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and / or coarse-grained reconfigurable architectures (CGRAs). Processors 1778 can be hosted by a deep learning cloud platform such as Google Cloud Platform™, Xilinx™, and Cirrascale™. Examples of processors 1778 include Google's Tensor Processing Unit (TPU)™, rackmount solutions like GX4 Rackmount Series™, GX17 Rackmount Series™, NVIDIA DGX-1™, Microsoft' Stratix V FPGA™, Graphcore's Intelligent Processor Unit (IPU)™, Qualcomm's Zeroth Platform™ with Snapdragon processors™, NVIDIA's Volta™, NVIDIA's DRIVE PX™, NVIDIA's JETSON TX1 / TX2 MODULE™, Intel's Nirvana™, Movidius VPU™, Fujitsu DPI™, ARM's DynamicIQ™, IBM TrueNorth™, Lambda GPU Server with Testa V100s™, and others.

[0286] Memory subsystem 1722 used in the storage subsystem 1710 can include a number of memories including a main random access memory (RAM) 1732 for storage of instructions and data during program execution and a read only memory (ROM) 1734 in which fixed instructions are stored. A file storage subsystem 1736 can provide persistent storage for program and data files, and can include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges. The modules implementing the functionality of certain implementations can be stored by file storage subsystem 1736 in the storage subsystem 1710, or in other machines accessible by the processor.

[0287] Bus subsystem 1755 provides a mechanism for letting the various components and subsystems of computer system 1700 communicate with each other as intended. Although bus subsystem 1755 is shown schematically as a single bus, alternative implementations of the bus subsystem can use multiple busses.

[0288] Computer system 1700 itself can be of varying types including a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a television, a mainframe, a server farm, a widely-distributed set of loosely networked computers, or any other data processing system or user device. Due to the ever-changing nature of computers and networks, the description of computer system 1700 depicted in Figure 17 is intended only as a specific example for purposes of illustrating the preferred implementations of the present 2026204972   25 Jun 2026 invention. Many other configurations of computer system 1700 are possible having more or less components than the computer system depicted in Figure 17. Particular Implementations

[0289] The technology disclosed attenuates spatial crosstalk from sensor pixels using equalization-based image processing techniques. The technology disclosed can be practiced as a system, method, or article of manufacture. One or more features of an implementation can be combined with the base implementation. Implementations that are not mutually exclusive are taught to be combinable. One or more features of an implementation can be combined with other implementations. This disclosure periodically reminds the user of these options. Omission from some implementations of recitations that repeat these options should not be taken as limiting the combinations taught in the preceding sections - these recitations are hereby incorporated forward by reference into each of the following implementations.

[0290] In one implementation, the technology disclosed proposes a computer-implemented method of attenuating spatial crosstalk from sensor pixels.

[0291] The technology disclosed resolves spatial crosstalk over sensor pixels in a pixel plane caused by periodically distributed fluorescent samples in a sample plane. Signal cones from the fluorescent samples are optically coupled to local grids of the sensor pixels through at least one lens. The signal cones overlap and impinge on the sensor pixels, thereby creating the spatial crosstalk.

[0292] The technology disclosed captures in at least one subpixel lookup table a characteristic spread of a characteristic signal cone projected through the lens and resulting contributions of the characteristic signal cone to fluorescence detected by sensor pixels in a local grid of the sensor pixels. The local grid of the sensor pixels is substantially concentric with a center of the characteristic signal cone.

[0293] The technology disclosed interpolates among a set of subpixel lookup tables that express the characteristic spread with subpixel resolution to generate an interpolated lookup table based on a target fluorescent sample center.

[0294] The technology disclosed isolates a signal from the target fluorescent sample that projects a center of a signal cone onto substantially a center of a target local grid of the sensor pixels by convolving the interpolated lookup table with sensor pixels in the target local grid.

[0295] The technology disclosed uses a sum of convolved contributions of the isolated signal as intensity of fluorescence from the target fluorescent sample.

