Patent Publication Number: US-2019171963-A1

Title: Bioinformatics Systems, Apparatuses, and Methods Executed on a Quantum Processing Platform

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 15/497,149, filed Apr. 25, 2017, which claims the benefit of U.S. Provisional Patent Application No. 62/462,869, filed Feb. 23, 2017, the disclosure of each of which are incorporated herein by reference in its entirety for all purposes. 
    
    
     FIELD OF THE DISCLOSURE 
     The subject matter described herein relates to bioinformatics, and more particularly to systems, apparatuses, and methods for implementing bioinformatic protocols, such as performing one or more functions for analyzing genomic data on an integrated circuit, such as on a hardware processing platform. 
     BACKGROUND TO THE DISCLOSURE 
     As described in detail herein, some major computational challenges for high-throughput DNA sequencing analysis is to address the explosive growth in available genomic data, the need for increased accuracy and sensitivity when gathering that data, and the need for fast, efficient, and accurate computational tools when performing analysis on a wide range of sequencing data sets derived from such genomic data. 
     Keeping pace with such increased sequencing throughput generated by Next Gen Sequencers has typically been manifested as multithreaded software tools that have been executed on ever greater numbers of faster processors in computer clusters with expensive high availability storage that requires substantial power and significant IT support costs. Importantly, future increases in sequencing throughput rates will translate into accelerating real dollar costs for these secondary processing solutions. 
     The devices, systems, and methods of their use described herein are provided, at least in part, so as to address these and other such challenges. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure is directed to devices, systems, and methods for employing the same in the performance of one or more genomics and/or bioinformatics protocols on data generated through a primary processing procedure, such as on genetic sequence data. For instance, in various aspects, the devices, systems, and methods herein provided are configured for performing secondary and/or tertiary analysis protocols on genetic data, such as data generated by the sequencing of RNA and/or DNA, e.g., by a Next Gen Sequencer (“NGS”). In particular embodiments, one or more secondary processing pipelines for processing genetic sequence data is provided. In other embodiments, one or more tertiary processing pipelines for processing genetic sequence data is provided, such as where the pipelines, and/or individual elements thereof, deliver superior sensitivity and improved accuracy on a wider range of sequence derived data than is currently available in the art. 
     For example, provided herein is a system, such as for executing one or more of a sequence and/or genomic analysis pipeline on genetic sequence data and/or other data derived therefrom. In various embodiments, the system may include one or more of an electronic data source that provides digital signals representing a plurality of reads of genetic and/or genomic data, such as where each of the plurality of reads of genomic data include a sequence of nucleotides. The system may further include a memory, e.g., a DRAM, or a cache, such as for storing one or more of the sequenced reads, one or a plurality of genetic reference sequences, and one or more indices of the one or more genetic reference sequences. The system may additionally include one or more integrated circuits, such as a FPGA, ASIC, or sASIC, and/or a CPU and/or a GPU, which integrated circuit, e.g., with respect to the FPGA, ASIC, or sASIC may be formed of a set of hardwired digital logic circuits that are interconnected by a plurality of physical electrical interconnects. The system may additionally include a quantum computing processing unit, for use in implementing one or more of the methods disclosed herein. 
     In various embodiments, one or more of the plurality of electrical interconnects may include an input to the one or more integrated circuits that may be connected or connectable, e.g., directly, via a suitable wired connection, or indirectly such as via a wireless network connection (for instance, a cloud or hybrid cloud), with the electronic data source. Regardless of a connection with the sequencer, an integrated circuit of the disclosure may be configured for receiving the plurality of reads of genomic data, e.g., directly from the sequencer or from an associated memory. The reads may be digitally encoded in a standard FASTQ or BCL file format. Accordingly, the system may include an integrated circuit having one or more electrical interconnects that may be a physical interconnect that includes a memory interface so as to allow the integrated circuit to access the memory. 
     Particularly, the hardwired digital logic circuit of the integrated circuit may be arranged as a set of processing engines, such as where each processing engine may be formed of a subset of the hardwired digital logic circuits so as to perform one or more steps in the sequence, genomic, and/or tertiary analysis pipeline, as described herein below, on the plurality of reads of genetic data as well as on other data derived therefrom. For instance, each subset of the hardwired digital logic circuits may be in a wired configuration to perform the one or more steps in the analysis pipeline. Additionally, where the integrated circuit is an FPGA, such steps in the sequence and/or further analysis process may involve the partial reconfiguration of the FPGA during the analysis process. 
     Particularly, the set of processing engines may include a mapping module, e.g., in a wired configuration, to access, according to at least some of the sequence of nucleotides in a read of the plurality of reads, the index of the one or more genetic reference sequences, from the memory via the memory interface, so as to map the read to one or more segments of the one or more genetic reference sequences based on the index. Additionally, the set of processing engines may include an alignment module in the wired configuration to access the one or more genetic reference sequences from the memory via the memory interface to align the read, e.g., the mapped read, to one or more positions in the one or more segments of the one or more genetic reference sequences, e.g., as received from the mapping module and/or stored in the memory. 
     Further, the set of processing engines may include a sorting module so as to sort each aligned read according to the one or more positions in the one or more genetic reference sequences. Furthermore, the set of processing engines may include a variant call module, such as for processing the mapped, aligned, and/or sorted reads, such as with respect to a reference genome, to thereby produce an HMM readout and/or variant call file for use with and/or detailing the variations between the sequenced genetic data and the reference genomic reference data. In various instances, one or more of the plurality of physical electrical interconnects may include an output from the integrated circuit for communicating result data from the mapping module and/or the alignment and/or sorting and/or variant call modules. 
     Particularly, with respect to the mapping module, in various embodiments, a system for executing a mapping analysis pipeline on a plurality of reads of genetic data using an index of genetic reference data is provided. In various instances, the genetic sequence, e.g., read, and/or the genetic reference data may be represented by a sequence of nucleotides, which may be stored in a memory of the system. The mapping module may be included within the integrated circuit and may be formed of a set of pre-configured and/or hardwired digital logic circuits that are interconnected by a plurality of physical electrical interconnects, which physical electrical interconnects may include a memory interface for allowing the integrated circuit to access the memory. In more particular embodiments, the hardwired digital logic circuits may be arranged as a set of processing engines, such as where each processing engine is formed of a subset of the hardwired digital logic circuits to perform one or more steps in the sequence analysis pipeline on the plurality of reads of genomic data. 
     For instance, in one embodiment, the set of processing engines may include a mapping module in a hardwired configuration, where the mapping module, and/or one or more processing engines thereof is configured for receiving a read of genomic data, such as via one or more of a plurality of physical electrical interconnects, and for extracting a portion of the read in such a manner as to generate a seed therefrom. In such an instance, the read may be represented by a sequence of nucleotides, and the seed may represent a subset of the sequence of nucleotides represented by the read. The mapping module may include or be connectable to a memory that includes one or more of the reads, one or more of the seeds of the reads, at least a portion of one or more of the reference genomes, and/or one or more indexes, such an index built from the one or more reference genomes. In certain instances, a processing engine of the mapping module employ the seed and the index to calculate an address within the index based on the seed. 
     Once an address has been calculated or otherwise derived and/or stored, such as in an onboard or offboard memory, the address may be accessed in the index in the memory so as to receive a record from the address, such as a record representing position information in the genetic reference sequence. This position information may then be used to determine one or more matching positions from the read to the genetic reference sequence based on the record. Then at least one of the matching positions may be output to the memory via the memory interface. 
     In another embodiment, a set of the processing engines may include an alignment module, such as in a pre-configured and/or hardwired configuration. In this instance, one or more of the processing engines may be configured to receive one or more of the mapped positions for the read data via one or more of the plurality of physical electrical interconnects. Then the memory (internal or external) may be accessed for each mapped position to retrieve a segment of the reference sequence/genome corresponding to the mapped position. An alignment of the read to each retrieved reference segment may be calculated along with a score for the alignment. Once calculated, at least one best-scoring alignment of the read may be selected and output. In various instances, the alignment module may also implement a dynamic programming algorithm when calculating the alignment, such as one or more of a Smith-Waterman algorithm, e.g., with linear or affine gap scoring, a gapped alignment algorithm, and/or a gapless alignment algorithm. In particular instances, the calculating of the alignment may include first performing a gapless alignment to each reference segment, and based on the gapless alignment results, selecting reference segments with which to further perform gapped alignments. 
     In various embodiments, a variant call module may be provided for performing improved variant call functions that when implemented in one or both of software and/or hardware configurations generate superior processing speed, better processed result accuracy, and enhanced overall efficiency than the methods, devices, and systems currently known in the art. Specifically, in one aspect, improved methods for performing variant call operations in software and/or in hardware, such as for performing one or more HMM operations on genetic sequence data, are provided. In another aspect, novel devices including an integrated circuit for performing such improved variant call operations, where at least a portion of the variant call operation is implemented in hardware, are provided. 
     Accordingly, in various instances, the methods disclosed herein may include mapping, by a first subset of hardwired and/or quantum digital logic circuits, a plurality of reads to one or more segments of one or more genetic reference sequences. Additionally, the methods may include accessing, by the integrated and/or quantum circuits, e.g., by one or more of the plurality of physical electrical interconnects, from the memory or a cache associated therewith, one or more of the mapped reads and/or one or more of the genetic reference sequences; and aligning, by a second subset of the hardwired and/or quantum digital logic circuits, the plurality of mapped reads to the one or more segments of the one or more genetic reference sequences. 
     In various embodiments, the method may additionally include accessing, by the integrated and/or quantum circuit, e.g., by one or more of the plurality of physical electrical interconnects from a memory or a cache associated therewith, the aligned plurality of reads. In such an instance the method may include sorting, by a third subset of the hardwired and/or quantum digital logic circuits, the aligned plurality of reads according to their positions in the one or more genetic reference sequences. In certain instances, the method may further include outputting, such as by one or more of the plurality of physical electrical interconnects of the integrated and/or quantum circuit, result data from the mapping and/or the aligning and/or the sorting, such as where the result data includes positions of the mapped and/or aligned and/or sorted plurality of reads. 
     In some instances, the method may additionally include using the obtained result data, such as by a further subset of the hardwired and/or quantum digital logic circuits, for the purpose of determining how the mapped, aligned, and/or sorted data, derived from the subject&#39;s sequenced genetic sample, differs from a reference sequence, so as to produce a variant call file delineating the genetic differences between the two samples. Accordingly, in various embodiments, the method may further include accessing, by the integrated and/or quantum circuit, e.g., by one or more of the plurality of physical electrical interconnects from a memory or a cache associated therewith, the mapped and/or aligned and/or sorted plurality of reads. In such an instance the method may include performing a variant call function, e.g., an HMM or paired HMM operation, on the accessed reads, by a third or fourth subset of the hardwired and/or quantum digital logic circuits, so as to produce a variant call file detailing how the mapped, aligned, and/or sorted reads vary from that of one or more reference, e.g., haplotype, sequences. 
     Accordingly, in accordance with particular aspects of the disclosure, presented herein is a compact hardware, e.g., chip based, or quantum accelerated platform for performing secondary and/or tertiary analyses on genetic and/or genomic sequencing data. Particularly, a platform or pipeline of hardwired and/or quantum digital logic circuits that have specifically been designed for performing secondary and/or tertiary genetic analysis, such as on sequenced genetic data, or genomic data derived therefrom, is provided. Particularly, a set of hardwired digital and/or quantum logic circuits, which may be arranged as a set of processing engines, may be provided, such as where the processing engines may be present in a preconfigured and/or hardwired and/or quantum configuration on a processing platform of the disclosure, and may be specifically designed for performing secondary mapping and/or aligning and/or variant call operations related to genetic analysis on DNA and/or RNA data, and/or may be specifically designed for performing other tertiary processing on the results data. 
     In particular instances, the present devices, systems, and methods of employing the same in the performance of one or more genomics and/or bioinformatics secondary and/or tertiary processing protocols, have been optimized so as to deliver an improvement in processing speed that is orders of magnitude faster than standard secondary processing pipelines that are implemented in software. Additionally, the pipelines and/or components thereof as set forth herein provide better sensitivity and accuracy on a wide range of sequence derived data sets for the purposes of genomics and bioinformatics processing. In various instances, one or more of these operations may be performed on by an integrated circuit that is part of or configured as a general purpose central processing unit and/or a graphics processing unit and/or a quantum processing unit. 
     For example, genomics and bioinformatics are fields concerned with the application of information technology and computer science to the field of genetics and/or molecular biology. In particular, bioinformatics techniques can be applied to process and analyze various genetic and/or genomic data, such as from an individual, so as to determine qualitative and quantitative information about that data that can then be used by various practitioners in the development of prophylactic, therapeutic, and/or diagnostic methods for preventing, treating, ameliorating, and/or at least identifying diseased states and/or their potential, and thus, improving the safety, quality, and effectiveness of health care on an individualized level. Hence, because of their focus on advancing personalized healthcare, genomics and bioinformatics fields promote individualized healthcare that is proactive, instead of reactive, and this gives the subject in need of treatment the opportunity to become more involved in their own wellness. An advantage of employing the genetics, genomics, and/or bioinformatics technologies disclosed herein is that the qualitative and/or quantitative analyses of molecular biological, e.g., genetic, data can be performed on a broader range of sample sets at a much higher rate of speed and often times more accurately, thus expediting the emergence of a personalized healthcare system. Particularly, in various embodiments, the genomics and/or bioinformatics related tasks may form a genomics pipeline that includes one or more of a whole genome analysis pipeline, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis pipeline, a metagenome analysis pipeline, a joint genotyping, and/or a GATK analysis pipeline. 
     Accordingly, to make use of these advantages there exists enhanced and more accurate software implementations for performing one or a series of such bioinformatics based analytical techniques, such as for deployment by a general purpose CPU and/or GPU and/or may be implemented in one or more quantum circuits of a quantum processing platform. However, common characteristics of traditionally configured software based bioinformatics methods and systems is that they are labor intensive, take a long time to execute on such general purpose processors, and are prone to errors. Therefore, bioinformatics systems as implemented herein that could perform these algorithms, such as implemented in software by a CPU and/or GPU of quantum processing unit in a less labor and/or processing intensive manner with a greater percentage accuracy would be useful. 
     Such implementations have been developed and are presented herein, such as where the genomics and/or bioinformatics analyses are performed by optimized software run on a CPU and/or GPU and/or quantum computer in a system that makes use of the genetic sequence data derived by the processing units and/or integrated circuits of the disclosure. Further, it is to be noted that the cost of analyzing, storing, and sharing this raw digital data has far outpaced the cost of producing it. Accordingly, also presented herein are “just in time” storage and/or retrieval methods that optimize the storage of such data in a manner that substitutes the speed of regenerating the data in exchange for the cost of storing such data collectively. Hence, the data generation, analysis, and “just in time” or “JIT” storage methods presented herein solve a key bottleneck that is a long felt but unmet obstacle standing between the ever-growing raw data generation and storage and the real medical insight being sought from it. 
     Presented herein, therefore, are systems, apparatuses, and methods for implementing genomics and/or bioinformatic protocols or portions thereof, such as for performing one or more functions for analyzing genomic data, for instance, on one or both of an integrated circuit, such as on a hardware processing platform, and a general purpose processor, such as for performing one or more bioanalytic operations in software and/or on firmware. For example, as set forth herein below, in various implementations, an integrated circuit and/or quantum circuit is provided so as to accelerate one or more processes in a primary, secondary, and/or tertiary processing platform. In various instances, the integrated circuit may be employed in performing genetic analytic related tasks, such as mapping, aligning, variant calling, compressing, decompressing, and the like, in an accelerated manner, and as such the integrated circuit may include a hardware accelerated configuration. Additionally, in various instances, an integrated and/or quantum circuit may be provided such as where the circuit is part of a processing unit that is configured for performing one or more genomics and/or bioinformatics protocols on the generated mapped and/or aligned and/or variant called data. 
     Particularly, in a first embodiment, a first integrated circuit may be formed of an FPGA, ASIC, and/or sASIC that is coupled to or otherwise attached to the motherboard and configured, or in the case of an FPGA may be programmable by firmware to be configured, as a set of hardwired digital logic circuits that are adapted to perform at least a first set of sequence analysis functions in a genomics analysis pipeline, such as where the integrated circuit is configured as described herein above to include one or more digital logic circuits that are arranged as a set of processing engines, which are adapted to perform one or more steps in a mapping, aligning, and/or variant calling operation on the genetic data so as to produce sequence analysis results data. The first integrated circuit may further include an output, e.g., formed of a plurality of physical electrical interconnects, such as for communicating the result data from the mapping and/or the alignment and/or other procedures to the memory. 
     Additionally, a second integrated and/or quantum circuit may be included, coupled to or otherwise attached to the motherboard, and in communication with the memory via a communications interface. The second integrated and/or quantum circuit may be formed as a central processing unit (CPU) or graphics processing unit (GPU) or quantum processing unit (QPU) that is configured for receiving the mapped and/or aligned and/or variant called sequence analysis result data and may be adapted to be responsive to one or more software algorithms that are configured to instruct the CPU or GPU to perform one or more genomics and/or bioinformatics functions of the genomic analysis pipeline on the mapped, aligned, and/or variant called sequence analysis result data. Specifically, the genomics and/or bioinformatics related tasks may form a genomics pipeline that includes one or more of a whole genome analysis pipeline, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis pipeline, a metagenome analysis pipeline, a joint genotyping, and/or a GATK analysis pipeline. 
     For instance, in one embodiment, the CPU and/or GPU and/or QPU of the second integrated circuit may include software that is configured for arranging the genome analysis pipeline for executing a whole genome analysis pipeline, such as a whole genome analysis pipeline that includes one or more of genome-wide variation analysis, whole-exome DNA analysis, whole transcriptome RNA analysis, gene function analysis, protein function analysis, protein binding analysis, quantitative gene analysis, and/or a gene assembly analysis. In certain instances, the whole genome analysis pipeline may be performed for the purposes of one or more of ancestry analysis, personal medical history analysis, disease diagnostics, drug discovery, and/or protein profiling. In a particular instance, the whole genome analysis pipeline is performed for the purposes of oncology analysis. In various instances, the results of this data may be made available, e.g. globally, throughout the system. 
     In various instances, the CPU and/or GPU and/or a quantum processing unit (QPU) of the second integrated and/or quantum circuit may include software that is configured for arranging the genome analysis pipeline for executing a genotyping analysis, such as a genotyping analysis including joint genotyping. For instance, the joint genotyping analysis may be performed using a Bayesian probability calculation, such as a Bayesian probability calculation that results in an absolute probability that a given determined genotype is a true genotype. In other instances, the software may be configured for performing a metagenome analysis so as to produce metagenome result data that may in turn be employed in the performance of a microbiome analysis. 
     In certain instances, the first and/or second integrated circuit and/or the memory may be housed on an expansion card, such as a peripheral component interconnect (PCI) card. For instance, in various embodiments, one or more of the integrated circuits may be one or more chips coupled to a PCIe card or otherwise associated with the motherboard. In various instances, the integrated and/or quantum circuit(s) and/or chip(s) may be a component within a sequencer or computer, or server, such as part of a server farm. In particular embodiments, the integrated and/or quantum circuit(s) and/or expansion card(s) and/or computer(s) and/or server(s) may be accessible via the internet, e.g., cloud. 
     Further, in some instances, the memory may be a volatile random access memory (RAM), e.g., a direct access memory (DRAM). Particularly, in various embodiments, the memory may include at least two memories, such as a first memory that is an HMEM, e.g., for storing the reference haplotype sequence data, and a second memory that is an RMEM, e.g., for storing the read of genomic sequence data. In particular instances, each of the two memories may include a write port and/or a read port, such as where the write port and the read port each accessing a separate clock. Additionally, each of the two memories may include a flip-flop configuration for storing a multiplicity of genetic sequence and/or processing result data. 
     Accordingly, in another aspect, the system may be configured for sharing memory resources amongst its component parts, such as in relation to performing some computational tasks via software, such as run by the CPU and/or GPU and/or quantum processing platform, and/or performing other computational tasks via firmware, such as via the hardware of an associated integrated circuit, e.g., FPGA, ASIC, and/or sASIC. This may be achieved in a number of different ways, such as by a direct loose or tight coupling between the CPU/GPU/QPU and the FPGA, e.g., chip or PCIe card. Such configurations may be particularly useful when distributing operations related to the processing of the large data structures associated with genomics and/or bioinformatics analyses to be used and accessed by both the CPU/GPU/QPU and the associated integrated circuit. Particularly, in various embodiments, when processing data through a genomics pipeline, as herein described, such as to accelerate overall processing function, timing, and efficiency, a number of different operations may be run on the data, which operations may involve both software and hardware processing components. 
     Consequently, data may need to be shared and/or otherwise communicated, between the software component(s) running on the CPU and/or GPU and/or QPU and/or the hardware component embodied in the chip, e.g., an FPGA. Accordingly, one or more of the various steps in the genomics and/or bioinformatics processing pipeline, or a portion thereof, may be performed by one device, e.g., the CPU/GPU/QPU, and one or more of the various steps may be performed by a hardwired device, e.g., the FPGA. In such an instance, the CPU/GPU/QPU and/or the FPGA may be communicably coupled in such a manner to allow the efficient transmission of such data, which coupling may involve the shared use of memory resources. To achieve such distribution of tasks and the sharing of information for the performance of such tasks, the various CPUs/GPUs/QPUs may be loosely or tightly coupled to one another and/or the hardware devices, e.g., FPGA, or other chip set, such as by a quick path interconnect. 
     Particularly, in various embodiments, a genomics analysis platform is provided. For instance, the platform may include a motherboard, a memory, and plurality of integrated and/or quantum circuits, such as forming one or more of a CPU/GPU/QPU, a mapping module, an alignment module, a sorting module, and/or a variant call module. Specifically, in particular embodiments, the platform may include a first integrated and/or quantum circuit, such as an integrated circuit forming a central processing unit (CPU) or graphics processing unit (GPU), or a quantum circuit forming a quantum processor, that is responsive to one or more software or other algorithms that are configured to instruct the CPU/GPU/QPU to perform one or more sets of genomics analysis functions, as described herein, such as where the CPU/GPU/QPU includes a first set of physical electronic interconnects to connect with the motherboard. In various instances, the memory may also be attached to the motherboard and may further be electronically connected with the CPU/GPU/QPU, such as via at least a portion of the first set of physical electronic interconnects. In such instances, the memory may be configured for storing a plurality of reads of genomic data, and/or at least one or more genetic reference sequences, and/or an index of the one or more genetic reference sequences. 
     Additionally, the platform may include one or more of another integrated circuit(s), such as where each of the other integrated circuit forms a field programmable gate array (FPGA) having a second set of physical electronic interconnects to connect with the CPU/GPU/QPU and the memory, such as via a point-to-point interconnect protocol. In such an instance, such as where the integrated circuit is an FPGA, the FPGA may be programmable by firmware to configure a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomics analysis functions, e.g., mapping, aligning, variant calling, etc. Particularly, the hardwired digital logic circuits of the FPGA may be arranged as a set of processing engines to perform one or more pre-configured steps in a sequence analysis pipeline of the genomics analysis, such as where the set(s) of processing engines include one or more of a mapping and/or aligning and/or variant call module, which modules may be formed of the separate or the same subsets of processing engines. 
     As indicated, the system may be configured to include one or more processing engines, and in various embodiments, an included processing engine may itself be configured for determining one or more transition probabilities for the sequence of nucleotides of the read of genomic sequence going from one state to another, such as from a match state to an indel state, or match state to a delete state, and/or back again such as from an insert or delete state back to a match state. Additionally, in various instances, the integrated circuit may have a pipelined configuration and/or may include a second and/or third and/or fourth subset of hardwired digital logic circuits, such as including a second set of processing engines, where the second set of processing engines includes a mapping module configured to map the read of genomic sequence to the reference haplotype sequence to produce a mapped read. A third subset of hardwired digital logic circuits may also be included such as where the third set of processing engines includes an aligning module configured to align the mapped read to one or more positions in the reference haplotype sequence. A fourth subset of hardwired digital logic circuits may additionally be included such as where the fourth set of processing engines includes a sorting module configured to sort the mapped and/or aligned read to its relative positions in the chromosome. Like above, in various of these instances, the mapping module and/or the aligning module and/or the sorting module, e.g., along with the variant call module, may be physically integrated on the expansion card. And in certain embodiments, the expansion card may be physically integrated with a genetic sequencer, such as a next gen sequencer and the like. 
     Accordingly, in one aspect, an apparatus for executing one or more steps of a sequence analysis pipeline, such as on genetic data, is provided wherein the genetic data includes one or more of a genetic reference sequence(s), such as a haplotype or hypothetical haplotype sequence, an index of the one or more genetic reference sequence(s), and/or a plurality of reads, such as of genetic and/or genomic data, which data may be stored in one or more shared memory devices, and/or processed by a distributed processing resource, such as a CPU/GPU/QPU and/or FPGA, which are coupled, e.g., tightly or loosely together. Hence, in various instances, the apparatus may include an integrated circuit, which integrated circuit may include one or more, e.g., a set, of hardwired digital logic circuits, wherein the set of hardwired digital logic circuits may be interconnected, such as by one or a plurality of physical electrical interconnects. 
     Accordingly, the system may be configured to include an integrated circuit formed of one or more digital logic circuits that are interconnected by a plurality of physical electrical interconnects, one or more of the plurality of physical electrical interconnects having one or more of a memory interface and/or cache, for the integrated circuit to access the memory and/or data stored thereon and to retrieve the same, such as in a cache coherent manner between the CPU/GPU/QPU and associated chip, e.g., FPGA. In various instances, the digital logic circuits may include at least a first subset of digital logic circuits, such as where the first subset of digital logic circuits may be arranged as a first set of processing engines, which processing engine may be configured for accessing the data stored in the cache and/or direct or indirectly coupled memory. For instance, the first set of processing engines may be configured to perform one or more steps in a mapping and/or aligning and/or sorting analysis, as described above, and/or an HMM analysis on the read of genomic sequence data and the haplotype sequence data. 
