KR20240131386A - Automatic switching of variant analysis model versions for genomic analysis applications - Google Patents

Abstract

The present disclosure describes a method, a non-transitory computer-readable medium, and a system that can flexibly and efficiently change versions of a variant analysis model for different genome analysis applications. For example, the disclosed system can determine a particular version of a variant analysis model indicated by a genome analysis application, and update a genome analysis device (e.g., an FPGA, a CPU) by installing the indicated version of the variant analysis model. The disclosed system can further execute the genome analysis application to analyze nucleotide base calls utilizing the version of the variant analysis model indicated by the genome analysis application.

Description

Automatic switching of variant analysis model versions for genomic analysis applications

Cross-reference to related applications

This application claims the benefit of and priority to U.S. Provisional Application No. 63/294,693, filed December 29, 2021, which is a continuation-in-part of U.S. Application No. 17/935,497, filed September 26, 2022, which claims priority to U.S. Provisional Application No. 63/294,693, filed December 29, 2021. The foregoing applications are incorporated herein by reference in their entirety.

In recent years, biotechnology companies and computer science institutions have improved hardware and software for generating diagnostics and performing other genomic analyses on nucleotide sequences of genomic samples. Some existing nucleotide base sequencing platforms and/or sequencing data analysis software (together and hereinafter, existing sequencing systems) generate nucleotide base calls from nucleotide reads of a sample nucleotide sequence and/or perform genomic analyses on the nucleotide base calls for a variety of purposes. For example, an existing sequencing system may implement a diagnostic sequencing analysis application to screen nucleotide sequences for a particular genetic condition (or for some other purpose) by detecting particular genetic markers within the nucleotide base calls of a sample sequence (e.g., mutation calls).

Despite recent advances, existing sequence analysis systems continue to exhibit many shortcomings or drawbacks. For example, many existing sequence analysis systems are rigidly bound to a particular version of a genome analysis model and cannot change the version of the genome analysis model without rendering other software applications incompatible with different versions of the model. Specifically, existing systems often utilize genome analysis models in the form of probabilistic models or some type of machine learning model (e.g., neural networks) to perform genome analysis on nucleotide sequences. In many cases, the genome analysis models of these existing systems have rigid architectures that are incompatible with specific genome analysis applications without modifying or retraining the models (e.g., to implement different analysis applications and/or perform different diagnostics). Such modification or retraining can be computationally expensive and time-consuming. As a result, some existing systems suffer from compatibility issues and are unable to run specific sequence analysis, secondary, and/or tertiary analysis applications that require specific model architectures and functions.

Some existing sequence analysis systems exhibit further inefficiencies and slow performance, at least in part because of their inflexible nature. For example, existing systems often require updating or rewriting genome analysis applications to achieve compatibility with available hardware and software. Where existing sequence analysis systems are capable of running multiple different genome analysis applications along with a genome analysis model, updating or rewriting the genome analysis application to be compatible with the genome analysis model often results in slow execution (requiring delays of days, weeks, or more) and sometimes renders the genome analysis application inoperable because different versions of the genome analysis application cannot be installed. For example, some existing sequence analysis systems require a client device to enter computer code command lines to change or update the version of the genome analysis model, which often can only be done with root access. In fact, rather than quickly adapting to available architectures on the fly, existing systems sometimes require a time-intensive process of retraining the model architecture and/or reprogramming all or part of the application before analysis of nucleotide base calls has even begun.

The present disclosure describes embodiments of a method, a non-transitory computer-readable medium, and a system that can flexibly and efficiently switch versions of a variant analysis model for specific requirements of a genome analysis application. For example, the disclosed system can determine a specific version of a variant analysis model indicated by a genome analysis application, and update a genome analysis device by installing the indicated version of the variant analysis model. As described below, the genome analysis device can take the form of a field programmable gate array (FPGA) or other device that operates with the variant analysis model to perform secondary and/or tertiary analysis of nucleotide base calls. In some cases, the disclosed system utilizes a container orchestration engine to implement the genome analysis application and automatically installs the required version of the variant analysis model (e.g., without user input related to installation). The disclosed system can further execute the genome analysis application to analyze nucleotide base calls (e.g., for a specific diagnosis or some other analysis) utilizing the version of the variant analysis model indicated by the genome analysis application and installed on the genome analysis device.

For detailed explanation, please refer to the drawings briefly explained below.
FIG. 1 illustrates a block diagram of a system environment including a model switching system according to one or more embodiments.
FIG. 2 illustrates an overview of a model switching system that identifies and installs a displayed version of a variant analysis model for executing a genome analysis application according to one or more embodiments.
FIG. 3 illustrates an exemplary flow of a model switching system that installs a displayed version of a mutation analysis model utilizing a container orchestration engine according to one or more embodiments.
FIG. 4 illustrates an exemplary flow of a model switching system for installing versions of a mutation analysis model on a genome analysis device according to one or more embodiments.
FIG. 5 illustrates an exemplary flow of a model switching system for installing different versions of a mutation analysis model for different workflow pods according to one or more embodiments.
FIG. 6 illustrates an exemplary architecture diagram of a model switching system associated with an overall sequence analysis environment according to one or more embodiments.
FIG. 7 illustrates a flowchart of a series of actions for identifying and installing a displayed version of a variant analysis model for executing a genome analysis application according to one or more embodiments.
FIG. 8 illustrates a block diagram of an exemplary computing device for implementing one or more embodiments of the present disclosure.
FIG. 9 illustrates a block diagram of an exemplary optical system for image-based genome sequencing according to one or more embodiments.
FIG. 10 illustrates an exemplary imager for image-based genome sequencing according to one or more embodiments.
FIG. 11 illustrates an exemplary diagram for performing image-based genome sequencing according to one or more embodiments.

The present disclosure describes an embodiment of a model switching system that updates a genome analysis device by installing a indicated version of a variant analysis model that is required or otherwise indicated by a genome analysis application. In a practical scenario, a genome analysis application often requires coordination with or operation of a genome analysis device, such as a field programmable gate array (FPGA) or a central processing unit (CPU), to perform sequence analysis, secondary analysis, and/or tertiary analysis functions. As a result, a genome analysis application often requires or calls upon specific functionality of a variant analysis model installed on the genome analysis device, where specific (e.g., older or newer) versions of the variant analysis model include different functionality (and different modifications to existing functionality). Accordingly, the model switching system may analyze an application specification (e.g., a workflow pod specification) that defines parameters for the genome analysis application to determine a indicated version of the variant analysis model (e.g., a version required to execute the genome analysis application).

Based on determining the indicated version of the variant analysis model, the model switching system can additionally install the indicated version on the genome analysis device. In some cases, the model switching system replaces a previously installed version of the variant analysis model, while in other cases, the model switching system installs the indicated version in addition to one or more previously installed versions. The model switching system can additionally execute a genome analysis application to analyze nucleotide base calling utilizing the indicated version of the variant analysis model.

As just mentioned, in certain implementations, the model switching system identifies a genomic analysis application for analyzing nucleotide base calls determined for a sample nucleotide sequence. For example, the model switching system identifies a genomic analysis application that performs a particular diagnosis (e.g., to detect markers for a genetic condition) or some other sequence analysis task or secondary analysis or tertiary analysis of nucleotide base calls. Additionally, the model switching system can determine compatibility requirements for the genomic analysis application that include a indicated version of a variant analysis model for executing the genomic analysis application. For example, the model switching system analyzes an application specification (e.g., a workflow pod specification) that defines parameters for the genomic analysis application, including an indicated version of a variant analysis model that is necessary to perform one or more functions of the genomic analysis application.

Based on determining the indicated version of the variant analysis model, the model switching system can additionally install the indicated version on a genome analysis device, such as an FPGA or CPU, that implements various components or functions of the variant analysis model and/or the genome analysis application. For example, the model switching system can update the genome analysis device to install the indicated version of the variant analysis model so that it is compatible with the genome analysis application. In some embodiments, the genome analysis device can be configured to execute only a single version of the installed variant analysis model at a time (e.g., if the genome analysis device is an FPGA with hardware that can only operate according to programming). Accordingly, in these or other embodiments, the model switching system can install the indicated version of the variant analysis model to replace a previously installed version. In certain embodiments, the genome analysis device can include multiple versions of the variant analysis model at a time, such that the model switching system can install the indicated version for a particular genome analysis application while maintaining one or more previously installed versions on the genome analysis device.

As further noted above, the model switching system can also execute a genome analysis application. In particular, the model switching system can execute a genome analysis application to perform certain diagnostics or some other analysis on nucleotide base calls for a sample nucleotide sequence. To execute the genome analysis application, the model switching system can utilize a displayed version of a variant analysis model installed on the genome analysis device. Indeed, in some cases, the model switching system utilizes the genome analysis device (along with the displayed version of the variant analysis model) to execute one or more functions of the genome analysis application and/or to execute one or more functions that utilize data in a particular format generated by the displayed version of the variant analysis model (e.g., sequence analysis data, such as nucleotide base calls).

As suggested above, embodiments of the model switching system provide several advantages, benefits, and/or improvements over existing sequence analysis systems. For example, in some embodiments, the model switching system utilizes or implements novel functionality not found in existing sequence analysis systems. Specifically, the model switching system can automatically identify and install indicated versions of variant analysis models associated with individual genome analysis applications or workflow pods. In contrast, existing sequence analysis systems cannot automatically adapt to applications that require different versions of variant analysis models. In fact, some existing sequence analysis systems instead require client devices to enter computer code command lines to change or update versions of variant analysis models. Such commands often require root access, which many client devices or end users do not have with respect to the variant analysis models.

By leveraging these new capabilities not found in existing sequence analysis systems, in some embodiments the model switching system is more flexible than many existing sequence analysis systems. While many existing systems are limited to a particular version of a genome analysis model and cannot automatically adapt to install new or different versions, the model switching system can automatically update the version of a variant analysis model installed on a genome analysis device (e.g., on an application-by-application basis). In fact, the model switching system utilizes a flexible architecture that includes a container orchestration engine that facilitates greater adaptability to update hardware and/or software components of a genome analysis device for executing genome analysis applications. As a result, the model switching system can further avoid or overcome some of the compatibility issues prevalent in existing sequence analysis systems that are unable to execute certain applications due to limitations in their variant analysis models or the computing devices that execute such variant analysis models.

The model switching system may further exhibit enhanced flexibility in adapting to different genome analysis applications. Specifically, the model switching system may determine an application-specific (or pod-specific) version of a variant analysis model indicated by each application (or workflow pod) to be sequentially executed. The model switching system may further repeatedly perform an automated process that does not require any user input regarding installation, i) determining an indicated version of a variant analysis model for each application (or workflow pod), ii) installing the indicated version for a particular application (or workflow pod), iii) executing the application utilizing the indicated version, iv) repeating the process of steps i) through iii) for subsequent applications (or workflow pods), and updating the version of the variant analysis model on the genome analysis device as needed.

At least in part due to the increased flexibility, in certain embodiments the model switching system also improves computational efficiency and speed over existing sequencing systems. For example, rather than wasting computational resources such as processing power and memory to modify or retrain model architectures as required by some existing sequencing systems, the model switching system utilizes a container orchestration engine that facilitates rapid and automatic updating and installation of model versions. Along these lines, the model switching system further improves speed over existing sequencing systems by avoiding or avoiding the need to continually modify or retrain model architectures (or waiting for another party with root access to perform an update to install a compatible version of the variant analysis model). In fact, the model switching system exhibits fast performance in automatically (e.g., without specific user input to determine the model version) and seamlessly determining a displayed version of a variant analysis model, and automatically (e.g., without specific user input to install or switch versions) and seamlessly installing the displayed version on a genome analysis device. In some implementations, the identifying process comprises:

As indicated by the foregoing discussion, the present disclosure utilizes various terms to describe the features and advantages of the model switching system. The following provides additional details regarding the meaning of such terms as used in the present disclosure. For example, the terms "sample nucleotide sequence" or "sample sequence" as used in the present disclosure refer to a nucleotide sequence (or a copy of such an isolated or extracted sequence) isolated or extracted from a sample organism. In particular, the sample nucleotide sequence comprises a segment of a nucleic acid polymer isolated or extracted from a sample organism, and is comprised of nitrogenous heterocyclic bases. For example, the sample nucleotide sequence may comprise segments of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or other polymeric forms of nucleic acids, or chimeric or hybrid forms of the nucleic acids mentioned below. More specifically, in some cases, the sample nucleotide sequence is found in a sample prepared or isolated by the kit and received by the sequencing device.

The term "sequencing data," as further used herein, refers to data or information relating to a nucleotide sequence for one or more genomic samples. For example, the sequence data may include data generated by a sequence analysis device and/or a variant analysis model. In some cases, the sequence data includes nucleotide reads, nucleotide base calls, and/or sequence metrics associated with the sample nucleotide sequence. In one or more embodiments, the sequence data is associated with a particular nucleotide sequence and is generated using the patented methods and processes of a genomic analysis platform that includes a variant analysis model implemented by a genomic sequence processing device.

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

The term "sequencing metric" as used herein refers to a quantitative measure or score that indicates the extent to which an individual nucleotide base call (or a sequence of nucleotide base calls) aligns, compares, or quantifies to a genomic coordinate or genomic region of a reference genome, to a nucleotide base call from a nucleotide read, or to a genome sequence or genome structure. For example, a sequencing metric includes a quantitative measure or score that indicates the extent to which (i) an individual nucleotide base call aligns, maps, or covers a genomic coordinate or a reference base of a reference genome; (ii) a nucleotide base call compares to a reference or alternate nucleotide read in terms of mapping, mismatch, base call quality, or other raw sequencing metrics; or (iii) the extent to which a genomic coordinate or region corresponding to a nucleotide base call has mappability, repetitive base call content, DNA structure, or other generalized metrics.

In this context, the term "nucleotide read" (or sometimes simply "read"), as used herein, refers to an inferred sequence of one or more nucleotide bases (or nucleotide base pairs) from all or a portion of a sample nucleotide sequence. In particular, a nucleotide read comprises a determined or predicted sequence of a nucleotide base call for a nucleotide fragment (or group of single clone nucleotide fragments) from a sequence analysis library corresponding to a genomic sample. For example, a model switching system determines a nucleotide read by generating nucleotide base calls for nucleotide bases that have passed through nanopores in a nucleotide-sample slide, or that have been determined via fluorescent tagging, or that have been determined from a well in a flow cell.

As noted, in some implementations, the model switching system utilizes a variant analysis model to generate nucleotide base calls for genomic coordinates and/or perform other analyses with respect to the nucleotide base calls. The term "variant analysis model," as used herein, refers to a model that includes an algorithm or set of algorithms for analyzing data about a sample nucleotide sequence (e.g., base call data). In some cases, the variant analysis model is a probabilistic model that generates sequence analysis data from nucleotide reads of a sample nucleotide sequence, including nucleotide base calls (e.g., variant calls) and associated metrics (e.g., base call quality metrics). For example, in some cases, the variant analysis model refers to a Bayesian probabilistic model that generates variant calls based on nucleotide reads of a sample nucleotide sequence. Such models may include a secondary analysis model performed by a server running variant calling software to align nucleotide reads of the sample to a reference genome, to determine genetic variation in the sample based on the aligned nucleotide reads to the reference genome, and to determine one or more of a quality metric, an allele frequency metric, or other sequence analysis metrics. The variant analysis model may similarly include a plurality of components including, but not limited to, various software applications or components for mapping and alignment, sorting, duplicate marking, read pileup depth computing, and variant calling. In some cases, the variant analysis model refers to an ILLUMINA DRAGEN model for variant calling functionality and a mapping and alignment functionality.

As noted, in some embodiments, the model switching system utilizes a container orchestration engine to orchestrate execution of the genome analysis application. The term "container orchestration engine" as used herein refers to a software engine or platform for automating the deployment, scaling, and management of containerized software services and applications. For example, the container orchestration engine may comprise a software application having a microservices architecture that executes individual workflow containers as part of a genome analysis application (e.g., a sequence analysis diagnostic workflow). The container orchestration engine may process each container separately to perform individual functions (e.g., containerized jobs) that may be compartmentalized into pieces and added to or removed from the workflow.