[0296] The technology disclosed then base calls the first target fluorescent sample using the intensity of fluorescence. The intensity of fluorescence is determined for the first target 2026204972   25 Jun 2026 fluorescent sample for each imaging channel in a plurality of imaging channels. Consider the four-channel chemistry that generates four images per sequencing cycle using four imaging channels. Then, for the first target fluorescent sample, four intensities of fluorescence are determined using the technology disclosed, as described above. Then, the four intensities of fluorescence are processed by a base caller to base call the first target fluorescent sample. Similarly, for two-channel chemistry, two intensities of fluorescence are used to base call the first target fluorescent sample.

[0297] The method described in this section and other sections of the technology disclosed can include one or more of the following features and / or features described in connection with additional methods disclosed. In the interest of conciseness, the combinations of features disclosed in this application are not individually enumerated and are not repeated with each base set of features. The reader will understand how features identified in this method can readily be combined with sets of base features identified as implementations in other sections of this application.

[0298] In some implementations, the periodically distributed fluorescent samples are arranged in a diamond shape. In other implementations, the periodically distributed fluorescent samples are arranged in a hexagonal shape.

[0299] Other implementations of the method described in this section can include a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described above. Yet another implementation of the method described in this section can include a system including memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above.

[0300] In another implementation, the technology disclosed proposes a computer-implemented method of base calling.

[0301] The technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The pixels include a center pixel that contains a center of the target cluster. Each pixel in the pixels is divisible into a plurality of subpixels.

[0302] Depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, the technology disclosed selects, from a bank of subpixel lookup tables, a subpixel lookup table that corresponds to the particular subpixel. The selected subpixel lookup table contains pixel coefficients that are configured to accept the intensity emissions from the target cluster and reject the intensity emissions from the adjacent clusters. 2026204972   25 Jun 2026

[0303] The technology disclosed element-wise multiplies the pixel coefficients to intensity values of the pixels in the image, and sums products of the multiplications to produce an output.

[0304] The technology disclosed uses the output to base call the target cluster.

[0305] Each of the features discussed in this particular implementation section for other implementations apply equally to this method implementation. As indicated above, all the method features are not repeated here and should be considered repeated by reference.

[0306] In some implementations, the technology disclosed further includes (i) selecting additional subpixel lookup tables, from the bank of subpixel look tables, which correspond to subpixels that are most contiguously adjacent to the particular subpixel, (ii) interpolating among pixel coefficients of the selected subpixel lookup table and the selected additional subpixel lookup tables and generating interpolated pixel coefficients that are configured to accept the intensity emissions from the target cluster and reject the intensity emissions from the adjacent clusters, (iii) element-wise multiplying the interpolated pixel coefficients to the intensity values of the pixels in the image and summing products of the multiplications to produce an output, and (iv) using the output to base call the target cluster.

[0307] In some implementations, the target cluster and the additional adjacent clusters are periodically distributed on a flow cell in a diamond shape and immobilized on wells of the flow cell. In other implementations, the target cluster and the additional adjacent clusters are periodically distributed on the flow cell in a hexagonal shape and immobilized on wells of the flow cell.

[0308] In some implementations, the interpolating is based on at least one of linear interpolation, bilinear interpolation, and bicubic interpolation.

[0309] In some implementations, pixel coefficients of subpixel lookup tables in the bank of subpixel lookup tables are learned as a result of training an equalizer using decision-directed equalization. In one implementation, the decision-directed equalization uses least square estimation as a loss function. In one implementation, the least square estimation minimizes a squared error using ground truth base calls. In one implementation, the ground truth base calls are modified to account for DC offset, amplification coefficient, and degree of polyclonality.

[0310] In some implementations, pixel coefficients of subpixel lookup tables in the bank of subpixel lookup tables are derived from a combination of (i) a single subpixel lookup table whose pixel coefficients are learned as a result of training an equalizer using decision-directed equalization, and (ii) a precalculated set of interpolation filters. Each interpolation filter in the set of interpolation filters respectively corresponds to each subpixel in the plurality of subpixels.

[0311] The technology disclosed further includes making the center of the target cluster substantially concentric with a center of the center pixel by (i) registering the image against a 2026204972   25 Jun 2026 template image and determining affine transformation and nonlinear transformation parameters, (ii) using the parameters to transform location coordinates of the target cluster and the additional adjacent clusters to image coordinates of the image and generating a transformed image with transformed pixels, and (iii) applying interpolation using the transformed location coordinates of the target cluster and the additional adjacent clusters to make their respective cluster centers substantially concentric with centers of respective transformed pixels that contain the cluster centers.