     More particularly, a first set of processing engines may include an HMM module, such as in a first configuration of the subset of digital logic circuits, which is adapted to access in the memory, e.g., via the memory interface, at least some of the sequence of nucleotides in the read of genomic sequence data and the haplotype sequence data, and may also be configured to perform the HMM analysis on the at least some of the sequence of nucleotides in the read of genomic sequence data and the at least some of the sequence of nucleotides in the haplotype sequence data so as to produce HMM result data. Additionally, the one or more of the plurality of physical electrical interconnects may include an output from the integrated circuit such as for communicating the HMM result data from the HMM module, such as to a CPU/GPU/QPU of a server or server cluster. 
     Accordingly, in one aspect, a method for executing a sequence analysis pipeline such as on genetic sequence data is provided. The genetic data may include one or more genetic reference or haplotype sequences, one or more indexes of the one or more genetic reference and/or haplotype sequences, and/or a plurality of reads of genomic data. The method may include one or more of receiving, accessing, mapping, aligning, sorting various iterations of the genetic sequence data and/or employing the results thereof in a method for producing one or more variant call files. For instance, in certain embodiments, the method may include receiving, on an input to an integrated circuit from an electronic data source, one or more of a plurality of reads of genomic data, wherein each read of genomic data may include a sequence of nucleotides. 
     In various instances, the integrated circuit may be formed of a set of hardwired digital logic circuits that may be arranged as one or more processing engines. In such an instance, a processing engine may be formed of a subset of the hardwired digital logic circuits that may be in a wired configuration. In such an instance, the processing engine may be configured to perform one or more pre-configured steps such as for implementing one or more of receiving, accessing, mapping, aligning, sorting various iterations of the genetic sequence data and/or employing the results thereof in a method for producing one or more variant call files. In some embodiments, the provided digital logic circuits may be interconnected such as by a plurality of physical electrical interconnects, which may include an input. 
     The method may further include accessing, by the integrated circuit on one or more of the plurality of physical electrical interconnects from a memory, data for performing one or more of the operations detailed herein. In various instances, the integrated circuit may be part of a chipset such as embedded or otherwise contained as part of an FPGA, ASIC, or structured ASIC, and the memory may be directly or indirectly coupled to one or both of the chip and/or a CPU/GPU/QPU associated therewith. For instance, the memory may be a plurality of memories one of each coupled to the chip and a CPU/GPU/QPU that is itself coupled to the chip, e.g., loosely. 
     In other instances, the memory may be a single memory that may be coupled to a CPU/GPU/QPU that is itself tightly coupled to the FPGA, e.g., via a tight processing interconnect or quick path interconnect, e.g., QPI, and thereby accessible to the FPGA, such as in a cache coherent manner. Accordingly, the integrated circuit may be directly or indirectly coupled to the memory so as to access data relevant to performing the functions herein presented, such as for accessing one or more of a plurality of reads, one or more genetic reference or theoretical reference sequences, and/or an index of the one or more genetic reference sequences, e.g., in the performance of a mapping operation. 
     Hence, in various instances, implementations of various aspects of the disclosure may include, but are not limited to: apparatuses, systems, and methods including one or more features as described in detail herein, as well as articles that comprise a tangibly embodied machine-readable medium operable to cause one or more machines (e.g., computers, etc.) to result in operations described herein. Similarly, computer systems are also described that may include one or more processors and/or one or more memories coupled to the one or more processors. Accordingly, computer implemented methods consistent with one or more implementations of the current subject matter can be implemented by one or more data processors residing in a single computing system or multiple computing systems containing multiple computers, such as in a computing or super-computing bank. 
     Such multiple computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g. the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, a physical electrical interconnect, or the like), via a direct connection between one or more of the multiple computing systems, etc. A memory, which can include a computer-readable storage medium, may include, encode, store, or the like one or more programs that cause one or more processors to perform one or more of the operations associated with one or more of the algorithms described herein. 
     The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. While certain features of the currently disclosed subject matter are described for illustrative purposes in relation to an enterprise resource software system or other business software solution or architecture, it should be readily understood that such features are not intended to be limiting. The claims that follow this disclosure are intended to define the scope of the protected subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations. 
         FIG. 1  depicts an HMM 3-state based model illustrating the transition probabilities of going from one state to another. 
         FIG. 2  depicts a high-level view of an integrated circuit of the disclosure including a HMM interface structure. 
         FIG. 3  depicts the integrated circuit of  FIG. 2 , showing an HMM cluster features in greater detail. 
         FIG. 4  depicts an overview of HMM related data flow throughout the system including both software and hardware interactions. 
         FIG. 5  depicts exemplary HMM cluster collar connections. 
         FIG. 6  depicts a high-level view of the major functional blocks within an exemplary HMM hardware accelerator. 
         FIG. 7  depicts an exemplary HMM matrix structure and hardware processing flow. 
         FIG. 8  depicts an enlarged view of a portion of  FIG. 2  showing the data flow and dependencies between nearby cells in the HMM M, I, and D state computations within the matrix. 
         FIG. 9  depicts exemplary computations useful for M, I, D state updates. 
         FIG. 10  depicts M, I, and D state update circuits, including the effects of simplifying assumptions of  FIG. 9  related to transition probabilities and the effect of sharing some M, I, D adder resources with the final sum operations. 
         FIG. 11  depicts Log domain M, I, D state calculation details. 
         FIG. 12  depicts an HMM state transition diagram showing the relation between GOP, GCP and transition probabilities. 
         FIG. 13  depicts an HMM Transprobs and Priors generation circuit to support the general state transition diagram of  FIG. 12 . 
         FIG. 14  depicts a simplified HMM state transition diagram showing the relation between GOP, GCP and transition probabilities. 
         FIG. 15  depicts a HMM Transprobs and Priors generation circuit to support the simplified state transition. 
         FIG. 16  depicts an exemplary theoretical HMM matrix and illustrates how such an HMM matrix may be traversed. 
         FIG. 17  presents a method for performing a multi-region joint detection pre-processing procedure. 
         FIG. 18  presents an exemplary method for computing a connection matrix such as in the pre-processing procedure of  FIG. 17 . 
         FIG. 19  is a graphical representation of the exemplary pileup pursuant to the connection matrix of  FIG. 18 . 
         FIG. 20  is a processing matrix for performing the pre-processing procedure of  FIG. 17 . 
         FIG. 21  is an example of a bubble formation in a De Brujin graph in accordance with the methods of  FIG. 20 . 
         FIG. 22  is an example of a variant pathway through an exemplary De Brujin graph. 
         FIG. 23  is a graphical representation of an exemplary sorting function. 
         FIG. 24  is another example of a processing matrix for a pruned multi-region joint detection procedure. 
         FIG. 25  illustrates a joint pileup of paired reads for two regions. 
         FIG. 26  sets forth a probability table in accordance with the disclosed herein. 
         FIG. 27  is a further example of a processing matrix for a multi-region joint detection procedure. 
         FIG. 28  represents a selection of candidate solutions for the joint pile up of  FIG. 25 . 
         FIG. 29  represents a further selection of candidate solutions for the pile up of  FIG. 28 , after a pruning function has been performed. 
         FIG. 30  represents the final candidates of  FIG. 28 , and their associated probabilities, after the performance of a MRJD function. 
         FIG. 31  illustrates the ROC curves for MRJD and a conventional detector. 
         FIG. 32  illustrates the same results of  FIG. 31  displayed as a function of the sequence similarity of the references. 
         FIG. 33A  depicts an exemplary architecture illustrating a loose coupling between a CPU and an FPGA of the disclosure. 
         FIG. 33B  depicts an exemplary architecture illustrating a tight coupling between a CPU and an FPGA of the disclosure. 
         FIG. 34A  depicts a direct coupling of a CPU and a FPGA of the disclosure. 
         FIG. 34B  depicts an alternative embodiment of the direct coupling of a CPU and a FPGA of  FIG. 34A . 
         FIG. 35  depicts an embodiment of a package of a combined CPU and FPGA, where the two devices share a common memory and/or cache. 
         FIG. 36  illustrates a core of CPUs sharing one or more memories and/or caches, wherein the CPUs are configured for communicating with one or more FPGAs that may also include a shared or common memory or caches. 
         FIG. 37  illustrates an exemplary method of data transfer throughout the system. 
         FIG. 38  depicts the embodiment of  FIG. 36  in greater detail. 
         FIG. 39  depicts an exemplary method for the processing of one or more jobs of a system of the disclosure. 
         FIG. 40  depicts a block diagram for a genomic infrastructure for onsite and/or cloud based genomics processing and analysis. 
         FIG. 41A  depicts a block diagram of a local and/or cloud based computing function of  FIG. 40  for a genomic infrastructure for onsite and/or cloud based genomics processing and analysis. 
         FIG. 41B  depicts the block diagram of  FIG. 41A  illustrating greater detail regarding the computing function for a genomic infrastructure for onsite and/or cloud based genomics processing and analysis. 
         FIG. 41C  depicts the block diagram of  FIG. 40  illustrating greater detail regarding the 3rd-Party analytics function for a genomic infrastructure for onsite and/or cloud based genomics processing and analysis. 
         FIG. 42A  depicts a block diagram illustrating a hybrid cloud configuration. 
         FIG. 42B  depicts the block diagram of  FIG. 42A  in greater detail, illustrating a hybrid cloud configuration. 
         FIG. 42C  depicts the block diagram of  FIG. 42A  in greater detail, illustrating a hybrid cloud configuration. 
         FIG. 43  depicts a block diagram illustrating a primary, secondary, and/or tertiary analysis pipeline as presented herein. 
         FIG. 44  depicts a flow diagram for an analysis pipeline of the disclosure. 
         FIG. 45  is a block diagram of a hardware processor architecture in accordance with an implementation of the disclosure. 
         FIG. 46  is a block diagram of a hardware processor architecture in accordance with another implementation. 
         FIG. 47  is a block diagram of a hardware processor architecture in accordance with yet another implementation. 
         FIG. 48  illustrates a genetic sequence analysis pipeline. 
         FIG. 49  illustrates processing steps using a genetic sequence analysis hardware platform. 
         FIG. 50A  illustrates an apparatus in accordance with an implementation of the disclosure. 
         FIG. 50B  illustrates another apparatus in accordance with an alternative implementation of the disclosure. 
         FIG. 51  illustrates a genomics processing system in accordance with an implementation. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     As summarized above, the present disclosure is directed to devices, systems, and methods for employing the same in the performance of one or more genomics and/or bioinformatics protocols, such as a mapping, aligning, sorting, and/or variant call protocol on data generated through a primary processing procedure, such as on genetic sequence data. For instance, in various aspects, the devices, systems, and methods herein provided are configured for performing secondary analysis protocols on genetic data, such as data generated by the sequencing of RNA and/or DNA, e.g., by a Next Gen Sequencer (“NGS”). In particular embodiments, one or more secondary processing pipelines for processing genetic sequence data is provided, such as where the pipelines, and/or individual elements thereof, may be implemented in software, hardware, or a combination thereof in a distributed and/or an optimized fashion so as to deliver superior sensitivity and improved accuracy on a wider range of sequence derived data than is currently available in the art. Additionally, as summarized above, the present disclosure is directed to devices, systems, and methods for employing the same in the performance of one or more genomics and/or bioinformatics tertiary protocols, such as a whole genome analysis protocol, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis pipeline, a metagenome analysis pipeline, a joint genotyping, and/or a GATK analysis protocol, such as on mapped, aligned, and/or other genetic sequence data, such as employing one or more variant call files. 
     Accordingly, provided herein are software and/or hardware e.g., chip based, accelerated platform analysis technologies for performing secondary and/or tertiary analysis of DNA/RNA sequencing data. More particularly, a platform, or pipeline, of processing engines, such as in a software implemented and/or hardwired configuration, which have specifically been designed for performing secondary genetic analysis, e.g., mapping, aligning, sorting, and/or variant calling; and/or may be specifically designed for performing tertiary genetic analysis, such as whole genome, genotyping, micro-array, exome, microbiome, epigenome, metagenome, joint genotyping, and/or a GATK analysis, such as with respect to genetic based sequencing data, which may have been generated in an optimized format that delivers an improvement in processing speed that is magnitudes faster than standard pipelines that are implemented in known software alone. Additionally, the pipelines presented herein provide better sensitivity and accuracy on a wide range of sequence derived data sets, such as on nucleic acid or protein derived sequences. 
     As indicated above, in various instances, it is a goal of bioinformatics processing to determine individual genomes and/or protein sequences of people, which determinations may be used in gene discovery protocols as well as for prophylaxis and/or therapeutic regimes to better enhance the livelihood of each particular person and human kind as a whole. Further, knowledge of an individual&#39;s genome and/or protein compellation may be used such as in drug discovery and/or FDA trials to better predict with particularity which, if any, drugs will be likely to work on an individual and/or which would be likely to have deleterious side effects, such as by analyzing the individual&#39;s genome and/or a protein profile derived therefrom and comparing the same with predicted biological response from such drug administration. 
     Such bioinformatics processing usually involves three well defined, but typically separate phases of information processing. The first phase, termed primary processing, involves DNA/RNA sequencing, where a subject&#39;s DNA and/or RNA is obtained and subjected to various processes whereby the subject&#39;s genetic code is converted to a machine-readable digital code, e.g., a FASTQ file. The second phase, termed secondary processing, involves using the subject&#39;s generated digital genetic code for the determination of the individual&#39;s genetic makeup, e.g., determining the individual&#39;s genomic nucleotide sequence. And the third phase, termed tertiary processing, involves performing one or more analyses on the subject&#39;s genetic makeup so as to determine therapeutically useful information therefrom. 
     Accordingly, once a subject&#39;s genetic code is sequenced, such as by a NextGen sequencer, so as to produce a machine readable digital representation of the subject&#39;s genetic code, e.g., in a FASTQ and/or BCL file format, it may be useful to further process the digitally encoded genetic sequence data obtained from the sequencer and/or sequencing protocol, such as by subjecting digitally represented data to secondary processing. This secondary processing, for instance, can be used to map and/or align and/or otherwise assemble an entire genomic and/or protein profile of an individual, such as where the individual&#39;s entire genetic makeup is determined, for instance, where each and every nucleotide of each and every chromosome is determined in sequential order such that the composition of the individual&#39;s entire genome has been identified. In such processing, the genome of the individual may be assembled such as by comparison to a reference genome, such as a reference standard, e.g., one or more genomes obtained from the human genome project or the like, so as to determine how the individual&#39;s genetic makeup differs from that of the referent(s). This process is commonly known as variant calling. As the difference between the DNA of any one person to another is 1 in 1,000 base pairs, such a variant calling process can be very labor and time intensive, requiring many steps that may need to be performed one after the other and/or simultaneously, such as in a pipeline, so to analyze the subject&#39;s genomic data and determine how that genetic sequence differs from a given reference. 
     In performing a secondary analysis pipeline, such as for generating a variant call file for a given query sequence of an individual subject; a genetic sample, e.g., DNA, RNA, protein sample, or the like may be obtained, form the subject. The subject&#39;s DNA/RNA may then be sequenced, e.g., by a NextGen Sequencer (NGS) and/or a sequencer-on-a-chip technology, e.g., in a primary processing step, so as to produce a multiplicity of read sequence segments (“reads”) covering all or a portion of the individual&#39;s genome, such as in an oversampled manner. The end product generated by the sequencing device may be a collection of short sequences, e.g., reads, that represent small segments of the subject&#39;s genome, e.g., short genetic sequences representing the individual&#39;s entire genome. As indicated, typically, the information represented by these reads may be an image file or in a digital format, such as in FASTQ, BCL, or other similar file format. 
     Particularly, in a typical secondary processing protocol, a subject&#39;s genetic makeup is assembled by comparison to a reference genome. This comparison involves the reconstruction of the individual&#39;s genome from millions upon millions of short read sequences and/or the comparison of the whole of the individual&#39;s DNA to an exemplary DNA sequence model. In a typical secondary processing protocol an image, FASTQ, and/or BCL file is received from the sequencer containing the raw sequenced read data. In order to compare the subject&#39;s genome to that of the standard reference genome, it needs to be determined where each of these reads map to the reference genome, such as how each is aligned with respect to one another, and/or how each read can also be sorted by chromosome order so as to determine at what position and in which chromosome each read belongs. One or more of these functions may take place prior to performing a variant call function on the entire full-length sequence, e.g., once assembled. Specifically, once it is determined where in the genome each read belongs, the full length genetic sequence may be determined, and then the differences between the subject&#39;s genetic code and that of the referent can be assessed. 
     For instance, reference based assembly in a typical secondary processing assembly protocol involves the comparison of sequenced genomic DNA/RNA of a subject to that of one or more standards, e.g., known reference sequences. Various mapping, aligning, sorting, and/or variant calling algorithms have been developed to help expedite these processes. These algorithms, therefore, may include some variation of one or more of: mapping, aligning, and/or sorting the millions of reads received from the image, FASTQ, and/or BCL file communicated by the sequencer, to determine where on each chromosome each particular read is located. It is noted that these processes may be implemented in software or hardware, such as by the methods and/or devices described in U.S. Pat. Nos. 9,014,989 and 9,235,680 both assigned to Edico Genome Corporation and incorporated by reference herein in their entireties. 
     Often a common feature behind the functioning of these various algorithms and/or hardware implementations is their use of an index and/or an array to expedite their processing function. 
     For example, with respect to mapping, a large quantity, e.g., all, of the sequenced reads may be processed to determine the possible locations in the reference genome to which those reads could possibly align. One methodology that can be used for this purpose is to do a direct comparison of the read to the reference genome so as to find all the positions of matching. Another methodology is to employ a prefix or suffix array, or to build out a prefix or suffix tree, for the purpose of mapping the reads to various positions in the reference genome. A typical algorithm useful in performing such a function is a Burrows-Wheeler transform, which is used to map a selection of reads to a reference using a compression formula that compresses repeating sequences of data. 
     A further methodology is to employ a hash table, such as where a selected subset of the reads, a k-mer of a selected length “k”, e.g., a seed, are placed in a hash table as keys and the reference sequence is broken into equivalent k-mer length portions and those portions and their location are inserted by an algorithm into the hash table at those locations in the table to which they map according to a hashing function. A typical algorithm for performing this function is “BLAST”, a Basic Local Alignment Search Tool. Such hash table based programs compare query nucleotide or protein sequences to one or more standard reference sequence databases and calculates the statistical significance of matches. In such manners as these, it may be determined where any given read is possibly located with respect to a reference genome. These algorithms are useful because they require less memory, fewer look ups, LUTs, and therefore require fewer processing resources and time in the performance of their functions, than would otherwise be the case, such as if the subject&#39;s genome were being assembled by direct comparison, such as without the use of these algorithms. 
     Additionally, an aligning function may be performed to determine out of all the possible locations a given read may map to on a genome, such as in those instances where a read may map to multiple positions in the genome, which is in fact the location from which it actually was derived, such as by being sequenced therefrom by the original sequencing protocol. This function may be performed on a number of the reads, e.g., mapped reads, of the genome and a string of ordered nucleotide bases representing a portion or the entire genetic sequence of the subject&#39;s DNA/RNA may be obtained. Along with the ordered genetic sequence a score may be given for each nucleotide in a given position, representing the likelihood that for any given nucleotide position, the nucleotide, e.g., “A”, “C”, “G”, “T” (or “U”), predicted to be in that position is in fact the nucleotide that belongs in that assigned position. Typical algorithms for performing alignment functions include Needleman-Wunsch and Smith-Waterman algorithms. In either case, these algorithms perform sequence alignments between a string of the subject&#39;s query genomic sequence and a string of the reference genomic sequence whereby instead of comparing the entire genomic sequences, one with the other, segments of a selection of possible lengths are compared. 
     Once the reads have been assigned a position, such as relative to the reference genome, which may include identifying to which chromosome the read belongs and/or its offset from the beginning of that chromosome, the reads may be sorted by position. This may enable downstream analyses to take advantage of the oversampling procedures described herein. All of the reads that overlap a given position in the genome will be adjacent to each other after sorting and they can be organized into a pileup and readily examined to determine if the majority of them agree with the reference value or not. If they do not, a variant can be flagged. 
     For instance, in various embodiments, the methods of the disclosure may include generating a variant call file (VCF) identifying one or more, e.g., all, of the genetic variants in the individual who&#39;s DNA/RNA were sequenced, e.g., relevant to one or more reference genomes. For instance, once the actual sample genome is known and compared to the reference genome, the variations between the two can be determined, and a list of all the variations/deviations between the reference genome(s) and the sample genome may be called out, e.g., a variant call file may be produced. Particularly, in one aspect, a variant call file containing all the variations of the subject&#39;s genetic sequence to the reference sequence(s) may be generated. 
     As indicated above, such variations between the two genetic sequences may be due to a number of reasons. Hence, in order to generate such a file, the genome of the subject must be sequenced and rebuilt prior to determining its variants. There are, however, several problems that may occur when attempting to generate such an assembly. For example, there may be problems with the chemistry, the sequencing machine, and/or human error that occur in the sequencing process. Furthermore, there may be genetic artifacts that make such reconstructions problematic. For instance, a typical problem with performing such assemblies is that there are sometimes huge portions of the genome that repeat themselves, such as long sections of the genome that include the same strings of nucleotides. Hence, because any genetic sequence is not unique everywhere, it may be difficult to determine where in the genome an identified read actually maps and aligns. Additionally, there may be a single nucleotide polymorphism (SNP), such as wherein one base in the subject&#39;s genetic sequence has been substituted for another; there may be more extensive substitutions of a plurality of nucleotides; there may be an insertion or a deletion, such as where one or a multiplicity of bases have been added to or deleted from the subject&#39;s genetic sequence, and/or there may be a structural variant, e.g., such as caused by the crossing of legs of two chromosomes, and/or there may simply be an offset causing a shift in the sequence. 
     Accordingly, there are two main possibilities for variation. For one, there is an actual variation at the particular location in question, for instance, where the person&#39;s genome is in fact different at a particular location than that of the reference, e.g., there is a natural variation due to an SNP (one base substitution), an Insertion or Deletion (of one or more nucleotides in length), and/or there is a structural variant, such as where the DNA material from one chromosome gets crossed onto a different chromosome or leg, or where a certain region gets copied twice in the DNA. Alternatively, a variation may be caused by there being a problem in the read data, either through chemistry or the machine, sequencer or aligner, or other human error. The methods disclosed herein may be employed in a manner so as to compensate for these types of errors, and more particularly so as to distinguish errors in variation due to chemistry, machine or human, and real variations in the sequenced genome. More specifically, the methods, apparatuses, and systems for employing the same, as here in described, have been developed so as to clearly distinguish between these two different types of variations and therefore to better ensure the accuracy of any call files generated so as to correctly identify true variants. 
     Hence, in particular embodiments, a platform of technologies for performing genetic analyses are provided where the platform may include the performance of one or more of: mapping, aligning, sorting, local realignment, duplicate marking, base quality score recalibration, variant calling, compression, and/or decompression functions. For instance, in various aspects a pipeline may be provided wherein the pipeline includes performing one or more analytic functions, as described herein, on a genomic sequence of one or more individuals, such as data obtained in an image file and/or a digital, e.g., FASTQ or BCL, file format from an automated sequencer. A typical pipeline to be executed may include one or more of sequencing genetic material, such as a portion or an entire genome, of one or more individual subjects, which genetic material may include DNA, ssDNA, RNA, rRNA, tRNA, and the like, and/or in some instances the genetic material may represent coding or non-coding regions, such as exomes and/or episomes of the DNA. The pipeline may include one or more of performing an image processing procedure, a base calling and/or error correction operation, such as on the digitized genetic data, and/or may include one or more of performing a mapping, an alignment, and/or a sorting function on the genetic data. In certain instances, the pipeline may include performing one or more of a realignment, a deduplication, a base quality or score recalibration, a reduction and/or compression, and/or a decompression on the digitized genetic data. In certain instances the pipeline may include performing a variant calling operation, such as a Hidden Markov Model, on the genetic data. 
     Accordingly, in certain instances, the implementation of one or more of these platform functions is for the purpose of performing one or more of determining and/or reconstructing a subject&#39;s consensus genomic sequence, comparing a subject&#39;s genomic sequence to a referent sequence, e.g., a reference or model genetic sequence, determining the manner in which the subject&#39;s genomic DNA or RNA differs from a referent, e.g., variant calling, and/or for performing a tertiary analysis on the subject&#39;s genomic sequence, such as for genome-wide variation analysis, gene function analysis, protein function analysis, e.g., protein binding analysis, quantitative and/or assembly analysis of genomes and/or transcriptomes, as well as for various diagnostic, and/or a prophylactic and/or therapeutic evaluation analyses. 
     As indicated above, in one aspect one or more of these platform functions, e.g., mapping, aligning, sorting, realignment, duplicate marking, base quality score recalibration, variant calling, compression, and/or decompression functions is configured for implementation in software. In some aspects, one or more of these platform functions, e.g., mapping, aligning, sorting, local realignment, duplicate marking, base quality score recalibration, decompression, variant calling, compression, and/or decompression functions is configured for implementation in hardware, e.g., firmware. In certain aspects, these genetic analysis technologies may employ improved algorithms that may be implemented by software that is run in a less processing intensive and/or less time consuming manner and/or with greater percentage accuracy, e.g., the hardware implemented functionality is faster, less processing intensive, and more accurate. 