In this context, the term "workflow container" (or sometimes simply "container") refers to a unit of software that packages code (and all of its dependencies) for portable distribution. For example, a workflow container includes a compartmentalized or containerized body of operable, portable, and executable code that performs a particular function or task. In some cases, a workflow container is executable to perform a function or task (e.g., a process or thread) that, for example, produces a particular output (e.g., a final output or an intermediate output that is fed to another container) from a piece of sequence analysis data. The model switching system may treat workflow containers individually, isolating some containers differently from others to allow and/or prevent access to sequence analysis data (e.g., within one or more workflow data sources) in a specific and customized manner. In some cases, a container refers to a NEXTFLOW container and/or a KUBERNETES container.

In this regard, the term "workflow pod" may refer to a unit of deployable software within a container orchestration engine that comprises a group of one or more workflow containers. In some cases, a workflow pod is the smallest deployable unit or denomination of software that a container orchestration engine can execute. Within a workflow pod, the constituent containers may share common network and/or common compute resources, such as storage locations and processing devices (e.g., genome analysis devices). In some cases, a workflow pod is part of a genome analysis application.

As noted, in some embodiments, the model switching system determines a displayed version of a variant analysis model for executing a genome analysis application. The term "genomic analysis application" as used herein may refer to a custom workflow content package that may be deployed by the model switching system, including workflow definitions, containerized jobs, custom user interfaces, custom microservices, and/or reference data. A genome analysis application may include or define a collection or workflow of jobs or functions that are organized and coordinated together to produce a set of diagnostic outputs from sequence analysis data for a sample nucleotide sequence. For example, a workflow of a genome analysis application may include various jobs that provide various functions, which may include secondary analysis, tertiary analysis, custom QC logic, reporting, or other desired functionality. In some cases, multiple entities may develop a single genome analysis application that may be deployed locally (e.g., on an edge server) or in the cloud. A genome analysis application may have a particular structure or file type (e.g., a tape archive or "TAR" file) that defines the workflow and other application data. In some embodiments, the genome analysis application is a single deployable unit that can be installed on a system that is completely disconnected from the Internet. The application package can be signed to ensure authenticity and validity. The package can be uploaded to the server via a UI portal (e.g., using a browser).

As noted, in some embodiments, the model switching system installs a displayed version of a variant analysis model on a genome analysis device. As used herein, the term "genomic analysis device" may refer to a processing device that executes functions or operations of a variant analysis model or a genome analysis application to perform analysis on sequence analysis data, such as nucleotide base calling. For example, the genome analysis device may execute all or part of a genome analysis application utilizing the variant analysis model to perform diagnostic analysis or some other analysis on the sequence analysis data. In some cases, the genome analysis device may include a computational hardware device, such as an FPGA, that includes an array of programmable logic blocks for executing functions according to a hardware description language (HDL). As another example, the genome analysis device may include programmable circuitry for executing instructions that constitute a software application or program, such as a genome analysis application. In some embodiments, the genome analysis device operates on a server separate from the sequence analysis device or instrument. In these or other embodiments, the genome analysis device is housed on a server that shares a local network with the sequence analysis device.

The following paragraphs describe the model switching system with reference to exemplary embodiments and exemplary drawings representing implementations. For example, FIG. 1 illustrates a schematic diagram of a system environment (or “environment”) (100) in which the model switching system (108) operates according to one or more embodiments. As illustrated, the environment (100) includes one or more local server device(s) (102) connected to a client device (110), a remote (e.g., cloud) server device(s) (120), and a sequence analysis device (114) via a network (116). While FIG. 1 illustrates one embodiment of the model switching system (108), the present disclosure describes alternative embodiments and configurations below.

As illustrated in FIG. 1, the local server device(s) (102), the client device (110), the server device(s) (120), and the sequence analysis device (114) may communicate with each other via a network (116). The network (116) includes any suitable network over which the computing devices can communicate. An exemplary network is discussed in further detail below with respect to FIG. 8.

As illustrated in FIG. 1 , the sequencing device (114) comprises a device for sequencing a nucleic acid polymer. In some embodiments, the sequencing device (114) analyzes nucleic acid segments or oligonucleotides extracted from a sample to generate nucleotide reads or other data, either directly or indirectly using computer-implemented methods and systems (described herein) on the sequencing device (114). More specifically, the sequencing device (114) receives and analyzes nucleic acid sequences extracted from a sample within a nucleotide-sample slide (e.g., a flow cell). In one or more embodiments, the sequencing device (114) sequences the nucleic acid polymer into nucleotide reads using sequencing-by-synthesis (SBS). Additionally or alternatively to communicating over a network (116), in some embodiments, the sequencing device (114) bypasses the network (116) and communicates directly with local server device(s) (102) (and/or client device (110)). In fact, in some embodiments, the sequence analysis device (114) and the local server device(s) (102) share a local network (e.g., housed on the same or different servers) as indicated by the dashed box, while the client device (110) communicates over a network (116) instead of sharing a local network.

As further illustrated in FIG. 1 , the local server device(s) (102) can generate, receive, analyze, store, and transmit digital data, such as data for determining nucleotide base calls, sequencing nucleic acid polymers, and/or executing a genome analysis application to analyze (e.g., perform diagnostics) a nucleotide sequence. As illustrated in FIG. 1 , the sequence analysis device (114) can transmit call data from the sequence analysis device (114) (and the local server device(s) (102) can receive the call data). The local server device(s) (102) can also communicate with the client device (110). In particular, the local server device(s) (102) can transmit data to the client device (110) that includes a variant call file or other information indicative of nucleotide base calls, sequence analysis metrics, error data, diagnostic information, or results generated by executing the genome analysis application.

In some embodiments, the server device(s) (102) comprises a local server device located at or near the same physical location as the sequence analysis device (114). Indeed, in some embodiments, the server device(s) (102) and the sequence analysis device (114) are integrated into the same computing device, as indicated by the dotted lines around the local server device(s) (102) and the sequence analysis device (114).

In some embodiments, the local server device(s) (102) comprises a set of distributed servers, rather than being located locally with the sequence analysis device (114), wherein the local server device(s) (102) comprises multiple server devices distributed across the network (116) and located at the same or different physical locations. As suggested, in some cases, the local server device(s) (102) houses the genome analysis platform (104). Additionally, the local server device(s) (102) may include a content server, an application server, a communication server, a web hosting server, or other types of servers.

As further illustrated in FIG. 1, the local server device(s) (102) may include a genome analysis platform (104) for generating and analyzing sequence analysis data. Typically, the genome analysis platform (104) includes a genome analysis device (106), such as an FPGA or CPU, that receives and executes a variant analysis model (107). In practice, the variant analysis model (107) may generate and/or analyze sequence analysis data, such as nucleotide base calls and/or sequence analysis metrics received from the sequence analysis device (114), to determine a nucleotide base sequence for a nucleic acid polymer. For example, the variant analysis model (107) may receive raw data from the sequence analysis device (114) and determine a nucleotide base sequence for a nucleic acid segment. In some embodiments, the variant analysis model (107) determines a sequence of nucleotide bases in a DNA and/or RNA segment or an oligonucleotide. Additionally, in addition to processing and determining a sequence for a nucleic acid polymer, the variant analysis model (107) also generates a variant call file representing one or more nucleotide base calls and/or variant calls for one or more genomic coordinates.

Additionally, in one or more embodiments, the genome analysis platform (104) includes a model switching system (108). As just mentioned, the model switching system (108) determines and installs a version of a variant analysis model (107) on the genome analysis device (106). For example, the model switching system (108) determines that a previously installed version of the variant analysis model (107) is incompatible with the genome analysis application and updates the genome analysis device (106) to include a compatible version. In some cases, the model switching system (108) determines a displayed version of the variant analysis model (107) from the genome analysis application and installs the displayed version on the genome analysis device (106).

In practice, in some embodiments, the model switching system (108) identifies or receives a genome analysis application (112). For example, the model switching system (108) receives the genome analysis application (112) via an upload on a web interface over the network (116). The genome analysis application (112) may include a number of workflow pods for performing tasks or functions in connection with sequence analysis data (e.g., for diagnostic purposes). In practice, the model switching system (108) may execute the genome analysis application (112) by utilizing an installed version of the variant analysis model (107) (on the genome analysis device (106)) to analyze sequence analysis data, such as nucleotide base calls and/or sequence analysis metrics (e.g., from the sequence analysis device (114) or the variant analysis model (107)) to generate or determine various diagnostic or other types of genome analyses. For example, the model switching system (108) performs diagnostic analysis of sequence analysis data for sample nucleotide sequences. In some cases, the model switching system (108) performs diagnostic analysis to diagnose or determine a predisposition to one or more diseases or genetic conditions.

As further illustrated in FIG. 1, the client device (110) can generate, store, receive, and transmit digital data. In particular, the client device (110) can receive sequence analysis metrics from the sequence analysis device (114). In addition, the client device (110) can communicate with the local server device(s) (102) to receive results of the genome analysis application and/or variant call files including nucleotide base calls and/or other metrics such as call quality, genotype representation, and genotype quality. Accordingly, the client device (110) can present or display information about the nucleotide base calls within a graphical user interface to a user associated with the client device (110). In addition, the client device (110) can generate and provide (e.g., upload via a network (116)) a genome analysis application (112) comprising one or more workflow pods for performing analysis of the sequence analysis data. In practice, the client device (110) can receive user interaction via a graphical user interface to select and arrange or configure workflow pods and/or containers for generating the genome analysis application (112). The client device (110) can also receive results of the genome analysis application (112) from local server device(s) (102) and display the results within the graphical user interface.

The client device (110) illustrated in FIG. 1 may include various types of client devices. For example, in some embodiments, the client device (110) includes a non-mobile device, such as a desktop computer or a server, or other types of client devices. In yet other embodiments, the client device (110) includes a mobile device, such as a laptop, tablet, cell phone, or smart phone. Additional details regarding the client device (110) are discussed below with respect to FIG. 8.

As mentioned, the client device (110) includes a genome analysis application (112). The genome analysis application (112) may be a web application or a native application (e.g., a mobile application, a desktop application) that is stored and executed on the client device (110). The genome analysis application (112) may include instructions that cause the client device (110) to receive data from the model switching system (108) and to present data from a variant call file for display on the client device (110). In addition, the genome analysis application (112) may instruct the client device (110) to display, when executing the genome analysis application (112), not only diagnostic results received from the server device(s), but also visualizations of workflow pods/containers for arranging within a visualization and/or external sequence analysis diagnostic workflow of the genome analysis application (112).

In some embodiments, the model switching system (108) may be located on the client device (110) or on the sequence analysis device (114) as part of the genome analysis application (112). Thus, in some embodiments, the model switching system (108) is implemented (e.g., by being located in whole or in part) by the client device (110). In yet other embodiments, the model switching system (108) is implemented by one or more other components of the environment (100), such as the sequence analysis device (114). In particular, the model switching system (108) may be implemented in various ways across the local server device(s) (102), the server device(s) (120), the network (116), the client device (110), and the sequence analysis device (114). For example, the model switching system (108) can be downloaded from local server device(s) (102) and/or server device(s) (120) to client devices (110) and/or sequence analysis devices (114), where all or part of the functionality of the model switching system (108) is performed on each respective device within the environment (100).

As further illustrated in FIG. 1 , the environment (100) includes a database (118). The database (118) can store information such as genome analysis applications, application results, variant call files, sample nucleotide sequences, and sequence analysis data such as nucleotide reads, nucleotide base calls, variant calls, and sequence analysis metrics. In some embodiments, the local server device(s) (102), client devices (110), and/or sequence analysis devices (114) communicate with the database (118) (e.g., via a network (116)) to store and/or access information such as genome analysis applications, application results, variant call files, sample nucleotide sequences, and sequence analysis data such as nucleotide reads, nucleotide base calls, variant calls, and sequence analysis metrics. In some cases, the database (118) also stores one or more models, such as different versions of a variant analysis model (107).

Additionally, the environment (100) includes server device(s) (120). In some embodiments, the server device(s) (120) can generate, receive, analyze, store, and transmit digital data, such as data for determining nucleotide base calls, sequencing nucleic acid polymers, and/or executing a genome analysis application to analyze (e.g., perform diagnostics) a nucleotide sequence. In some cases, the sequence analysis device (114) can transmit call data from the sequence analysis device (114) (and the server device(s) (120) can receive call data). The server device(s) (120) can also communicate with the client device (110). In particular, the server device(s) (120) can transmit data to the client device (110) that includes a variant call file or other information indicative of nucleotide base calls, sequence analysis metrics, error data, diagnostic information, or results generated by executing a genome analysis application. As illustrated, the server device(s) (120) can accommodate a variant analysis model (107). In some embodiments, the server device(s) (120) also or alternatively accommodates one or more components of a genome analysis platform (104) (operating in conjunction with other components on the local server device(s) (102). Additionally, the server device(s) (120) can include a content server, an application server, a communication server, a web hosting server, or other types of servers.

Although FIG. 1 illustrates components of the environment (100) communicating over a network (116), in certain implementations, components of the environment (100) may also communicate directly with each other, bypassing the network (116). For example, and as noted above, in some implementations, the client device (110) communicates directly with the sequence analysis device (114). Additionally, in some embodiments, the client device (110) communicates directly with the model switching system (108). Additionally, the model switching system (108) may access one or more databases housed or accessed by the local server device(s) (102) or server device(s) (120) or elsewhere in the environment (100).

As noted, in certain described embodiments, the model switching system (108) installs a marked version of a variant analysis model for executing a genome analysis application. In particular, the model switching system (108) identifies a marked version of a genome analysis application and installs that version on a genome analysis device. FIG. 2 illustrates an exemplary overview of installing a marked version of a variant analysis model for executing a genome analysis application according to one or more embodiments. Additional details regarding individual acts described in connection with FIG. 2 are provided hereinafter with reference to the subsequent drawings.

As illustrated in FIG. 2 , the model switching system (108) performs an act (202) to identify a genome analysis application. In particular, the model switching system (108) identifies a genome analysis application (e.g., a genome analysis application (112)) for analyzing sequence analysis data, such as nucleotide base calling or sequence analysis metrics, as part of a diagnostic process or for some other purpose. In some cases, the model switching system (108) receives the genome analysis application from a client device (110) or from a server that communicates with the client device (110) (e.g., via a network (116)). For example, the model switching system (108) (or the genome analysis platform (104)) provides a web interface by which the client device (110) generates and uploads a genome analysis application, such as the genome analysis application (112).

As further illustrated in FIG. 2, the model switching system (108) performs an action (204) to determine an indicated version of a variant analysis model (e.g., variant analysis model (107)) from a genome analysis application. Specifically, the model switching system (108) determines a version of a variant analysis model that is indicated by the genome analysis application and/or is required to execute or perform one or more functions of the genome analysis application. In some cases, the model switching system (108) determines the indicated version by analyzing an application specification that defines one or more parameters for executing the genome analysis application. For example, the model switching system (108) identifies a version label within the application specification that indicates a version of a variant analysis model that is required or preferred for executing the genome analysis application.

For example, an application specification may refer to metadata or software code that accompanies or is part of a genome analysis application and defines application parameters (e.g., using labels). Such application parameters may include versions of variant analysis models required to run the application (or specific workflow pods within the application), computing resources such as genome analysis devices required and/or accessible by the application, definitions of workflow pods/containers, or other application-specific (or pod-specific) information. A single genome analysis application may include one or more application specifications, where a single application specification may define parameters for the entire application or for a set of one or more workflow pods within the application (with other application specifications defining parameters for other workflow pods).

As further illustrated in FIG. 2 , in some embodiments, the model switching system (108) performs act (206) to determine an available version of a variant analysis model. More specifically, based on determining a displayed version of a variant analysis model for executing a genome analysis application, the model switching system (108) determines whether the displayed version is available. For example, the model switching system (108) utilizes a variant analysis model manager (e.g., a particular container associated with a container reconciliation engine implemented by the model switching system (108) or the genome analysis platform (104)) to access a repository of versions of the variant analysis model (e.g., within the database (118). In some embodiments, the model switching system (108) thus determines an available version of the variant analysis model stored within the repository. The model switching system (108) may further compare the stored version with the displayed version for the genome analysis application.