[0312] The technology disclosed further includes producing the output for each image in a plurality of images captured using respective imaging channels at a particular sequencing cycle, and base calling the target cluster using the output respectively produced for each image.

[0313] Other implementations of the method described in this section can include a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described above. Yet another implementation of the method described in this section can include a system including memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above.

[0314] We disclose the following clauses: 1.     A computer-implemented method of base calling, the method including: accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, the pixels including a center pixel that contains a center of the target cluster, and each pixel in the pixels divisible into a plurality of subpixels; depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, selecting, from a bank of subpixel lookup tables, a subpixel lookup table that corresponds to the particular subpixel, the selected subpixel lookup table containing pixel coefficients that are configured to maximize a signal-to-noise ratio; element-wise multiplying the pixel coefficients to intensity values of the pixels in the image and summing products of the multiplications to produce an output, the pixel coefficients serving as weights and the output being a weighted sum of the intensity values; and using the output to base call the target cluster. 2026204972   25 Jun 2026 2.     The computer-implemented method of clause 1, wherein the signal maximized in the signal-to-noise ratio is the intensity emissions from the target cluster, and the noise minimized in the signal-to-noise ratio is the intensity emissions from the adjacent clusters. 3.     The computer-implemented method of clause 1, wherein the element-wise multiplication adds a bias for given set of equalizer coefficients. 4.     The computer-implemented method of clause 3, wherein the bias is a DC offset that averages background noise intensity. 5.     The computer-implemented method of clause 1, further including: selecting additional subpixel lookup tables, from the bank of subpixel look tables, which correspond to subpixels that are most contiguously adjacent to the particular subpixel; interpolating among pixel coefficients of the selected subpixel lookup table and the selected additional subpixel lookup tables and generating interpolated pixel coefficients that are configured to maximize the signal-to-noise ratio; element-wise multiplying the interpolated pixel coefficients to the intensity values of the pixels in the image and summing products of the multiplications to produce an output, the interpolated pixel coefficients serving as weights and the output being a weighted sum of the intensity values; and using the output to base call the target cluster. 6.     The computer-implemented method of clause 1, wherein the target cluster and the additional adjacent clusters are periodically distributed on a flow cell in a diamond shape and immobilized on wells of the flow cell. 7.     The computer-implemented method of clause 6, wherein the target cluster and the additional adjacent clusters are periodically distributed on the flow cell in a hexagonal shape and immobilized on wells of the flow cell. 8.     The computer-implemented method of clause 1, wherein the interpolating is based on at least one of linear interpolation, bilinear interpolation, and bicubic interpolation. 9.     The computer-implemented method of clause 1, wherein pixel coefficients of subpixel lookup tables in the bank of subpixel lookup tables are learned as a result of training an equalizer using at least one of least squares estimation, ordinary least squares, least-mean squares, and recursive least-squares. In other implementations, other estimation algorithms and adaptive algorithms can be used to train the equalizer. 2026204972   25 Jun 2026 10. The computer-implemented method of clause 9, further including training the equalizer in an offline mode in which the pixel coefficients of subpixel lookup tables are fixed after being trained on batches of training data from a previously executed sequencing run. 11. The computer-implemented method of clause 10, further including training the equalizer in an online mode in which the pixel coefficients of subpixel lookup tables are iteratively updated as training data from an ongoing sequencing run becomes available. 12. The computer-implemented method of clause 11, further including accessing base-wise intensity distributions of each of the four bases A, C, G, and T generated during prior base calling of images in the training data, selecting respective centers of the base-wise intensity distributions as base-wise ground truth target intensities, and using the base-wise ground truth target intensities to train the equalizer. 13. The computer-implemented method of clause 12, further including pre-training the equalizer in the offline mode and retraining the equalizer in the online mode. 14. The computer-implemented method of clause 9, further including generating the lookup tables in the bank of subpixel lookup tables by together applying a single set of equalizer coefficients and a precalculated set of interpolation filters, including interpolating pixel intensities to generate inputs for the equalizer. This includes calculating pixel weights for clusters that have a substantially different alignment with respect to pixels compared to the trained equalizer coefficients, by using interpolated pixel intensity values to generate the equalizer inputs. Interpolation and equalizer filter responses can be convolved together for an efficient implementation with a single shared LUT. In other implementations, the interpolation filter calculation can be done directly, without binning to subpixels. 