     For instance, in certain embodiments, improved algorithms for performing such secondary and/or tertiary processing, as disclosed herein, are provided. The improved algorithms are directed to more efficiently and/or more accurately performing one or more of mapping, aligning, sorting and/or variant calling functions, such as on an image file and/or a digital representation of DNA/RNA sequence data obtained from a sequencing platform, such as in a FASTQ or BCL file format obtained from an automated sequencer such as one of those set forth above. In particular embodiments, the improved algorithms may be directed to more efficiently and/or more accurately performing one or more of local realignment, duplicate marking, base quality score recalibration, variant calling, compression, and/or decompression functions. Further, as described in greater detail herein below, in certain embodiments, these genetic analysis technologies may employ one or more algorithms, such as improved algorithms, that may be implemented by one or more of software and/or hardware that is run in a less processing intensive and/or less time consuming manner and/or with greater percentage accuracy than various traditional software implementations for doing the same. In various instances, improved algorithms for implementation on a quantum processing platform are provided. 
     Hence, in various aspects, presented herein are systems, apparatuses, and methods for implementing bioinformatics protocols, such as for performing one or more functions for analyzing genetic data, such as genomic data, for instance, via one or more optimized algorithms and/or on one or more optimized integrated and/or quantum circuits, such as on one or more hardware processing platforms. In one instance, systems and methods are provided for implementing one or more algorithms, e.g., in software and/or in firmware and/or by a quantum processing circuit, for the performance of one or more steps for analyzing genomic data in a bioinformatics protocol, such as where the steps may include the performance of one or more of: mapping, aligning, sorting, local realignment, duplicate marking, base quality score recalibration, variant calling, compression, and/or decompression; and may further include one or more steps in a tertiary processing platform. Accordingly, in certain instances, methods, including software, firmware, hardware, and/or quantum processing algorithms for performing the methods, are presented herein where the methods involve the performance of an algorithm, such as an algorithm for implementing one or more genetic analysis functions such as mapping, aligning, sorting, realignment, duplicate marking, base quality score recalibration, variant calling, compression, decompression, and/or one or more tertiary processing protocols where the algorithm, e.g., including firmware, has been optimized in accordance with the manner in which it is to be implemented. 
     In particular, where the algorithm is to be implemented in a software solution, the algorithm and/or its attendant processes, has been optimized so as to be performed faster and/or with better accuracy for execution by that media. Likewise, where the functions of the algorithm are to be implemented in a hardware solution, e.g., as firmware, the hardware has been designed to perform these functions and/or their attendant processes in an optimized manner so as to be performed faster and/or with better accuracy for execution by that media. Further, where the algorithm is to be implemented in a quantum processing solution, the algorithm and/or its attendant processes, has been optimized so as to be performed faster and/or with better accuracy for execution by that media. These methods, for instance, can be employed such as in an iterative mapping, aligning, sorting, variant calling, and/or tertiary processing procedure. In another instance, systems and methods are provided for implementing the functions of one or more algorithms for the performance of one or more steps for analyzing genomic data in a bioinformatics protocol, as set forth herein, wherein the functions are implemented on a hardware and/or quantum accelerator, which may or may not be coupled with one or more general purpose processors and/or super computers and/or quantum computers. 
     More specifically, in some instances, methods and/or machinery for implementing those methods, for performing secondary analytics on data pertaining to the genetic composition of a subject are provided. In one instance, the analytics to be performed may involve reference based reconstruction of the subject genome. For instance, referenced based mapping involves the use of a reference genome, which may be generated from sequencing the genome of a single or multiple individuals, or it may be an amalgamation of various people&#39;s DNA/RNA that have been combined in such a manner so as to produce a prototypical, standard reference genome to which any individual&#39;s genetic material, e.g., DNA/RNA, may be compared, for example, so as to determine and reconstruct the individual&#39;s genetic sequence and/or for determining the difference between their genetic makeup and that of the standard reference, e.g., variant calling. 
     Particularly, a reason for performing a secondary analysis on a subject&#39;s sequenced DNA/RNA is to determine how the subject&#39;s DNA/RNA varies from that of the reference, such as to determine one, a multiplicity, or all, of the differences in the nucleotide sequence of the subject from that of the reference. For instance, the differences between the genetic sequences of any two random persons is 1 about in 1,000 base pairs, which when taken in view of the entire genome of over 3 billion base pairs amounts to a variation of up to 3,000,000 divergent base pairs per person. Determining these differences may be useful such as in a tertiary analysis protocol, for instance, so as to predict the potential for the occurrence of a diseased state, such as because of a genetic abnormality, and/or the likelihood of success of a prophylactic or therapeutic modality, such as based on how a prophylactic or therapeutic is expected to interact with the subject&#39;s DNA or the proteins generated therefrom. In various instances, it may be useful to perform both a de novo and a reference based reconstruction of the subject&#39;s genome so as to confirm the results of one against the other, and to, where desirable, enhance the accuracy of a variant calling protocol. 
     Accordingly, in one aspect, in various embodiments, once the subject&#39;s genome has been reconstructed and/or a VCF has been generated, such data may then be subjected to tertiary processing so as to interpret it, such as for determining what the data means with respect to identifying what diseases this person may or may have the potential for suffer from and/or for determining what treatments or lifestyle changes this subject may want to employ so as to ameliorate and/or prevent a diseased state. For example, the subject&#39;s genetic sequence and/or their variant call file may be analyzed to determine clinically relevant genetic markers that indicate the existence or potential for a diseased state and/or the efficacy of a proposed therapeutic or prophylactic regimen may have on the subject. This data may then be used to provide the subject with one or more therapeutic or prophylactic regimens so as to better the subject&#39;s quality of life, such as treating and/or preventing a diseased state. 
     Particularly, once one or more of an individual&#39;s genetic variations are determined, such variant call file information can be used to develop medically useful information, which in turn can be used to determine, e.g., using various known statistical analysis models, health related data and/or medical useful information, e.g., for diagnostic purposes, e.g., diagnosing a disease or potential therefore, clinical interpretation (e.g., looking for markers that represent a disease variant), whether the subject should be included or excluded in various clinical trials, and other such purposes. More particularly, in various instances, the generated genomics and/or bioinformatics processed results data may be employed in the performance of one or more genomics and/or bioinformatics tertiary protocols, such as a whole genome analysis protocol, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis pipeline, a metagenome analysis pipeline, a joint genotyping, and/or a GATK analysis protocol. 
     As there are a finite number of diseased states that are caused by genetic malformations, in tertiary processing variants of a certain type, e.g., those known to be related to the onset of diseased states, can be queried for, such as by determining if one or more genetic based diseased markers are included in the variant call file of the subject. Consequently, in various instances, the methods herein disclosed may involve analyzing, e.g., scanning, the VCF and/or the generated sequence, against a known disease sequence variant, such as in a data base of genomic markers therefore, so as to identify the presence of the genetic marker in the VCF and/or the generated sequence, and if present to make a call as to the presence or potential for a genetically induced diseased state. Since there are a large number of known genetic variations and a large number of individual&#39;s suffering from diseases caused by such variations, in some embodiments, the methods disclosed herein may entail the generation of one or more databases linking sequenced data for an entire genome and/or a variant call file pertaining thereto, e.g., such as from an individual or a plurality of individuals, and a diseased state and/or searching the generated databases to determine if a particular subject has a genetic composition that would predispose them to having such diseased state. Such searching may involve a comparison of one entire genome with one or more others, or a fragment of a genome, such as a fragment containing only the variations, to one or more fragments of one or more other genomes such as in a database of reference genomes or fragments thereof. 
     Therefore, in various instances, a pipeline of the disclosure may include one or more modules, wherein the modules are configured for performing one or more functions, such as an image processing or a base calling and/or error correction operation and/or a mapping and/or an alignment, e.g., a gapped or gapless alignment, and/or a sorting function on genetic data, e.g., sequenced genetic data. And in various instances, the pipeline may include one or more modules, wherein the modules are configured for performing one more of a local realignment, a deduplication, a base quality score recalibration, a variant calling, e.g., HMM, a reduction and/or compression, and/or a decompression on the genetic data. Additionally, the pipeline may include one or more modules, wherein the modules are configured for performing a tertiary analysis protocol, such as a whole genome analysis protocol, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis pipeline, a metagenome analysis pipeline, a joint genotyping, and/or a GATK analysis protocol. 
     Many of these modules may either be performed by software or on hardware or remotely, e.g., via software or hardware, such as on the cloud or a remote server and/or server bank, such as a quantum computing cluster. Additionally, many of these steps and/or modules of the pipeline are optional and/or can be arranged in any logical order and/or omitted entirely. For instance, the software and/or hardware disclosed herein may or may not include an image processing and/or a base calling or sequence correction algorithm, such as where there may be concern that such functions may result in a statistical bias. Consequently the system may include or may not include the base calling and/or sequence correction function, respectively, dependent on the level of accuracy and/or efficiency desired. And as indicated above, one or more of the pipeline functions may be employed in the generation of a genomic sequence of a subject such as through a reference based genomic reconstruction. Also as indicated above, in certain instances, the output from the pipeline is a variant call file indicating a portion or all the variants in a genome or a portion thereof. 
     Particularly, once the reads are assigned a position relative to the reference genome, which may include identifying to which chromosome the read belongs and its offset from the beginning of that chromosome, they may be sorted, such as by position. This enables downstream analyses to take advantage of the various oversampling protocols described herein. All of the reads that overlap a given position in the genome may be positioned adjacent to each other after sorting and they can be piled up and readily examined to determine if the majority of them agree with the reference value or not. If they do not, as indicated above, a variant can be flagged. 
     Accordingly, as indicated above with respect to mapping, the image file, BCL file, and/or FASTQ file obtained from the sequencer is comprised of a plurality, e.g., millions to a billion or more, of reads consisting of short strings of nucleotide sequence data representing a portion or the entire genome of an individual. Mapping, in general, involves plotting the reads to all the locations in the reference genome to where there is a match. For example, dependent on the size of the read there may be one or a plurality of locations where the read substantially matches a corresponding sequence in the reference genome. Hence, the mapping and/or other functions disclosed herein may be configured for determining where out of all the possible locations one or more reads may match to in the reference genome is actually the true location to where they map. 
     For instance, in various instances, an index of a reference genome may be generated or otherwise provided, so that the reads or portions of the reads may be looked up, e.g., within a Look-Up Table (LUT), in reference to the index, thereby retrieving indications of locations in the reference, so as to map the reads to the reference. Such an index of the reference can be constructed in various forms and queried in various manners. In some methods, the index may include a prefix and/or a suffix tree. In particular methods, the index may be derived from a Burrows/Wheeler transform of the reference. Hence, alternatively, or in addition to employing a prefix or a suffix tree, a Burrows/Wheeler transform can be performed on the data. For instance, a Burrows/Wheeler transform may be used to store a tree-like data structure abstractly equivalent to a prefix and/or suffix tree, in a compact format, such as in the space allocated for storing the reference genome. In various instances, the data stored is not in a tree-like structure, but rather the reference sequence data is in a linear list that may have been scrambled into a different order so as to transform it in a very particular way such that the accompanying algorithm allows the reference to be searched with reference to the sample reads so as to effectively walk the “tree”. 
     Additionally, in various instances, the index may include one or more hash tables, and the methods disclosed herein may include a hash function that may be performed on one or more portions of the reads in an effort to map the reads to the reference, e.g., to the index of the reference. For instance, alternatively, or in addition to utilizing one or both a prefix/suffix tree and/or a Burrows/Wheeler transform on the reference genome and subject sequence data, so as to find where the one maps against the other, another such method involves the production of a hash table index and/or the performance of a hash function. The hash table index may be a large reference structure that is built up from sequences of the reference genome that may then be compared to one or more portions of the read to determine where the one may match to the other. Likewise, the hash table index may be built up from portions of the read that may then be compared to one or more sequences of the reference genome and thereby used to determine where the one may match to the other. 
     Implementation of a hash table is a fast method for performing a pattern match. Each lookup takes a nearly constant amount of time to perform. Such method may be contrasted with the Burrows-Wheeler method which may require many probes (the number may vary depending on how many bits are required to find a unique pattern) per query to find a match, or a binary search method that takes log 2(N) probes where N is the number of seed patterns in the table. Further, even though the hash function can break the reference genome down into segments of seeds of any given length, e.g., 28 base pairs, and can then convert the seeds into a digital, e.g., binary, representation of 56 bits, not all 56 bits need be accessed entirely at the same time or in the same way. For instance, the hash function can be implemented in such a manner that the address for each seed is designated by a number less than 56 bits, such as about 18 to about 44 or 46 bits, such as about 20 to about 40 bits, such as about 24 to about 36 bits, including about 28 to about 32 or about 30 bits may be used as an initial key or address so as to access the hash table. For example, in certain instances, about 26 to about 29 bits may be used as a primary access key for the hash table, leaving about 27 to about 30 bits left over, which may be employed as a means for double checking the first key, e.g., if both the first and second keys arrive at the same cell in the hash table, then it is relatively clear that said location is where they belong. 
     For instance, a first portion of the digitally represented seed, e.g., about 26 to about 32, such as about 29 bits, can form a primary access key and be hashed and may be looked up in a first step. And, in a second step, the remaining about 27 to about 30 bits, e.g., a secondary access key, can be inserted into the hash table, such as in a hash chain, as a means for confirming the first pass. Accordingly, for any seed, its original address bits may be hashed in a first step, and the secondary address bits may be used in a second, confirmation step. In such an instance, the first portion of the seeds can be inserted into a primary record location, and the second portion may be fit into the table in a secondary record chain location. And, as indicated above, in various instances, these two different record locations may be positionally separated, such as by a chain format record. 
     In particular instances, a brute force linear scan can be employed to compare the reference to the read, or portions thereof. However, using a brute force linear search to scan the reference genome for locations where a seed matches, over 3 billion locations may have to be checked. Which searching can be performed, in accordance with the methods disclosed herein, in software or hardware. Nevertheless, by using a hashing approach, as set forth herein, each seed lookup can occur in approximately a constant amount of time. Often, the location can be ascertained in a few, e.g., a single access. However, in cases where multiple seeds map to the same location in the table, e.g., they are not unique enough, a few additional accesses may be made to find the seed being currently looked up. Hence, even though there can be 30M or more possible locations for a given 100 nucleotide length read to match up to, with respect to a reference genome, the hash table and hash function can quickly determine where that read is going to show up in the reference genome. By using a hash table index, therefore, it is not necessary to search the whole reference genome, e.g., by brute force, to determine where the read maps and aligns. 
     In view of the above, any suitable hash function may be employed for these purposes, however, in various instances, the hash function used to determine the table address for each seed may be a cyclic redundancy check (CRC) that may be based on a 2 k-bit primitive polynomial, as indicated above. Alternatively, a trivial hash function mapper may be employed such as by simply dropping some of the 2 k bits. However, in various instances, the CRC may be a stronger hash function that may better separate similar seeds while at the same time avoiding table congestion. This may especially be beneficial where there is no speed penalty when calculating CRCs such as with the dedicated hardware described herein. In such instances, the hash record populated for each seed may include the reference position where the seed occurred, and the flag indicating whether it was reverse complemented before hashing. 
     The output returned from the performance of a mapping function may be a list of possibilities as to where one or more, e.g., each, read maps to one or more reference genomes. For instance, the output for each mapped read may be a list of possible locations the read may be mapped to a matching sequence in the reference genome. In various embodiments, an exact match to the reference for at least a piece, e.g., a seed of the read, if not all of the read may be sought. Accordingly, in various instances, it is not necessary for all portions of all the reads to match exactly to all the portions of the reference genome. 
     As described herein, all of these operations may be performed via software or may be hardwired, such as into an integrated circuit, such as on a chip, for instance as part of a circuit board. For instance, the functioning of one or more of these algorithms may be embedded onto a chip, such as into a FPGA (field programmable gate array) or ASIC (application specific integrated circuit) chip, and may be optimized so as to perform more efficiently because of their implementation in such hardware. Additionally, one or more, e.g., two or all three, of these mapping functions may form a module, such as a mapping module, that may form part of a system, e.g., a pipeline, that is used in a process for determining an actual entire genomic sequence, or a portion thereof, of an individual. 
     An advantage of implementing the hash module in hardware is that the processes may be accelerated and therefore performed in a much faster manner. For instance, where software may include various instructions for performing one or more of these various functions, the implementation of such instructions often requires data and instructions to be stored and/or fetched and/or read and/or interpreted, such as prior to execution. As indicated above, however, and described in greater detail herein, a chip can be hardwired to perform these functions without having to fetch, interpret, and/or perform one or more of a sequence of instructions. Rather, the chip may be wired to perform such functions directly. Accordingly, in various aspects, the disclosure is directed to a custom hardwired machine that may be configured such that portions or all of the above described mapping, e.g., hashing, module may be implemented by one or more network circuits, such as integrated circuits hardwired on a chip, such as an FPGA or ASIC. 
     For example, in various instances, the hash table index may be constructed and the hash function may be performed on a chip, and in other instances, the hash table index may be generated off of the chip, such as via software run by a host CPU, but once generated it is loaded onto or otherwise made accessible to the hardware and employed by the chip, such as in running the hash module. Particularly, in various instances, the chip, such as an FPGA, may be configured so as to be tightly coupled to the host CPU, such as by a low latency interconnect, such as a QPI interconnect. More particularly, the chip and CPU may be configured so as to be tightly coupled together in such a manner so as to share one or more memory resources, e.g., a DRAM, in a cache coherent configuration, as described in more detail below. In such an instance, the host memory may build and/or include the reference index, e.g., the hash table, which may be stored in the host memory but be made readily accessible to the FPGA such as for its use in the performance of a hash or other mapping function. In particular embodiments, one or both of the CPU and the FPGA may include one or more caches or registers that may be coupled together so as to be in a coherent configuration such that stored data in one cache may be substantially mirrored by the other. 
     Accordingly, in view of the above, at run-time, one or more previously constructed hash tables, e.g., containing an index of a reference genome, or a constructed or to be constructed hash table, may be loaded into onboard memory or may at least be made accessible by its host application, as described in greater detail herein below. In such an instance, reads, e.g., stored in FASTQ file format, may be sent by the host application to the onboard processing engines, e.g., a memory or cache or other register associated therewith, such as for use by a mapping and/or alignment and/or sorting engine, such as where the results thereof may be sent to and used for performing a variant call function. With respect thereto, as indicated above, in various instances, a pile up of overlapping seeds may be generated, e.g., via a seed generation function, and extracted from the sequenced reads, or read-pairs, and once generated the seeds may be hashed, such as against an index, and looked up in the hash table so as to determine candidate read mapping positions in the reference. 
     More particularly, in various instances, a mapping module may be provided, such as where the mapping module is configured to perform one or more mapping functions, such as in a hardwired configuration. Specifically, the hardwired mapping module may be configured to perform one or more functions typically performed by one or more algorithms run on a CPU, such as the functions that would typically be implemented in a software based algorithm that produces a prefix and/or suffix tree, a Burrows-Wheeler Transform, and/or runs a hash function, for instance, a hash function that makes use of, or otherwise relies on, a hash-table indexing, such as of a reference, e.g., a reference genome sequence. In such instances, the hash function may be structured so as to implement a strategy, such as an optimized mapping strategy that may be configured to minimize the number of memory accesses, e.g., large-memory random accesses, being performed so as to thereby maximize the utility of the on-board or otherwise associated memory bandwidth, which may fundamentally be constrained such as by space within the chip architecture. 
     Further, in certain instances, in order to make the system more efficient, the host CPU/GPU/QPU may be tightly coupled to the associated hardware, e.g., FPGA, such as by a low latency interface, e.g., Quick Path Interconnect (“QPI”), so as to allow the processing engines of the integrated circuit to have ready access to host memory. In particular instances, the interaction between the host CPU and the coupled chip and their respective associated memories, e.g., one or more DRAMs, may be configured so as to be cache coherent. Hence, in various embodiments, an integrated circuit may be provided wherein the integrated circuit has been pre-configured, e.g., prewired, in such a manner as to include one or more digital logic circuits that may be in a wired configuration, which may be interconnected, e.g., by one or a plurality of physical electrical interconnects, and in various embodiments, the hardwired digital logic circuits may be arranged into one or more processing engines so as to form one or more modules, such as a mapping module. 
     Accordingly, in various instances, a mapping module may be provided, such as in a first pre-configured wired, e.g., hardwired, configuration, where the mapping module is configured to perform various mapping functions. For instance, the mapping module may be configured so as to access, at least some of a sequence of nucleotides in a read of a plurality of reads, derived from a subject&#39;s sequenced genetic sample, and/or a genetic reference sequence, and/or an index of one or more genetic reference sequences, from a memory or a cache associated therewith, e.g., via a memory interface, such as a process interconnect, for instance, a Quick Path Interconnect, and the like. The mapping module may further be configured for mapping the read to one or more segments of the one or more genetic reference sequences, such as based on the index. For example, in various particular embodiments, the mapping algorithm and/or module presented herein, may be employed to build, or otherwise construct a hash table whereby the read, or a portion thereof, of the sequenced genetic material from the subject may be compared with one or more segments of a reference genome, so as to produce mapped reads. In such an instance, once mapping has been performed, an alignment may be performed. 
     For example, after it has been determined where all the possible matches are for the seeds against the reference genome, it must be determined which out of all the possible locations a given read may match to is in fact the correct position to which it aligns. Hence, after mapping there may be a multiplicity of positions that one or more reads appear to match in the reference genome. Consequently, there may be a plurality of seeds that appear to be indicating the exact same thing, e.g., they may match to the exact same position on the reference, if you take into account the position of the seed in the read. The actual alignment, therefore, must be determined for each given read. This determination may be made in several different ways. 
     In one instance, all the reads may be evaluated so as to determine their correct alignment with respect to the reference genome based on the positions indicated by every seed from the read that returned position information during the mapping, e.g., hash lookup, process. However, in various instances, prior to performing an alignment, a seed chain filtering function may be performed on one or more of the seeds. For instance, in certain instances, the seeds associated with a given read that appear to map to the same general place as against the reference genome may be aggregated into a single chain that references the same general region. All of the seeds associated with one read may be grouped into one or more seed chains such that each seed is a member of only one chain. It is such chain(s) that then cause the read to be aligned to each indicated position in the reference genome. 
     Specifically, in various instances, all the seeds that have the same supporting evidence indicating that they all belong to the same general location(s) in the reference may be gathered together to form one or more chains. The seeds that group together, therefore, or at least appear as they are going to be near one another in the reference genome, e.g., within a certain band, will be grouped into a chain of seeds, and those that are outside of this band will be made into a different chain of seeds. Once these various seeds have been aggregated into one or more various seed chains, it may be determined which of the chains actually represents the correct chain to be aligned. This may be done, at least in part, by use of a filtering algorithm that is a heuristic designed to eliminate weak seed chains which are highly unlikely to be the correct one. 
     The outcome from performing one or more of these mapping, filtering, and/or editing functions is a list of reads which includes for each read a list of all the possible locations to where the read may matchup with the reference genome. Hence, a mapping function may be performed so as to quickly determine where the reads of the image file, BCL file, and/or FASTQ file obtained from the sequencer map to the reference genome, e.g., to where in the whole genome the various reads map. However, if there is an error in any of the reads or a genetic variation, you may not get an exact match to the reference and/or there may be several places one or more reads appear to match. It, therefore, must be determined where the various reads actually align with respect to the genome as a whole. 
     Accordingly, after mapping and/or filtering and/or editing, the location positions for a large number of reads have been determined, where for some of the individual reads a multiplicity of location positions have been determined, and it now needs to be determined which out of all the possible locations is in fact the true or most likely location to which the various reads align. Such aligning may be performed by one or more algorithms, such as a dynamic programming algorithm that matches the mapped reads to the reference genome and runs an alignment function thereon. An exemplary aligning function compares one or more, e.g., all of the reads, to the reference, such as by placing them in a graphical relation to one another, e.g., such as in a table, e.g., a virtual array or matrix, where the sequence of one of the reference genome or the mapped reads is placed on one dimension or axis, e.g., the horizontal axis, and the other is placed on the opposed dimensions or axis, such as the vertical axis. A conceptual scoring wave front is then passed over the array so as to determine the alignment of the reads with the reference genome, such as by computing alignment scores for each cell in the matrix. 
     The scoring wave front represents one or more, e.g., all, the cells of a matrix, or a portion of those cells, which may be scored independently and/or simultaneously according to the rules of dynamic programming applicable in the alignment algorithm, such as Smith-Waterman, and/or Needleman-Wunsch, and/or related algorithms. Alignment scores may be computed sequentially or in other orders, such as by computing all the scores in the top row from left to right, followed by all the scores in the next row from left to right, etc. In this manner the diagonally sweeping diagonal wave front represents an optimal sequence of batches of scores computed simultaneously or in parallel in a series of wave front steps. 
     For instance, in one embodiment, a window of the reference genome containing the segment to which a read was mapped may be placed on the horizontal axis, and the read may be positioned on the vertical axis. In a manner such as this an array or matrix is generated, e.g., a virtual matrix, whereby the nucleotide at each position in the read may be compared with the nucleotide at each position in the reference window. As the wave front passes over the array, all potential ways of aligning the read to the reference window are considered, including if changes to one sequence would be required to make the read match the reference sequence, such as by changing one or more nucleotides of the read to other nucleotides, or inserting one or more new nucleotides into one sequence, or deleting one or more nucleotides from one sequence. 
     An alignment score, representing the extent of the changes that would be required to be made to achieve an exact alignment, is generated, wherein this score and/or other associated data may be stored in the given cells of the array. Each cell of the array corresponds to the possibility that the nucleotide at its position on the read axis aligns to the nucleotide at its position on the reference axis, and the score generated for each cell represents the partial alignment terminating with the cell&#39;s positions in the read and the reference window. The highest score generated in any cell represents the best overall alignment of the read to the reference window. In various instances, the alignment may be global, where the entire read must be aligned to some portion of the reference window, such as using a Needleman-Wunsch or similar algorithm; or in other instances, the alignment may be local, where only a portion of the read may be aligned to a portion of the reference window, such as by using a Smith-Waterman or similar algorithm. 