Additionally, as illustrated in FIG. 2 , the model switching system (108) performs an act (208) of installing a marked version of a variant analysis model. In some embodiments, the model switching system (108) performs act (208) in response to performing act (206). In other embodiments, the model switching system (108) performs act (208) in response to act (204) (e.g., without performing act (206)). The model switching system (108) installs the marked version to achieve compatibility for executing a genome analysis application. For example, the model switching system (108) updates a genome analysis device (e.g., a device that accommodates a single version of a variant analysis model at a time or a device that accommodates multiple versions of a variant analysis model at a time) by installing the marked version of the variant analysis model. In some cases, the model switching system (108) replaces a previously installed version of a variant analysis model with the indicated version (e.g., so that the indicated version is accommodated on the genome analysis device in place of a previous version), while in other cases, the model switching system (108) installs the indicated version in addition to one or more previously installed versions.

In certain embodiments, the model switching system (108) performs one or more of the actions (204-208) automatically, i.e., without requiring or receiving user input to initiate or perform the particular actions or operations. For example, the model switching system (108) automatically determines a displayed version of a variant analysis model upon identifying or receiving a genome analysis application, without requiring or receiving user input to initiate identifying the displayed version. Additionally (or alternatively), the model switching system (108) automatically determines an available version of a variant analysis model in the repository upon determining the displayed version (or upon identifying the genome analysis application), without requiring or receiving user interaction to initiate determining an available version of the variant analysis model. Additionally (or alternatively) still, the model switching system (108) automatically installs a displayed version of a variant analysis model upon determining an available version (or upon determining a displayed version), without requiring or receiving user input to initiate installation of the displayed version.

As further illustrated in FIG. 2, the model switching system (108) performs actions (210) to execute a genome analysis application. In particular, the model switching system (108) utilizes a displayed version of a variant analysis model installed on the genome analysis device to perform various functions (as defined by the workflow pod and/or container) of the genome analysis application. For example, the model switching system (108) implements the variant analysis model to provide data for performing (e.g., for use by) certain aspects of the genome analysis application utilizing the genome analysis device. In some cases, the model switching system (108) may additionally utilize the genome analysis device to perform one or more aspects of the genome analysis application.

When executing a genome analysis application, the model switching system (108) determines or generates application results that include diagnostic or other information generated or inferred from the sequence analysis data, such as nucleotide base calls (e.g., “AATG”) of the sample nucleotide sequence. In certain embodiments, the model switching system (108) executes the genome analysis application in the form of a diagnostic application that meets regulatory security and analysis standards established by a regulatory agency for in vitro diagnostics (IVDs) (e.g., standards established by the U.S. Food and Drug Administration (FDA) or some other agency). Similarly, the model switching system (108) can execute applications for investigational use only (IUO) analyses and research use only (RUO) analyses.

As further illustrated in FIG. 2, the model switching system (108) can iterate through the acts (202-210) for multiple genome analysis applications. Specifically, the model switching system (108) can perform or execute the multiple genome analysis applications sequentially (e.g., by scheduling computing resources such as FPGAs or CPUs). In practice, for each application in the sequence, the model switching system (108) can perform acts (202-210) before moving on to the next application. In some embodiments, the model switching system (108) automatically iterates through the multiple applications, one at a time, without requiring or receiving user input to proceed to the next application and/or without requiring or receiving user input to perform/repeat each act (202-210) for each successive application.

As noted, in certain illustrated embodiments, the model switching system (108) utilizes a particular container orchestration engine to switch between versions of a variant analysis model. In particular, the model switching system (108) utilizes a container orchestration engine to determine a version of a variant analysis model indicated by a genome analysis application and to execute a containerized function to install the indicated version on a genome analysis device. FIG. 3 illustrates an exemplary flow for utilizing a container orchestration engine to identify and install a version of a variant analysis model according to one or more embodiments.

As illustrated in FIG. 3 , the model switching system (108) identifies or receives a genome analysis application (302) (e.g., a genome analysis application (112)). In addition, the model switching system (108) identifies an application specification (304) within or accompanying the genome analysis application (302). For example, the model switching system (108) identifies an application specification (304) that defines parameters for executing the genome analysis application (302), such as a version label that specifies a version of a variant analysis model, a container label that specifies a workflow container or workflow pod within the genome analysis application (302), and a resource label that specifies a computing resource for executing the genome analysis application (302) (e.g., a genome analysis device). In some cases, the genome analysis application (302) includes (or corresponds to) a single application specification (e.g., an application specification (304)). In other cases, the genome analysis application (302) includes (or corresponds to) multiple application specifications, each defining parameters for one or more constituent workflow pods within the genome analysis application (302).

As further illustrated in FIG. 3, the model switching system (108) utilizes the container orchestration engine API (306) to orchestrate or coordinate the identification and installation of indicated versions of variant analysis models. For example, the model switching system (108) utilizes the container orchestration engine API (306) comprising various workflow pods and/or workflow containers (which are part of a workflow pod) to analyze the application specification (304), identify indicated versions of variant analysis models, install the indicated versions, and run the genome analysis application utilizing the indicated versions.

In some cases, the container orchestration engine API (306) includes an admission controller that includes functionality to install a version of a mutation analysis model. For example, the admission controller may communicate with another component of the container orchestration engine (or the model switching system (108)) to provide instructions to install a indicated version of the mutation analysis model. In these or other cases, the container orchestration engine API (306) includes a mutation admission webhook that modifies or augments the behavior of the genome analysis application (302) with a custom callback (e.g., by modifying the application specification (304)). For example, the mutation admission webhook includes instructions that provide instructions to, or are accessible by, the mutation webhook controller (308) to modify or augment the application specification (304).

In practice, the model switching system (108) utilizes the mutation webhook controller (308) to generate a modified application specification (310) from the application specification (304) (e.g., as a workflow pod within the container orchestration engine). In practice, the mutation webhook controller (308) may include computer code to key off a pod label (e.g., an indication of a mutation analysis model version) to modify the application specification (304) to include pod settings for a version of a mutation analysis model (e.g., hostPID and hostIPC). Additionally, the mutation webhook controller (308) includes code to effect injection of an initialization workflow container, which queries the mutation analysis model manager (322) to install the model version indicated by the pod label. Thus, the mutation webhook controller (308) ensures that the version of the mutation analysis model (107) installed on the operating system matches the version running on the pod corresponding to the pod label.

For example, the model switching system (108) generates a modified application specification (310) to include a specialized workflow container called an initialization workflow container (indicated as “InitContainer” in FIG. 3 ). In practice, the model switching system (108) generates and adds the initialization workflow container to the modified application specification (310). In certain cases, the initialization workflow container includes instructions for communicating with other components of the model switching system (108) (or the genome analysis platform (104)) (e.g., other workflow containers, devices, or network locations). For example, the model switching system (108) utilizes the initialization workflow container to communicate with a variant analysis model manager to install a displayed version of a variant analysis model.

In practice, the model switching system (108) utilizes the mutation webhook controller (308) to add a version label for a version of a mutation analysis model that is required or preferred for executing the genome analysis application (302) to the modified application specification (310). The model switching system (108) also utilizes the mutation webhook controller (308) to add a resource label to the modified application specification (310) that indicates a resource, such as an FPGA or other genome analysis device, that is required or preferred for executing the genome analysis application (302).

Accordingly, the mutation webhook controller (308) identifies and/or selects a genome analysis device by identifying and/or adding a resource label within the modified application specification (310). As illustrated, the mutation webhook controller (308) further communicates with a mutation analysis model manager (322) to provide instructions to install a displayed version of the mutation analysis model. In one or more implementations, the mutation webhook controller (308) and/or the mutation analysis model manager (322) are housed on the same server as the mutation analysis model (e.g., the server device(s) (120) or the local server device(s) (102)).

In one or more embodiments, the mutation webhook controller (308) identifies or detects any genomics analysis application or workflow pod requesting a mutation analysis model, and mutates its corresponding application specification (or pod specification) by adding an initialization container to install the indicated version of the mutation analysis model. In some embodiments, the model switching system (108) generates a modified application specification (utilizing the mutation webhook controller (308)) for each individual genomics analysis application requesting a mutation analysis model and/or generates a modified pod specification for each individual workflow pod requesting a mutation analysis model.

As further illustrated in FIG. 3, the model switching system (108) utilizes a resource manager (312) to access or utilize a genome analysis device resource (320) (as specified by the modified application specification (310)). Specifically, the model switching system (108) identifies a resource label within the modified application specification (310) and utilizes a particular workflow container, called the resource manager (312), to access the resource identified in the label. In practice, the model switching system (108) defines a genome analysis device, such as an FPGA or CPU, as a schedulable resource for access via the container orchestration engine. Accordingly, the resource manager (312) accesses a genome analysis device resource (320) associated with the resource indicated in the modified application specification (310) (e.g., a workflow container that communicates with and interfaces with the genome analysis device). In some cases, the modified application specification (310) indicates an FPGA or CPU or some other genomic analysis device for executing the genomic analysis application (302) (or a particular workflow pod), and thus the resource manager (312) accesses or communicates with the designated device (or other resource) to facilitate execution of the genomic analysis application (302) (or a particular workflow pod).

As further illustrated in FIG. 3, the model switching system (108) executes a workflow pod (314) as part of the genome analysis application (302). In particular, the model switching system (108) identifies a workflow pod (314) associated with a modified application specification (310) and executes functions for one or more workflow containers within the workflow pod (314). Specifically, the model switching system (108) executes functions for the workflow initiation container (316) and the workflow container (318). In certain embodiments, the model switching system (108) executes or performs functions defined by the workflow initiation container (316) prior to executing functions of other workflow containers (e.g., the workflow container (318)) within the workflow pod (314) or within the genome analysis application (302).

In practice, as described, the model switching system (108) creates a workflow initialization container (316) and adds it to the workflow pod (314) to initialize the workflow pod (314) to execute the workflow container (318) utilizing an appropriate (e.g., indicated) version of the variant analysis model on the requested resource (e.g., the genome analysis device). For example, in the example illustrated in FIG. 3 , in the model switching system (108), the workflow initialization container (316) communicates with the variant analysis model manager (322) to install version 3.8.2 of the variant analysis model for executing the workflow container (318). By executing the workflow initialization container (316) prior to the workflow container (318), the model switching system (108) ensures that the workflow pod (314) utilizes or has access to an appropriate version of the variant analysis model to perform the functions defined by the configuration container, such as the workflow container (318).

As just mentioned, the workflow initiation container (316) communicates with the variant analysis model manager (322) (running on the host operating system) to provide instructions to install a marked version of a variant analysis model. In practice, the variant analysis model manager (322) receives instructions to mark or otherwise determine a marked version of a variant analysis model for executing a workflow pod (314) (or a genome analysis application (302)). The variant analysis model manager (322) further accesses a repository of variant analysis model versions to determine whether the marked version is available. When it determines that the marked version is available, the variant analysis model manager (322) installs the marked version on the genome analysis device, either replacing a previously installed version or adding the marked version to one or more previously installed versions. The mutation analysis model manager (322) may additionally monitor the installation status and provide indications to the mutation webhook controller (308) and/or other components of the model switching system (108) to initiate execution of the genome analysis application (302) (or workflow pod (314)) when the installation is complete.

As noted above, in certain illustrated embodiments, the model switching system (108) installs versions of variant analysis models indicated by genome analysis applications. In particular, the model switching system (108) installs different versions for different genome analysis applications as requested by their respective applications. FIG. 4 illustrates an exemplary flow for installing indicated versions of variant analysis models according to one or more embodiments.

As illustrated in FIG. 4, the model switching system (108) utilizes a variant analysis model manager (406) (e.g., a variant analysis model manager (322)) to install a displayed version (408) of a variant analysis model on a genome analysis device (410) (e.g., a genome analysis device (106)). As described, the model switching system (108) determines a displayed version of a variant analysis model and accesses a repository of variant analysis model versions (404) within a database (402) (e.g., a database (118)). Additionally, the model switching system (108) utilizes the variant analysis model manager (406) to install a displayed version (408) from the database (402) on the genome analysis device (410).

In some cases, the database (402) is local to the genome analysis device (410) and/or the variant analysis model (e.g., on the same server or within a local network), while in other cases the database (402) is remote (e.g., not on the same server or within a local network). Accordingly, the model switching system (108) may instruct a server (e.g., a server that houses or interfaces with the genome analysis device (410)) to install the indicated version (408) based on determining that the indicated version is stored within a remote repository.

In some embodiments, the variant analysis model manager (406) installs the indicated version (408) to replace a previously installed version of the variant analysis model (e.g., a previously installed version A (412)). For example, the variant analysis model manager (406) determines that the indicated version (408) is different from a previously installed version of the variant analysis model and therefore installs the indicated version (408). In certain cases, the genome analysis device (410) can only accommodate a single version of a variant analysis model at a time. Accordingly, the variant analysis model manager (406) replaces a previously installed version A (412) with the indicated version (408).

Additionally, the model switching system (108) executes the genome analysis application (or a workflow pod of the genome analysis application) by implementing a displayed version (408) of the variant analysis model on the genome analysis device (410). For example, the model switching system (108) determines compute availability, such as whether the genome analysis device (410) is currently performing a task (wherein the genome analysis device (410) is limited to a single task at a time). Based on compute availability, the model switching system (108) further schedules execution of the genome analysis application for immediate execution or for a later execution (e.g., in sequence behind one or more other tasks queued to the genome analysis device (410) from the model switching system (108) or from some other component of the genome analysis platform (104). In practice, the model switching system (108) can determine the computational availability of the genome analysis device (410) at each step involving the genome analysis device (410), such as installing a displayed version of a variant analysis model and executing a genome analysis application or workflow pod utilizing the displayed version (e.g., utilizing the variant analysis model manager (322)).

When executing a genome analysis application, the model switching system (108) may determine that the genome analysis device (410) is no longer occupied and is free to be scheduled again for another genome analysis application. Accordingly, the model switching system (108) may analyze the other genome analysis application to identify a new marked version of a variant analysis model (e.g., as specified by the application specification) for the new application. The model switching system (108) may additionally install the new marked version (when it determines that the new marked version is among the available variant analysis model versions (404)) and execute the new genome analysis application. Additionally, the model switching system (108) may repeat the process of identifying the marked version, installing the marked version, and sequentially executing the genome analysis application for multiple genome analysis applications.

In one or more embodiments, the genome analysis device (410) may accommodate (or communicate with a database that accommodates) multiple versions of variant analysis models. For example, the genome analysis device (410) may interface with a database of variant analysis model versions (e.g., database (118)) that is installed locally (e.g., on a shared server device or within a shared local network) or on a cloud server. As illustrated, the genome analysis device (410) interfaces with a cloud database that includes a previously installed version B (414) and a previously installed version C (416). In some cases, the genome analysis device (410) may execute the genome analysis application by accessing the previously installed version B (414) and/or the previously installed version C (416) to perform functions for one or more workflow pods defined within the genome analysis application. In one or more instances, the genome analysis device (410) may interface with a cloud database (or a local database or some other local memory) to alternate between different installed versions of a variant analysis model (e.g., to run different genome analysis applications or workflow pods).

As noted, in certain embodiments, the model switching system (108) installs different versions of a variant analysis model for different workflow pods (e.g., within a single genome analysis application or within different genome analysis applications). In particular, the model switching system (108) may identify multiple workflow pods within a genome analysis application and may analyze a single application specification that defines different versions of a variant analysis model to execute for each of the workflow pods. Alternatively, the model switching system (108) may analyze individual application specifications (or pod specifications) that define variant analysis model versions for individual workflow pods. FIG. 5 illustrates an exemplary flow for installing different versions of a variant analysis model for different workflow pods according to one or more embodiments.