15. The computer-implemented method of clause 1, further including making the center of the target cluster substantially concentric with a center of the center pixel by: registering the image against a template image and determining affine transformation and nonlinear transformation parameters; using the parameters to transform location coordinates of the target cluster and the additional adjacent clusters to image coordinates of the image and generating a transformed image with transformed pixels; and applying interpolation using the transformed location coordinates of the target cluster and the additional adjacent clusters to make their respective cluster centers substantially 2026204972   25 Jun 2026 concentric with centers of respective transformed pixels that contain the cluster centers. 16. The computer-implemented method of clause 4, further including producing the output for each image in a plurality of images captured using respective imaging channels and / or color channels at a particular sequencing cycle, and base calling the target cluster using the output respectively produced for each image. 17. A computer-implemented method of recovering an underlying signal from a fluorescent sample positioned in a sample plane from a signal that is corrupted by surrounding fluorescent sources also in the sample plane, the method including: capturing in at least one subpixel lookup table a characteristic collection of illumination in an image plane by a sensor pixel array based on sampling that takes into account corruption from the surrounding fluorescent sources and then generating a set of lookup tables for the characteristic collection of illumination by the sensor pixel array when a center coordinate of the fluorescent sample is at positions distributed over a center pixel of the sensor array, the positions distributed relative to a center of coordinate of the center pixel; receiving an image that has the center coordinate of the fluorescent sample somewhere in the center pixel of the sensor pixel array, wherein the image is corrupted by the surrounding fluorescent sources, and receiving the center coordinate of the fluorescent sample within the center pixel; calculating an interpolated table of characteristic collection of illumination by a sensor pixel array customized to the received center coordinate of the fluorescent sample based on interpolating between lookup tables in the set of lookup table; recovering a signal from the target fluorescent sample that projects a center of a signal cone onto substantially a center of a target local grid of the sensor pixels by elementwise multiplying the interpolated lookup table with sensor pixels in the target local grid; using a sum of products of the element-wise multiplications as intensity of fluorescence from the target fluorescent sample; and base calling the first target fluorescent sample using the intensity of fluorescence. 1.     A computer-implemented method of base calling, the method including: accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters; 2026204972   25 Jun 2026 selecting a lookup table that contains pixel coefficients that are configured to maximize a signal-to-noise ratio; convolving the pixel coefficients with intensity values of the pixels in the image to produce an output; and base calling the target cluster based on the output. 2.     The computer-implemented method of claim 1, wherein the signal maximized in the signal-to-noise ratio is the intensity emissions from the target cluster, and the noise minimized in the signal-to-noise ratio is the intensity emissions from the adjacent clusters, plus additional noise sources. 3.     The computer-implemented method of claim 1, wherein the pixels include a center pixel that contains a center of the target cluster, and each pixel in the pixels is divisible into a plurality of subpixels. 4.     The computer-implemented method of claim 3, wherein the lookup table is a subpixel lookup table. 5.     The computer-implemented method of claim 4, further including: depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, selecting, from a bank of subpixel lookup tables, the subpixel lookup table that corresponds to the particular subpixel, the selected subpixel lookup table containing the pixel coefficients; element-wise multiplying the pixel coefficients to the intensity values of the pixels in the image and summing products of the multiplications to produce the output, the pixel coefficients serving as weights and the output being a weighted sum of the intensity values; and using the output to base call the target cluster, including generating the output for each imaging channel in a plurality of imaging channels and base calling the target cluster using the output for each imaging channel. 6.     The computer-implemented method of claim 5, wherein the element-wise multiplication adds a bias for given set of equalizer coefficients, wherein the bias is a DC offset that averages background noise intensity. 