     Accordingly, in various instances, an alignment function may be performed, such as on the data obtained from the mapping module. Hence, in various instances, an alignment function may form a module, such as an alignment module, that may form part of a system, e.g., a pipeline, that is used, such as in addition with a mapping module, in a process for determining the actual entire genomic sequence, or a portion thereof, of an individual. For instance, the output returned from the performance of the mapping function, such as from a mapping module, e.g., the list of possibilities as to where one or more or all of the reads maps to one or more positions in one or more reference genomes, may be employed by the alignment function so as to determine the actual sequence alignment of the subject&#39;s sequenced DNA. 
     Such an alignment function may at times be useful because, as described above, often times, for a variety of different reasons, the sequenced reads do not always match exactly to the reference genome. For instance, there may be an SNP (single nucleotide polymorphism) in one or more of the reads, e.g., a substitution of one nucleotide for another at a single position; there may be an “indel,” insertion or deletion of one or more bases along one or more of the read sequences, which insertion or deletion is not present in the reference genome; and/or there may be a sequencing error (e.g., errors in sample prep and/or sequencer read and/or sequencer output, etc.) causing one or more of these apparent variations. Accordingly, when a read varies from the reference, such as by an SNP or indel, this may be because the reference differs from the true DNA sequence sampled, or because the read differs from the true DNA sequence sampled. The problem is to figure out how to correctly align the reads to the reference genome given the fact that in all likelihood the two sequences are going to vary from one another in a multiplicity of different ways. 
     In various instances, the input into an alignment function, such as from a mapping function, such as a prefix/suffix tree, or a Burrows/Wheeler transform, or a hash table and/or hash function, may be a list of possibilities as to where one or more reads may match to one or more positions of one or more reference sequences. For instance, for any given read, it may match any number of positions in the reference genome, such as at  1  location or 16, or 32, or 64, or 100, or 500, or 1,000 or more locations where a given read maps to in the genome. However, any individual read was derived, e.g., sequenced, from only one specific portion of the genome. Hence, in order to find the true location from where a given particular read was derived, an alignment function may be performed, e.g., a Smith-Waterman gapped or gapless alignment, a Needleman-Wunsch alignment, etc., so as to determine where in the genome one or more of the reads was actually derived, such as by comparing all of the possible locations where a match occurs and determining which of all the possibilities is the most likely location in the genome from which the read was sequenced, on the basis of which location&#39;s alignment score is greatest. 
     As indicated, typically, an algorithm is used to perform such an alignment function. For example, a Smith-Waterman and/or a Needleman-Wunsch alignment algorithm may be employed to align two or more sequences against one another. In this instance, they may be employed in a manner so as to determine the probabilities that for any given position where the read maps to the reference genome that the mapping is in fact the position from where the read originated. Typically these algorithms are configured so as to be performed by software, however, in various instances, such as herein presented, one or more of these algorithms can be configured so as to be executed in hardware, as described in greater detail herein below. 
     In particular, the alignment function operates, at least in part, to align one or more, e.g., all, of the reads to the reference genome despite the presence of one or more portions of mismatches, e.g., SNPs, insertions, deletions, structural artifacts, etc. so as to determine where the reads are likely to fit in the genome correctly. For instance, the one or more reads are compared against the reference genome, and the best possible fit for the read against the genome is determined, while accounting for substitutions and/or indels and/or structural variants. However, to better determine which of the modified versions of the read best fits against the reference genome, the proposed changes must be accounted for, and as such a scoring function may also be performed. 
     For example, a scoring function may be performed, e.g., as part of an overall alignment function, whereby as the alignment module performs its function and introduces one or more changes into a sequence being compared to another, e.g., so as to achieve a better or best fit between the two, for each change that is made so as to achieve the better alignment, a number is detracted from a starting score, e.g., either a perfect score, or a zero starting score, in a manner such that as the alignment is performed the score for the alignment is also determined, such as where matches are detected the score is increased, and for each change introduced a penalty is incurred, and thus, the best fit for the possible alignments can be determined, for example, by figuring out which of all the possible modified reads fits to the genome with the highest score. Accordingly, in various instances, the alignment function may be configured to determine the best combination of changes that need to be made to the read(s) to achieve the highest scoring alignment, which alignment may then be determined to be the correct or most likely alignment. 
     In view of the above, there are, therefore, at least two goals that may be achieved from performing an alignment function. One is a report of the best alignment, including position in the reference genome and a description of what changes are necessary to make the read match the reference segment at that position, and the other is the alignment quality score. For instance, in various instances, the output from a the alignment module may be a Compact Idiosyncratic Gapped Alignment Report, e.g., a CIGAR string, wherein the CIGAR string output is a report detailing all the changes that were made to the reads so as to achieve their best fit alignment, e.g., detailed alignment instructions indicating how the query actually aligns with the reference. Such a CIGAR string readout may be useful in further stages of processing so as to better determine that for the given subject&#39;s genomic nucleotide sequence, the predicted variations as compared against a reference genome are in fact true variations, and not just due to machine, software, or human error. 
     As set forth above, in various embodiments, alignment is typically performed in a sequential manner, wherein the algorithm and/or firmware receives read sequence data, such as from a mapping module, pertaining to a read and one or more possible locations where the read may potentially map to the one or more reference genomes, and further receives genomic sequence data, such as from one or more memories, such as associated DRAMs, pertaining to the one or more positions in the one or more reference genomes to which the read may map. In particular, in various embodiments, the mapping module processes the reads, such as from a FASTQ file, and maps each of them to one or more positions in the reference genome to where they may possibly align. The aligner then takes these predicted positions and uses them to align the reads to the reference genome, such as by building a virtual array by which the reads can be compared with the reference genome. 
     In performing this function the aligner evaluates each mapped position for each individual read and particularly evaluates those reads that map to multiple possible locations in the reference genome and scores the possibility that each position is the correct position. It then compares the best scores, e.g., the two best scores, and makes a decision as to where the particular read actually aligns. For instance, in comparing the first and second best alignment scores, the aligner looks at the difference between the scores, and if the difference between them is great, then the confidence score that the one with the bigger score is correct will be high. However, where the difference between them is small, e.g., zero, then the confidence score in being able to tell from which of the two positions the read actually is derived is low, and more processing may be useful in being able to clearly determine the true location in the reference genome from where the read is derived. 
     Hence, the aligner in part is looking for the biggest difference between the first and second best confidence scores in making its call that a given read maps to a given location in the reference genome. Ideally, the score of the best possible choice of alignment is significantly greater than the score for the second best alignment for that sequence. There are many different ways an alignment scoring methodology may be implemented, for instance, each cell of the array may be scored or a sub-portion of cells may be scored, such as in accordance with the methods disclosed herein. In various instances, scoring parameters for nucleotide matches, nucleotide mismatches, insertions, and deletions may have any various positive or negative or zero values. In various instances, these scoring parameters may be modified based on available information. For instance, accurate alignments may be achieved by making scoring parameters, including any or all of nucleotide match scores, nucleotide mismatch scores, gap (insertion and/or deletion) penalties, gap open penalties, and/or gap extend penalties, vary according to a base quality score associated with the current read nucleotide or position. For example, score bonuses and/or penalties could be made smaller when a base quality score indicates a high probability a sequencing or other error being present. Base quality sensitive scoring may be implemented, for example, using a fixed or configurable lookup-table, accessed using a base quality score, which returns corresponding scoring parameters. 
     In a hardware implementation in an integrated circuit, such as an FPGA or ASIC, a scoring wave front may be implemented as a linear array of scoring cells, such as 16 cells, or 32 cells, or 64 cells, or 128 cells or the like. Each of the scoring cells may be built of digital logic elements in a wired configuration to compute alignment scores. Hence, for each step of the wave front, for instance, each clock cycle, or some other fixed or variable unit of time, each of the scoring cells, or a portion of the cells, computes the score or scores required for a new cell in the virtual alignment matrix. Notionally, the various scoring cells are considered to be in various positions in the alignment matrix, corresponding to a scoring wave front as discussed herein, e.g., along a straight line extending from bottom-left to top-right in the matrix. As is well understood in the field of digital logic design, the physical scoring cells and their comprised digital logic need not be physically arranged in like manner on the integrated circuit. 
     Accordingly, as the wave front takes steps to sweep through the virtual alignment matrix, the notional positions of the scoring cells correspondingly update each cell, for example, notionally “moving” a step to the right, or for example, a step downward in the alignment matrix. All scoring cells make the same relative notional movement, keeping the diagonal wave front arrangement intact. Each time the wave front moves to a new position, e.g., with a vertical downward step, or a horizontal rightward step in the matrix, the scoring cells arrive in new notional positions, and compute alignment scores for the virtual alignment matrix cells they have entered. In such an implementation, neighboring scoring cells in the linear array are coupled to communicate query (read) nucleotides, reference nucleotides, and previously calculated alignment scores. The nucleotides of the reference window may be fed sequentially into one end of the wave front, e.g., the top-right scoring cell in the linear array, and may shift from there sequentially down the length of the wave front, so that at any given time, a segment of reference nucleotides equal in length to the number of scoring cells is present within the cells, one successive nucleotide in each successive scoring cell. 
     For instance, each time the wave front steps horizontally, another reference nucleotide is fed into the top-right cell, and other reference nucleotides shift down-left through the wave front. This shifting of reference nucleotides may be the underlying reality of the notional movement of the wave front of scoring cells rightward through the alignment matrix. Hence, the nucleotides of the read may be fed sequentially into the opposite end of the wave front, e.g. the bottom-left scoring cell in the linear array, and shift from there sequentially up the length of the wave front, so that at any given time, a segment of query nucleotides equal in length to the number of scoring cells is present within the cells, one successive nucleotide in each successive scoring cell. Likewise, each time the wave front steps vertically, another query nucleotide is fed into the bottom-left cell, and other query nucleotides shift up-right through the wave front. This shifting of query nucleotides is the underlying reality of the notional movement of the wave front of scoring cells downward through the alignment matrix. Accordingly, by commanding a shift of reference nucleotides, the wave front may be moved a step horizontally, and by commanding a shift of query nucleotides, the wave front may be moved a step vertically. Hence, to produce generally diagonal wave front movement, such as to follow a typical alignment of query and reference sequences without insertions or deletions, wave front steps may be commanded in alternating vertical and horizontal directions. 
     Accordingly, neighboring scoring cells in the linear array may be coupled to communicate previously calculated alignment scores. In various alignment scoring algorithms, such as a Smith-Waterman or Needleman-Wunsch, or such variant, the alignment score(s) in each cell of the virtual alignment matrix may be calculated using previously calculated scores in other cells of the matrix, such as the three cells positioned immediately to the left of the current cell, above the current cell, and diagonally up-left of the current cell. When a scoring cell calculates new score(s) for another matrix position it has entered, it must retrieve such previously calculated scores corresponding to such other matrix positions. These previously calculated scores may be obtained from storage of previously calculated scores within the same cell, and/or from storage of previously calculated scores in the one or two neighboring scoring cells in the linear array. This is because the three contributing score positions in the virtual alignment matrix (immediately left, above, and diagonally up-left) would have been scored either by the current scoring cell, or by one of its neighboring scoring cells in the linear array. 
     For instance, the cell immediately to the left in the matrix would have been scored by the current scoring cell, if the most recent wave front step was horizontal (rightward), or would have been scored by the neighboring cell down-left in the linear array, if the most recent wave front step was vertical (downward). Similarly, the cell immediately above in the matrix would have been scored by the current scoring cell, if the most recent wave front step was vertical (downward), or would have been scored by the neighboring cell up-right in the linear array, if the most recent wave front step was horizontal (rightward). Particularly, the cell diagonally up-left in the matrix would have been scored by the current scoring cell, if the most recent two wave front steps were in different directions, e.g., down then right, or right then down, or would have been scored by the neighboring cell up-right in the linear array, if the most recent two wave front steps were both horizontal (rightward), or would have been scored by the neighboring cell down-left in the linear array, if the most recent two wave front steps were both vertical (downward). 
     Accordingly, by considering information on the last one or two wave front step directions, a scoring cell may select the appropriate previously calculated scores, accessing them within itself, and/or within neighboring scoring cells, utilizing the coupling between neighboring cells. In a variation, scoring cells at the two ends of the wave front may have their outward score inputs hard-wired to invalid, or zero, or minimum-value scores, so that they will not affect new score calculations in these extreme cells. A wave front being thus implemented in a linear array of scoring cells, with such coupling for shifting reference and query nucleotides through the array in opposing directions, in order to notionally move the wave front in vertical and horizontal, e.g., diagonal, steps, and coupling for accessing scores previously computed by neighboring cells in order to compute alignment score(s) in new virtual matrix cell positions entered by the wave front, it is accordingly possible to score a band of cells in the virtual matrix, the width of the wave front, such as by commanding successive steps of the wave front to sweep it through the matrix. 
     For a new read and reference window to be aligned, therefore, the wave front may begin positioned inside the scoring matrix, or, advantageously, may gradually enter the scoring matrix from outside, beginning e.g., to the left, or above, or diagonally left and above the top-left corner of the matrix. For instance, the wave front may begin with its top-left scoring cell positioned just left of the top-left cell of the virtual matrix, and the wave front may then sweep rightward into the matrix by a series of horizontal steps, scoring a horizontal band of cells in the top-left region of the matrix. When the wave front reaches a predicted alignment relationship between the reference and query, or when matching is detected from increasing alignment scores, the wave front may begin to sweep diagonally down-right, by alternating vertical and horizontal steps, scoring a diagonal band of cells through the middle of the matrix. When the bottom-left wave front scoring cell reaches the bottom of the alignment matrix, the wave front may begin sweeping rightward again by successive horizontal steps, until some or all wave front cells sweep out of the boundaries of the alignment matrix, scoring a horizontal band of cells in the bottom-right region of the matrix. 
     One or more of such alignment procedures may be performed by any suitable alignment algorithm, such as a Needleman-Wunsch alignment algorithm and/or a Smith-Waterman alignment algorithm that may have been modified to accommodate the functionality herein described. In general both of these algorithms and those like them basically perform, in some instances, in a similar manner. For instance, as set forth above, these alignment algorithms typically build the virtual array in a similar manner such that, in various instances, the horizontal top boundary may be configured to represent the genomic reference sequence, which may be laid out across the top row of the array according to its base pair composition. Likewise, the vertical boundary may be configured to represent the sequenced and mapped query sequences that have been positioned in order, downwards along the first column, such that their nucleotide sequence order is generally matched to the nucleotide sequence of the reference to which they mapped. The intervening cells may then be populated with scores as to the probability that the relevant base of the query at a given position, is positioned at that location relative to the reference. In performing this function, a swath may be moved diagonally across the matrix populating scores within the intervening cells and the probability for each base of the query being in the indicated position may be determined. 
     With respect to a Needleman-Wunsch alignment function, which generates optimal global (or semi-global) alignments, aligning the entire read sequence to some segment of the reference genome, the wave front steering may be configured such that it typically sweeps all the way from the top edge of the alignment matrix to the bottom edge. When the wave front sweep is complete, the maximum score on the bottom edge of the alignment matrix (corresponding to the end of the read) is selected, and the alignment is backtraced to a cell on the top edge of the matrix (corresponding to the beginning of the read). In various of the instances disclosed herein, the reads can be any length long, can be any size, and there need not be extensive read parameters as to how the alignment is performed, e.g., in various instances, the read can be as long as a chromosome. In such an instance, however, the memory size and chromosome length may be limiting factor. 
     With respect to a Smith-Waterman algorithm, which generates optimal local alignments, aligning the entire read sequence or part of the read sequence to some segment of the reference genome, this algorithm may be configured for finding the best scoring possible based on a full or partial alignment of the read. Hence, in various instances, the wave front-scored band may not extend to the top and/or bottom edges of the alignment matrix, such as if a very long read had only seeds in its middle mapping to the reference genome, but commonly the wave front may still score from top to bottom of the matrix. Local alignment is typically achieved by two adjustments. First, alignment scores are never allowed to fall below zero (or some other floor), and if a cell score otherwise calculated would be negative, a zero score is substituted, representing the start of a new alignment. Second, the maximum alignment score produced in any cell in the matrix, not necessarily along the bottom edge, is used as the terminus of the alignment. The alignment is backtraced from this maximum score up and left through the matrix to a zero score, which is used as the start position of the local alignment, even if it is not on the top row of the matrix. 
     In view of the above, there are several different possible pathways through the virtual array. In various embodiments, the wave front starts from the upper left corner of the virtual array, and moves downwards towards identifiers of the maximum score. For instance, the results of all possible aligns can be gathered, processed, correlated, and scored to determine the maximum score. When the end of a boundary or the end of the array has been reached and/or a computation leading to the highest score for all of the processed cells is determined (e.g., the overall highest score identified) then a backtrace may be performed so as to find the pathway that was taken to achieve that highest score. For example, a pathway that leads to a predicted maximum score may be identified, and once identified an audit may be performed so as to determine how that maximum score was derived, for instance, by moving backwards following the best score alignment arrows retracing the pathway that led to achieving the identified maximum score, such as calculated by the wave front scoring cells. 
     This backwards reconstruction or backtrace involves starting from a determined maximum score, and working backward through the previous cells navigating the path of cells having the scores that led to achieving the maximum score all the way up the table and back to an initial boundary, such as the beginning of the array, or a zero score in the case of local alignment. During a backtrace, having reached a particular cell in the alignment matrix, the next backtrace step is to the neighboring cell, immediately leftward, or above, or diagonally up-left, which contributed the best score that was selected to construct the score in the current cell. In this manner, the evolution of the maximum score may be determined, thereby figuring out how the maximum score was achieved. The backtrace may end at a corner, or an edge, or a boundary, or may end at a zero score, such as in the upper left hand corner of the array. Accordingly, it is such a back trace that identifies the proper alignment and thereby produces the CIGAR strand readout that represents how the sample genomic sequence derived from the individual, or a portion thereof, matches to, or otherwise aligns with, the genomic sequence of the reference DNA. 
     Once it has been determined where each read is mapped, and further determined where each read is aligned, e.g., each relevant read has been given a position and a quality score reflecting the probability that the position is the correct alignment, such that the nucleotide sequence for the subject&#39;s DNA is known, then the order of the various reads and/or genomic nucleic acid sequence of the subject may be verified, such as by performing a back trace function moving backwards up through the array so as to determine the identity of every nucleic acid in its proper order in the sample genomic sequence. Consequently, in some aspects, the present disclosure is directed to a back trace function, such as is part of an alignment module that performs both an alignment and a back trace function, such as a module that may be part of a pipeline of modules, such as a pipeline that is directed at taking raw sequence read data, such as form a genomic sample form an individual, and mapping and/or aligning that data, which data may then be sorted. 
     To facilitate the backtrace operation, it is useful to store a scoring vector for each scored cell in the alignment matrix, encoding the score-selection decision. For classical Smith-Waterman and/or Needleman-Wunsch scoring implementations with linear gap penalties, the scoring vector can encode four possibilities, which may optionally be stored as a 2-bit integer from 0 to 3, for example: 0=new alignment (null score selected); 1=vertical alignment (score from the cell above selected, modified by gap penalty); 2=horizontal alignment (score from the cell to the left selected, modified by gap penalty); 3=diagonal alignment (score from the cell up and left selected, modified by nucleotide match or mismatch score). Optionally, the computed score(s) for each scored matrix cell may also be stored (in addition to the maximum achieved alignment score which is standardly stored), but this is not generally necessary for backtrace, and can consume large amounts of memory. Performing backtrace then becomes a matter of following the scoring vectors; when the backtrace has reached a given cell in the matrix, the next backtrace step is determined by the stored scoring vector for that cell, e.g.: 0=terminate backtrace; 1=backtrace upward; 2=backtrace leftward; 3=backtrace diagonally up-left. 
     Such scoring vectors may be stored in a two-dimensional table arranged according to the dimensions of the alignment matrix, wherein only entries corresponding to cells scored by the wave front are populated. Alternatively, to conserve memory, more easily record scoring vectors as they are generated, and more easily accommodate alignment matrices of various sizes, scoring vectors may be stored in a table with each row sized to store scoring vectors from a single wave front of scoring cells, e.g. 128 bits to store 64 2-bit scoring vectors from a 64-cell wave front, and a number of rows equal to the maximum number of wave front steps in an alignment operation. Additionally, for this option, a record may be kept of the directions of the various wavefront steps, e.g., storing an extra, e.g., 129.sup.th, bit in each table row, encoding e.g., 0 for vertical wavefront step preceding this wavefront position, and 1 for horizontal wavefront step preceding this wavefront position. This extra bit can be used during backtrace to keep track of which virtual scoring matrix positions the scoring vectors in each table row correspond to, so that the proper scoring vector can be retrieved after each successive backtrace step. When a backtrace step is vertical or horizontal, the next scoring vector should be retrieved from the previous table row, but when a backtrace step is diagonal, the next scoring vector should be retrieved from two rows previous, because the wavefront had to take two steps to move from scoring any one cell to scoring the cell diagonally right-down from it. 
     In the case of affine gap scoring, scoring vector information may be extended, e.g. to 4 bits per scored cell. In addition to the e.g., 2-bit score-choice direction indicator, two 1-bit flags may be added, a vertical extend flag, and a horizontal extend flag. According to the methods of affine gap scoring extensions to Smith-Waterman or Needleman-Wunsch or similar alignment algorithms, for each cell, in addition to the primary alignment score representing the best-scoring alignment terminating in that cell, a ‘vertical score’ should be generated, corresponding to the maximum alignment score reaching that cell with a final vertical step, and a ‘horizontal score’ should be generated, corresponding to the maximum alignment score reaching that cell with a final horizontal step; and when computing any of the three scores, a vertical step into the cell may be computed either using the primary score from the cell above minus a gap-open penalty, or using the vertical score from the cell above minus a gap-extend penalty, whichever is greater; and a horizontal step into the cell may be computed either using the primary score from the cell to the left minus a gap-open penalty, or using the horizontal score from the cell to the left minus a gap-extend penalty, whichever is greater. In cases where the vertical score minus a gap extend penalty is selected, the vertical extend flag in the scoring vector should be set, e.g. ‘1’, and otherwise it should be unset, e.g. ‘0’. 
     In cases when the horizontal score minus a gap extend penalty is selected, the horizontal extend flag in the scoring vector should be set, e.g. ‘1’, and otherwise it should be unset, e.g. ‘0’. During backtrace for affine gap scoring, any time backtrace takes a vertical step upward from a given cell, if that cell&#39;s scoring vector&#39;s vertical extend flag is set, the following backtrace step must also be vertical, regardless of the scoring vector for the cell above. Likewise, any time backtrace takes a horizontal step leftward from a given cell, if that cell&#39;s scoring vector&#39;s horizontal extend flag is set, the following backtrace step must also be horizontal, regardless of the scoring vector for the cell to the left. Accordingly, such a table of scoring vectors, e.g. 129 bits per row for 64 cells using linear gap scoring, or 257 bits per row for 64 cells using affine gap scoring, with some number NR of rows, is adequate to support backtrace after concluding alignment scoring where the scoring wavefront took NR steps or fewer. 
     For example, when aligning 300-nucleotide reads, the number of wavefront steps required may always be less than 1024, so the table may be 257.times.1024 bits, or approximately 32 kilobytes, which in many cases may be a reasonable local memory inside the integrated circuit. But if very long reads are to be aligned, e.g. 100,000 nucleotides, the memory requirements for scoring vectors may be quite large, e.g. 8 megabytes, which may be very costly to include as local memory inside the integrated circuit. For such support, scoring vector information may be recorded to bulk memory outside the integrated circuit, e.g. DRAM, but then the bandwidth requirements, e.g. 257 bits per clock cycle per aligner module, may be excessive, which may bottleneck and dramatically reduce aligner performance. Accordingly, it is desirable to have a method for disposing of scoring vectors before completing alignment, so their storage requirements can be kept bounded, e.g. to perform incremental backtraces, generating incremental partial CIGAR strings for example, from early portions of an alignment&#39;s scoring vector history, so that such early portions of the scoring vectors may then be discarded. The challenge is that the backtrace is supposed to begin in the alignment&#39;s terminal, maximum scoring cell, which unknown until the alignment scoring completes, so any backtrace begun before alignment completes may begin from the wrong cell, not along the eventual final optimal alignment path. 
     Hence, a method is given for performing incremental backtrace from partial alignment information, e.g., comprising partial scoring vector information for alignment matrix cells scored so far. From a currently completed alignment boundary, e.g., a particular scored wave front position, backtrace is initiated from all cell positions on the boundary. Such backtrace from all boundary cells may be performed sequentially, or advantageously, especially in a hardware implementation, all the backtraces may be performed together. It is not necessary to extract alignment notations, e.g., CIGAR strings, from these multiple backtraces; only to determine what alignment matrix positions they pass through during the backtrace. In an implementation of simultaneous backtrace from a scoring boundary, a number of 1-bit registers may be utilized, corresponding to the number of alignment cells, initialized e.g., all to ‘1’s, representing whether any of the backtraces pass through a corresponding position. For each step of simultaneous backtrace, scoring vectors corresponding to all the current ‘1’s in these registers, e.g. from one row of the scoring vector table, can be examined, to determine a next backtrace step corresponding to each ‘1’ in the registers, leading to a following position for each ‘1’ in the registers, for the next simultaneous backtrace step. 