As illustrated in FIG. 5 , the model switching system (108) utilizes the variant analysis model manager (516) (e.g., the variant analysis model manager (406 or 322)) to identify a represented version of a variant analysis model and to install the represented version on a genome analysis device (518) (e.g., the genome analysis device (410 or 106)). More specifically, the model switching system (108) analyzes the genome analysis application (502) to identify a plurality of workflow pods, such as workflow pod A (504), workflow pod B (508), and workflow pod C (512). Additionally, the model switching system (108) identifies one or more application specifications (or pod specifications) that specify or define the respective represented versions for executing the respective workflow pods (A through C). In certain instances, the workflow pods (A through C) represent different versions of the variant analysis model because their respective genome analysis functions require different components or functionality belonging to the different versions of the variant analysis model.

For example, the model switching system (108) identifies an indicated version A (506) from the application specification associated with workflow pod A (504). Additionally, the model switching system (108) identifies an indicated version B (510) associated with workflow pod B (508). Additionally, the model switching system (108) identifies an indicated version C (514) associated with workflow pod C (512). In one or more embodiments, the model switching system (108) accesses a database (520) (e.g., the database (118)) that contains or stores a repository of variant analysis model versions (522). The model switching system (108) further compares the available variant analysis model versions (522) with the indicated versions (A through C) (all at once or one at a time sequentially) to determine whether the indicated versions (A through C) are available within the variant analysis model versions (522).

In some embodiments, the model switching system (108) further utilizes the variant analysis model manager (516) in an iterative and sequential manner to identify and install the indicated version for each corresponding workflow pod to be executed via the genome analysis device (518). Specifically, the model switching system (108) identifies and installs the indicated version A (506) on the genome analysis device (518), and further utilizes the genome analysis device (518) to execute workflow pod A (504) utilizing the indicated version A installed on the genome analysis device (518). Upon completion of execution of workflow pod A (504), the model switching system (108) identifies the indicated version B (510), verifies its availability among the variant analysis model versions (522), installs it on the genome analysis device (518), and executes workflow pod B (508) utilizing the indicated version B, thereby continuing sequentially with workflow pod B (508).

Likewise, the model switching system (108) repeats the process for workflow pod C (512) by identifying the indicated version C (514), verifying that the indicated version C (514) is available within the variant analysis model version (522), installing the indicated version C (514) onto the genome analysis device (518), and executing the indicated version C (514) utilizing the indicated version C (514) installed on the genome analysis device (518). As illustrated, the model switching system (108) can repeat this process for any number of workflow pods identified within the genome analysis application (502) (e.g., workflow pods specifying different indicated versions of the variant analysis model).

As noted above, in certain embodiments, the model switching system (108) utilizes containers and pods to determine and update versions of variant analysis models. In particular, the model switching system (108) can update model versions based on requirements or parameters for a genome analysis application. FIG. 6 illustrates an exemplary diagram of components of a system architecture (e.g., installed on a local server device), applications, devices, and containers involved in updating model versions and executing applications according to one or more embodiments.

As illustrated in FIG. 6 , the model switching system (108) utilizes a sequence analysis device (e.g., a sequence analysis device (114)) to communicate with various components or systems and perform sequence analysis operations used and/or directed by a version of a variant analysis model on a local server (e.g., a genome analysis device (106) on a local server device(s) (102)). For example, the model switching system (108) communicates with a BaseSpace Sequencing Hub (“BSSH”) or a Research Only Operations (“RUO”) cloud-based interface and laboratory information management system (“LIMS”) to generate base calls for nucleotide bases of a genome sample.

The model switching system (108) performs real-time analysis ("RTA") of the sample based on information from the BSSH RUO and/or the LIMS. More specifically, the model switching system (108) performs the RTA to determine base calls, variant calls, and/or various metrics from nucleotide bases of the genomic sample according to a sequence analysis plan. Based on the RTA, the model switching system (108) generates a binary base call ("BCL") file that includes raw data generated and output by one or more sequence analysis runs (e.g., via the RTA). In practice, the BCL file may represent base calls, variant calls, and/or other sequence analysis information for interpretation by the variant analysis model and/or some other system.

To organize or schedule a sequence analysis run of the RTA, the model switching system (108) provides control software (e.g., including a user interface) for planning or scheduling a sequence analysis run for a particular sample. In practice, the model switching system (108) provides control software and a user interface for planning one or more sequence analysis runs for testing genomic samples for particular genetic markers or genetic traits, for example, according to planning parameters. For example, the control software allows a user to specify parameters for a sequence analysis run and/or to test for particular markers. As illustrated, the model switching system (108) can integrate the control software for a sequence analysis device with a user interface web portal (including a standalone web browser and integration of the control software) for interfacing with the sequence analysis device to plan a sequence analysis run.

In some cases, the model switching system (108) facilitates local planning for sequence analysis executions where the planning software (e.g., control software) is hosted by a local edge server. In these or other cases, the model switching system (108) facilitates cloud planning for sequence analysis executions where the planning software (e.g., control software) is hosted on a cloud server rather than on the local server device(s) (102). In a similar manner, execution of the variant analysis models may be either local-based or cloud-based depending on whether the server hosting the variant analysis models is a local server (e.g., local server device(s) (102)) or a cloud-based server (e.g., server device(s) (120)).

In one or more embodiments, the model switching system (108) facilitates version updates or version switching for variant analysis models for dark sites or other locations that have computing devices that are not connected to the Internet or a wide area network (WAN). For example, the model switching system (108) accommodates situations where (i) a genome sequencing system, such as a primary analysis system, a secondary analysis system, or a tertiary analysis system, is not connected to the Internet for privacy, security, and/or other purposes, and (ii) includes multiple versions of a variant analysis model stored on the relevant system. Specifically, the model switching system (108) can generate and provide a graphical user interface that facilitates downloading an installer (e.g., an executable digital file for installing software on a computing device) for a version of a variant analysis model (or multiple installers for multiple versions of a variant analysis model) and copying the installer onto the model switching system (108) (e.g., onto the genome analysis device (106)). Specifically, the model switching system (108) can guide the installation process using a graphical user interface without requiring Internet or WAN access to execute the installer for the relevant or selected model version (e.g., to copy and/or execute the installer). For example, the model switching system (108) can provide multiple installers for different versions of a variant analysis model, and can automatically (or manually via input within the graphical user interface) select, copy, and/or execute the appropriate installer for updating or switching between model versions based on parameters or requirements of different genomic analysis applications.

As further illustrated in FIG. 6 , the system architecture (600) of the model switching system (108) includes or communicates with containers or systems associated with one or more core services. In fact, as illustrated, the model switching system (108) includes services of the system architecture (600). To manage or coordinate the various services of the system architecture (600) of the model switching system (108), the system architecture (600) includes a container orchestration engine (601) (e.g., K3S or Kubernetes) for managing and implementing the various pods and containers associated with performing genomic analysis and/or updating model versions for variant analysis models. As described, the model switching system (108) utilizes the container orchestration engine (601) to orchestrate or coordinate the identification and installation of indicated versions of variant analysis models (e.g., as indicated by applications of third-party systems). The model switching system (108) may also perform other functions including user management, application management, execution management, mutation analysis model management, device management, data copying, and audit logging.

For example, the model switching system (108) utilizes a container orchestration engine (601) that includes various workflow pods and/or workflow containers (that are part of the workflow pods) to analyze application specifications, identify indicated versions of variant analysis models, install the indicated versions, and run genome analysis applications utilizing the indicated versions. In some cases, the container orchestration engine (601) includes an admission controller that includes functionality for installing versions of variant analysis models. For example, the admission controller may communicate with other components of the container orchestration engine (601) (or the model switching system (108)) to provide commands to install indicated versions of variant analysis models.

For example, the system architecture (600) includes a user management service (602) (e.g., a set of one or more user management pods or containers). The user management service (602) performs various processes or functions to provide a single sign-on ("SSO") experience across the system. Specifically, the user management service (602) may include one or more containers or pods that contain or access user information for third-party systems, for example, to determine a version of a variant analysis model currently installed on a genome analysis device and/or to determine a required version of a genome analysis model. Based on the determination of the installed version and/or the required version, the user management service (602) may communicate with other services of the system architecture (600) to initiate the installation of a new version that matches the required version, as well as the removal of the current version.

Additionally, the system architecture (600) includes or utilizes an application management service (604) that communicates with the container orchestration engine (601). For example, the application management service (604) manages application package installations for version upgrades and removal of previous model versions. In some cases, the application management service (604) further includes a resource manager (e.g., resource manager (312)). As described, the resource manager may access or utilize genomic analysis device resources as specified by the modified application specification. Specifically, the resource manager identifies a resource label for accessing a specified resource, such as an FPGA or CPU, as a schedulable resource for access via the container orchestration engine. In some cases, the application management service (604) includes (or receives from a third party system) an application specification that represents an FPGA or CPU or some other genomic analysis device for executing a genomic analysis application (or a particular workflow pod), so that the resource manager accesses or communicates with the designated device (or other resource) to facilitate execution of the genomic analysis application (or a particular workflow pod).

As further illustrated, the system architecture (600) includes or utilizes an execution management and orchestration service (606). Specifically, the execution management and orchestration service (606) includes one or more containers or pods for facilitating and executing a sequence analysis run, a primary analysis, a secondary analysis, or a tertiary analysis. In practice, the execution management and orchestration service (606) includes computer code or instructions for executing a sequence analysis run (and/or additional analysis) depending on the installed version of the variant analysis model. For example, the execution management and orchestration service (606) communicates with the workflow engine (614) to execute a customized workflow for an application, such as an application associated with a third-party system (e.g., an oncology analysis application such as the TSO500 application, a QC application, or other application). The execution management and orchestration service (606) further includes code for communicating with a data copy service (612) to perform genome analysis or to copy input and output sequence analysis data (e.g., from a BCL file generated by a sequence analysis device) for storage in a database, such as a local network attached storage ("NAS"), server message block ("SMB"), or common internet file system ("CIFS").

In addition, the system architecture (600) includes a variant analysis model management service (608). In particular, the variant analysis model management service (608) includes one or more containers or pods for managing a variant analysis model (e.g., a variant analysis model (107)) for performing genome analysis. For example, the variant analysis model management service (608) implements a specific version installed for detecting genetic markers for specific conditions in a sample genome sequence. In addition, the variant analysis model management service (608) performs model peripheral management such as license acquisition, self-testing, and version certification for the variant analysis model.

As further illustrated in FIG. 6 , the system architecture (600) includes a device management service (610). In one or more embodiments, the device management service (610) includes one or more containers or pods for pairing and monitoring devices used as part of a sequence analysis workflow and/or a post-sequencing genome analysis workflow. For example, the device management service (610) manages devices and/or variant analysis models of sequence analysis devices to pair compatible devices with indicated versions of the variant analysis models (or vice versa). The system architecture (600) further includes an audit logging service (616) for monitoring and logging the performance of the devices, components of the variant analysis models, and/or containers within the application workflow. For example, the audit logging service (616) detects and logs errors or other audit information associated with the system architecture (600).

As suggested above in the description of FIG. 1, using the system architecture (600) illustrated in FIG. 6, the model switching system (108) may be deployed locally on an edge server (e.g., a local server device (102)) or in the cloud, such as a cloud-based server hosting Illumina Connected Analytics ("ICA") and/or a cloud-based server of Amazon Web Services ("AWS"). For example, the model switching system (108) may be run locally on the local server device(s) (102) as part of planning software that plans resources based on user input for sequence analysis runs or other analyses, and the variant analysis model (107) may likewise be run locally on the local server device(s) (102) to analyze the BCL data and determine variant calls or other metrics. In contrast, the model switching system (108) can be executed remotely on server device(s) (120) as part of the planning software, and the mutation analysis model (107) can be executed locally on local server device(s) (102) to analyze BCL data and determine mutation calls.

Referring now to FIG. 7 , that diagram illustrates an exemplary flow diagram of a series of acts for identifying and installing a represented version of a variant analysis model for executing a genome analysis application according to one or more embodiments. While FIG. 7 illustrates the acts according to one embodiment, alternative implementations may omit, add, reorder, and/or modify any of the acts illustrated in FIG. 7 . The acts of FIG. 7 may be performed as part of a method. Alternatively, a non-transitory computer-readable storage medium may include instructions that, when executed by one or more processors, cause a computing device to perform the acts illustrated in FIG. 7 . In another embodiment, a system includes at least one processor, and a non-transitory computer-readable medium including instructions that, when executed by the one or more processors, cause the system to perform the acts of FIG. 7 .

As illustrated in FIG. 7, the series of acts (700) includes an act of identifying a genome analysis application (702). In particular, the act (702) may include identifying a genome analysis application for analyzing nucleotide base calls determined for a sample nucleotide sequence. The act (702) may involve identifying or receiving a genome analysis application from a client device, wherein the client device arranges or generates the genome analysis application for upload or transmission to a genome analysis platform.

As further illustrated in FIG. 7 , the series of acts (700) includes an act (704) of determining a marked version of a variant analysis model for executing a genome analysis application. In particular, the act (704) may include determining a marked version of a variant analysis model for executing a genome analysis application indicated by an application specification that defines one or more parameters for the genome analysis application. For example, the act (704) may involve analyzing the application specification to identify a version label that specifies a marked version of the variant analysis model. In some cases, the act (704) includes utilizing a variant analysis model manager to analyze the application specification to identify a version label that specifies a marked version of the variant analysis model.

Additionally, the series of acts (700) includes an act (706) of installing a marked version of a variant analysis model. In particular, act (706) may include installing the marked version of the variant analysis model for execution in place of a previously installed version of the variant analysis model, based on determining the marked version of the variant analysis model. For example, act (706) may involve updating a field programmable gate array to include the marked version of the variant analysis model for performing genomic analysis. In some cases, act (706) involves automatically installing the marked version of the variant analysis model by utilizing a variant analysis model manager to determine available versions of the variant analysis model and initiate installation of the marked version from the available versions.

In one or more embodiments, the series of acts (700) comprises determining that the indicated version of the variant analysis model is different from a previously installed version of the variant analysis model, wherein only a single version of the variant analysis model can be installed at a time. In these or other embodiments, act (706) may involve installing the indicated version of the variant analysis model based on determining that the indicated version is different from the previously installed version. In one or more cases, act (706) involves replacing the previously installed version of the variant analysis model with the indicated version of the variant analysis model. In certain cases, act (706) involves installing the indicated version of the variant analysis model in addition to the previously installed version, such that the indicated version and the previously installed version of the variant analysis model are installed on one or more servers of the system.

In one or more embodiments, act (706) involves utilizing a variant analysis model manager hosted on a server shared by the variant analysis model to initiate installation of the indicated version of the variant analysis model. Act (706) may involve determining that the indicated version of the variant analysis model is stored in a remote repository that stores multiple versions of the variant analysis model. In some cases, act (706) may involve providing a command to the server to install the indicated version of the variant analysis model based on determining that the indicated version is stored in the remote repository.

In some embodiments, the series of acts (700) includes determining that the indicated version of the variant analysis model installed on the genome analysis device is different from a previously installed version of the variant analysis model, wherein the genome analysis device can be configured to run a single version of the variant analysis model at a time. Act (706) can further involve installing the indicated version of the variant analysis model on the genome analysis device based on the determination that the indicated version is different from the previously installed version. The series of acts (700) can also include selecting the genome analysis device as a location for installing the indicated version of the variant analysis model by utilizing a mutation webhook controller to identify a resource label within an application specification that specifies the genome analysis device.

As further illustrated in FIG. 7, the series of acts (700) includes executing a genome analysis application utilizing a displayed version of a variant analysis model (708). In particular, the act (708) may include executing the genome analysis application to analyze nucleotide base calls utilizing the displayed version of the variant analysis model. In some embodiments, the act (708) involves determining the computational availability of a genome analysis device that accommodates the variant analysis model for executing the genome analysis application and scheduling execution of the genome analysis application by the genome analysis device based on the computational availability of the genome analysis device. In certain instances, the series of acts (700) includes receiving sequence analysis data including nucleotide base calls from a sequence analysis device. The act (708) may further involve executing the genome analysis application to analyze nucleotide base calls utilizing a field programmable gate array configured to execute the displayed version of the variant analysis model.