7.     The computer-implemented method of claim 5, further including: selecting additional subpixel lookup tables, from the bank of subpixel look tables, which correspond to subpixels that are contiguously adjacent to the particular subpixel; 2026204972   25 Jun 2026 generating, based on pixel coefficients of the selected subpixel lookup table and the selected additional subpixel lookup tables, interpolated pixel coefficients that are configured to maximize the signal-to-noise ratio; convolving the interpolated pixel coefficients with the intensity values of the pixels in the image to produce an output; and base calling the target cluster based on the output. 8.     The computer-implemented method of claim 7, further including: element-wise multiplying the interpolated pixel coefficients to the intensity values of the pixels in the image and summing products of the multiplications to produce the output, the interpolated pixel coefficients serving as weights and the output being a weighted sum of the intensity values. 9.     The computer-implemented method of claim 1, further including training an equalizer using at least one of least squares estimation, ordinary least squares, least-mean squares, and recursive least-squares to generate the pixel coefficients. 10. The computer-implemented method of claim 9, further including training the equalizer in an offline mode in which the pixel coefficients of subpixel lookup tables are fixed after being trained on batches of training data from a previously executed sequencing run. 11. The computer-implemented method of claim 10, further including training the equalizer in an online mode in which the pixel coefficients of subpixel lookup tables are iteratively updated during an ongoing sequencing run. 12. The computer-implemented method of claim 11, further including accessing base-wise intensity distributions of each of the four bases A, C, G, and T generated during prior base calling of images in the training data, selecting respective centers of the base-wise intensity distributions as base-wise ground truth target intensities for corresponding color channels, and using the base-wise ground truth target intensities to train the equalizer. 13. The computer-implemented method of claim 12, further including pre-training the equalizer in the offline mode and retraining the equalizer in the online mode. 14. The computer-implemented method of claim 9, further including generating the lookup tables in the bank of subpixel lookup tables by together applying a single set of equalizer coefficients and a precalculated set of interpolation filters, including interpolating pixel intensities to generate inputs for the equalizer. 2026204972   25 Jun 2026 15. The computer-implemented method of claim 1, further including making the center of the target cluster concentric with a center of the center pixel by: registering the image against a template image and determining affine transformation and nonlinear transformation parameters; using the parameters to transform location coordinates of the target cluster and the additional adjacent clusters to image coordinates of the image and generating a transformed image with transformed pixels; and applying interpolation using the transformed location coordinates of the target cluster and the additional adjacent clusters to make their respective cluster centers concentric with centers of respective transformed pixels that contain the cluster centers. 16. A non-transitory computer readable storage medium impressed with computer program instructions to perform base calling, the instructions, when executed on a processor, implement a method comprising: accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters; selecting a lookup table that contains pixel coefficients that are configured to maximize a signal-to-noise ratio; convolving the pixel coefficients with intensity values of the pixels in the image to produce an output; and base calling the target cluster based on the output. 17. The non-transitory computer readable storage medium of claim 16, wherein the signal maximized in the signal-to-noise ratio is the intensity emissions from the target cluster, and the noise minimized in the signal-to-noise ratio is the intensity emissions from the adjacent clusters, plus additional noise sources. 18. The non-transitory computer readable storage medium of claim 16, implementing the method further comprising training an equalizer using at least one of least squares estimation, ordinary least squares, least-mean squares, and recursive least-squares to generate the pixel coefficients. 19. A system including one or more processors coupled to memory, the memory loaded with computer instructions to perform base calling, the instructions, when executed on the processors, implement actions comprising: accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters; 2026204972   25 Jun 2026 selecting a lookup table that contains pixel coefficients that are configured to maximize a signal-to-noise ratio; convolving the pixel coefficients with intensity values of the pixels in the image to produce an output; and base calling the target cluster based on the output. 20. The system of claim 19, further implementing actions comprising training an equalizer using at least one of least squares estimation, ordinary least squares, least-mean squares, and recursive least-squares to generate the pixel coefficients.