     Importantly, it is easily possible for multiple ‘1’s in the registers to merge into common positions, corresponding to multiple of the simultaneous backtraces merging together onto common backtrace paths. Once two or more of the simultaneous backtraces merge together, they remain merged indefinitely, because henceforth they will utilize scoring vector information from the same cell. It has been observed, empirically and for theoretical reasons, that with high probability, all of the simultaneous backtraces merge into a singular backtrace path, in a relatively small number of backtrace steps, which e.g. may be a small multiple, e.g.  8 , times the number of scoring cells in the wavefront. For example, with a 64-cell wavefront, with high probability, all backtraces from a given wavefront boundary merge into a single backtrace path within 512 backtrace steps. Alternatively, it is also possible, and not uncommon, for all backtraces to terminate within the number, e.g. 512, of backtrace steps. 
     Accordingly, the multiple simultaneous backtraces may be performed from a scoring boundary, e.g. a scored wavefront position, far enough back that they all either terminate or merge into a single backtrace path, e.g. in 512 backtrace steps or fewer. If they all merge together into a singular backtrace path, then from the location in the scoring matrix where they merge, or any distance further back along the singular backtrace path, an incremental backtrace from partial alignment information is possible. Further backtrace from the merge point, or any distance further back, is commenced, by normal singular backtrace methods, including recording the corresponding alignment notation, e.g., a partial CIGAR string. This incremental backtrace, and e.g., partial CIGAR string, must be part of any possible final backtrace, and e.g., full CIGAR string, that would result after alignment completes, unless such final backtrace would terminate before reaching the scoring boundary where simultaneous backtrace began, because if it reaches the scoring boundary, it must follow one of the simultaneous backtrace paths, and merge into the singular backtrace path, now incrementally extracted. 
     Therefore, all scoring vectors for the matrix regions corresponding to the incrementally extracted backtrace, e.g., in all table rows for wave front positions preceding the start of the extracted singular backtrace, may be safely discarded. When the final backtrace is performed from a maximum scoring cell, if it terminates before reaching the scoring boundary (or alternatively, if it terminates before reaching the start of the extracted singular backtrace), the incremental alignment notation, e.g. partial CIGAR string, may be discarded. If the final backtrace continues to the start of the extracted singular backtrace, its alignment notation, e.g., CIGAR string, may then be grafted onto the incremental alignment notation, e.g., partial CIGAR string. Furthermore, in a very long alignment, the process of performing a simultaneous backtrace from a scoring boundary, e.g., scored wave front position, until all backtraces terminate or merge, followed by a singular backtrace with alignment notation extraction, may be repeated multiple times, from various successive scoring boundaries. The incremental alignment notation, e.g. partial CIGAR string, from each successive incremental backtrace may then be grafted onto the accumulated previous alignment notations, unless the new simultaneous backtrace or singular backtrace terminates early, in which case accumulated previous alignment notations may be discarded. The eventual final backtrace likewise grafts its alignment notation onto the most recent accumulated alignment notations, for a complete backtrace description, e.g. CIGAR string. 
     Accordingly, in this manner, the memory to store scoring vectors may be kept bounded, assuming simultaneous backtraces always merge together in a bounded number of steps, e.g. 512 steps. In rare cases where simultaneous backtraces fail to merge or terminate in the bounded number of steps, various exceptional actions may be taken, including failing the current alignment, or repeating it with a higher bound or with no bound, perhaps by a different or traditional method, such as storing all scoring vectors for the complete alignment, such as in external DRAM. In a variation, it may be reasonable to fail such an alignment, because it is extremely rare, and even rarer that such a failed alignment would have been a best-scoring alignment to be used in alignment reporting. 
     In an optional variation, scoring vector storage may be divided, physically or logically, into a number of distinct blocks, e.g. 512 rows each, and the final row in each block may be used as a scoring boundary to commence a simultaneous backtrace. Optionally, a simultaneous backtrace may be required to terminate or merge within the single block, e.g. 512 steps. Optionally, if simultaneous backtraces merge in fewer steps, the merged backtrace may nevertheless be continued through the whole block, before commencing an extraction of a singular backtrace in the previous block. Accordingly, after scoring vectors are fully written to block N, and begin writing to block N+1, a simultaneous backtrace may commence in block N, followed by a singular backtrace and alignment notation extraction in block N−1. If the speed of the simultaneous backtrace, the singular backtrace, and alignment scoring are all similar or identical, and can be performed simultaneously, e.g., in parallel hardware in an integrated circuit, then the singular backtrace in block N−1 may be simultaneous with scoring vectors filling block N+2, and when block N+3 is to be filled, block N−1 may be released and recycled. 
     Thus, in such an implementation, a minimum of 4 scoring vector blocks may be employed, and may be utilized cyclically. Hence, the total scoring vector storage for an aligner module may be 4 blocks of 257×512 bits each, for example, or approximately 64 kilobytes. In a variation, if the current maximum alignment score corresponds to an earlier block than the current wavefront position, this block and the previous block may be preserved rather than recycled, so that a final backtrace may commence from this position if it remains the maximum score; having an extra 2 blocks to keep preserved in this manner brings the minimum, e.g., to 6 blocks. 
     In another variation, to support overlapped alignments, the scoring wave front crossing gradually from one alignment matrix to the next as described above, additional blocks, e.g. 1 or 2 additional blocks, may be utilized, e.g., 8 blocks total, e.g., approximately 128 kilobytes. Accordingly, if such a limited number of blocks, e.g., 4 blocks or 8 blocks, is used cyclically, alignment and backtrace of arbitrarily long reads is possible, e.g., 100,000 nucleotides, or an entire chromosome, without the use of external memory for scoring vectors. It is to be understood, such as with reference to the above, that although a mapping function may in some instances have been described, such as with reference to a mapper, and/or an alignment function may have in some instances been described, such as with reference to an aligner, these different functions may be performed sequentially by the same architecture, which has commonly been referenced in the art as an aligner. Accordingly, in various instances, both the mapping function and the aligning function, as herein described may be performed by a common architecture that may be understood to be an aligner, especially in those instances wherein to perform an alignment function, a mapping function need first be performed. 
     In various instances, the devices, systems, and their methods of use of the present disclosure may be configured for performing one or more of a full-read gapless and/or gapped alignments that may then be scored so as to determine the appropriate alignment for the reads in the dataset. For instance, in various instances, a gapless alignment procedure may be performed on data to be processed, which gapless alignment procedure may then be followed by one or more of a gapped alignment, and/or by a selective Smith-Waterman alignment procedure. For instance, in a first step, a gapless alignment chain may be generated. As described herein, such gapless alignment functions may be performed quickly, such as without the need for accounting for gaps, which after a first step of performing a gapless alignment, may then be followed by then performing a gapped alignment. 
     For example, an alignment function may be performed in order to determine how any given nucleotide sequence, e.g., read, aligns to a reference sequence without the need for inserting gaps in one or more of the reads and/or reference. An important part of performing such an alignment function is determining where and how there are mismatches in the sequence in question versus the sequence of the reference genome. However, because of the great homology within the human genome, in theory, any given nucleotide sequence is going to largely match a representative reference sequence. Where there are mismatches, these will likely be due to a single nucleotide polymorphism, which is relatively easy to detect, or they will be due to an insertion or deletion in the sequences in question, which are much more difficult to detect. 
     Consequently, in performing an alignment function, the majority of the time, the sequence in question is going to match the reference sequence, and where there is a mismatch due to an SNP, this will easily be determined. Hence, a relatively large amount of processing power is not required to perform such analysis. Difficulties arise, however, where there are insertions or deletions in the sequence in question with respect to the reference sequence, because such insertions and deletions amount to gaps in the alignment. Such gaps require a more extensive and complicated processing platform so as to determine the correct alignment. Nevertheless, because there will only be a small percentage of indels, only a relatively smaller percentage of gapped alignment protocols need be performed as compared to the millions of gapless alignments performed. Hence, only a small percentage of all of the gapless alignment functions result in a need for further processing due to the presence of an indel in the sequence, and therefore will need a gapped alignment. 
     When an indel is indicated in a gapless alignment procedure, only those sequences get passed on to an alignment engine for further processing, such as an alignment engine configured for performing an advanced alignment function, such as a Smith Waterman alignment (SWA). Thus, because either a gapless or a gapped alignment is to be performed, the devices and systems disclosed herein are a much more efficient use of resources. More particularly, in certain embodiments, both a gapless and a gapped alignment may be performed on a given selection of sequences, e.g., one right after the other, then the results are compared for each sequence, and the best result is chosen. Such an arrangement may be implemented, for instance, where an enhancement in accuracy is desired, and an increased amount of time and resources for performing the required processing is acceptable. 
     Particularly, in various instances, a first alignment step may be performed without engaging a processing intensive Smith Waterman function. Hence, a plurality of gapless alignments may be performed in a less resource intensive, less time consuming manner, and because less resources are needed less space need be dedicated for such processing on the chip. Thus, more processing may be performed, using less processing elements, requiring less time, therefore, more alignments can be done, and better accuracy can be achieved. More particularly, less chip resource-implementations for performing Smith Waterman alignments need be dedicated using less chip area, as it does not require as much chip area for the processing elements required to perform gapless alignments as it does for performing a gapped alignment. As the chip resource requirements go down, the more processing can be performed in a shorter period of time, and with the more processing that can be performed, the better the accuracy can be achieved. 
     Accordingly, in such instances, a gapless alignment protocol, e.g., to be performed by suitably configured gapless alignment resources, may be employed. For example, as disclosed herein, in various embodiments, an alignment processing engine is provided such as where the processing engine is configured for receiving digital signals, e.g., representing one or more reads of genomic data, such as digital data denoting one or more nucleotide sequences, from an electronic data source, and mapping and/or aligning that data to a reference sequence, such as by first performing a gapless alignment function on that data, which gapless alignment function may then be followed, if necessary, by a gapped alignment function, such as by performing a Smith Waterman alignment protocol. 
     Consequently, in various instances, a gapless alignment function is performed on a contiguous portion of the read, e.g., employing a gapless aligner, and if the gapless alignment goes from end to end, e.g., the read is complete, a gapped alignment is not performed. However, if the results of the gapless alignment are indicative of their being an indel present, e.g., the read is clipped or otherwise incomplete, then a gapped alignment may be performed. Thus, the ungapped alignment results may be used to determine if a gapped alignment is needed, for instance, where the ungapped alignment is extended into a gap region but does not extend the entire length of the read, such as where the read may be clipped, e.g., soft clipped to some degree, and where clipped then a gapped alignment may be performed. 
     Hence, in various embodiments, based on the completeness and alignment scores, it is only if the gapless alignment ends up being clipped, e.g., does not go end to end, that a gapped alignment is performed. More particularly, in various embodiments, the best identifiable gapless and/or gapped alignment score may be estimated and used as a cutoff line for deciding if the score is good enough to warrant further analysis, such as by performing a gapped alignment. Thus, the completeness of alignment, and its score, may be employed such that a high score is indicative of the alignment being complete, and therefore, ungapped, and a lower score is indicative of the alignment not being complete, and a gapped alignment needing to be performed. Hence, where a high score is attained a gapped alignment is not performed, but only when the score is low enough is the gapped alignment performed. Of course, in various instances a brute force alignment approach may be employed such that the number of gapped and/or gapless aligners are deployed in the chip architecture, so as to allow for a greater number of alignments to be performed, and thus a larger amount of data may be looked at. 
     More particularly, in various embodiments, each mapping and/or aligning engine may include one or more, e.g., two Smith-Waterman, aligner modules. In certain instances, these modules may be configured so as to support global (end-to-end) gapless alignment and/or local (clipped) gapped alignment, perform affine gap scoring, and can be configured for generating unclipped score bonuses at each end. Base-quality sensitive match and mismatch scoring may also be supported. Where two alignment modules are included, e.g., as part of the integrated circuit, for example, each Smith-Waterman aligner may be constructed as an anti-diagonal wavefront of scoring cells, which wavefront ‘moves’ through a virtual alignment rectangle, scoring cells that it sweeps through. 
     However, for longer reads, the Smith-Waterman wavefront may also be configured to support automatic steering, so as to track the best alignment through accumulated indels, such as to ensure that the alignment wavefront and cells being scored do not escape the scoring band. In the background, logic engines may be configured to examine current wavefront scores, find the maximums, flag the subsets of cells over a threshold distance below the maximum, and target the midpoint between the two extreme flags. In such an instance, auto-steering may be configured to run diagonally when the target is at the wavefront center, but may be configured to run straight horizontally or vertically as needed to re-center the target if it drifts, such as due to the presence of indels. 
     The output from the alignment module is a SAM (Text) or BAM (e.g., binary version of a SAM) file along with a mapping quality score (MAPA), which quality score reflects the confidence that the predicted and aligned location of the read to the reference is actually where the read is derived. Accordingly, once it has been determined where each read is mapped, and further determined where each read is aligned, e.g., each relevant read has been given a position and a quality score reflecting the probability that the position is the correct alignment, such that the nucleotide sequence for the subject&#39;s DNA is known as well as how the subject&#39;s DNA differs from that of the reference (e.g., the CIGAR string has been determined), then the various reads representing the genomic nucleic acid sequence of the subject may be sorted by chromosome location, so that the exact location of the read on the chromosomes may be determined. Consequently, in some aspects, the present disclosure is directed to a sorting function, such as may be performed by a sorting module, which sorting module may be part of a pipeline of modules, such as a pipeline that is directed at taking raw sequence read data, such as form a genomic sample form an individual, and mapping and/or aligning that data, which data may then be sorted. 
     More particularly, once the reads have been assigned a position, such as relative to the reference genome, which may include identifying to which chromosome the read belongs and/or its offset from the beginning of that chromosome, the reads may be sorted by position. Sorting may be useful, such as in downstream analyses, whereby all of the reads that overlap a given position in the genome may be formed into a pile up so as to be adjacent to one another, such as after being processed through the sorting module, whereby it can be readily determined if the majority of the reads agree with the reference value or not. Hence, where the majority of reads do not agree with the reference value a variant call can be flagged. Sorting, therefore, may involve one or more of sorting the reads that align to the relatively same position, such as the same chromosome position, so as to produce a pileup, such that all the reads that cover the same location are physically grouped together; and may further involve analyzing the reads of the pileup to determine where the reads may indicate an actual variant in the genome, as compared to the reference genome, which variant may be distinguishable, such as by the consensus of the pileup, from an error, such as a machine read error or error an error in the sequencing methods which may be exhibited by a small minority of the reads. 
     Once the data has been obtained there are one or more other modules that may be run so as to clean up the data. For instance, one module that may be included, for example, in a sequence analysis pipeline, such as for determining the genomic sequence of an individual, may be a local realignment module. For example, it is often difficult to determine insertions and deletions that occur at the end of the read. This is because the Smith-Waterman or equivalent alignment process lacks enough context beyond the indel to allow the scoring to detect its presence. Consequently, the actual indel may be reported as one or more SNPs. In such an instance, the accuracy of the predicted location for any given read may be enhanced by performing a local realignment on the mapped and/or aligned and/or sorted read data. 
     In such instances, pileups may be used to help clarify the proper alignment, such as where a position in question is at the end of any given read, that same position is likely to be at the middle of some other read in the pileup. Accordingly, in performing a local realignment the various reads in a pileup may be analyzed so as to determine if some of the reads in the pile up indicate that there was an insertion or a deletion at a given position where an other read does not include the indel, or rather includes a substitution, at that position, then the indel may be inserted, such as into the reference, where it is not present, and the reads in the local pileup that overlap that region may be realigned to see if collectively a better score is achieved then when the insertion and/or deletion was not there. If there is an improvement, the whole set of reads in the pileup may be reviewed and if the score of the overall set has improved then it is clear to make the call that there really was an indel at that position. In a manner such as this, the fact that there is not enough context to more accurately align a read at the end of a chromosome, for any individual read, may be compensated for. Hence, when performing a local realignment, one or more pileups where one or more indels may be positioned are examined, and it is determined if by adding an indel at any given position the overall alignment score may be enhanced. 
     Another module that may be included, for example, in a sequence analysis pipeline, such as for determining the genomic sequence of an individual, may be a duplicate marking module. For instance, a duplicate marking function may be performed so as to compensate for chemistry errors that may occur during the sequencing phase. For example, as described above, during some sequencing procedures nucleic acid sequences are attached to beads and built up from there using labeled nucleotide bases. Ideally there will be only one read per bead. However, sometimes multiple reads become attached to a single bead and this results in an excessive number of copies of the attached read. This phenomenon is known as read duplication. 
     After an alignment is performed and the results obtained, and/or a sorting function, local realignment, and/or a de-duplication is performed, a variant call function may be employed on the resultant data. For instance, a typical variant call function or parts thereof may be configured so as to be implemented in a software and/or hardwired configuration, such as on an integrated circuit. Particularly, variant calling is a process that involves positioning all the reads that align to a given location on the reference into groupings such that all overlapping regions from all the various aligned reads form a “pile up.” Then the pileup of reads covering a given region of the reference genome are analyzed to determine what the most likely actual content of the sampled individual&#39;s DNA/RNA is within that region. This is then repeated, step wise, for every region of the genome. The determined content generates a list of differences termed “variations” or “variants” from the reference genome, each with an associated confidence level along with other metadata. 
     The most common variants are single nucleotide polymorphisms (SNPs), in which a single base differs from the reference. SNPs occur at about 1 in 1000 positions in a human genome. Next most common are insertions (into the reference) and deletions (from the reference), or “indels” collectively. These are more common at shorter lengths, but can be of any length. Additional complications arise, however, because the collection of sequenced segments (“reads”) is random, some regions will have deeper coverage than others. There are also more complex variants that include multi-base substitutions, and combinations of indels and substitutions that can be thought of as length-altering substitutions. Standard software based variant callers have difficulty identifying all of these, and with various limits on variant lengths. More specialized variant callers in both software and/or hardware are needed to identify longer variations, and many varieties of exotic “structural variants” involving large alterations of the chromosomes. 
     However, variant calling is a difficult procedure to implement in software, and worlds of magnitude more difficult to deploy in hardware. In order to account for and/or detect these types of errors, typical variant callers may perform one or more of the following tasks. For instance, they may come up with a set of hypothesis genotypes (content of the one or two chromosomes at a locus), use Bayesian calculations to estimate the posterior probability that each genotype is the truth given the observed evidence, and report the most likely genotype along with its confidence level. As such variant callers may be simple or complex. Simpler variant callers look only at the column of bases in the aligned read pileup at the precise position of a call being made. More advanced variant callers are “haplotype based callers”, which may be configured to take into account context, such as in a window, around the call being made. 
     A “haplotype” is particular DNA content (nucleotide sequence, list of variants, etc.) in a single common “strand”, e.g. one of two diploid strands in a region, and a haplotype based caller considers the Bayesian implications of which differences are linked by appearing in the same read. Accordingly, a variant call protocol, as proposed herein, may implement one or more improved functions such as those performed in a Genome Analysis Tool Kit (GATK) haplotype caller and/or using a Hidden Markov Model (HMM) tool and/or a De Bruijn Graph function, such as where one or more these functions typically employed by a GATK haplotype caller, and/or a HMM tool, and/or a De Bruijn Graph function may be implemented in software and/or in hardware. 
     More particularly, as implemented herein, various different variant call operations may be configured so as to be performed in software or hardware, and may include one or more of the following steps. For instance, variant call function may include an active region identification, such as for identifying places where multiple reads disagree with the reference, and for generating a window around the identified active region, so that only these regions may be selected for further processing. Additionally, localized haplotype assembly may take place, such as where, for each given active region, all the overlapping reads may be assembled into a “De Bruijn graph” (DBG) matrix. From this DBG, various paths through the matrix may be extracted, where each path constitutes a candidate haplotype, e.g., hypotheses, for what the true DNA sequence may be on at least one strand. Further, haplotype alignment may take place, such as where each extracted haplotype candidate may be aligned, e.g., Smith-Waterman aligned, back to the reference genome, so as to determine what variation(s) from the reference it implies. Furthermore, a read likelihood calculation may be performed, such as where each read may be tested against each haplotype, or hypothesis, to estimate a probability of observing the read assuming the haplotype was the true original DNA sampled. 
     With respect to these processes, the read likelihood calculation will typically be the most resource intensive and time consuming operation to be performed, often requiring a pair HMM evaluation. Additionally, the constructing of De Bruijn graphs for each pileup of reads, with associated operations of identifying locally and globally unique K-mers, as described below may also be resource intensive and/or time consuming. Accordingly, in various embodiments, one or more of the various calculations involved in performing one or more of these steps may be configured so as to be implemented in optimized software fashion or hardware, such as for being performed in an accelerated manner by an integrated circuit, as herein described. 
     As indicated above, in various embodiments, a Haplotype Caller of the disclosure, implemented in software and/or in hardware or a combination thereof may be configured to include one or more of the following operations: Active Region Identification, Localized Haplotype Assembly, Haplotype Alignment, Read Likelihood Calculation, and/or Genotyping. For instance, the devices, systems, and/or methods of the disclosure may be configured to perform one or more of a mapping, aligning, and/or a sorting operation on data obtained from a subject&#39;s sequenced DNA/RNA to generate mapped, aligned, and/or sorted results data. This results data may then be cleaned up, such as by performing a de duplication operation on it and/or that data may be communicated to one or more dedicated haplotype caller processing engines for performing a variant call operation, including one or more of the aforementioned steps, on that results data so as to generate a variant call file with respect thereto. Hence, all the reads that have been sequenced and/or been mapped and/or aligned to particular positions in the reference genome may be subjected to further processing so as to determine how the determined sequence differs from a reference sequence at any given point in the reference genome. 
     Accordingly, in various embodiments, a device, system, and/or method of its use, as herein disclosed, may include a variant or haplotype caller system that is implemented in a software and/or hardwired configuration to perform an active region identification operation on the obtained results data. Active region identification involves identifying and determining places where multiple reads, e.g., in a pile up of reads, disagree with a reference, and further involves generating one or more windows around the disagreements (“active regions”) such that the region within the window may be selected for further processing. For example, during a mapping and/or aligning step, identified reads are mapped and/or aligned to the regions in the reference genome where they are expected to have originated in the subject&#39;s genetic sequence. 
     However, as the sequencing is performed in such a manner so as to create an oversampling of sequenced reads for any given region of the genome, at any given position in the reference sequence may be seen a pile up of any and/all of the sequenced reads that line up and align with that region. All of these reads that align and/or overlap in a given region or pile up position may be input into the variant caller system. Hence, for any given read being analyzed, the read may be compared to the reference at its suspected region of overlap, and that read may be compared to the reference to determine if it shows any difference in its sequence from the known sequence of the reference. If the read lines up to the reference, without any insertions or deletions and all the bases are the same, then the alignment is determined to be good. 
     Hence, for any given mapped and/or aligned read, the read may have bases that are different from the reference, e.g., the read may include one or more SNPs, creating a position where a base is mismatched; and/or the read may have one or more of an insertion and/or deletion, e.g., creating a gap in the alignment. Accordingly, in any of these instances, there will be one or more mismatches that need to be accounted for by further processing. Nevertheless, to save time and increase efficiency, such further processing should be limited to those instances where a perceived mismatch is non-trivial, e.g., a non-noise difference. In determining the significance of a mismatch, places where multiple reads in a pile up disagree from the reference may be identified as an active region, a window around the active region may then be used to select a locus of disagreement that may then be subjected to further processing. The disagreement, however, should be non-trivial. This may be determined in many ways, for instance, the non-reference probability may be calculated for each locus in question, such as by analyzing base match vs mismatch quality scores, such as above a given threshold deemed to be a sufficiently significant amount of indication from those reads that disagree with the reference in a significant way. 
     For instance, if 30 of the mapped and/or aligned reads all line up and/or overlap so as to form a pile up at a given position in the reference, e.g., an active region, and only 1 or 2 out of the 30 reads disagrees with the reference, then the minimal threshold for further processing may be deemed to not have been met, and the non-agreeing read(s) can be disregarded in view of the 28 or 29 reads that do agree. However, if 3 or 4, or 5, or 10, or more of the reads in the pile up disagree, then the disagreement may be statistically significant enough to warrant further processing, and an active region around the identified region(s) of difference might be determined. In such an instance, an active region window ascertaining the bases surrounding that difference may be taken to give enhanced context to the region surrounding the difference, and additional processing steps, such as performing a Gaussian distribution and sum of non-reference probabilities distributed across neighboring positions, may be taken to further investigate and process that region to figure out if and active region should be declared and if so what variances from the reference actually are present within that region if any. Therefore, the determining of an active region identifies those regions where extra processing may be needed to clearly determine if a true variance or a read error has occurred. 
     Particularly, because in many instances it is not desirable to subject every region in a pile up of sequences to further processing, an active region can be identified whereby it is only those regions where extra processing may be needed to clearly determine if a true variance or a read error has occurred that may be determined as needing of further processing. And, as indicated above, it may be the size of the supposed variance that determines the size of the window of the active region. For instance, in various instances, the bounds of the active window may vary from 1 or 2 or about 10 or 20 or even about 25 or about 50 to about 200 or about 300, or about 500 or about 1000 bases long or more, where it is only within the bounds of the active window that further processing is taking place. Of course, the size of the active window can be any suitable length so long as it provides the context to determine the statistical importance of a difference. 
     Hence, if there is only one or two isolated differences, then the active window may only need to cover one or more to a few dozen bases in the active region so as to have enough context to make a statistical call that an actual variant is present. However, if there is a cluster or a bunch of differences, or if there are indels present for which more context is desired, then the window may be configured so as to be larger. In either instance, it may be desirable to analyze any and all the differences that might occur in clusters, so as to analyze them all in one or more active regions, because to do so can provide supporting information about each individual difference and will save processing time by decreasing the number of active windows engaged. In various instances, the active region boundaries may be determined by active probabilities that pass a given threshold, such as about 0.00001 or about 0.00001 or about 0.0001 or less to about 0.002 or about 0.02 or about 0.2 or more. And if the active region is longer than a given threshold, e.g., about 300-500 bases or 1000 bases or more, then the region can be broken up into sub-regions, such as by sub-regions defined by the locus with the lowest active probability score. 