In one or more embodiments, the series of acts (700) comprises identifying a plurality of workflow pods, wherein two or more of the plurality of workflow pods specify different versions of the mutation analysis model to perform their respective functions. In these or other embodiments, the series of acts (700) comprises repeatedly installing the different versions of the mutation analysis model for sequential execution of each of the plurality of workflow pods. The series of acts (700) may comprise utilizing a mutation webhook controller to modify an application specification for a genome analysis application to include instructions for identifying the indicated versions of the mutation analysis model and initializing installation of the indicated versions of the mutation analysis model. Modifying the application specification may comprise adding an initialization workflow container to the application specification that communicates with a mutation analysis model manager to install the indicated versions of the mutation analysis model.

In certain embodiments, the series of acts (700) includes identifying a plurality of additional genomic analysis applications for analyzing nucleotide base calls determined for the sample nucleotide sequence, each of the plurality of additional genomic analysis applications specifying a different version of the variant analysis model. Additionally, the series of acts (700) may include sequentially executing each of the plurality of additional genomic analysis applications, iteratively, such as installing a version of the variant analysis model for a current genomic analysis application among the plurality of additional genomic analysis applications to replace a version from a previous genomic analysis application among the plurality of additional genomic analysis applications, and executing the current genomic analysis application utilizing the version of the variant analysis model for the current genomic analysis application.

The methods described herein can be used in conjunction with a variety of nucleic acid sequencing techniques. Particularly applicable techniques are those in which nucleic acids are attached to fixed locations on an array so that their relative positions do not change, and the array is imaged repeatedly. For example, embodiments in which images are obtained in different color channels that correspond to different labels used to distinguish one nucleotide base type from another are particularly applicable. In some embodiments, the process of determining the nucleotide sequence of a target nucleic acid (i.e., a nucleic acid polymer) can be an automated process. Preferred embodiments include sequence-by-synthesis ("SBS") techniques.

SBS technology generally involves enzymatic elongation of a nascent nucleic acid strand through repeated addition of nucleotides to a template strand. In traditional SBS methods, a single nucleotide monomer may be provided to a target nucleotide in the presence of a polymerase in each delivery. However, in the methods described herein, more than one type of nucleotide monomer may be provided to a target nucleic acid in the presence of a polymerase in a delivery.

SBS can utilize nucleotide monomers having a terminator moiety or nucleotide monomers lacking any terminator moiety. Methods utilizing nucleotide monomers lacking a terminator include, for example, sequencing and pyrosequencing using γ-phosphate labeled nucleotides, as described in more detail below. In methods utilizing nucleotide monomers lacking a terminator, the number of nucleotides added in each cycle is generally variable and depends on the template sequence and the mode of nucleotide delivery. In SBS techniques utilizing nucleotide monomers having a terminator moiety, the terminator may be substantially irreversible under the sequencing conditions employed, as is the case in traditional Sanger sequencing utilizing dideoxynucleotides, or the terminator may be reversible, as is the case in the sequencing methods developed by Solexa (now Illumina, Inc.).

SBS technology can utilize nucleotide monomers with or without a label moiety. Accordingly, incorporation events can be detected based on a property of the label, such as fluorescence of the label; a property of the nucleotide monomer, such as molecular weight or charge; a byproduct of nucleotide incorporation, such as release of pyrophosphate; etc. In embodiments where two or more different nucleotides are present in the sequencing reagent, the different nucleotides may be distinguishable from one another, or alternatively, the two or more different labels may not be distinguishable under the detection technology being used. For example, the different nucleotides present in the sequencing reagent may have different labels and be distinguishable using appropriate optical equipment, such as exemplified by the sequencing method developed by Solexa (now Illumina, Inc.).

A preferred embodiment comprises a pyrosequencing technique. Pyrosequencing detects the release of inorganic pyrophosphate (PPi) when specific nucleotides are incorporated into a new strand (Ronaghi, M., Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996) "Real-time DNA sequencing using detection of pyrophosphate release." Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001) "Pyrosequencing sheds light on DNA sequencing." Genome Res. 11(1), 3-11; Ronaghi, M., Uhlen, M. and Nyren, P. (1998) "A sequencing method based on real-time pyrophosphate." Science 281(5375), 363; U.S. Patent No. 6,210,891; U.S. Patent (U.S. Pat. No. 6,258,568 and U.S. Pat. No. 6,274,320, the disclosures of which are incorporated herein by reference in their entireties). In pyrosequencing, the released PPi is immediately converted to adenosine triphosphate (ATP) by ATP sulfurylase, which can be detected, and the level of ATP produced is detected via luciferase-generated photons. The nucleic acids to be sequenced can be attached to features in the array, and the array can be imaged to capture the chemiluminescent signal generated by incorporation of the nucleotides at the features in the array. The array can be imaged after treatment with a particular nucleotide type (e.g., A, T, C, or G). The images obtained after addition of each nucleotide type differ with respect to which features in the array are detected. These differences in the images reflect the different sequence contents of the features in the array. However, the relative positions of the individual features remain unchanged in the images. The images can be stored, processed, and analyzed using the methods described herein. For example, the images obtained after processing the array with each different nucleotide type can be processed in the same manner as exemplified herein for images obtained from different detection channels in the case of a reversible terminator-based sequencing method.

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

Preferably, in a reversible terminator-based sequence analysis embodiment, the label does not substantially inhibit extension under SBS reaction conditions. However, the detection label may be removed, for example, by cleavage or degradation. After the labels are incorporated into the arrayed nucleic acid features, images may be captured. In a particular embodiment, each cycle involves simultaneously delivering four different nucleotide types to the array, each nucleotide type having a spectrally distinct label. Four images may then be acquired, each using a detection channel that is selective for one of the four different labels. Alternatively, the different nucleotide types may be sequentially added, and an image of the array may be acquired between each addition step. In such an embodiment, each image will represent a nucleic acid feature incorporated with a particular type of nucleotide. Different features may be present or absent in different images because the sequence content of each feature is different. However, the relative positions of the features remain unchanged in the images. Images obtained from such a reversible terminator-SBS method may be stored, processed, and analyzed as described herein. After the image capture step, the label can be removed and the reversible terminator moiety can be removed for subsequent nucleotide addition and detection cycles. Removing the label after detection in a particular cycle and before a subsequent cycle can provide the advantage of reducing background signal and crosstalk between cycles. Examples of useful label and removal methods are described below.

In certain embodiments, some or all of the nucleotide monomers may comprise a reversible terminator. In such embodiments, the reversible terminator/cleavable fluorophore may comprise a fluorophore linked to the ribose moiety via a 3' ester bond (as described in Metzker, Genome Res. 15:1767-1776 (2005), which is incorporated herein by reference). Another approach has separated the terminator chemistry from the cleavage of the fluorescent label (as described in Ruparel et al., Proc Natl Acad Sci USA 102: 5932-7 (2005), which is incorporated herein by reference in its entirety). Ruparel et al. described the development of reversible terminators that utilize a small 3' allyl group to block extension, but which can be readily deblocked by a brief treatment with a palladium catalyst. The fluorophore is attached to the base via a photocleavable linker that is readily cleaved by exposure to long wavelength UV light for 30 seconds. Thus, disulfide reduction or photocleavage can be used as the cleavable linker. Another approach to reversible termination is to use the spontaneous termination that occurs after placement of a bulky dye on the dNTP. The presence of a charged bulky dye on the dNTP can act as an effective terminator through steric and/or electrostatic hindrance. Once an incorporation event has occurred, further incorporation is prevented unless the dye is removed. Cleavage of the dye removes the fluorine and effectively reverses the termination. Examples of modified nucleotides are also described in U.S. Pat. No. 7,427,673 and U.S. Pat. No. 7,057,026, the disclosures of which are incorporated herein by reference in their entireties.

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

Some embodiments may utilize detection of four different nucleotides using less than four different labels. For example, SBS may be performed utilizing the methods and systems described in the materials incorporated herein by reference in U.S. Patent Application Publication No. 2013/0079232. In a first example, a pair of nucleotide types may be detected at the same wavelength, but are distinguished based on an intensity difference of one member of the pair relative to the other member, or based on a change (e.g., via chemical modification, photochemical modification, or physical modification) in one member of the pair that causes an apparent signal to appear or disappear relative to the signal detected for the other member of the pair. In a second example, three of the four different nucleotide types may be detected under certain conditions, while the fourth nucleotide type has no detectable label under those conditions, or is minimally detected under those conditions (e.g., minimal detection due to background fluorescence, etc.). The incorporation of the first three nucleotide types into the nucleic acid can be determined based on the presence of their respective signals, and the incorporation of the fourth nucleotide type into the nucleic acid can be determined based on the absence or minimal detection of any signal. As a third example, one nucleotide type can comprise a label(s) that is detected in two different channels, while the other nucleotide type is not detected in one or more channels. The three exemplary configurations described above are not to be considered mutually exclusive, and can be used in various combinations. An exemplary embodiment combining all three examples is a fluorescence-based SBS method using a first nucleotide type detected in a first channel (e.g., dATP having a label detected in the first channel when excited by the first excitation wavelength), a second nucleotide type detected in a second channel (e.g., dCTP having a label detected in the second channel when excited by the second excitation wavelength), a third nucleotide type detected in both the first and second channels (e.g., dTTP having at least one label detected in both channels when excited by the first and/or second excitation wavelengths), and a fourth nucleotide type lacking a label that is not detected or minimally detected in either channel (e.g., dGTP having no label).

Additionally, sequence analysis data can be obtained using a single channel, as described in the materials included in U.S. Patent Application Publication No. 2013/0079232. In this so-called one-dye sequence analysis approach, a first nucleotide type is labeled but the label is removed after the first image is generated, a second nucleotide type is labeled only after the first image is generated, a third nucleotide type remains labeled in both the first and second images, and a fourth nucleotide type remains unlabeled in both images.

Some embodiments may utilize sequence analysis by ligation technology. This technology utilizes DNA ligase to incorporate oligonucleotides and identify the incorporation of such oligonucleotides. The oligonucleotides typically have different labels that correlate with the identity of specific nucleotides in the sequence to which the oligonucleotides hybridize. As with other SBS methods, an array of nucleic acid features can be treated with labeled sequencing reagents and then images can be obtained. Each image represents nucleic acid features that have incorporated a particular type of label. Different features may or may not be present in different images because the sequence content of each feature is different, but the relative positions of the features remain unchanged in the images. Images obtained from ligation-based sequencing methods can be stored, processed, and analyzed as described herein. Exemplary SBS systems and methods that can be utilized with the methods and systems described herein are described in U.S. Pat. Nos. 6,969,488, 6,172,218, and 6,306,597, the disclosures of which are incorporated herein by reference in their entireties.

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

Some embodiments may utilize methods involving real-time monitoring of DNA polymerase activity. Nucleotide incorporation may be detected via fluorescence resonance energy transfer (FRET) interactions between a fluorophore-bearing polymerase and a γ-phosphate labeled nucleotide, for example, as described in U.S. Pat. Nos. 7,329,492 and 7,211,414, each of which is incorporated herein by reference, or nucleotide incorporation may be detected using a zero-mode waveguide, for example, as described in U.S. Pat. No. 7,315,019, which is incorporated herein by reference, and a fluorescent nucleotide analogue and an engineered polymerase, for example, as described in U.S. Pat. No. 7,405,281 and U.S. Patent Publication No. 2008/0107082, which are each incorporated herein by reference. Illumination can be restricted to a zeptoliter-scale volume around the surface-tethered polymerase so that incorporation of fluorescently labeled nucleotides can be observed with low background (Levene, M.J. et al. "Zero-mode waveguides for single-molecule analysis at high concentrations." Science 299, 682-686 (2003)); Lundquist, P. M. et al. "Parallel confocal detection of single molecules in real time." Opt. Lett. 33, 1026-1028 (2008)); Korlach, J. et al. "Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nano structures." Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008)], the disclosures of which are incorporated herein by reference in their entirety). Images obtained from such methods can be stored, processed, and analyzed as described herein.

Some SBS embodiments involve detection of a proton released upon incorporation of a nucleotide into an extension product. For example, sequencing based on detection of a released proton can utilize commercially available electrical detectors and related technology from Ion Torrent (Guilford, CT, a Life Technologies subsidiary), or the sequencing methods and systems described in U.S. Patent Application Publication No. 2009/0026082 A1; U.S. Patent Application Publication No. 2009/0127589 A1; U.S. Patent Application Publication No. 2010/0137143 A1; or U.S. Patent Application Publication No. 2010/0282617 A1, each of which is incorporated herein by reference. The methods set forth herein for amplifying a target nucleic acid using kinetic exclusion can readily be applied to devices used to detect protons. More specifically, the methods set forth herein can be used to produce clonal populations of amplicons used to detect protons.

The SBS method is advantageously performed in a multiplex format, allowing multiple different target nucleic acids to be manipulated simultaneously. In certain embodiments, different target nucleic acids can be processed in a common reaction vessel or on the surface of a specific substrate. This facilitates the delivery of sequencing reagents, removal of unreacted reagents, and detection of incorporation events in a multiplexed manner. In embodiments using surface-bound target nucleic acids, the target nucleic acids can be present in an array format. In an array format, the target nucleic acids can be bound to a surface in a typically spatially distinguishable manner. The target nucleic acids can be bound by direct covalent binding, attachment to a bead or other particle, or binding to a polymerase or other molecule attached to the surface. The array can contain a single copy of the target nucleic acid at each site (also referred to as a feature), or multiple copies having the same sequence can be present at each site or feature. The multiple copies can be generated by amplification methods, such as bridge amplification or emulsion PCR, as described in more detail below.

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

One advantage of the methods presented herein is that they provide rapid and efficient detection of multiple target nucleic acids in parallel. Accordingly, the present disclosure provides an integrated system capable of producing and detecting nucleic acids using techniques known in the art, such as those exemplified above. Accordingly, the integrated system of the present disclosure can include fluidic components capable of delivering amplification reagents and/or sequencing reagents to one or more immobilized DNA fragments, and the system includes components such as pumps, valves, reservoirs, fluidic lines, and the like. A flow cell can be configured and/or used in an integrated system for detecting target nucleic acids. Exemplary flow cells are described, for example, in U.S. Patent Application Publication No. 2010/0111768 A1 and U.S. Patent Application No. 13/273,666, each of which is incorporated herein by reference. As an example of a flow cell, one or more of the fluidic components of the integrated system can be used in an amplification method and a detection method. For example, in a nucleic acid sequencing implementation, one or more of the fluidic components of the integrated system can be used for the delivery of sequencing reagents in an amplification method as presented herein and a sequencing method as exemplified above. Alternatively, the integrated system can include separate fluidic systems for performing the amplification method and performing the detection method. Examples of integrated sequencing systems that can both generate amplified nucleic acids and determine the sequence of the nucleic acids include, without limitation, the MiSeqTM platform (Illumina, Inc., San Diego, CA) and the devices described in U.S. patent application Ser. No. 13/273,666, which is incorporated herein by reference.

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

The nucleic acid sample may include high molecular weight material, such as genomic DNA (gDNA). The sample may include low molecular weight material, such as nucleic acid molecules obtained from FFPE or archived DNA samples. In other embodiments, the low molecular weight material includes enzymatically or mechanically fragmented DNA. The sample may include cell-free circulating DNA. In some embodiments, the sample may include nucleic acid molecules obtained from biopsies, tumors, scrapings, swabs, blood, mucus, urine, plasma, semen, hair, laser capture microdissections, surgical excisions, and other clinical or laboratory-derived samples. In some embodiments, the sample may be an epidemiological, agricultural, forensic, or pathogenic sample. In some embodiments, the sample may include nucleic acid molecules obtained from an animal, such as a human or mammalian source. In other embodiments, the sample may include nucleic acid molecules obtained from a non-mammalian source, such as a plant, bacteria, virus, or fungus. In some embodiments, the source of the nucleic acid molecules may be an archived or extinct sample or species.