[0315] While the present invention is disclosed by reference to the preferred implementations and examples detailed above, it is to be understood that these examples are intended in an illustrative rather than in a limiting sense. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims.

[0316] What is claimed is:

Claims

1. A system comprising:at least one processor; anda non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:receive, for a sequencing cycle, an image of pixels depicting intensity emissions from a target cluster and intensity emissions from adjacent clusters;select a set of coefficients that correspond to a target pixel depicting the intensity emissions from the target cluster and corresponding to a location of the target cluster;adjust, for the target pixel, the set of coefficients to generate:a pixel-specific coefficient specific to the target pixel for the target cluster; anda set of pixel-specific coefficients specific to a set of pixels for the adjacent clusters;determine, for the target cluster, a corrected signal based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and intensity values of the intensity emissions from the target cluster and the adjacent clusters; anddetermine, for the sequencing cycle, a base call for the target cluster based on the corrected signal for the target cluster.

2. The system of claim 1, further comprising instructions that, when executed by theat least one processor, cause the system to receive the image of pixels depicting the intensity712026204972   25 Jun 2026emissions from the target cluster overlapping with one or more of the intensity emissions from the adjacent clusters.

3. The system of claim 1 or claim 2, further comprising instructions that, whenexecuted by the at least one processor, cause the system to receive the image of pixels depicting the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters by receiving an image patch of pixels depicting a region of a sample plane comprising the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters.

4. The system of any one of claims 1-3, further comprising instructions that, whenexecuted by the at least one processor, cause the system to adjust, for the target pixel, the set of coefficients to generate the pixel-specific coefficient and the set of pixel-specific coefficients by generating a subpixel-specific coefficient specific to the target pixel and representing a characteristic signal for the target cluster and a set of subpixel-specific coefficients specific to the set of pixels for the adjacent clusters.

5. The system of any one of claims 1-4, further comprising instructions that, whenexecuted by the at least one processor, cause the system to determine, for the target cluster, the corrected signal based on the pixel-specific coefficient, the set of pixel-specific coefficients, and the intensity values of the intensity emissions from the target cluster and the adjacent clusters by:determining, from tables of predetermined pixel-specific coefficients, interpolated pixelspecific coefficients for an array of pixels from the image of pixels; andmultiplying the interpolated pixel-specific coefficients and intensity values corresponding to the array of pixels.722026204972   25 Jun 20266.     The system of claim 5, further comprising instructions that, when executed by theat least one processor, cause the system to:determine the interpolated pixel-specific coefficients for the array of pixels by interpolating between the tables of predetermined pixel-specific coefficients for the array of pixels customized to a center coordinate of the target cluster;multiply the interpolated pixel-specific coefficients and the intensity values of the array of pixels by element-wise multiplying the interpolated pixel-specific coefficients and the intensity values corresponding to the array of pixels; andsum products of element-wise multiplications to determine one or more adjusted intensity values for the target cluster.

7. The system of claim 6, further comprising instructions that, when executed by theat least one processor, cause the system to determine the base call for the target cluster based on the one or more adjusted intensity values for the target cluster.

8. The system of any one of claims 1-7, further comprising instructions that, whenexecuted by the at least one processor, cause the system to determine, for the target cluster, the corrected signal by:determining a first adjusted intensity value for the target cluster in a first imaging channel based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and one or more intensity values of the intensity emissions from the target cluster and the adjacent clusters;732026204972   25 Jun 2026determining a second adjusted intensity value for the target cluster in a second imaging channel based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and one or more intensity values of the intensity emissions from the target cluster and the adjacent clusters; anddetermine the base call for the target cluster based on the first adjusted intensity value and the second adjusted intensity value.

9. The system of any one of claims 1-8, further comprising instructions that, whenexecuted by the at least one processor, cause the system to select the set of coefficients by selecting a lookup table comprising pixel coefficients corresponding to the location of the target cluster.

10. A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause a computing system to:receive, for a sequencing cycle, an image of pixels depicting intensity emissions from a target cluster and intensity emissions from adjacent clusters;select a set of coefficients that correspond to a target pixel depicting the intensity emissions from the target cluster and corresponding to a location of the target cluster;adjust, for the target pixel, the set of coefficients to generate:a pixel-specific coefficient specific to the target pixel for the target cluster; anda set of pixel-specific coefficients specific to a set of pixels for the adjacent clusters;determine, for the target cluster, a corrected signal based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and intensity values of the intensity emissions from the target cluster and the adjacent clusters; and742026204972   25 Jun 2026determine, for the sequencing cycle, a base call for the target cluster based on the corrected signal for the target cluster.