     In various instances, after an active region is identified, a localized haplotype assembly procedure may be performed. For instance, in each active region, all the piled up and/or overlapping reads may be assembled into a “De Bruijn Graph” (DBG). A DBG may be a directed graph based on all the reads that overlapped the selected active region, which active region may be about 200 or about 300 to about 400 or about 500 bases long or more, within which active region the presence and/or identity of variants are to be determined. In various instances, as indicated above, the active region can be extended, e.g., by including another about 100 or about 200 or more bases in each direction of the locus in question so as to generate an extended active region, such as where additional context surrounding a difference may be desired. Accordingly, it is from the active region window, extended or not, that all of the reads that have portions that overlap the active region are piled up, e.g., to produce a pileup, the overlapping portions are identified, and the read sequences are threaded into the haplotype caller system and are thereby assembled together in the form of a De Bruin graph, much like the pieces of a puzzle. 
     Accordingly, for any given active window there will be reads that form a pile up such that en masse the pile up will include a sequence pathway through which the overlapping regions of the various overlapping reads in the pile up covers the entire sequence within the active window. Hence, at any given locus in the active region, there will be a plurality of reads overlapping that locus, albeit any given read may not extend the entire active region. The result of this is that various regions of various reads within a pileup are employed by the DBG in determining whether a variant actually is present or not for any given locus in the sequence within the active region. As it is within the active window that this determination is being made, it is those portions of any given read within the borders of the active window that are considered, and those portions that are outside of the active window may be discarded. 
     As indicated, it is those sections of the reads that overlap the reference within the active region that are fed into the DBG system. The DBG system then assembles the reads like a puzzle into a graph, and then for each position in the sequence, it is determined based on the collection of overlapping reads for that position, whether there is a match or a mismatch for any given, and if there is a mismatch, what the probability of that mismatch is. For instance, where there are discrete places where segments of the reads in the pile up overlap each other, they may be aligned to one another based on their areas of matching, and from stringing or stitching the matching reads together, as determined by their points of matching, it can be established for each position within that segment, whether and to what extent the reads at any given position match or mismatch each other. Hence, if two or more reads being compiled line up and match each other identically for a while, a graph having a single string will result; however, when the two or more reads come to a point of difference, a branch in the graph will form, and two or more divergent strings will result, until matching between the two or more reads resumes. 
     Hence, the pathways through the graph are often not a straight line. For instance, where the k-mers of a read varies from the k-mers of the reference and/or the k-mers from one or more overlapping reads, e.g., in the pileup, a “bubble” will be formed in the graph at the point of difference resulting in two divergent strings that will continue along two different path lines until matching between the two sequences resumes. Each vertex may be given a weighted score identifying how many times the respective k-mers overlap in all of the reads in the pileup. Particularly, each pathway extending through the generated graph from one side to the other may be given a count. And where the same k-mers are generated from a multiplicity of reads, e.g., where each k-mer has the same sequence pattern, they may be accounted for in the graph by increasing the count for that pathway where the k-mer overlaps an already existing k-mer pathway. Hence, where the same k-mer is generated from a multiplicity of overlapping reads having the same sequence, the pattern of the pathway between the graph will be repeated over and over again and the count for traversing this pathway through the graph will be increased incrementally in correspondence therewith. In such an instance, the pattern is only recorded for the first instance of the k-mer, and the count is incrementally increased for each k-mer that repeats that pattern. In this mode the various reads in the pile up can be harvested to determine what variations occur and where. 
     In a manner such as this, a graph matrix may be formed by taking all possible N base k-mers, e.g., 10 base k-mers, which can be generated from each given read by sequentially walking the length of the read in ten base segments, where the beginning of each new ten base segment is off set by one base from the last generated 10 base segment. This procedure may then be repeated by doing the same for every read in the pile up within the active window. The generated k-mers may then be aligned with one another such that areas of identical matching between the generated k-mers are matched to the areas where they overlap, so as to build up a data structure, e.g., graph, that may then be scanned and the percentage of matching and mismatching may be determined. Particularly, the reference and any previously processed k-mers aligned therewith may be scanned with respect to the next generated k-mer to determine if the instant generated k-mer matches and/or overlaps any portion of a previously generated k-mer, and where it is found to match the instant generated k-mer can then be inserted into the graph at the appropriate position. 
     Once built, the graph can be scanned and it may be determined based on this matching whether any given SNPs and/or indels in the reads with respect to the reference are likely to be an actual variation in the subject&#39;s genetic code or the result of a processing or other error. For instance, if all or a significant portion of the k-mers, of all or a significant portion of all of the reads, in a given region include the same SNP and/or indel mismatch, but differ from the reference in the same manner, then it may be determined that there is an actually SNP and/or indel variation in the subject&#39;s genome as compared to the reference genome. However, if only a limited number of k-mers from a limited number of reads evidence the artifact, it is likely to be caused by machine and/or processing and/or other error and not indicative of a true variation at the position in question. 
     As indicated, where there is a suspected variance, a bubble will be formed within the graph. Specifically, where all of the k-mers within all of a given region of reads all match the reference, they will line up in such a manner as to form a linear graph. However, where there is a difference between the bases at a given locus, at that locus of difference that graph will branch. This branching may be at any position within the k-mer, and consequently at that point of difference the 10 base k-mer, including that difference, will diverge from the rest of the k-mers in the graph. In such an instance, a new node, forming a different pathway through the graph will be formed. 
     Hence, where everything may have been agreeing, e.g., the sequence in the given new k-mer being graphed is matching the sequence to which it aligns in the graph, up to the point of difference the pathway for that k-mer will match the pathway for the graph generally and will be linear, but post the point of difference, a new pathway through the graph will emerge to accommodate the difference represented in the sequence of the newly graphed k-mer. This divergence being represented by a new node within the graph. In such an instance, any new k-mers to be added to the graph that match the newly divergent pathway will increase the count at that node. Hence, for every read that supports the arc, the count will be increased incrementally. 
     In various of such instances, the k-mer and/or the read it represents will once again start matching, e.g., after the point of divergence, such that there is now a point of convergence where the k-mer begins matching the main pathway through the graph represented by the k-mers of the reference sequence. For instance, naturally after a while the read(s) that support the branched node should rejoin the graph over time. Thus, over time, the k-mers for that read will rejoin the main pathway again. More particularly, for an SNP at a given locus within a read, the k-mer starting at that SNP will diverge from the main graph and will stay separate for about 10 nodes, because there are 10 bases per k-mer that overlap that locus of mismatching between the read and the reference. Hence, for an SNP, at the 11 th  position, the k-mers covering that locus within the read will rejoin the main pathway as exact matching is resumed. Consequently, it will take ten shifts for the k-mers of a read having an SNP at a given locus to rejoin the main graph represented by the reference sequence. 
     As indicated above, there is typically one main path or line or backbone that is the reference path, and where there is a divergence a bubble is formed at a node where there is a difference between a read and the backbone graph. Thus there are some reads that diverge from the backbone and form a bubble, which divergence may be indicative of the presence of a variant. As the graph is processed, bubbles within bubbles within bubbles may be formed along the reference backbone, so that they are stacked up and a plurality of pathways through the graph may be created. In such an instance, there may be a main path represented by the reference backbone, one path of a first divergence, and a further path of a second divergence within the first divergence, all within a given window, each pathway through the graph may represent an actual variation or may be an artifact such as caused by sequencing error, and/or PCR error, and/or a processing error, and the like. 
     Once such a graph has been produced, it must be determined which pathways through the graph represent actual variations present within the sample genome and which are mere artifacts. Albeit, it is expected that reads containing handling or machine errors will not be supported by the majority of reads in the sample pileup, however, this is not always the case. For instance, errors in PCR processing may typically be the result of a cloning mistake that occurs when preparing the DNA sample, such mistakes tend to result in an insertion and/or a deletion being added to the cloned sequence. Such indel errors may be more consistent among reads, and can wind up with generating multiple reads that have the same error from this mistake in PCR cloning. Consequently, a higher count line for such a point of divergence may result because of such errors. 
     Hence, once a graph matrix has been formed, with many paths through the graph, the next stage is to traverse and thereby extract all of the paths through the graph, e.g., left to right. One path will be the reference backbone, but there will be other paths that follow various bubbles along the way. All paths must be traversed and their count tabulated. For instance, if the graph includes a pathway with a two level bubble in one spot and a three level bubble in another spot, there will be (2×3) 6  paths through that graph. So each of the paths will individually need to be extracted, which extracted paths are termed as candidate haplotypes. Such candidate haplotypes represent theories for what could really be representative of the subject&#39;s actual DNA that was sequenced, and the following processing steps, including one or more of haplotype alignment, read likelihood calculation, and/or genotyping may be employed to test these theories so as to find out the probabilities that anyone and/or each of these theories is correct. The implementation of a De Bruijn graph reconstruction therefore represents a way to reliably extract a good set of hypotheses to test. 
     For instance, in performing a variant call function, as disclosed herein, an active region identification operation may be implemented, such as for identifying places where multiple reads in a pile up within a given region disagree with the reference, and for generating a window around the identified active region, so that only these regions may be selected for further processing. Additionally, localized haplotype assembly may take place, such as where, for each given active region, all the overlapping reads in the pile up may be assembled into a “De Bruijn graph” (DBG) matrix. From this DBG, various paths through the matrix may be extracted, where each path constitutes a candidate haplotype, e.g., hypotheses, for what the true DNA sequence may be on at least one strand. 
     Further, haplotype alignment may take place, such as where each extracted haplotype candidate may be aligned, e.g., Smith-Waterman aligned, back to the reference genome, so as to determine what variation(s) from the reference it implies. Furthermore, a read likelihood calculation may be performed, such as where each read may be tested against each haplotype, to estimate a probability of observing the read assuming the haplotype was the true original DNA sampled. Finally, a genotyping operation may be implement, and a variant call file produced. As indicated above, any or all of these operations may be configured so as to be implemented in an optimized manner in software and/or in hardware, and in various instances, because of the resource intensive and time consuming nature of building a DBG matrix and extracting candidate haplotypes therefrom, and/or because of the resource intensive and time consuming nature of performing a haplotype alignment and/or a read likelihood calculation, which may include the engagement of an Hidden Markov Model (HMM) evaluation, these operations (e.g., localized haplotype assembly, and/or haplotype alignment, and/or read likelihood calculation) or a portion thereof may be configured so as to have one or more functions of their operation implemented in a hardwired form, such as for being performed in an accelerated manner by an integrated circuit as described herein. In various instances, these tasks may be configured to be implemented by one or more quantum circuits such as in a quantum computing device. 
     Accordingly, in various instances, the devices, systems, and methods for performing the same may be configured so as to perform a haplotype alignment and/or a read likelihood calculation. For instance, as indicated, each extracted haplotype may be aligned, such as Smith-Waterman aligned, back to the reference genome, so as to determine what variation(s) from the reference it implies. In various exemplary instances, scoring may take place, such as in accordance with the following exemplary scoring parameters: a match=20.0; a mismatch=−15.0; a gap open −26.0; and a gap extend=−1.1, other scoring parameters may be used. Accordingly, in this manner, a CIGAR strand may be generated and associated with the haplotype to produce an assembled haplotype, which assembled haplotype may eventually be used to identify variants. Accordingly, in a manner such as this, the likelihood of a given read being associated with a given haplotype may be calculated for all read/haplotype combinations. In such instances, the likelihood may be calculated using a Hidden Markov Model (HMM). 
     For instance, the various assembled haplotypes may be aligned in accordance with a dynamic programing model similar to a SW alignment. In such an instance, a virtual matrix may be generated such as where the candidate haplotype, e.g., generated by the DBG, may be positioned on one axis of a virtual array, and the read may be positioned on the other axis. The matrix may then be filled out with the scores generated by traversing the extracted paths through the graph and calculating the probabilities that any given path is the true path. Hence, in such an instance, a difference in this alignment protocol from a typical SW alignment protocol is that with respect to finding the most likely path through the array, a maximum likelihood calculation is used, such as a calculation performed by an HMM model that is configured to provide the total probability for alignment of the reads to the haplotype. Hence, an actual CIGAR strand alignment, in this instance, need not be produced. Rather all possible alignments are considered and their possibilities are summed. The pair HMM evaluation is resource and time intensive, and thus, implementing its operations within a hardwired configuration within an integrated circuit or via quantum circuits on a quantum computing platform is very advantageous. 
     For example, each read may be tested against each candidate haplotype, so as to estimate a probability of observing the read assuming the haplotype is the true representative of the original DNA sampled. In various instances, this calculation may be performed by evaluating a “pair hidden Markov model” (HMM), which may be configured to model the various possible ways the haplotype candidate might have been modified, such as by PCR or sequencing errors, and the like, and a variation introduced into the read observed. In such instances, the HMM evaluation may employ a dynamic programming method to calculate the total probability of any series of Markov state transitions arriving at the observed read in view of the possibility that any divergence in the read may be the result of an error model. Accordingly, such HMM calculations may be configured to analyze all the possible SNPs and Indels that could have been introduced into one or more of the reads, such as by amplification and/or sequencing artifacts. 
     Particularly, paired HMM considers in a virtual matrix all the possible alignments of the read to the reference candidate haplotypes along with a probability associated with each of them, where all probabilities are added up. The sum of all of the probabilities of all the variants along a given path is added up to get one overarching probability for each read. This process is then performed for every pair, for every haplotype, read pair. For example, if there is a six pile up cluster overlapping a given region, e.g., a region of six haplotype candidates, and if the pile up includes about one hundred reads, 600 HMM operations will then need to be performed. More particularly, if there are 6 haplotypes then there are going to be 6 branches through the path and the probability that each one is the correct pathway that matches the subject&#39;s actual genetic code for that region must be calculated. Consequently, each pathway for all of the reads must be considered, and the probability for each read that you would arrive at this given haplotype is to be calculated. 
     The pair Hidden Markov Model is an approximate model for how a true haplotype in the sampled DNA may transform into a possible different detected read. It has been observed that these types of transformations are a combination of SNPs and indels that have been introduced into the genetic sample set by the PCR process, by one or more of the other sample preparation steps, and/or by an error caused by the sequencing process, and the like. As can be seen with respect to  FIG. 1 , to account for these types of errors, an underlying 3-state base model may be employed, such as where: (M=alignment match, I=insertion, D=deletion), further where any transition is possible except I&lt;-&gt;D. 
     As can be seen with respect to  FIG. 1 , the 3-state base model transitions are not in a time sequence, but rather are in a sequence of progression through the candidate haplotype and read sequences, beginning at position 0 in each sequence, where the first base is position 1. A transition to M implies position +1 in both sequences; a transition to I implies position +1 in the read sequence only; and a transition to D implies position +1 in the haplotype sequence only. The same 3-state model may be configured to underlie the Smith-Waterman and/or Needleman-Wunsch alignments, as herein described, as well. Accordingly, such a 3-state model, as set forth herein, may be employed in a SW and/or NW process thereby allowing for affine gap (indel) scoring, in which gap opening (entering the I or D state) is assumed to be less likely than gap extension (remaining in the I or D state). Hence, in this instance, the pair HMM can be seen as alignment, and a CIGAR string may be produced to encode a sequence of the various state transitions. 
     In various instances, the 3-state base model may be complicated by allowing the transition probabilities to vary by position. For instance, the probabilities of all M transitions may be multiplied by the prior probabilities of observing the next read base given its base quality score, and the corresponding next haplotype base. In such an instance, the base quality scores may translate to a probability of a sequencing SNP error. When the two bases match, the prior probability is taken as one minus this error probability, and when they mismatch, it is taken as the error probability divided by 3, since there are 3 possible SNP results. 
     The above discussion is regarding an abstract “Markovish” model. In various instances, the maximum-likelihood transition sequence may also be determined, which is termed herein as an alignment, and may be performed using a Needleman-Wunsch or other dynamic programming algorithm. But, in various instances, in performing a variant calling function, as disclosed herein, the maximum likelihood alignment, or any particular alignment, need not be a primary concern. Rather, the total probability may be computed, for instance, by computing the total probability of observing the read given the haplotype, which is the sum of the probabilities of all possible transition paths through the graph, from read position zero at any haplotype position, to the read end position, at any haplotype position, each component path probability being simply the product of the various constituent transition probabilities. 
     Finding the sum of pathway probabilities may also be performed by employing a virtual array and using a dynamic programming algorithm, as described above, such that in each cell of a (0 . . . N)×(0 . . . M) matrix, there are three probability values calculated, corresponding to M, D, and I transition states. (Or equivalently, there are 3 matrices.) The top row (read position zero) of the matrix may be initialized to probability 1.0 in the D states, and 0.0 in the I and M states; and the rest of the left column (haplotype position zero) may be initialized to all zeros. (In software, the initial D probabilities may be set near the double-precision max value, e.g. 2̂1020, so as to avoid underflow, but this factor may be normalized out later.) 
     This 3-to-1 computation dependency restricts the order that cells may be computed. They can be computed left to right in each row, progressing through rows from top to bottom, or top to bottom in each column, progressing rightward. Additionally, they may be computed in anti-diagonal wavefronts, where the next step is to compute all cells (n,m) where n+m equals the incremented step number. This wavefront order has the advantage that all cells in the anti-diagonal may be computed independently of each other. The bottom row of the matrix then, at the final read position, may be configured to represent the completed alignments. In such an instance, the Haplotype Caller will work by summing the I and M probabilities of all bottom row cells. In various embodiments, the system may be set up so that no D transitions are permitted within the bottom row, or a D transition probability of 0.0 may be used there, so as to avoid double counting. 
     As described herein, in various instances, each HMM evaluation may operate on a sequence pair, such as on a candidate haplotype and a read pair. For instance, within a given active region, each of a set of haplotypes may be HMM-evaluated vs. each of a set of reads. In such an instance, the software and/or hardware input bandwidth may be reduced and/or minimized by transferring the set of reads and the set of haplotypes once, and letting the software and/or hardware generate the N×M pair operations. In certain instances, a Smith-Waterman evaluator may be configured to queue up individual HMM operations, each with its own copy of read and haplotype data. A Smith-Waterman (SW) alignment module may be configured to run the pair HMM calculation in linear space or may operate in log probability space. This is useful to keep precision across the huge range of probability values with fixed-point values. However, in other instances, floating point operations may be used. 
     There are three parallel multiplications (e.g., additions in log space), then two serial additions (˜5-6 stage approximation pipelines), then an additional multiplication. In such an instance, the full pipeline may be about L=12-16 cycles long. The I &amp; D calculations may be about half the length. The pipeline may be fed a multiplicity of input probabilities, such as 2 or 3 or 5 or 7 or more input probabilities each cycle, such as from one or more already computed neighboring cells (M and/or D from the left, M and/or I from above, and/or M and/or I and/or D from above-left). It may also include one or more haplotype bases, and/or one or more read bases such as with associated parameters, e.g., pre-processed parameters, each cycle. It outputs the M &amp; I &amp; D result set for one cell each cycle, after fall-through latency. 
     As indicated above, in performing a variant call function, as disclosed herein, a De Bruijn Graph may be formulated, and when all of the reads in a pile up are identical, the DBG will be linear. However, where there are differences, the graph will form “bubbles” that are indicative of regions of differences resulting in multiple paths diverging from matching the reference alignment and then later re-joining in matching alignment. From this DBG, various paths may be extracted, which form candidate haplotypes, e.g., hypotheses for what the true DNA sequence may be on at least one strand, which hypotheses may be tested by performing an HMM, or modified HMM, operation on the data. Further still, a genotyping function may be employed such as where the possible diploid combinations of the candidate haplotypes may be formed, and for each of them, a conditional probability of observing the entire read pileup may be calculated. These results may then be fed into a Bayesian formula module to calculate an absolute probability that each genotype is the truth, given the entire read pileup observed. 
     Hence, in accordance with the devices, systems, and methods of their use described herein, in various instances, a genotyping operation may be performed, which genotyping operation may be configured so as to be implemented in an optimized manner in software and/or in hardware and/or by a quantum processing unit. For instance, the possible diploid combinations of the candidate haplotypes may be formed, and for each combination, a conditional probability of observing the entire read pileup may be calculated, such as by using the constituent probabilities of observing each read given each haplotype from the pair HMM evaluation. The results of these calculations feed into a Bayesian formula so as to calculate an absolute probability that each genotype is the truth, given the entire read pileup observed. 
     Accordingly, in various aspects, the present disclosure is directed to a system for performing a haplotype or variant call operation on generated and/or supplied data so as to produce a variant call file with respect thereto. Specifically, as described herein above, in particular instances, a variant call file may be a digital or other such file that encodes the difference between one sequence and another, such as a the difference between a sample sequence and a reference sequence. Specifically, in various instances, the variant call file may be a text file that sets forth or otherwise details the genetic and/or structural variations in a person&#39;s genetic makeup as compared to one or more reference genomes. 
     For instance, a haplotype is a set of genetic, e.g., DNA and/or RNA, variations, such as polymorphisms that reside in a person&#39;s chromosomes and as such may be passed on to offspring and thereby inherited together. Particularly, a haplotype can refer to a combination of alleles, e.g., one of a plurality of alternative forms of a gene such as may arise by mutation, which allelic variations are typically found at the same place on a chromosome. Hence, in determining the identity of a person&#39;s genome it is important to know which form of various different possible alleles a specific person&#39;s genetic sequence codes for. In particular instances, a haplotype may refer to one or more, e.g., a set, of nucleotide polymorphisms (e.g., SNPs) that may be found at the same position on the same chromosome. 
     Typically, in various embodiments, in order to determine the genotype, e.g., allelic haplotypes, for a subject, as described herein and above, a software based algorithm may be engaged, such as an algorithm employing a haplotype call program, e.g., GATK, for simultaneously determining SNPs and/or insertions and/or deletions, i.e., indels, in an individual&#39;s genetic sequence. In particular, the algorithm may involve one or more haplotype assembly protocols such as for local de-novo assembly of a haplotype in one or more active regions of the genetic sequence being processed. Such processing typically involves the deployment of a processing function called a Hidden Markov Model (HMM) that is a stochastic and/or statistical model used to exemplify randomly changing systems such as where it is assumed that future states within the system depend only on the present state and not on the sequence of events that precedes it. 
     In such instances, the system being modeled bears the characteristics or is otherwise assumed to be a Markov process with unobserved (hidden) states. In particular instances, the model may involve a simple dynamic Bayesian network. Particularly, with respect to determining genetic variation, in its simplest form, there is one of four possibilities for the identity of any given base in a sequence being processed, such as when comparing a segment of a reference sequence, e.g., a hypothetical haplotype, and that of a subject&#39;s DNA or RNA, e.g., a read derived from a sequencer. However, in order to determine such variation, in a first instance, a subject&#39;s DNA/RNA must be sequenced, e.g., via a Next Gen Sequencer (“NGS”), to produce a readout or “reads” that identify the subject&#39;s genetic code. Next, once the subject&#39;s genome has been sequenced to produce one or more reads, the various reads, representative of the subject&#39;s DNA and/or RNA need to be mapped and/or aligned, as herein described above in great detail. The next step in the process then is to determine how the genes of the subject that have just been determined, e.g., having been mapped and/or aligned, vary from that of a prototypical reference sequence. In performing such analysis, therefore, it is assumed that the read potentially representing a given gene of a subject is a representation of the prototypical haplotype albeit with various SNPs and/or indels that are to presently be determined. 
     Specifically, in particular aspects, devices, systems, and/or methods for practicing the same, such as for performing a haplotype and/or variant call function, such as deploying an HMM function, for instance, in an accelerated haplotype caller is provided. In various instances, in order to overcome these and other such various problems known in the art, the HMM accelerator herein presented may be configured to be operated in a manner so as to be implemented in software, implemented in hardware, or a combination of being implemented and/or otherwise controlled in part by software and/or in part by hardware and/or may include quantum computing implementations. For instance, in a particular aspect, the disclosure is directed to a method by which data pertaining to the DNA and/or RNA sequence identity of a subject and/or how the subject&#39;s genetic information may differ from that of a reference genome may be determined. 
     In such an instance, the method may be performed by the implementation of a haplotype or variant call function, such as employing an HMM protocol. Particularly, the HMM function may be performed in hardware, software, or via one or more quantum circuits, such as on an accelerated device, in accordance with a method described herein. In such an instance, the HMM accelerator may be configured to receive and process the sequenced, mapped, and/or aligned data, to process the same, e.g., to produce a variant call file, as well as to transmit the processed data back throughout the system. Accordingly, the method may include deploying a system where data may be sent from a processor, such as a software-controlled CPU or GPU or even a QPU, to a haplotype caller implementing an accelerated HMM, which haplotype caller may be deployed on a microprocessor chip, such as an FPGA, ASIC, or structured ASIC or implemented by one or more quantum circuits. The method may further include the steps for processing the data to produce HMM result data, which results may then be fed back to the CPU and/or GPU and/or QPU. 
     Particularly, in one embodiment, as can be seen with respect to  FIG. 2 , a bioinformatics pipeline system including an HMM accelerator is provided. For instance, in one instance, the bioinformatics pipeline system may be configured as a variant call system  1 . The system is illustrated as being implemented in hardware, but may also be implemented via one or more quantum circuits, such as of a quantum computing platform. Specifically,  FIG. 2  provides a high level view of an HMM interface structure. In particular embodiments, the variant call system  1  is configured to accelerate at least a portion of a variant call operation, such as an HMM operation. Hence, in various instances, the variant call system may be referenced herein as an HMM system  1 . The system  1  includes a server having one or more central processing units (CPU/GPU/QPU)  1000  configured for performing one or more routines related to the sequencing and/or processing of genetic information, such as for comparing a sequenced genetic sequence to one or more reference sequences. 