In addition, the methods and compositions disclosed herein may be useful for amplifying nucleic acid samples having low-quality nucleic acid molecules, such as fragmented and/or fragmented genomic DNA from a forensic sample. 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 may include forensic samples obtained by law enforcement, one or more members of the military, or personnel thereof. The nucleic acid sample may be crude DNA or purified samples containing lysates, such as those derived from oral swabs, paper, fibers, or other substrates that may be soaked with saliva, blood, or other bodily fluids. As such, in some embodiments, the nucleic acid sample may include small amounts or fragmented portions of DNA, such as genomic DNA. In some embodiments, the target sequence may be present in one or more bodily fluids, including but not limited to blood, sputum, plasma, semen, urine, and serum. In some embodiments, the target sequences can be obtained from hair, skin, tissue samples, autopsy, or remains of a victim. In some embodiments, the nucleic acids comprising one or more target sequences can be obtained from a deceased animal or human. In some embodiments, the target sequences can include nucleic acids obtained from non-human DNA, such as microbial, plant, or entomological DNA. In some embodiments, the target sequences or amplified target sequences are directed to human identification purposes. In some embodiments, the present disclosure generally relates to methods for identifying a characteristic of a forensic sample. In some embodiments, the present disclosure generally relates to methods for human identification using one or more of the 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 or human identification sample containing at least one target sequence can be amplified using any one or more of the target-specific primers disclosed herein or using the primer design criteria outlined herein.

The components of the model switching system (108) may include software, hardware, or both. For example, the components of the model switching system (108) may include one or more instructions stored on a computer-readable storage medium and executable by a processor of one or more computing devices (e.g., client device (110), local server device(s) (102), and/or server device(s) (120)). When executed by the one or more processors, the computer-executable instructions of the model switching system (108) may cause the computing devices to perform the bubble detection methods described herein. Alternatively, the components of the model switching system (108) may include hardware, such as a special purpose processing device that performs a particular function or group of functions. Additionally or alternatively, the components of the model switching system (108) may include a combination of computer-executable instructions and hardware.

Additionally, components of the model switching system (108) that perform the functions described herein in connection with the model switching system (108) may be implemented, for example, as part of a standalone application, as a module of an application, as a plug-in for an application, as a library function or function callable by other applications, and/or as a cloud computing model. Thus, components of the model switching system (108) may be implemented as part of a standalone application on a personal computing device or a mobile device. Additionally or alternatively, components of the model switching system (108) may be implemented in any application that provides sequence analysis services, including but not limited to Illumina BaseSpace, Illumina DRAGEN, or Illumina TruSight software. "Illumina," "BaseSpace," "DRAGEN," and "TruSight" are registered trademarks or trademarks of Illumina, Inc. in the U.S. and/or other countries.

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

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

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

A "network" is defined as one or more data links that enable the transfer of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided to a computer over a network or other communications connection (whether wired, wireless, or a combination of wired and wireless), the computer considers the connection to be a transmission medium. Transmission media can include networks and/or data links that can be used to convey desired program code means in the form of computer-executable instructions or data structures and can be accessed by general-purpose or special-purpose computers. Combinations of the above should also be included within the scope of computer-readable media.

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

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

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

Embodiments of the present disclosure may also be implemented in a cloud computing environment. In this description, "cloud computing" is defined as a model that enables on-demand network access to a shared pool of configurable computing resources. For example, cloud computing may be used in the marketplace to provide ubiquitous and convenient on-demand access to a shared pool of configurable computing resources. The shared pool of configurable computing resources may be rapidly provisioned through virtualization, launched with little management effort or service provider interaction, and then scaled accordingly.

A cloud computing model can be comprised of various characteristics, such as on-demand self-service, broad network access, resource pooling, rapid elasticity, metered services, etc. A cloud computing model can also expose various service models, such as Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS). A cloud computing model can also be deployed using various deployment models, such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this description and claims, a "cloud computing environment" is an environment in which cloud computing is used.

FIG. 8 illustrates a block diagram of a computing device (800) that may be configured to perform one or more of the methods described above. It will be appreciated that one or more computing devices, such as the computing device (800), may implement the model switching system (108) and the genome analysis platform (104). As illustrated in FIG. 8, the computing device (800) may include a processor (802), a memory (804), a storage device (806), an I/O interface (808), and a communication interface (810), which may be communicatively coupled via a communication infrastructure (812). In certain embodiments, the computing device (800) may include fewer or more components than illustrated in FIG. 8. The components of the computing device (800) illustrated in FIG. 8 are described in more detail in the following paragraphs.

In one or more embodiments, the processor (802) includes hardware for executing instructions, such as instructions that constitute a computer program. By way of example, and not limitation, to execute instructions for dynamically modifying a workflow, the processor (802) may retrieve (or fetch) instructions from an internal register, an internal cache, a memory (804), or a storage device (806), decode them, and execute them. The memory (804) may be volatile or nonvolatile memory used to store data, metadata, and programs for execution by the processor(s). The storage device (806) includes a storage device, such as a hard disk, a flash disk drive, or other digital storage device, for storing data or instructions for performing the methods described herein.

The I/O interface (808) allows a user to provide input to the computing device (800), receive output therefrom, and otherwise transmit and receive data to and from the computing device (800). The I/O interface (808) may include a mouse, a keypad or keyboard, a touch screen, a camera, an optical scanner, a network interface, a modem, any other known I/O device, or a combination of such I/O interfaces. The I/O interface (808) may include one or more devices for providing output to the user, including but not limited to a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I/O interface (808) is configured to provide graphical data to the display for presentation to the user. The graphical data may represent one or more graphical user interfaces and/or any other graphical content that can provide a particular implementation.

The communication interface (810) may include hardware, software, or both. In any case, the communication interface (810) may provide one or more interfaces for communication (e.g., packet-based communications) between the computing device (800) and one or more other computing devices or networks. By way of example, and not limitation, the communication interface (810) may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as WI-FI.

Additionally, the communication interface (810) may facilitate communication with various types of wired or wireless networks. The communication interface (810) may also facilitate communication using various communication protocols. The communication infrastructure (812) may also include hardware, software, or both that interconnect components of the computing device (800). For example, the communication interface (810) may utilize one or more networks and/or protocols to enable a plurality of computing devices connected by a particular infrastructure to communicate with each other to perform one or more aspects of the methods described herein. By way of example, a sequence analysis method may allow a plurality of devices (e.g., a client device, a sequence analysis device, and a server device(s)) to exchange information, such as sequence analysis data and error notifications.

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

The present disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The described embodiments are to be considered in all respects merely illustrative and not restrictive. For example, the methods described herein may be performed with fewer or more steps/acts, or the steps/acts may be performed in a different order. Furthermore, the steps/acts described herein may be repeated or performed in parallel with each other or with other instances of the same or similar steps/acts. Accordingly, the scope of the present application is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be considered to be included within their scope.

The term "object" as used herein includes anything suitable for imaging, viewing, analyzing, examining, or profiling using the optical systems described herein. By way of example only, an object may include a semiconductor wafer or chip, a recordable medium, a sample, a flow cell, a microparticle, a slide, or a microarray. An object generally includes one or more surfaces and/or one or more interfaces on which a user may wish to image, view, analyze, examine, and/or determine a profile. An object may have a surface or interface having relief features such as wells, pits, ridges, bumps, beads, and the like.

As indicated above in the description of "sample", the sample may be imaged or scanned for subsequent analysis. In certain embodiments, the sample may comprise a biological or chemical material of interest, and optionally may comprise an optical substrate supporting the biological or chemical material. As such, the sample may or may not comprise an optical substrate. The term "biological material or chemical material" as used herein is not intended to be limiting, and may include a variety of biological or chemical materials suitable for imaging or examination using the optical systems described herein. For example, biological or chemical materials include biomolecules, such as nucleosides, nucleic acids, polynucleotides, oligonucleotides, proteins, enzymes, polypeptides, antibodies, antigens, ligands, receptors, polysaccharides, carbohydrates, polyphosphates, nanopores, organelles, lipid layers, cells, tissues, organisms, and biologically active chemical compound(s), such as analogs or mimics of the aforementioned species.

The biological or chemical material may be supported by an optical substrate. The term "optical substrate" as used herein is not intended to be limiting and may include a variety of materials that support the biological or chemical material and allow at least one of the biological or chemical material to be observed, imaged, and examined. For example, the optical substrate may include a transparent material that reflects a portion of incident light and refracts a portion of the incident light. Alternatively, the optical substrate may be a mirror, for example, that entirely reflects incident light so that no light is transmitted through the optical substrate. Typically, the optical substrate has a flat surface. However, the optical substrate may have a surface having relief features such as wells, pits, ridges, bumps, beads, and the like.

In an exemplary embodiment, the optical substrate is a flow cell having a flow channel through which nucleic acids are sequenced. However, in alternative embodiments, the optical substrate can include one or more slides, flat chips (e.g., as used in microarrays), or microparticles. When the optical substrate includes a plurality of microparticles supporting biological or chemical substances, the microparticles can be supported by another optical substrate, such as a slide or a grooved plate. In certain embodiments, the optical substrate includes a diffraction grating-based encoded optical identification element similar or identical to that described in co-pending U.S. patent application Ser. No. 10/661,234, filed Sep. 12, 2003, entitled Diffraction Grating Based Optical Identification Element, which is incorporated herein by reference in its entirety and is described in more detail below. The bead cells or plates for holding the optical identification elements can be similar or identical to those described in co-pending U.S. patent application Ser. No. 10/661,836, filed Sep. 12, 2003, entitled "Method and Apparatus for Aligning Microbeads for Investigation" and U.S. Pat. No. 7,164,533, issued Jan. 16, 2007, entitled "Hybrid Random Bead/Chip-Based Microarray", as well as U.S. patent application Ser. No. 60/609,583, filed Sep. 13, 2004, entitled "Improved Method and Apparatus for Aligning Microbeads for Investigation" and U.S. patent application Ser. No. 60/1010,910, filed Sep. 17, 2004, entitled "Method and Apparatus for Aligning Microbeads for Investigation", each of which is incorporated herein by reference in its entirety.

The term "optical component" or "focusing component" as used herein includes various elements that affect the transmission of light. The optical components can be, for example, reflectors, dichroics, beam splitters, collimators, lenses, filters, wedges, prisms, mirrors, and the like.

For example, the optical systems described herein can be configured to include various components and assemblies such as those described in PCT Application No. PCT/US07/07991, filed Mar. 30, 2007, entitled "System and Device for Sequencing by Synthetic Analysis" and/or can be configured to include various components and assemblies such as those described in PCT Application No. PCT/US2008/077850, filed Sep. 26, 2008, entitled "Fluorescence Excitation and Detection Systems and Methods", the entire subject matter of all of which is incorporated herein by reference in its entirety. In certain embodiments, the optical systems can include various components and assemblies such as those described in U.S. Pat. No. 7,329,860, the entire subject matter of which is incorporated herein by reference in its entirety. The optical system may also include various components and assemblies as described in U.S. patent application Ser. No. 12/638,770, filed December 15, 2009, the entire subject matter of which is incorporated herein by reference in its entirety.

In certain embodiments, the methods and optical systems described herein may be used for nucleic acid sequencing. For example, a sequence-by-synthesis (SBS) protocol is particularly applicable. In SBS, a plurality of fluorescently labeled modified nucleotides are used to sequence dense clusters (potentially millions of clusters) of amplified DNA present on a surface of an optical substrate (e.g., a surface that at least partially defines a channel in a flow cell). The flow cell may contain a nucleic acid sample for sequencing, wherein the flow cell is positioned within a suitable flow cell holder. The sample for sequencing may take the form of single nucleic acid molecules that are individually resolvable, a population of amplified nucleic acid molecules in the form of clusters or other features, or a bead attached to one or more nucleic acid molecules. The nucleic acids may be prepared to include oligonucleotide primers that are adjacent to an unknown target sequence. To initiate a first SBS sequencing cycle, one or more differently labeled nucleotides and a DNA polymerase, etc., may be flowed into/through the flow cell by a fluid flow subsystem (not shown). A single type of nucleotide can be added at a time, or the nucleotides used in the sequencing procedure can be specifically designed to have reversible termination properties, so that each cycle of the sequencing reaction can occur simultaneously in the presence of several types of labeled nucleotides (e.g., A, C, T, G). The nucleotides can include a detectable label moiety, such as a fluorophore. When four nucleotides are mixed together, the polymerase can select the correct base to incorporate, and each sequence is extended by a single base. One or more lasers can excite the nucleic acid, inducing fluorescence. The fluorescence emitted from the nucleic acid is based on the fluorophores of the incorporated bases, and different fluorophores can emit light at different wavelengths. Exemplary sequencing methods are described, for example, in the literature [Bentley et al., Nature 456:53-59 (2008)], WO 04/018497; U.S. Pat. No. 7,057,026; International Publication No. WO 91/06678; International Publication No. WO 07/123,744; U.S. Patent No. 7,329,492; U.S. Patent No. 7,211,414; U.S. Patent No. 7,315,019; U.S. Patent No. 7,405,281, and U.S. Patent Application Publication No. 2008/0108082, each of which is incorporated herein by reference.

Other sequencing techniques applicable to the use of the methods and systems described herein are pyrosequencing, nanopore sequencing, and sequencing-by-ligation. Particularly useful exemplary pyrosequencing techniques and samples are described in U.S. Pat. No. 6,210,891; U.S. Pat. No. 6,258,568; U.S. Pat. No. 6,274,320, and Ronaghi, Genome Research 11:3-11 (2001), each of which is incorporated herein by reference. Also useful exemplary nanopore techniques and samples are described in Deamer et al., Acc. Chem. Res. 35:817-825 (2002); Li et al., Nat. Mater. 2:611-615 (2003); Soni et al., Clin Chem. 53:1996-2001 (2007), Healy et al., Nanomed. 2:459-481 (2007)] and Cockroft et al., J. am. Chem. Soc. 130:818-820; and U.S. Pat. No. 7,001,792, each of which is incorporated herein by reference. Any of a variety of samples may be used in such systems, such as substrates having beads generated by emulsion PCR, substrates having zero mode waveguides, substrates having biological nanopores in lipid bilayers, solid substrates having synthetic nanopores, and others known in the art. Such samples are described in the context of various sequencing techniques in the references cited above and further in U.S. Patent Application Publication Nos. 2005/0042648; U.S. Patent Application Publication No. 2005/0079510; U.S. Patent Application Publication No. 2005/0130173; and WO 05/010145, each of which is incorporated herein by reference.

In another embodiment, the optical system described herein can be utilized for the detection of a sample comprising a microarray. The microarray can include a population of different probe molecules attached to one or more substrates such that the different probe molecules can be distinguished from one another based on their relative positions. The array can include a different probe molecule or population of probe molecules, each located at a different addressable location on the substrate. Alternatively, the microarray can include separate optical substrates, such as beads, each carrying a different probe molecule or population of probe molecules that can be identified based on the location of the optical substrate on the surface to which the substrate is attached or based on the location of the substrate in a liquid. Exemplary arrays in which separate substrates are located on a surface include Sentrix® arrays or Sentrix® Beadchip arrays available from Illumina®, Inc. (San Diego, Calif.), or those described in U.S. Pat. Nos. 6,266,459, 6,355,431, 6,770,441, and 6,859,570; and others including beads in wells, as described in PCT International Publication No. WO 00/63437, each of which is incorporated herein by reference. Other arrays having particles on a surface include those described in U.S. Patent Application Publication No. 2005/0227252; International Publication No. WO 05/033681; and International Publication No. WO 04/024328, each of which is incorporated herein by reference.

Any of a variety of microarrays known in the art, including, for example, the microarrays described herein, can be used in embodiments of the present invention. A typical microarray includes regions, sometimes called features, each of which has a population of probes. The population of probes for each region is typically homogeneous, having a single type of probe, although in some embodiments the populations can be heterogeneous. The regions or features of an array are typically discrete and spatially separated from one another. The size of the probe regions and/or the spacing between the regions can vary, such that the array can be high-density, medium-density, or low-density. A high-density array is characterized as having regions separated by less than about 15 μm. A medium-density array has regions separated by about 15 to 30 μm, while a low-density array has regions separated by more than 30 μm. An array useful in the present invention can have segments separated by less than 100 μm, 50 μm, 10 μm, 5 μm, 1 μm, or 0.5 μm. The device or method of one embodiment of the present invention can be used to image the array with sufficient resolution to distinguish segments within said density or range of densities.