11. The non-transitory computer readable storage medium of claim 10, further storing instructions that, when executed by the at least one processor, cause the computing system to receive the image of pixels depicting the intensity emissions from the target cluster overlapping with one or more of the intensity emissions from the adjacent clusters.

12. The non-transitory computer readable storage medium of claim 10 or claim 11, further storing instructions that, when executed by the at least one processor, cause the computing system to receive the image of pixels depicting the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters by receiving an image patch of pixels depicting a region of a sample plane comprising the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters.

13. The non-transitory computer readable storage medium of any one of claims 10-12, further storing instructions that, when executed by the at least one processor, cause the computing system to adjust, for the target pixel, the set of coefficients to generate the pixel-specific coefficient and the set of pixel-specific coefficients by generating a subpixel-specific coefficient specific to the target pixel representing a characteristic signal for the target cluster and a set of subpixelspecific coefficients specific to the set of pixels for the adjacent clusters.

14. The non-transitory computer readable storage medium of any one of claims 10-13, further storing instructions that, when executed by the at least one processor, cause the computing752026204972   25 Jun 2026system to determine, for the target cluster, the corrected signal based on the pixel-specific coefficient, the set of pixel-specific coefficients, and the intensity values of the intensity emissions from the target cluster and the adjacent clusters by:determining, from tables of predetermined pixel-specific coefficients, interpolated pixelspecific coefficients for an array of pixels from the image of pixels; andmultiplying the interpolated pixel-specific coefficients and intensity values corresponding to the array of pixels.

15. The non-transitory computer readable storage medium of claim 14, further storing instructions that, when executed by the at least one processor, cause the computing system to:determine the interpolated pixel-specific coefficients for the array of pixels by interpolating between the tables of predetermined pixel-specific coefficients for the array of pixels customized to a center coordinate of the target cluster;multiply the interpolated pixel-specific coefficients and the intensity values of the array of pixels by element-wise multiplying the interpolated pixel-specific coefficients and the intensity values corresponding to the array of pixels; andsum products of element-wise multiplications to determine one or more adjusted intensity values for the target cluster.

16. The non-transitory computer readable storage medium of claim 15, further storing instructions that, when executed by the at least one processor, cause the computing system to determine the base call for the target cluster based on the one or more adjusted intensity values for the target cluster.762026204972   25 Jun 202617. A computer-implemented method comprising:receiving, for a sequencing cycle, an image of pixels depicting intensity emissions from a target cluster and intensity emissions from adjacent clusters;selecting a set of coefficients that correspond to a target pixel depicting the intensity emissions from the target cluster and corresponding to a location of the target cluster;adjusting, for the target pixel, the set of coefficients to generate:a pixel-specific coefficient specific to the target pixel for the target cluster; anda set of pixel-specific coefficients specific to a set of pixels for the adjacent clusters; determining, for the target cluster, a corrected signal based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and intensity values of the intensity emissions from the target cluster and the adjacent clusters; anddetermining, for the sequencing cycle, a base call for the target cluster based on the corrected signal for the target cluster.

18. The computer-implemented method of claim 17, wherein receiving the image of pixels depicting the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters comprises receiving an image patch of pixels depicting a region of a sample plane comprising the intensity emissions from the target cluster and the intensity emissions from the adjacent clusters.

19. The computer-implemented method of claim 17 or claim 18, wherein determining, for the target cluster, the corrected signal comprises:determining a first adjusted intensity value for the target cluster in a first imaging channel based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific 772026204972   25 Jun 2026coefficients specific to the set of pixels, and one or more intensity values of the intensity emissions from the target cluster and the adjacent clusters; anddetermining a second adjusted intensity value for the target cluster in a second imaging channel based on the pixel-specific coefficient specific to the target pixel, the set of pixel-specific coefficients specific to the set of pixels, and one or more intensity values of the intensity emissions from the target cluster and the adjacent clusters.

20. The computer-implemented method of claim 19, wherein determining the base call comprises determining the base call for the target cluster based on the first adjusted intensity value and the second adjusted intensity value.Illumina, Inc.Patent Attorneys for the ApplicantSPRUSON & FERGUSON78

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