     Additionally, the system  1  includes a peripheral device  2 , such as an expansion card, that includes a microchip  7 , such as an FPGA, ASIC, or sASIC. In some instances, one or more quantum circuits may be provided and configured for performing the various operations set forth herein. It is also to be noted that the term ASIC may refer equally to a structured ASIC (sASIC), where appropriate. The peripheral device  2  includes an interconnect  3  and a bus interface  4 , such as a parallel or serial bus, which connects the CPU/GPU/QPU  1000  with the chip  7 . For instance, the device  2  may comprise a peripheral component interconnect, such as a PCI, PCI-X, PCIe, or QPI (quick path interconnect), and may include a bus interface  4 , that is adapted to operably and/or communicably connect the CPU/GPU/QPU  1000  to the peripheral device  2 , such as for low latency, high data transfer rates. Accordingly, in particular instances, the interface may be a peripheral component interconnect express (PCIe)  4  that is associated with the microchip  7 , which microchip includes an HMM accelerator  8 . For example, in particular instances, the HMM accelerator  8  is configured for performing an accelerated HMM function, such as where the HMM function, in certain embodiments, may at least partially be implemented in the hardware of the FPGA, AISC, or sASIC or via one or more suitably configured quantum circuits. 
     Specifically,  FIG. 2  presents a high-level figure of an HMM accelerator  8  having an exemplary organization of one or more engines  13 , such as a plurality of processing engines  13   a - 13   m+1 , for performing one or more processes of a variant call function, such as including an HMM task. Accordingly, the HMM accelerator  8  may be composed of a data distributor  9 , e.g., CentCom, and one or a multiplicity of processing clusters  11 - 11   n+1  that may be organized as or otherwise include one or more instances  13 , such as where each instance may be configured as a processing engine, such as a small engine  13   a - 13   m+1 . For instance, the distributor  9  may be configured for receiving data, such as from the CPU/GPU/QPU  1000 , and distributing or otherwise transferring that data to one or more of the multiplicity of HMM processing clusters  11 . 
     Particularly, in certain embodiments, the distributor  9  may be positioned logically between the on-board PCIe interface  4  and the HMM accelerator module  8 , such as where the interface  4  communicates with the distributor  9  such as over an interconnect or other suitably configured bus  5 , e.g., PCIe bus. The distributor module  9  may be adapted for communicating with one or more HMM accelerator clusters  11  such as over one or more cluster buses  10 . For instance, the HMM accelerator module  8  may be configured as or otherwise include an array of clusters  11   a - 11   n+1  such as where each HMM cluster  11  may be configured as or otherwise includes a cluster hub  11  and/or may include one or more instances  13 , which instance may be configured as a processing engine  13  that is adapted for performing one or more operations on data received thereby. Accordingly, in various embodiments, each cluster  11  may be formed as or otherwise include a cluster hub  11   a - 11   n+1 , where each of the hubs may be operably associated with multiple HMM accelerator engine instances  13   a - 13   m+1 , such as where each cluster hub  11  may be configured for directing data to a plurality of the processing engines  13   a - 13   m+1  within the cluster  11 . 
     In various instances, the HMM accelerator  8  is configured for comparing each base of a subject&#39;s sequenced genetic code, such as in read format, with the various known or generated candidate haplotypes of a reference sequence and determining the probability that any given base at a position being considered either matches or doesn&#39;t match the relevant haplotype, e.g., the read includes an SNP, an insertion, or a deletion, thereby resulting in a variation of the base at the position being considered. Particularly, in various embodiments, the HMM accelerator  8  is configured to assign transition probabilities for the sequence of the bases of the read going between each of these states, Match (“M”), Insert (“I”), or Delete (“D”) as described in greater detail herein below. 
     More particularly, dependent on the configuration, the HMM acceleration function may be implemented in either software, such as by the CPU/GPU/QPU  1000  and/or microchip  7 , and/or may be implemented in hardware and may be present within the microchip  7 , such as positioned on the peripheral expansion card or board  2 . In various embodiments, this functionality may be implemented partially as software, e.g., run by the CPU/GPU/QPU  1000 , and partially as hardware, implemented on the chip  7  or via one or more quantum processing circuits. Accordingly, in various embodiments, the chip  7  may be present on the motherboard of the CPU/GPU/QPU  1000 , or it may be part of the peripheral device  2 , or both. Consequently, the HMM accelerator module  8  may include or otherwise be associated with various interfaces, e.g.,  3 ,  5 ,  10 , and/or  12  so as to allow the efficient transfer of data to and from the processing engines  13 . 
     Accordingly, as can be seen with respect to  FIGS. 2 and 3 , in various embodiments, a microchip  7  configured for performing a variant, e.g., haplotype, call function is provided. The microchip  7  may be associated with a CPU/GPU/QPU  1000  such as directly coupled therewith, e.g., included on the motherboard of a computer, or indirectly coupled thereto, such as being included as part of a peripheral device  2  that is operably coupled to the CPU/GPU/QPU  1000 , such as via one or more interconnects, e.g.,  3 ,  4 ,  5 ,  10 , and/or  12 . In this instance, the microchip  7  is present on the peripheral device  2 . It is to be understood that although configured as a microchip, the accelerator could also be configured as one or more quantum circuits of a quantum processing unit, wherein the quantum circuits are configured as one or more processing engines for performing one or more of the functions disclosed herein. 
     Hence, the peripheral device  2  may include a parallel or serial expansion bus  4  such as for connecting the peripheral device  2  to the central processing unit (CPU/GPU/QPU)  1000  of a computer and/or server, such as via an interface  3 , e.g., DMA. In particular instances, the peripheral device  2  and/or serial expansion bus  4  may be a Peripheral Component Interconnect express (PCIe) that is configured to communicate with or otherwise include the microchip  7 , such as via connection  5 . As described herein, the microchip  7  may at least partially be configured as or may otherwise include an HMM accelerator  8 . The HMM accelerator  8  may be configured as part of the microchip  7 , e.g., as hardwired and/or as code to be run in association therewith, and is configured for performing a variant call function, such as for performing one or more operations of a Hidden Markov Model, on data supplied to the microchip  7  by the CPU/GPU/QPU  1000 , such as over the PCIe interface  4 . Likewise, once one or more variant call functions have been performed, e.g., one or more HMM operations run, the results thereof may be transferred from the HMM accelerator  8  of the chip  7  over the bus  4  to the CPU/GPU/QPU  1000 , such as via connection  3 . 
     For instance, in particular instances, a CPU/GPU/QPU  1000  for processing and/or transferring information and/or executing instructions is provided along with a microchip  7  that is at least partially configured as an HMM accelerator  8 . The CPU/GPU/QPU  1000  communicates with the microchip  7  over an interface  5  that is adapted to facilitate the communication between the CPU/GPU/QPU  1000  and the HMM accelerator  8  of the microchip  7  and therefore may communicably connect the CPU/GPU/QPU  1000  to the HMM accelerator  8  that is part of the microchip  7 . To facilitate these functions, the microchip  7  includes a distributor module  9 , which may be a CentCom, that is configured for transferring data to a multiplicity of HMM engines  13 , e.g., via one or more clusters  11 , where each engine  13  is configured for receiving and processing the data, such as by running an HMM protocol thereon, computing final values, outputting the results thereof, and repeating the same. In various instances, the performance of an HMM protocol may include determining one or more transition probabilities, as described herein below. Particularly, each HMM engine  13  may be configured for performing a job such as including one or more of the generating and/or evaluating of an HMM virtual matrix to produce and output a final sum value with respect thereto, which final sum expresses the probable likelihood that the called base matches or is different from a corresponding base in a hypothetical haplotype sequence, as described herein below. 
       FIG. 3  presents a detailed depiction of the HMM cluster  11  of  FIG. 2 . In various embodiments, each HMM cluster  11  includes one or more HMM instances  13 . One or a number of clusters may be provided, such as desired in accordance with the amount of resources provided, such as on the chip or quantum computing processor. Particularly, a HMM cluster may be provided, where the cluster is configured as a cluster hub  11 . The cluster hub  11  takes the data pertaining to one or more jobs  20  from the distributor  9 , and is further communicably connected to one or more, e.g., a plurality of, HMM instances  13 , such as via one or more HMM instance busses  12 , to which the cluster hub  11  transmits the job data  20 . 
     The bandwidth for the transfer of data throughout the system may be relatively low bandwidth process, and once a job  20  is received, the system  1  may be configured for completing the job, such as without having to go off chip  7  for memory. In various embodiments, one job  20   a  is sent to one processing engine  13   a  at any given time, but several jobs  20   a-n  may be distributed by the cluster hub  11  to several different processing engines  13   a - 13   m+1 , such as where each of the processing engines  13  will be working on a single job  20 , e.g., a single comparison between one or more reads and one or more haplotype sequences, in parallel and at high speeds. As described below, the performance of such a job  20  may typically involve the generation of a virtual matrix whereby the subject&#39;s “read” sequences may be compared to one or more, e.g., two, hypothetical haplotype sequences, so as to determine the differences there between. In such instances, a single job  20  may involve the processing of one or more matrices having a multiplicity of cells therein that need to be processed for each comparison being made, such as on a base by base basis. As the human genome is about 3 billion base pairs, there may be on the order of 1 to 2 billion different jobs to be performed when analyzing a 30λ oversampling of a human genome (which is equitable to about 20 trillion cells in the matrices of all associated HMM jobs). 
     Accordingly, as described herein, each HMM instance  13  may be adapted so as to perform an HMM protocol, e.g., the generating and processing of an HMM matrix, on sequence data, such as data received thereby from the CPU/GPU/QPU  1000 . For example, as explained above, in sequencing a subject&#39;s genetic material, such as DNA or RNA, the DNA/RNA is broken down into segments, such as up to about 100 bases in length. The identity of these 100 base segments are then determined, such as by an automated sequencer, and “read” into a FASTQ text based file or other format that stores both each base identity of the read along with a Phred quality score (e.g., typically a number between 0 and 63 in log scale, where a score of 0 indicates the least amount of confidence that the called base is correct, with scores between 20 to 45 generally being acceptable as relatively accurate). 
     Particularly, as indicated above, a Phred quality score is a quality indicator that measures the quality of the identification of the nucleobase identities generated by the sequencing processor, e.g., by the automated DNA/RNA sequencer. Hence, each read base includes its own quality, e.g., Phred, score based on what the sequencer evaluated the quality of that specific identification to be. The Phred represents the confidence with which the sequencer estimates that it got the called base identity correct. This Phred score is then used by the implemented HMM module  8 , as described in detail below, to further determine the accuracy of each called base in the read as compared to the haplotype to which it has been mapped and/or aligned, such as by determining its Match, Insertion, and/or Deletion transition probabilities, e.g., in and out of the Match state. It is to be noted that in various embodiments, the system  1  may modify or otherwise adjust the initial Phred score prior to the performance of an HMM protocol thereon, such as by taking into account neighboring bases/scores and/or fragments of neighboring DNA and allowing such factors to influence the Phred score of the base, e.g., cell, under examination. 
     In such instances, as can be seen with respect to  FIG. 4 , the system  1 , e.g., computer/quantum software, may determine and identify various active regions  500   n  within the sequenced genome that may be explored and/or otherwise subjected to further processing as herein described, which may be broken down into jobs  20   n  that may be parallelized amongst the various cores and available threads  1007  throughout the system  1 . For instance, such active regions  500  may be identified as being sources of variation between the sequenced and reference genomes. Particularly, the CPU/GPU/QPU  1000  may have multiple threads  1007  running, identifying active regions  500   a ,  500   b , and  500   c , compiling and aggregating various different jobs  20   n  to be worked on, e.g., via a suitably configured aggregator  1008 , based on the active region(s)  500   a - c  currently being examined. Any suitable number of threads  1007  may be employed so as to allow the system  1  to run at maximum efficiency, e.g., the more threads present the less active time spent waiting. 
     Once identified, compiled, and/or aggregated, the threads  1007 / 1008  will then transfer the active jobs  20  to the data distributor  9 , e.g., CentCom, of the HMM module  8 , such as via PCIe interface  4 , e.g., in a fire and forget manner, and will then move on to a different process while waiting for the HMM  8  to send the output data back so as to be matched back up to the corresponding active region  500  to which it maps and/or aligns. The data distributor  9  will then distribute the jobs  20  to the various different HMM clusters  11 , such as on a job-by-job manner. If everything is running efficiently, this may be on a first in first out format, but such does not need to be the case. For instance, in various embodiments, raw jobs data and processed job results data may be sent through and across the system as they become available. 
     Particularly, as can be seen with respect to  FIGS. 2, 3, and 4 , the various job data  20  may be aggregated into 4K byte pages of data, which may be sent via the PCIe  4  to and through the CentCom  9  and on to the processing engines  13 , e.g., via the clusters  11 . The amount of data being sent may be more or less than 4K bytes, but will typically include about 100 HMM jobs per 4K (e.g., 1024) page of data. Particularly, these data then get digested by the data distributor  9  and are fed to each cluster  11 , such as where one 4K page is sent to one cluster  11 . However, such need not be the case as any given job  20  may be sent to any given cluster  11 , based on the clusters that become available and when. 
     Accordingly, the cluster  11  approach as presented here efficiently distributes incoming data to the processing engines  13  at high-speed. Specifically, as data arrives at the PCIe interface  4  from the CPU/GPU/QPU  1000 , e.g., over DMA connection  3 , the received data may then be sent over the PCIe bus  5  to the CentCom distributor  9  of the variant caller microchip  7 . The distributor  9  then sends the data to one or more HMM processing clusters  11 , such as over one or more cluster dedicated buses  10 , which cluster  11  may then transmit the data to one or more processing instances  13 , e.g., via one or more instance buses  12 , such as for processing. In this instance, the PCIe interface  4  is adapted to provide data through the peripheral expansion bus  5 , distributor  9 , and/or cluster  10  and/or instance  12  busses at a rapid rate, such as at a rate that can keep one or more, e.g., all, of the HMM accelerator instances  13   a−(m+1)  within one or more, e.g., all, of the HMM clusters  11   a−(n+1)  busy, such as over a prolonged period of time, e.g., full time, during the period over which the system  1  is being run, the jobs  20  are being processed, and whilst also keeping up with the output of the processed HMM data that is to be sent back to one or more CPUs  1000 , over the PCIe interface  4 . 
     For instance, any inefficiency in the interfaces  3 ,  5 ,  10 , and/or  12  that leads to idle time for one or more of the HMM accelerator instances  13  may directly add to the overall processing time of the system  1 . Particularly, when analyzing a human genome, there may be on the order of two or more billion different jobs  20  that need to be distributed to the various HMM clusters  11  and processed over the course of a time period, such as under 1 hour, under 45 minutes, under 30 minutes, under 20 minutes including 15 minutes, 10 minutes, 5 minutes, or less. 
     Accordingly,  FIG. 4  sets forth an overview of an exemplary data flow throughout the software and/or hardware of the system  1 , as described generally above. As can be seen with respect to  FIG. 4 , the system  1  may be configured in part to transfer data, such as between the PCIe interface  4  and the distributor  9 , e.g., CentCom, such as over the PCIe bus  5 . Additionally, the system  1  may further be configured in part to transfer the received data, such as between the distributor  9  and the one or more HMM clusters  11 , such as over the one or more cluster buses  10 . Hence, in various embodiments, the HMM accelerator  8  may include one or more clusters  11 , such as one or more clusters  11  configured for performing one or more processes of an HMM function. In such an instance, there is an interface, such as a cluster bus  10 , that connects the CentCom  9  to the HMM cluster  11 . 
     For instance,  FIG. 5  is a high level diagram depicting the interface in to and out of the HMM module  8 , such as into and out of a cluster module. As can be seen with respect to  FIG. 5 , each HMM cluster  11  may be configured to communicate with, e.g., receive data from and/or send final result data, e.g., sum data, to the CentCom data distributor  9  through a dedicated cluster bus  10 . Particularly, any suitable interface or bus  5  may be provided so long as it allows the PCIe interface  4  to communicate with the data distributor  9 . More particularly, the bus  5  may be an interconnect that includes the interpretation logic useful in talking to the data distributor  9 , which interpretation logic may be configured to accommodate any protocol employed to provide this functionality. Specifically, in various instances, the interconnect may be configured as a PCIe bus  5 . 
     Additionally, the cluster  11  may be configured such that single or multiple clock domains may be employed therein, and hence, one or more clocks may be present within the cluster  11 . In particular instances, multiple clock domains may be provided. For example, a slower clock may be provided, such as for communications, e.g., to and from the cluster  11 . Additionally, a faster, e.g., a high speed, clock may be provided which may be employed by the HMM instances  13  for use in performing the various state calculations described herein. 
     Particularly, in various embodiments, as can be seen with respect to  FIG. 5 , the system  1  may be set up such that, in a first instance, as the data distributor  9  leverages the existing CentCom IP, a collar, such as a gasket, may be provided, where the gasket is configured for translating signals to and from the CentCom interface  5  from and to the HMM cluster interface or bus  10 . For instance, an HMM cluster bus  10  may communicably and/or operably connect the CPU/GPU  1000  to the various clusters  11  of the HMM accelerator module  8 . Hence, as can be seen with respect to  FIG. 5 , structured write and/or read data for each haplotype and/or for each read may be sent throughout the system  1 . 
     Following a job  20  being input into the HMM engine, an HMM engine  13  may typically start either: a) immediately, if it is IDLE, or b) after it has completed its currently assigned task. It is to be noted that each HMM accelerator engine  13  can handle ping and pong inputs (e.g., can be working on one data set while the other is being loaded), thus minimizing downtime between jobs. Additionally, the HMM cluster collar  11  may be configured to automatically take the input job  20  sent by the data distributor  9  and assign it to one of the HMM engine instances  13  in the cluster  11  that can receive a new job. There need not be a control on the software side that can select a specific HMM engine instance  13  for a specific job  20 . However, in various instances, the software can be configured to control such instances. 
     Accordingly, in view of the above, the system  1  may be streamlined when transferring the results data back to the CPU/GPU/QPU, and because of this efficiency there is not much data that needs to go back to the CPU/GPU/QPU to achieve the usefulness of the results. This allows the system to achieve about a 30 minute or less, such as about a 25 or about a 20 minute or less, for instance, about a 18 or about a 15 minute or less, including about a 10 or about a 7 minute or less, even about a 5 or about a 3 minute or less variant call operation, dependent on the system configuration. 
       FIG. 6  presents a high-level view of various functional blocks within an exemplary HMM engine  13  within a hardware accelerator  8 , on the FPGA or ASIC  7 . Specifically, within the hardware HMM accelerator  8  there are multiple clusters  11 , and within each cluster  11  there are multiple engines  13 .  FIG. 6  presents a single instance of an HMM engine  13 . As can be seen with respect to  FIG. 6 , the engine  13  may include an instance bus interface  12 , a plurality of memories, e.g., an HMEM  16  and an RMEM  18 , various other components  17 , HMM control logic  15 , as well as a result output interface  19 . Particularly, on the engine side, the HMM instance bus  12  is operably connected to the memories, HMEM  16  and RMEM  18 , and may include interface logic that communicates with the cluster hub  11 , which hub is in communications with the distributor  9 , which in turn is communicating with the PCIe interface  4  that communicates with the variant call software being run by the CPU/GPU and/or server  1000 . The HMM instance bus  12 , therefore, receives the data from the CPU  1000  and loads it into one or more of the memories, e.g., the HMEM and RMEM. This configuration may also be implemented in one or more quantum circuits and adapted accordingly. 
     In these instances, enough memory space should be allocated such that at least one or two or more haplotypes, e.g., two haplotypes, may be loaded, e.g., in the HMEM  16 , per given read sequence that is loaded, e.g., into the RMEM  18 , which when multiple haplotypes are loaded results in an easing of the burden on the PCIe bus  5  bandwidth. In particular instances, two haplotypes and two read sequences may be loaded into their respective memories, which would allow the four sequences to be processed together in all relevant combinations. In other instances four, or eight, or sixteen sequences, e.g., pairs of sequences, may be loaded, and in like manner be processed in combination, such as to further ease the bandwidth when desired. 
     Additionally, enough memory may be reserved such that a ping-pong structure may be implemented therein such that once the memories are loaded with a new job  20   a , such as on the ping side of the memory, a new job signal is indicated, and the control logic  15  may begin processing the new job  20   a , such as by generating the matrix and performing the requisite calculations, as described herein and below. Accordingly, this leaves the pong side of the memory available so as to be loaded up with another job  20   b , which may be loaded therein while the first job  20   a  is being processed, such that as the first job  20   a  is finished, the second job  20   b  may immediately begin to be processed by the control logic  15 . 
     In such an instance, the matrix for job  20   b  may be preprocessed so that there is virtually no down time, e.g., one or two clock cycles, from the ending of processing of the first job  20   a , and the beginning of processing of the second job  20   b . Hence, when utilizing both the ping and pong side of the memory structures, the HMEM  16  may typically store  4  haplotype sequences, e.g., two a piece, and the RMEM  18  may typically store  2  read sequences. This ping-pong configuration is useful because it simply requires a little extra memory space, but allows for a doubling of the throughput of the engine  13 . 
     During and/or after processing the memories  16 ,  18  feed into the transition probabilities calculator and lookup table (LUT) block  17   a , which is configured for calculating various information related to “Priors” data, as explained below, which in turn feeds the Prior results data into the M, I, and D state calculator block  17   b , for use when calculating transition probabilities. One or more scratch RAMs  17   c  may also be included, such as for holding the M, I, and D states at the boundary of the swath, e.g., the values of the bottom row of the processing swath, which as indicated, in various instances, may be any suitable amount of cells, e.g., about 10 cells, in length so as to be commensurate with the length of the swath  35 . 
     Additionally, a separate results output interface block  19  may be included so that when the sums are finished they, e.g., a 4 32-bit word, can immediately be transmitted back to the variant call software of the CPU/GPU/QPU  1000 . It is to be noted that this configuration may be adapted so that the system  1 , specifically the M, I, and D calculator  17   b  is not held up waiting for the output interface  19  to clear, e.g., so long as it does not take as long to clear the results as it does to perform the job  20 . Hence, in this configuration, there may be three pipeline steps functioning in concert to make an overall systems pipeline, such as loading the memory, performing the MID calculations, and outputting the results. Further, it is noted that any given HMM engine  13  is one of many with their own output interface  19 , however they may share a common interface  10  back to the data distributor  9 . Hence, the cluster hub  11  will include management capabilities to manage the transfer (“xfer”) of information through the HMM accelerator  8  so as to avoid collisions. 
     Accordingly, the following details the processes being performed within each module of the HMM engines  13  as it receives the haplotype and read sequence data, processes it, and outputs results data pertaining to the same, as generally outlined above. Specifically, the high-bandwidth computations in the HMM engine  13 , within the HMM cluster  11 , are directed to computing and/or updating the match (M), insert (I), and delete (D) state values, which are employed in determining whether the particular read being examined matches the haplotype reference as well as the extent of the same, as described above. Particularly, the read along with the Phred score anf GOP value for each base in the read is transmitted to the cluster  11  from the distributor  9  and is thereby assigned to a particular processing engine  13  for processing. These data are then used by the M, I, and D calculator  17  of the processing engine  13  to determine whether the called base in the read is more or less likely to be correct and/or to be a match to its respective base in the haplotype, or to be the product of a variation, e.g., an insert or deletion; and/or if there is a variation, whether such variation is the likely result of a true variability in the haplotype or rather an artifact of an error in the sequence generating and/or mapping and/or aligning systems. 
     As indicated above, a part of such analysis includes the MID calculator  17  determining the transition probabilities from one base to another in the read going from one M, I, or D state to another in comparison to the reference, such as from a matching state to another matching state, or a matching state to either an insertion state or to a deletion state. In making such determinations each of the associated transition probabilities is determined and considered when evaluating whether any observed variation between the read and the reference is a true variation and not just some machine or processing error. For these purposes, the Phred score for each base being considered is useful in determining the transition probabilities in and out of the match state, such as going from a match state to an insert or deletion, e.g., a gapped, state in the comparison. Likewise, the transition probabilities of continuing a gapped state or going from a gapped state, e.g., an insert or deletion state, back to a match state are also determined. In particular instances, the probabilities in or out of the delete or insert state, e.g., exiting a gap continuation state, may be a fixed value, and may be referenced herein as the gap continuation probability or penalty. Nevertheless, in various instances, such gap continuation penalties may be floating and therefore subject to change dependent on the accuracy demands of the system configuration. 
     Accordingly, as depicted with respect to  FIGS. 7 and 8  each of the M, I, and D state values are computed for each possible read and haplotype base pairing. In such an instance, a virtual matrix  30  of cells containing the read sequence being evaluated on one axis of the matrix and the associated haplotype sequence on the other axis may be formed, such as where each cell in the matrix represents a base position in the read and haplotype reference. Hence, if the read and haplotype sequences are each 100 bases in length, the matrix  30  will include 100 by 100 cells, a given portion of which may need to be processed in order to determine the likelihood and/or extent to which this particular read matches up with this particular reference. Hence, once virtually formed, the matrix  30  may then be used to determine the various state transitions that take place when moving from one base in the read sequence to another and comparing the same to that of the haplotype sequence, such as depicted in  FIGS. 7 and 8 . Specifically, the processing engine  13  is configured such that a multiplicity of cells may be processed in parallel and/or sequential fashion when traversing the matrix with the control logic  15 . For instance, as depicted in  FIG. 7 , a virtual processing swath  35  is propagated and moves across and down the matrix  30 , such as from left to right, processing the individual cells of the matrix  30  down the right to left diagonal. 
     More specifically, as can be seen with respect to  FIG. 7 , each individual virtual cell within the matrix  30  includes an M, I, and D state value that needs to be calculated so as to asses the nature of the identity of the called base, and as depicted in  FIG. 7  the data dependencies for each cell in this process may clearly be seen. Hence, for determining a given M state of a present cell being processed, the Match, Insert, and Delete states of the cell diagonally above the present cell need to be pushed into the present cell and used in the calculation of the M state of the cell presently being calculated (e.g., thus, the diagonal downwards, forwards progression through the matrix is indicative of matching). 