Additional examples of commercially available microarrays that may be used include, for example, Affymetrix® GeneChip® microarrays, or microarrays disclosed in, for example, U.S. Patent Nos. 5,324,633; 5,744,305; 5,451,683; 5,482,867; 5,491,074; 5,624,711; 5,795,716; 5,831,070; 5,856,101; 5,858,659; 5,874,219; 5,968,740; 5,974,164; 5,981,185; 5,981,956; 6,025,601; Other microarrays synthesized according to a technique sometimes referred to as Very Large Scale Immobilized Polymer Synthesis (VLSIPS™) technology, as described in U.S. Pat. Nos. 6,033,860; 6,090,555; 6,136,269; 6,022,963; 6,083,697; 6,291,183; 6,309,831; 6,416,949; 6,428,752 and 6,482,591 (each of which is incorporated herein by reference). Spotted microarrays may also be used in the methods according to one embodiment of the present invention. An exemplary spotted microarray is a CodeLink™ array available from Amersham Biosciences. Another useful microarray is one manufactured using an inkjet printing method, such as SurePrint™ Technology available from Agilent Technologies.

The systems and methods described herein can be used to detect the presence of a particular target molecule in a sample contacted with a microarray. This may be determined, for example, based on binding of a labeled target analyte to a particular probe of the microarray, or may be determined due to target-dependent modification of a particular probe to incorporate, remove, or change a label at the probe location. Any one of several assays can be used to identify or characterize a target using a microarray, as described, for example, in U.S. Patent Application Publication Nos. 2003/0108867; 2003/0108900; 2003/0170684; 2003/0207295; or 2005/0181394, each of which is incorporated herein by reference.

For example, exemplary labels that may be detected according to embodiments of the present invention when present on a microarray include, but are not limited to, chromophores; luminophores; fluorophores; optically encoded nanoparticles; particles encoded with a diffraction grating; electrochemiluminescent labels such as Ru(bpy) ³²⁺ ; or moieties that may be detected based on their optical properties. Fluorophores that may be useful include, for example, europium and terbium, fluorescein, rhodamine, tetramethylrhodamine, eosin, erythrosine, coumarin, methyl-coumarin, pyrene, malachite green, Cy3, Cy5, stilbene, Lucifer yellow, Cascade Blue™, Texas Red, Alexa dyes, phycoerythrin, bodipy, and the like as described in Haugland, Molecular Probes Handbook , (Eugene, Oreg.) 6th Edition; Fluorescent lanthanide complexes, including those known in the art, such as those described in The Synthegen catalog (Houston, Tex.), Lakowicz, Principles of Fluorescence Spectroscopy, 2nd Ed., Plenum Press New York (1999)], or International Publication No. WO 98/59066, each of which is incorporated herein by reference.

In certain embodiments, the optical system can be configured to perform Time Delay Integration (TDI), for example in a line scanning embodiment, as described, for example, in U.S. Patent No. 7,329,860, the entire subject matter of which is incorporated herein by reference in its entirety. For example, the optical assembly can have a 0.75 NA lens and a focus accuracy of +/- 125 to 500 nm. The resolution can be 50 to 100 nm. The system can obtain 1,000 to 10,000 measurements per second in an unfiltered state.

While the embodiments have been illustrated with respect to the detection of samples comprising biological or chemical substances supported by an optical substrate, it will be appreciated that other samples may be analyzed, examined, or imaged by the embodiments described herein. Other exemplary samples include, but are not limited to, biological samples such as cells or tissues, electronic chips such as those used in computer processors, and the like. Some examples of applications include microscopy, satellite scanners, high-resolution cloning, fluorescence image acquisition, nucleic acid analysis and sequencing, DNA sequencing, sequencing-by-synthesis, imaging of microarrays, imaging of holographically encoded microparticles, and the like.

In another embodiment, the optical system may be configured to inspect an object to determine specific features or structures of the object. For example, the optical system may be used to inspect a surface of an object (e.g., a semiconductor chip, a silicon wafer) to determine if there are any deviations or defects on the surface.

FIG. 9 illustrates a block diagram of an optical system (900) formed according to one embodiment. By way of example only, the optical system (900) may be a sample imager that images a sample of interest for analysis. In another embodiment, the optical system (900) may be a profilometer that determines a surface profile (e.g., topography) of the object. Additionally, various other types of optical systems may utilize the mechanisms and systems described herein. In the illustrated embodiment, the optical system (900) includes an optical assembly (906), an object holder (902) for supporting an object (910) near a focal plane (FP) of the optical assembly (906), and a stage controller (915) configured to move the object holder (902) laterally (along the X-axis and/or Y-axis extending toward the ground) or vertically/elevationally along the Z-axis. The optical system (900) may also include a system controller or computing system (920) operably coupled to the optical assembly (906), the stage controller (915), and/or the object holder (902).

In certain embodiments, the optical system (900) is a sample imager configured to image a sample. Although not shown, the sample imager may include other subsystems or devices for performing various analysis protocols. By way of example only, the sample may include a flow cell having a flow channel. The sample imager may include a fluid control system including a liquid reservoir fluidly coupled to the flow channel via a fluid network. The sample imager may also include a temperature control system that may have a heater/cooler configured to control the temperature of the sample and/or the temperature of a fluid flowing through the sample. The temperature control system may include a sensor that detects the temperature of the fluid.

As illustrated, the optical assembly (906) is configured to direct input light toward the object (910) and to receive and direct output light toward one or more detectors. The output light may be input light that is at least one that is reflected or refracted by the object (910), and/or the output light may be light emitted from the object (910). To direct the input light, the optical assembly (906) may include at least one reference light source (912) and at least one excitation light source (914) that direct light, such as a light beam having a predetermined wavelength, through one or more optical components of the optical assembly (906). The optical assembly (906) may include various optical components, including a conjugate lens (918) for directing the input light toward the object (910) and directing the output light toward the detector(s).

In an exemplary embodiment, a reference light source (912) may be used by a distance measurement system or focus adjustment system (or focus mechanism) of the optical system (900), and an excitation light source (914) may be used to excite biological or chemical substances of the object (910) if the object (910) comprises a biological or chemical sample. The excitation light source (914) may be arranged to illuminate the bottom surface of the object (910), as in TIRF imaging, or may be arranged to illuminate the top surface of the object (910), as in epi-fluorescence imaging. As illustrated in FIG. 9 , a conjugate lens (918) directs input light into a focal region (922) within a focal plane (FP). The lens (918) has an optical axis (924) and is positioned a working distance (WD ₁ ) from the object (910) measured along the optical axis (924). The stage controller (915) can move the object (910) in the Z direction to adjust the working distance (WD ₁ ), for example, so that a portion of the object (910) is within the focal area (922).

To determine whether an object (910) is in focus (i.e., sufficiently within a focal region (922) or focal plane (FP), the optical assembly (906) is configured to direct at least one pair of optical beams into a focal region (922) where the object (910) is approximately located. The object (910) reflects the optical beams. More specifically, an exterior surface of the object (910) or an interface within the object (910) reflects the optical beams. The reflected optical beams then return to and propagate through the lens (918). As illustrated, each optical beam has an optical path that includes a portion that has not yet been reflected by the object (910) and a portion that has been reflected by the object (910). The portion of the optical path prior to reflection is designated as incident optical beams (930a and 932a) and is indicated by arrows pointing toward the object (910). Portions of the optical paths reflected by the object (910) are designated as reflected optical beams (930b and 932b) and are indicated by arrows pointing away from the object (910). For purposes of illustration, the optical beams (930a, 930b, 932a, and 932b) are shown as having different optical paths within the lens (918) and near the object (910). However, in an exemplary embodiment, the optical beams (930a and 932b) are configured to propagate in opposite directions and have identical or substantially overlapping optical paths within the lens (918) and near the object (910), and the optical beams (930b and 932a) are configured to propagate in opposite directions and have identical or substantially overlapping optical paths within the lens (918) and near the object (910).

In the embodiment illustrated in FIG. 9, the light beams (930a, 930b, 932a, and 932b) pass through the same lens used for imaging. In an alternative embodiment, the light beams used for distance measurement or focus determination may pass through a different lens that is not used for imaging. In this alternative embodiment, the lens (918) is dedicated to passing the beams (930a, 930b, 932a, and 932b) through for distance measurement or focus determination, and a separate lens (not shown) is used to image the object (910). Similarly, it will be appreciated that the systems and methods described herein for focus determination and distance measurement may occur using a common objective lens shared with the imaging optics, or alternatively, the objective lens illustrated herein may be dedicated to focus determination or distance measurement.

The reflected light beams (930b and 932b) propagate through the lens (918) and may optionally be further directed by other optical components of the optical assembly (906). As illustrated, the reflected light beams (930b and 932b) are detected by at least one focus detector (944). In the illustrated embodiment, both reflected light beams (930b and 932b) are detected by a single focus detector (944). The reflected light beams can be used to determine a relative separation (RS ₁ ). For example, the relative separation (RS ₁ ) can be determined as the distance (i.e., the separation distance) separating the beam spots from the reflected light beams (930b and 932b) impinging on the focus detector (944). The relative separation (RS ₁ ) can be used to determine the degree of focus of the optical system (900) with respect to the object (910). However, in an alternative embodiment, each reflected light beam (930b and 932b) can be detected by a separate corresponding focus detector (944), and the relative separation (RS ₁ ) can be determined based on the position of the beam spot on the corresponding focus detector (944).

If the object (910) is not within sufficient focus, the computing system (920) may operate the stage controller (915) to move the object holder (902) to a desired position. Alternatively or additionally to moving the object holder (902), the optical assembly (906) may be translated in the Z direction and/or along the XY plane.

For example, if the object (910) is positioned above the focal plane (FP) (or focal region (922)), the object (910) can be translated relatively to the focal plane (FP) by a distance ΔZ ₁ , or if the object (910) is positioned below the focal plane (FP) (or focal region (922)), the object (910) can be translated relatively to the focal plane (FP) by a distance ΔZ ₂ . In some embodiments, the optical system (900) can replace the lens (918) with another lens (918) or other optical component to translate the focal region (922) of the optical assembly (906).

The examples presented above and in FIG. 9 are presented in connection with a system for adjusting focus or determining a degree of focus. The system is also useful for determining a working distance (WD ₁ ) between an object (910) and a lens (918). In such an embodiment, the focus detector (944) may function as a working distance detector, and the distance separating the beam spots on the working distance detector may be used to determine the working distance between the object (910) and the lens (918). For convenience of description, various embodiments of the system and method are illustrated herein in connection with adjusting focus or determining a degree of focus. It will be appreciated that the system and method may also be used to determine a working distance between an object and a lens. Likewise, the system and method may also be used to determine a surface profile of an object.

In an exemplary embodiment, during operation, the light source (914) directs input light (not shown) at the object (910) to excite a fluorescently labeled biological or chemical substance. The label of the biological or chemical substance provides an optical signal (940) having a predetermined wavelength(s) (also referred to as an optical emission). The optical signal (940) is received by the lens (918) and then directed by other optical components of the optical assembly (906) to at least one object detector (942). While the illustrated embodiment shows only one object detector (942), the object detector (942) may include multiple detectors. For example, the object detector (942) may include a first detector configured to detect one or more optical wavelengths and a second detector configured to detect one or more different optical wavelengths. The optical assembly (906) may include a lens/filter assembly that directs the different optical signals along different optical paths toward corresponding object detectors. Such optical systems are described in detail in PCT Application No. PCT/US07/07991, filed Mar. 30, 2007, entitled "System and Device for Sequencing by Synthetic Analysis" and PCT Application No. PCT/US2008/077850, filed Sep. 26, 2008, entitled "Fluorescence Excitation and Detection System and Method", the entire subject matter of both of which are incorporated herein by reference in their entireties.

The object detector (942) transmits object data associated with the detected optical signal (940) to the computing system (920). The computing system (920) can then record, process, analyze, and/or transmit the data to a computing system or other user, including a remote computing system via a communication line (e.g., the Internet). For example, the object data may include image data that is processed to generate image(s) of the object (910). The images can then be analyzed by a user of the computing system and/or the optical system (900). In other embodiments, the object data may include optical emissions from biological or chemical substances, as well as light that is at least one of reflected or refracted by an optical substrate or other component. For example, the optical signal (940) may include light reflected by an encoded microparticle, such as the holographically encoded optical identification element described above.

In some embodiments, a single detector may provide both functions as described above with respect to the object and focus detectors (942 and 944). For example, a single detector may detect both a reflected light beam (930b, 932b) and a light signal (940).

The optical system (900) may include a user interface (925) for interacting with a user via the computing system (920). For example, the user interface (925) may include a display (not shown) for presenting and requesting information to the user, and a user input device (not shown) for receiving user input.

The computing system (920) may include, among other things, an object analysis module (950) and a focus adjustment module (952). The focus adjustment module (952) is configured to receive focus data acquired by the focus detector (944). The focus data may include signals representing beam spots incident on the focus detector (944). The data may be processed to determine a relative separation (e.g., a separation distance between the beam spots). The degree of focus of the optical system (900) with respect to the object (910) may then be determined based on the relative separation. In certain embodiments, a working distance (WD ₁ ) between the object (910) and the lens (918) may be determined. Similarly, the object analysis module (950) may receive object data acquired by the object detector (942). The object analysis module may process or analyze the object data to generate an image of the object.

Additionally, the computing system (920) may include any processor-based or microprocessor-based system, including systems using a microcontroller, a reduced instruction set computer (RISC), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), logic circuits, and any other circuitry or processor capable of performing the functionality described herein. The above examples are merely illustrative and are not intended to limit the definition and/or meaning of the term "system controller" in any way. In an exemplary embodiment, the computing system (920) executes a set of instructions stored in one or more storage elements, memories, or modules for at least one of acquiring and analyzing object data. The storage elements may be in the form of an information source or a physical memory element within the optical system (900).

The instruction set may include various commands that instruct the optical system (900) to perform a particular protocol. For example, the instruction set may include various commands for performing an analysis and imaging the object (910) or for determining a surface profile of the object (910). The instruction set may be in the form of a software program. The terms "software" and "firmware" as used herein 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 types of memory are merely exemplary and are therefore not limiting as to the types of memory that may be used for storing computer programs.

As described above, the light source (914) here generates excitation light directed at the object (910). The light source (914) here can generate one or more laser beams at one or more predetermined excitation wavelengths. The light can be moved in a raster pattern across portions of the object (910), such as groups of rows and columns of the object (910). Alternatively, the excitation light can illuminate one or more entire regions of the object (910) at one time and then continuously stop through the regions in a “step and shoot” scanning pattern. Line scanning can also be used, as described, for example, in U.S. Pat. No. 7,329,860, the entire subject matter of which is incorporated herein by reference in its entirety. The object (910) generates an optical signal (940) that can include optical emissions generated in response to illumination of a label within the object (910) and/or light reflected or refracted by an optical substrate of the object (910). Alternatively, the optical signal (940) may be generated without illumination based entirely on the emission properties of a material within the object (910) (e.g., a radioactive or chemiluminescent component of the object).

The object and focus detectors (942 and 944) may be, for example, photodiodes or cameras. In some embodiments of the present disclosure, the detectors (942 and 944) may include a camera having a 1 million pixel CCD-based optical imaging system, such as a 1002 x 1004 CCD camera with 8 gm pixels, which can optionally image an area of 0.4 x 0.4 mm per tile using excitation light having a laser spot size of 0.5 x 0.5 mm (e.g., a square spot, or a 0.5 mm diameter circle, or an elliptical spot, etc.) at 20× magnification. The camera optionally can have more or less than 1 million pixels, for example, a 4 million pixel camera can be used. In many embodiments, the readout speed of the camera should be as fast as possible, for example, a data rate of 10 MHz or higher, for example, 20 or 30 MHz. More pixels generally mean more surface area, which means that more sequence reactions or other optically detectable events can be imaged simultaneously in a single exposure. In certain embodiments, a CCD camera/TIRF laser can collect about 6400 images to examine 1600 tiles (since imaging is optionally performed in four different colors per cycle using a combination of filters, dichroics, and detectors as described herein). For a 1 million pixel CCD, a particular image can optionally include about 5,000 to 50,000 randomly spaced unique nucleic acid clusters (i.e., images of the flow cell surface). At an imaging speed of 2 seconds per tile for four colors and a cluster density of 25,000 per tile, the system herein can optionally quantify about 45 million features per hour. Imaging speeds can be improved at faster imaging speeds and higher cluster densities. For example, for a 20 MHz readout rate of the camera and image-resolved clusters every 20 pixels, the readout could be 1 million clusters per second. The detector may be configured for time delay integration (TDI), for example in a line scanning embodiment, as described in U.S. Pat. No. 7,329,860, the entire subject matter of which is incorporated herein by reference in its entirety. Other useful detectors include, but are not limited to, an optical quadrant photodiode detector, such as one having a 2×2 array of individual photodiode active regions fabricated on a single chip, examples of which are available from Pacific Silicon Sensor, Westlake Village, Calif.; or a position-sensitive detector, such as one having a monolithic PIN photodiode having uniform resistance in one or two dimensions, examples of which are available from Hamamatsu Photonics, K.K., Hamamatsu City, Japan.

FIG. 10 is a perspective view of a formed sample imager (1000) according to one embodiment. As depicted, the sample imager (1000) includes an imager base (1002) that supports a stage (1004) having a sample holder (1006). The sample holder (1006) is configured to support one or more optical substrates (1008) during an imaging session. The optical substrates (1008) are illustrated as a flow cell in FIG. 10 , although other sample substrates may be used.

The sample imager (1000) also includes a housing (1010) (illustrated in dashed lines) and a strut (1012) supporting the housing (1010). The housing (1010) may enclose at least a portion of an optical assembly (1014) therein. The optical assembly (1014) may include a focus assembly (1016) and a sample detector assembly (1030). For example, the focus assembly (1016) may include an autofocus line scan camera that receives a reflected light beam to determine the focus of the sample imager (1000). The sample imager (1000) may also include an alignment mirror (1024) (illustrated in FIG. 10 as a K4 camera) that directs light toward the sample detector (1032); and a filter wheel (1022).

FIG. 11 illustrates an implementation of a sequence analysis system (1110) configured to process a molecular sample that can be sequenced to determine the components of the sample, the order of the components, and to roughly determine the structure of the sample. The system includes a device (1112) for receiving and processing a biological sample. A sample source (1114) provides a sample (1116), which in many cases includes a tissue sample. The sample source can include an individual or subject, such as a human, animal, microorganism, plant, or other donor (including environmental samples), or any other subject that includes an organic molecule of interest whose sequence is to be determined. Of course, the system can also be used with samples other than those taken from an organism, including synthetic molecules. In many cases, the molecule will include DNA, RNA, or other molecules having base pairs whose sequences define genes and mutations having a particular function that is ultimately of interest.

A sample (1116) is introduced into a sample/library preparation system (1118). This system can separate, digest, and otherwise prepare the sample for analysis. The resulting library comprises molecules of interest of a length that facilitates sequencing. The resulting library is then provided to an instrument (1112) where sequencing is performed. In practice, the library, sometimes referred to as a template, is combined with reagents in an automated or semi-automated process and then introduced into a flow cell prior to sequencing.

In the embodiment illustrated in FIG. 11, the device includes a flow cell or array (1120) that accommodates a sample library. The flow cell includes one or more fluidic channels that allow sequence chemistry to occur, including amplification and attachment of molecules of the library at locations or sites that can be detected during a sequencing operation. For example, the flow cell/array (1120) may include a sequence template immobilized on one or more surfaces at the locations or sites. The "flow cell" may include a patterned array, such as a microarray, nanoarray, or the like. In practice, the locations or sites may be arranged in a regular repeating pattern, a complex non-repeating pattern, or a random arrangement on one or more surfaces of the support. To allow sequence chemistry to occur, the flow cell also allows for the introduction of materials, such as various reagents, buffers, and other reaction media used for reactions, flushing, etc. Materials may flow through the flow cell and contact molecules of interest at individual sites.

In the device, the flow cell (1120) is mounted on a movable stage (1122) that can move in one or more directions, as indicated in this embodiment by reference numeral 1124. The flow cell (1120) may be provided in the form of a removable and replaceable cartridge that can interface with ports on the movable stage (1122) or other components of the system, for example, to allow reagents and other fluids to be delivered to or from the flow cell (1120). The stage is associated with an optical detection system (1126) that can direct radiation or light (1128) to the flow cell during sequencing. The optical detection system can use a variety of methods, such as fluorescence microscopy, to detect analytes placed at sites on the flow cell. As a non-limiting example, the optical detection system (1126) can use confocal line scanning to generate progressively pixelated image data that can be analyzed to locate individual sites on the flow cell and determine the type of nucleotide most recently attached or incorporated at each site. Other imaging techniques may also be used, such as techniques in which one or more radiographic points are scanned along the sample or techniques using a “step and shoot” imaging approach. The optical detection system (1126) and stage (1122) may cooperate to maintain the flow cell and detection system in a static relationship while acquiring area images, or as noted, the flow cell may be scanned in any suitable mode (e.g., point scanning, line scanning, “step and shoot” scanning).

While many different techniques may be used to image or, more generally, detect molecules at a site, the presently contemplated implementation utilizes confocal optical imaging at a wavelength that excites a fluorescent tag. The excited tag, due to its absorption spectrum, returns a fluorescent signal due to its emission spectrum. The optical detection system (1126) is configured to capture this signal, process the pixelated image data at a resolution that allows analysis of the site of signal emission, and process and store the generated image data (or data derived therefrom).

In a sequencing operation, the cyclic operation or process is implemented in an automated or semi-automated manner, for example, by using a single nucleotide or oligonucleotide to initiate a reaction, followed by flushing, imaging, and deblocking to prepare for the next cycle. A sample library prepared for sequencing and fixed to a flow cell may undergo a number of such cycles before all useful information is extracted from the library. An optical detection system (1126) may use electronic detection circuitry (e.g., a camera or image processing electronics or chips) to generate image data from scans of the flow cell (and portions thereof) during each cycle of the sequencing operation. The generated image data is then analyzed to locate individual sites in the image data, and the molecules present at those sites, as represented by groups or clusters of pixels in the image data at those locations, are analyzed and characterized, for example, by reference to a particular color or wavelength of light (a characteristic emission spectrum of a particular fluorescent tag). For example, in a DNA or RNA sequencing application, four common nucleotides may be represented by distinguishable fluorescence emission spectra (wavelengths or wavelength ranges of light). Subsequently, each emission spectrum can be assigned a value corresponding to its nucleotide. Based on this analysis, by tracking the periodic values determined for each site, individual nucleotides and their order can be determined for each site. These sequences can then be further processed to assemble longer segments, including genes, chromosomes, etc. The terms "automatic" and "semiautomatic" as used herein mean that once an operation is initiated or once a process comprising an operation is initiated, the operation is performed by system programming or configuration with little or no human interaction.

In the illustrated embodiment, reagents (1130) are aspirated or drawn into the flow cell through a valve (1132). The valve can access reagents from a container or receiver in which the reagents are stored, such as via a pipette or sipper (not shown in FIG. 11 ). The valve (1132) can allow for selection of reagents based on a predetermined sequence of operations to be performed. The valve can additionally be instructed to direct reagents through the flow path (1134) into the flow cell (1120). An outlet or effluent flow path (1136) directs used reagents from the flow cell. In the illustrated embodiment, a pump (1138) serves to move the reagents through the system. The pump may also provide other useful functions, such as metering reagents or other fluids through the system, aspirating air or other fluids, etc. An additional valve (1140) downstream of the pump (1138) allows for appropriate directing of the used reagent to a waste container or receiver (1142).

The device further includes various circuitry that assists in directing the operation of various system components, monitoring the operation of the components via feedback from sensors, collecting image data, and at least in part processing the image data. In the exemplary embodiment illustrated in FIG. 11, the control/supervisory system (1144) includes a control system (1146) and a data acquisition and analysis system (1148). Both systems may include one or more processors (e.g., digital processing circuitry such as a microprocessor, a multi-core processor, an FPGA, or other suitable processing circuitry) and associated memory circuitry (1150) (e.g., a solid state memory device, a dynamic memory device, an on- and/or off-board memory device, etc.) that can store machine-executable instructions for controlling one or more computers, processors, or other similar logic devices to provide particular functions. A special purpose or general purpose computer may at least partially comprise the control system and the data acquisition and analysis system. The control system may include circuitry configured (e.g., programmed) to process commands for, for example, the fluidic devices, the optical devices, the stage control, and other useful functions of the device. A data acquisition and analysis system (1148) interfaces with the optical detection system to command movement of the optical detection system or the stage, or both, to emit light for periodic detection, to receive and process the returned signal, etc. The device may also include various interfaces, indicated by reference numeral (1152), such as an operator interface that allows for control and monitoring of the device, sample transfer, execution of automated or semi-automated sequencing operations, report generation, etc. Finally, in the embodiment of FIG. 11, an external network or system (1154) may be connected to and cooperate with the device, for example, for analysis, control, monitoring, providing services, and other tasks.

Although FIG. 11 illustrates a single flow cell and fluid path and a single optical detection system (1126), it will be noted that some devices may accommodate more than one flow cell and fluid path. For example, the presently contemplated implementation provides two such arrangements to enhance sequencing and throughput. In practice, any number of flow cells and paths may be provided. They may utilize the same or different reagent containers, waste containers, control systems, image analysis systems, etc. Where multiple fluid systems are provided, the multiple fluid systems may be controlled individually or in a coordinated manner.

Claims (20)

As a system,
at least one processor; and
A non-transitory computer-readable medium comprising: a computer-readable medium configured to cause the system, when executed by at least one processor, to:
Identifying a genome analysis application for analyzing nucleotide base calls determined for a sample nucleotide sequence;
Determining a version of a variant analysis model for executing said genome analysis application indicated by an application specification defining one or more parameters for said genome analysis application;
Based on determining the indicated version of the mutation analysis model, installing the indicated version of the mutation analysis model for execution instead of a previously installed version of the mutation analysis model;
A system comprising instructions for executing said genome analysis application to analyze said nucleotide base calls utilizing said indicated version of said mutation analysis model. A system according to claim 1, further comprising instructions that, when executed by said at least one processor, cause the system to install said indicated version of said variant analysis model by updating a field programmable gate array to include said indicated version of said variant analysis model for performing genome analysis. A system according to claim 1, further comprising instructions that, when executed by said at least one processor, cause the system to automatically install said indicated version of said variant analysis model by utilizing a variant analysis model manager to determine available versions of said variant analysis model and initiate installation of said indicated version from said available versions. A system according to claim 1, further comprising instructions that, when executed by said at least one processor, cause the system to determine said indicated version of said variant analysis model for executing said genome analysis application by analyzing said application specification to identify a version label designating said indicated version of said variant analysis model. In the first paragraph, when executed by the at least one processor, the system causes:
determines that the indicated version of the said mutation analysis model is different from the previously installed version of the said mutation analysis model (only a single version of the said mutation analysis model can be installed at a time);
A system further comprising instructions to cause the indicated version of the mutation analysis model to be installed based on a determination that the indicated version is different from the previously installed version. A system according to claim 1, further comprising instructions that, when executed by said at least one processor, cause said system to replace said previously installed version of said mutation analysis model with said indicated version of said mutation analysis model. A system according to claim 1, further comprising instructions that, when executed by said at least one processor, cause said system to install said indicated version of said mutation analysis model in addition to said previously installed version, such that said indicated version and said previously installed version are installed on one or more servers of said system. As a computer implementation method,
A step of identifying a genome analysis application for analyzing nucleotide base calls determined for a sample nucleotide sequence;
A step of determining a indicated version of a variant analysis model for executing said genome analysis application indicated by an application specification defining one or more parameters for said genome analysis application;
Based on the step of determining the indicated version of the mutation analysis model, installing the indicated version of the mutation analysis model for execution instead of the previously installed version of the mutation analysis model; and
A computer implemented method comprising the step of executing said genome analysis application to analyze said nucleotide base calls utilizing said indicated version of said mutation analysis model. A computer-implemented method in claim 8, wherein the step of installing the indicated version of the mutation analysis model comprises the step of utilizing a mutation analysis model manager accommodated on a server shared by the mutation analysis model to initiate installation of the indicated version of the mutation analysis model. In Article 8,
A step of determining the computational availability of a genome analysis device that accommodates the mutation analysis model for executing the genome analysis application; and
A computer-implemented method further comprising the step of scheduling execution of the genome analysis application by the genome analysis device based on the computational availability of the genome analysis device. In Article 8,
A step of identifying a plurality of workflow pods, at least two of said plurality of workflow pods specifying different versions of said mutation analysis model to perform their respective functions; and
A computer-implemented method further comprising the step of repeatedly installing said different versions of said mutation analysis model to sequentially execute each of said plurality of workflow pods. A computer-implemented method, further comprising the step of utilizing a mutation webhook controller to modify an application specification for the genome analysis application to include instructions for identifying the indicated version of the mutation analysis model and initializing installation of the indicated version of the mutation analysis model. A computer-implemented method in claim 12, wherein the step of modifying the application specification comprises the step of adding an initialization workflow container to the application specification that communicates with a variation analysis model manager to install the indicated version of the variation analysis model. A computer-implemented method in claim 8, wherein the step of determining the indicated version of the variant analysis model to execute the genome analysis application comprises the step of utilizing a variant analysis model manager to analyze the application specifications to identify a version label designating the indicated version of the variant analysis model. A non-transitory computer-readable medium, which, when executed by at least one processor, causes a system to:
Identifying a genome analysis application for analyzing nucleotide base calls determined for a sample nucleotide sequence;
Determining a version of a variant analysis model for executing said genome analysis application indicated by an application specification defining one or more parameters for said genome analysis application;
Based on determining the indicated version of the mutation analysis model, installing the indicated version of the mutation analysis model for execution instead of a previously installed version of the mutation analysis model;
A non-transitory computer-readable medium comprising instructions for executing the genome analysis application to analyze the nucleotide base calls utilizing the indicated version of the mutation analysis model. In the 15th paragraph, when executed by the at least one processor, the system causes:
determining that the indicated version of the mutation analysis model is stored in a remote storage that stores multiple versions of the mutation analysis model; and
A non-transitory computer-readable medium further comprising instructions for causing the server to install the indicated version of the variant analysis model based on the step of determining that the indicated version is stored within the remote repository. In the 15th paragraph, when executed by the at least one processor, the system causes:
Further comprising instructions for identifying a plurality of additional genomic analysis applications for analyzing said nucleotide base calls determined for said sample nucleotide sequence, each of said plurality of additional genomic analysis applications specifying a different version of said variant analysis model; and
sequentially executing each of the above multiple additional genome analysis applications,
installing a version of said variant analysis model for a current genome analysis application among said plurality of additional genome analysis applications to replace a version from a previous genome analysis application among said plurality of additional genome analysis applications; and
A non-transitory computer-readable medium further comprising the step of executing said current genome analysis application utilizing said version of said variant analysis model for said current genome analysis application. In the 15th paragraph, when executed by the at least one processor, the system causes:
Receiving sequence analysis data including the nucleotide base calls from a sequence analysis device;
A non-transitory computer-readable medium further comprising instructions for executing said genome analysis application to analyze said nucleotide base calls utilizing a field programmable gate array configured to execute said indicated version of said mutation analysis model. In the 15th paragraph, when executed by the at least one processor, the system causes:
determining that the indicated version of the mutation analysis model installed on the genome analysis device is different from the previously installed version of the mutation analysis model (wherein the genome analysis device can be configured to run a single version of the mutation analysis model at a time);
A non-transitory computer-readable medium further comprising instructions for causing the displayed version of the mutation analysis model to be installed on the genome analysis device based on a determination that the displayed version is different from the previously installed version. A non-transitory computer-readable medium further comprising instructions that, when executed by said at least one processor, cause said system to select a genome analysis device as a location for installing said indicated version of said mutation analysis model by utilizing a mutation webhook controller to identify a resource label within said application specification that specifies said genome analysis device.

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