     However, for determining the I state, only the Match and Insert states for the cell directly above the present cell need be pushed into the present cell being processed (thus, the vertical downwards “gapped” progression when continuing in an insertion state). Likewise, for determining the D state, only the Match and Delete states for the cell directly left of the present cell need be pushed into the present cell (thus, the horizontal cross-wards “gapped” progression when continuing in a deletion state). As can be seen with respect to  FIG. 7 , after computation of cell  1  (the shaded cell in the top most row) begins, the processing of cell  2  (the shaded cell in the second row) can also begin, without waiting for any results from cell  1 , because there is no data dependencies between this cell in row  2  and the cell of row  1  where processing begins. This forms a reverse diagonal  35  where processing proceeds downwards and to the left, as shown by the red arrow. This reverse diagonal  35  processing approach increases the processing efficiency and throughput of the overall system. Likewise, the data generated in cell  1 , can immediately be pushed forward to the cell down and forward to the right of the top most cell  1 , thereby advancing the swath  35  forward. 
     For instance,  FIG. 7  depicts an exemplary HMM matrix structure  35  showing the hardware processing flow. The matrix  35  includes the haplotype base index, e.g., containing 36 bases, positioned to run along the top edge of the horizontal axis, and further includes the base read index, e.g., 10 bases, positioned to fall along the side edge of the vertical axis in such a manner to from a structure of cells where a selection of the cells may be populated with an M, I, and D probability state, and the transition probabilities of transitioning from the present state to a neighboring state. In such an instance, as described in greater detail above, a move from a match state to a match state results in a forwards diagonal progression through the matrix  30 , while moving from a match state to an insertion state results in a vertical downwards progressing gap, and a move from a match state to a deletion state results in a horizontal progressing gap. Hence, as depicted in  FIG. 8 , for a given cell, when determining the match, insert, and delete states for each cell, the match, insert, and delete probabilities of its three adjoining cells are employed. 
     The downwards arrow in  FIG. 7  represents the parallel and sequential nature of the processing engine(s) that are configured so as to produce a processing swath or wave  35  that moves progressively along the virtual matrix in accordance with the data dependencies, see  FIGS. 7 and 8 , for determining the M, I, and D states for each particular cell in the structure  30 . Accordingly, in certain instances, it may be desirable to calculate the identities of each cell in a downwards and diagonal manner, as explained above, rather than simply calculating each cell along a vertical or horizontal axis exclusively, although this can be done if desired. This is due to the increased wait time, e.g., latency, that would be required when processing the virtual cells of the matrix  35  individually and sequentially along the vertical or horizontal axis alone, such as via the hardware configuration. 
     For instance, in such an instance, when moving linearly and sequentially through the virtual matrix  30 , such as in a row by row or column by column manner, in order to process each new cell the state computations of each preceding cell would have to be completed, thereby increasing latency time overall. However, when propagating the M, I, D probabilities of each new cell in a downwards and diagonal fashion, the system  1  does not have to wait for the processing of its preceding cell, e.g., of row one, to complete before beginning the processing of an adjoining cell in row two of the matrix. This allows for parallel and sequential processing of cells in a diagonal arrangement to occur, and further allows the various computational delays of the pipeline associated with the M, I, and D state calculations to be hidden. Accordingly, as the swath  35  moves across the matrix  30  from left to right, the computational processing moves diagonally downwards, e.g., towards the left (as shown by the arrow in  FIG. 7 ). This configuration may be particularly useful for hardware and/or quantum circuit implementations, such as where the memory and/or clock-by-clock latency are a primary concern. 
     In these configurations, the actual value output from each call of an HMM engine  13 , e.g., after having calculated the entire matrix  30 , may be a bottom row (e.g., Row  35  of  FIG. 21 ) containing M, I, and D states, where the M and I states may be summed (the D states may be ignored at this point having already fulfilled their function in processing the calculations above), so as to produce a final sum value that may be a single probability that estimates, for each read and haplotype index, the probability of observing the read, e.g., assuming the haplotype was the true original DNA sampled. 
     Particularly, the outcome of the processing of the matrix  30 , e.g., of  FIG. 7 , may be a single value representing the probability that the read is an actual representation of that haplotype. This probability is a value between 0 and 1 and is formed by summing all of the M and I states from the bottom row of cells in the HMM matrix  30 . Essentially, what is being assessed is the possibility that something could have gone wrong in the sequencer, or associated DNA preparation methods prior to sequencing, so as to incorrectly produce a mismatch, insertion, or deletion into the read that is not actually present within the subject&#39;s genetic sequence. In such an instance, the read is not a true reflection of the subject&#39;s actual DNA. 
     Hence, accounting for such production errors, it can be determined what any given read actually represents with respect to the haplotype, and thereby allows the system to better determine how the subject&#39;s genetic sequence, e.g., en masse, may differ from that of a reference sequence. For instance, many haplotypes may be run against many read sequences, generating scores for all of them, and determining based on which matches have the best scores, what the actual genomic sequence identity of the individual is and/or how it truly varies from a reference genome. 
     More particularly,  FIG. 8  depicts an enlarged view of a portion of the HMM state matrix  30  from  FIG. 7 . As shown in  FIG. 8 , given the internal composition of each cell in the matrix  30 , as well as the structure of the matrix as a whole, the M, I, and D state probability for any given “new” cell being calculated is dependent on the M, I, and D states of several of its surrounding neighbors that have already been calculated. Particularly, as shown in greater detail with respect to  FIGS. 1 and 16 , in an exemplary configuration, there may be an approximately a 0.9998 probability of going from a match state to another match state, and there may be only a 0.0001 probability (gap open penalty) of going from a match state to either an insertion or a deletion, e.g., gapped, state. Further, when in either a gapped insertion or gapped deletion state there may be only a 0.1 probability (gap extension or continuation penalty) of staying in that gapped state, while there is a 0.9 probability of returning to a match state. It is to be noted that according to this model, all of the probabilities in to or out of a given state should sum to one. Particularly, the processing of the matrix  30  revolves around calculating the transition probabilities, accounting for the various gap open or gap continuation penalties and a final sum is calculated. 
     Hence, these calculated state transition probabilities are derived mainly from the directly adjoining cells in the matrix  30 , such as from the cells that are immediately to the left of, the top of, and diagonally up and left of that given cell presently being calculated, as seen in  FIG. 16 . Additionally, the state transition probabilities may in part be derived from the “Phred” quality score that accompanies each read base. These transition probabilities, therefore, are useful in computing the M, I, and D state values for that particular cell, and likewise for any associated new cell being calculated. It is to be noted that as described herein, the gap open and gap continuation penalties may be fixed values, however, in various instances, the gap open and gap continuation penalties may be variable and therefore programmable within the system, albeit by employing additional hardware resources dedicated to determining such variable transition probability calculations. Such instances may be useful where greater accuracy is desired. Nevertheless, when such values are assumed to be constant, smaller resource usage and/or chip size may be achieved, leading to greater processing speed, as explained below. 
     Accordingly, there is a multiplicity of calculations and/or other mathematical computations, such as multiplications and/or additions, which are involved in deriving each new M, I, and D state value. In such an instance, such as for calculating maximum throughput, the primitive mathematical computations involved in each M, I, and D transition state calculation may be pipelined. Such pipelining may be configured in a way that the corresponding clock frequencies are high, but where the pipeline depth may be non-trivial. Further, such a pipeline may be configured to have a finite depth, and in such instances it may take more than one clock cycle to complete the operations. 
     For instance, these computations may be run at high speeds inside the processor  7 , such as at about 300 MHz. This may be achieved such as by pipelining the FPGA or ASIC heavily with registers so little mathematical computation occurs between each flip-flop. This pipeline structure results in multiple cycles of latency in going from the input of the match state to the output, but given the reverse diagonal computing structure, set forth in  FIG. 7  above, these latencies may be hidden over the entire HMM matrix  30 , such as where each cell represents one clock cycle. 
     Hence, the number of M, I, and D state calculations may be limited. In such an instance, the processing engine  13  may be configured in such a manner that a grouping, e.g., swath  35 , of cells in a number of rows of the matrix  30  may be processed as a group (such as in a down-and-left-diagonal fashion as illustrated by the arrow in  FIG. 7 ) before proceeding to the processing of a second swath below, e.g., where the second swath contains the same number of cells in rows to be processed as the first. In a manner such as this, a hardware implementation of an accelerator  8 , as described herein, may be adapted so as to make the overall system more efficient, as described above. 
     Particularly,  FIG. 9  sets forth an exemplary computational structure for performing the various state processing calculations herein described. More particularly,  FIG. 9  sets forth three dedicated logic blocks  17  of the processing engine  13  for computing the state computations involved in generating each M, I, and D state value for each particular cell, or grouping of cells, being processed in the HMM matrix  30 . These logic blocks may be implemented in hardware, but in some instances, may be implemented in software, such as for being performed by one or more quantum circuits. As can be seen with respect to  FIG. 9 , the match state computation  15   a  is more involved than either of the insert  15   b  or deletion  15   c  computations, this is because in calculating the match state  15   a  of the present cell being processed, all of the previous match, insert, and delete states of the adjoining cells along with various “Priors” data are included in the present match computation (see  FIGS. 9 and 10 ), whereas only the match and either the insert and delete states are included in their respective calculations. Hence, as can be seen with respect to  FIG. 9 , in calculating a match state, three state multipliers, as well as two adders, and a final multiplier, which accounts for the Prior, e.g. Phred, data are included. However, for calculating the I or D state, only two multipliers and one adder are included. It is noted that in hardware, multipliers are more resource intensive than adders. 
     Accordingly, to various extents, the M, I, and D state values for processing each new cell in the HMM matrix  30  uses the knowledge or pre-computation of the following values, such as the “previous” M, I, and D state values from left, above, and/or diagonally left and above of the currently-being-computed cell in the HMM matrix. Additionally, such values representing the prior information, or “Priors”, may at least in part be based on the “Phred” quality score, and whether the read base and the reference base at a given cell in the matrix  30  match or are different. Such information is particularly useful when determining a match state. Specifically, as can be seen with respect to  FIG. 9 , in such instances, there are basically seven “transition probabilities” (M-to-M, I-to-M, D-to-M, I-to-I, M-to-I, D-to-D, and M-to-D) that indicate and/or estimate the probability of seeing a gap open, e.g., of seeing a transition from a match state to an insert or delete state; seeing a gap close; e.g., going from an insert or delete state back to a match state; and seeing the next state continuing in the same state as the previous state, e.g., Match-to-Match, Insert-to-Insert, Delete-to-Delete. 
     The state values (e.g., in any cell to be processed in the HMM matrix  30 ), Priors, and transition probabilities are all values in the range of [0, 1]. Additionally, there are also known starting conditions for cells that are on the left or top edge of the HMM matrix  30 . As can be seen from the logic  15   a  of  FIG. 9 , there are four multiplication and two addition computations that may be employed in the particular M state calculation being determined for any given cell being processed. Likewise, as can be seen from the logic of  15   b  and  15   c  there are two multiplications and one addition involved for each I state and each D state calculation, respectively. Collectively, along with the priors multiplier this sums to a total of eight multiplications and four addition operations for the M, I, and D state calculations associated with each single cell in the HMM matrix  8  to be processed. 
     The final sum output, e.g., row  34  of  FIG. 16 , of the computation of the matrix  30 , e.g., for a single job  20  of comparing one read to one or two haplotypes, is the summation of the final M and I states across the entire bottom row  34  of the matrix  30 , which is the final sum value that is output from the HMM accelerator  8  and delivered to the CPU/GPU/QPU  1000 . This final summed value represents how well the read matches the haplotype(s). The value is a probability, e.g., of less than one, for a single job  20   a  that may then be compared to the output resulting from another job  20   b  such as form the same active region  500 . It is noted that there are on the order of 20 trillion HMM cells to evaluate in a “typical” human genome at  30 X coverage, where these 20 trillion HMM cells are spread across about 1 to 2 billion HMM matrices  30  of all associated HMM jobs  20 . 
     The results of such calculations may then be compared one against the other so as to determine, in a more precise manner, how the genetic sequence of a subject differs, e.g., on a base by base comparison, from that of one or more reference genomes. For the final sum calculation, the adders already employed for calculating the M, I, and/or D states of the individual cells may be re-deployed so as to compute the final sum value, such as by including a mux into a selection of the re-deployed adders thereby including one last additional row, e.g., with respect to calculation time, to the matrix so as to calculate this final sum, which if the read length is 100 bases amounts to about a 1% overhead. In alternative embodiments, dedicated hardware resources can be used for performing such calculations. In various instances, the logic for the adders for the M and D state calculations may be deployed for calculating the final sum, which D state adder may be efficiently deployed since it is not otherwise being used in the final processing leading to the summing values. 
     In certain instances, these calculations and relevant processes may be configured so as to correspond to the output of a given sequencing platform, such as including an ensemble of sequencers, which as a collective may be capable of outputting (on average) a new human genome at 30× coverage every 28 minutes (though they come out of the sequencer ensemble in groups of about 150 genomes every three days). In such an instance, when the present mapping, aligning, and variant calling operations are configured to fit within such a sequencing platform of processing technologies, a portion of the 28 minutes (e.g., about 10 minutes) it takes for the sequencing cluster to sequence a genome, may be used by a suitably configured mapper and/or aligner, as herein described, so as to take the image/BCL/FASTQ file results from the sequencer and perform the steps of mapping and/or aligning the genome, e.g., post-sequencer processing. That leaves about 18 minutes of the sequencing time period for performing the variant calling step, of which the HMM operation is the main computational component, such as prior to the nucleotide sequencer sequencing the next genome, such as over the next 28 minutes. Accordingly, in such instances, 18 minutes may be budgeted to computing the 20 trillion HMM cells that need to be processed in accordance with the processing of a genome, such as where each of the HMM cells to be processed includes about twelve mathematical operations (e.g., eight multiplications and/or four addition operations). Such a throughput allows for the following computational dynamics (20 trillion HMM cells)×(12 math ops per cell)/(18 minutes×60 seconds/minute), which is about 222 billion operations per second of sustained throughput. 
       FIG. 10  sets forth the logic blocks  17  of the processing engine of  FIG. 9  including exemplary M, I, and D state update circuits that present a simplification of the circuit provided in  FIG. 9 . The system may be configured so as to not be memory-limited, so a single HMM engine instance  13  (e.g., that computes all of the single cells in the HMM matrix  30  at a rate of one cell per clock cycle, on average, plus overheads) may be replicated multiple times (at least 65-70 times to make the throughput efficient, as described above). Nevertheless, to minimize the size of the hardware, e.g., the size of the chip  2  and/or its associated resource usage, and/or in a further effort to include as many HMM engine instances  13  on the chip  2  as desirable and/or possible, simplifications may be made with regard to the logic blocks  15   a ′- c ′ of the processing instance  13  for computing one or more of the transition probabilities to be calculated. 
     In particular, it may be assumed that the gap open penalty (GOP) and gap continuation penalty (GCP), as described above, such as for inserts and deletes are the same and are known prior to chip configuration. This simplification implies that the I-to-M and D-to-M transition probabilities are identical. In such an instance, one or more of the multipliers, e.g., set forth in  FIG. 9 , may be eliminated, such as by pre-adding I and D states before multiplying by a common Indel-to-M transition probability. For instance, in various instances, if the I and D state calculations are assumed to be the same, then the state calculations per cell can be simplified as presented in  FIG. 10 . Particularly, if the I and D state values are the same, then the I state and the D state may be added and then that sum may be multiplied by a single value, thereby saving a multiply. This may be done because, as seen with respect to  FIG. 10 , the gap continuation and/or close penalties for the I and D states are the same. However, as indicated above, the system can be configured to calculate different values for both the I and D transition state probabilities, and in such an instance, this simplification would not be employed. 
     Additionally, in a further simplification, rather than dedicate chip or other computing resources configured specifically to perform the final sum operation at the bottom of the HMM matrix, the present HMM accelerator  8  may be configured so as to effectively append one or more additional rows to the HMM matrix  30 , with respect to computational time, e.g., overhead, it takes to perform the calculation, and may also be configured to “borrow” one or more adders from the M-state  15   a  and D-state  15   c  computation logic such as by MUXing in the final sum values to the existing adders as needed, so as to perform the actual final summing calculation. In such an instance, the final logic, including the M logic  15   a , I logic  15   b , and D logic  15   c  blocks, which blocks together form part of the HMM MID instance  17 , may include 7 multipliers and 4 adders along with the various MUXing involved. 
     Accordingly,  FIG. 10  sets forth the M, I, and D state update circuits  15   a ′,  15   b ′, and  15   c ′ including the effects of simplifying assumptions related to transition probabilities, as well as the effect of sharing various M, I, and/or D resources, e.g., adder resources, for the final sum operations. A delay block may also be added to the M-state path in the M-state computation block, as shown in  FIG. 10 . This delay may be added to compensate for delays in the actual hardware implementations of the multiply and addition operations, and/or to simplify the control logic, e.g.,  15 . 
     As shown in  FIGS. 9 and 10 , these respective multipliers and/or adders may be floating point multipliers and adders. However, in various instances, as can be seen with respect to  FIG. 11 , a log domain configuration may be implemented where in such configuration all of the multiplies turn into adds.  FIG. 11  presents what log domain calculation would look like if all the multipliers turned into adders, e.g.,  15   a ″,  15   b ″, and  15   c ″, such as occurs when employing a log domain computational configuration. Particularly, all of the multiplier logic turns into an adder, but the adder itself turns into or otherwise includes a function where the function such as: f(a,b)=max(a,b)−log 2 (1+2̂(−[a−b]), such as where the log portion of the equation may be maintained within a LUT whose depth and physical size is determined by the precision required. 
     Given the typical read and haplotype sequence lengths as well as the values typically seen for read quality (Phred) scores and for the related transition probabilities, the dynamic range requirements on the internal HMM state values may be quite severe. For instance, when implementing the HMM module in software, various of the HMM jobs  20  may result in underruns, such as when implemented on single-precision (32-bit) floating-point state values. This implies a dynamic range that is greater than 80 powers of 10, thereby requiring the variant call software to bump up to double-precision (64-bit) floating point state values. However, full 64-bit double-precision floating-point representation may, in various instances, have some negative implications, such as if compact, high-speed hardware is to be implemented, both storage and compute pipeline resource requirements will need to be increased, thereby occupying greater chip space, and/or slowing timing. In such instances, a fixed-point-only linear-domain number representation may be implemented. Nevertheless, the dynamic range demands on the state values, in this embodiment, make the bit widths involved in certain circumstances less than desirable. Accordingly, in such instances, fixed-point-only log-domain number representation may be implemented, as described herein. 
     In such a scheme, as can be seen with respect to  FIG. 11 , instead of representing the actual state value in memory and computations, the -log-base-2 of the number may be represented. This may have several advantages, including employing multiply operations in linear space that translate into add operations in log space; and/or this log domain representation of numbers inherently supports wider dynamic range with only small increases in the number of integer bits. These log-domain M, I, D state update calculations are set forth in  FIGS. 11 and 12 . 
     As can be seen when comparing the logic  17  configuration of  FIG. 11  with that of  FIG. 9 , the multiply operations go away in the log-domain. Rather, they are replaced by add operations, and the add operations are morphed into a function that can be expressed as a max operation followed by a correction factor addition, e.g., via a LUT, where the correction factor is a function of the difference between the two values being summed in the log-domain. Such a correction factor can be either computed or generated from the look-up-table. Whether a correction factor computation or look-up-table implementation is more efficient to be used depends on the required precision (bit width) on the difference between the sum values. In particular instances, therefore, the number of log-domain bits for state representation can be in the neighborhood of 8 to 12 integer bits plus 6 to 24 fractional bits, depending on the level of quality desired for any given implementation. This implies somewhere between 14 and 36 bits total for log-domain state value representation. Further, it has been determined that there are log-domain fixed-point representations that can provide acceptable quality and acceptable hardware size and speed. 
     In various instances, one read sequence is typically processed for each HMM job  20 , which as indicated may include a comparison against two haplotype sequences. And like above for the haplotype memory, a ping-pong structure may also be used in the read sequence memory  18  to allow various software implemented functions the ability to write new HMM job information  20   b  while a current job  20   a  is still being processed by the HMM engine instance  13 . Hence, a read sequence storage requirement may be for a single 1024×32 two-port memory (such as one port for write, one port for read, and/or separate clocks for write and read ports). 
     Particularly, as described above, in various instances, the architecture employed by the system  1  is configured such that in determining whether a given base in a sequenced sample genome matches that of a corresponding base in one or more reference genomes, a virtual matrix  30  is formed, wherein the reference genome is theoretically set across a horizontal axis, while the sequenced reads, representing the sample genome, is theoretically set in descending fashion down the vertical axis. Consequently, in performing an HMM calculation, the HMM processing engine  13 , as herein described, is configured to traverse this virtual HMM matrix  30 . Such processing can be depicted as in  FIG. 7 , as a swath  35  moving diagonally down and across the virtual array performing the various HMM calculations for each cell of the virtual array, as seen in  FIG. 8 . 
     More particularly, this theoretical traversal involves processing a first grouping of rows of cells  35   a  from the matrix  30  in its entirety, such as for all haplotype and read bases within the grouping, before proceeding down to the next grouping of rows  35   b  (e.g., the next group of read bases). In such an instance, the M, I, and D state values for the first grouping are stored at the bottom edge of that initial grouping of rows so that these M, I, and D state values can then be used to feed the top row of the next grouping (swath) down in the matrix  30 . In various instances, the system  1  may be configured to allow up to 1008 length haplotypes and/or reads in the HMM accelerator  8 , and since the numerical representation employs W-bits for each state, this implies a 1008 word×W-bit memory for M, I, and D state storage. 
     Accordingly, as indicated, such memory could be either a single-port or double-port memory. Additionally, a cluster-level, scratch pad memory, e.g., for storing the results of the swath boundary, may also be provided. For instance, in accordance with the disclosure above, the memories discussed already are configured for a per-engine-instance  13  basis. In particular HMM implementations, multiple engine instances  13   a−   (n+1)  may be grouped into a cluster  11  that is serviced by a single connection, e.g., PCIe bus  5 , to the PCIe interface  4  and DMA  3  via CentCom  9 . Multiple clusters  11   a−   (n+1)  can be instantiated so as to more efficiently utilize PCIe bandwidth using the existing CentCom  9  functionality. 
     Hence, in a typical configuration, somewhere between 16 and 64 engines  13   m  are instantiated within a cluster  11   n , and one to four clusters might be instantiated in a typical FPGA/ASIC implementation of the HMM  8  (e.g., depending on whether it is a dedicated HMM FPGA image or whether the HMM has to share FPGA real estate with the sequencer/mapper/aligner and/or other modules, as herein disclosed). In particular instances, there may be a small amount of memory used at the cluster-level  11  in the HMM hardware. This memory may be used as an elastic First In First Out (“FIFO”) to capture output data from the HMM engine instances  13  in the cluster and pass it on to CentCom  9  for further transmittal back to the software of the CPU  1000  via the DMA  3  and PCIe  4 . In theory, this FIFO could be very small (on the order of two 32-bit words), as data are typically passed on to CentCom  9  almost immediately after arriving in the FIFO. However, to absorb potential disrupts in the output data path, the size of this FIFO may be made parametrizable. In particular instances, the FIFO may be used with a depth of 512 words. Thus, the cluster-level storage requirements may be a single 512×32 two-port memory (separate read and write ports, same clock domain). 
       FIG. 12  sets forth the various HMM state transitions  17   b  depicting the relationship between Gap Open Penalties (GOP), Gap Close Penalties (GCP), and transition probabilities involved in determining whether and how well a given read sequence matches a particular haplotype sequence. In performing such an analysis, the HMM engine  13  includes at least three logic blocks  17   b , such as a logic block for determining a match state  15   a , a logic block for determining an insert state  15   b , and a logic block for determining a delete state  15   c . These M, I, and D state calculation logic  17  when appropriately configured function efficiently to avoid high-bandwidth bottlenecks, such as of the HMM computational flow. However, once the M, I, D core computation architecture is determined, other system enhancements may also be configured and implemented so as to avoid the development of other bottlenecks within the system. 
     Particularly, the system  1  may be configured so as to maximize the process of efficiently feeding information from the computing core  1000  to the variant caller module  2  and back again, so as not to produce other bottlenecks that would limit overall throughput. One such block that feeds the HMM core M, I, D state computation logic  17  is the transition probabilities and priors calculation block. For instance, as can be seen with respect to  FIG. 9 , each clock cycle employs the presentation of seven transition probabilities and one Prior at the input to the M, I, D state computation block  15   a . However, after the simplifications that result in the architecture of  FIG. 10 , only four unique transition probabilities and one Prior are employed for each clock cycle at the input of the M, I, D state computation block. Accordingly, in various instances, these calculations may be simplified and the resulting values generated. Thus, increasing throughput, efficiency, and reducing the possibility of a bottleneck forming at this stage in the process. 
     Additionally, as described above, the Priors are values generated via the read quality, e.g., Phred score, of the particular base being investigated and whether, or not, that base matches the hypothesis haplotype base for the current cell being evaluated in the virtual HMM matrix  30 . The relationship can be described via the equations bellow: First, the read Phred in question may be expressed as a probability=10̂(−(read Phred/10)). Then the Prior can be computed based on whether the read base matches the hypothesis haplotype base: If the read base and hypothesis haplotype base match: Prior=1−read Phred expressed as a probability. Otherwise: Prior=(read Phred expressed as probability)/3. The divide-by-three operation in this last equation reflects the fact that there are only four possible bases (A, C, G, T). Hence, if the read and haplotype base did not match, then it must be one of the three remaining possible bases that does match, and each of the three possibilities is modeled as being equally likely. 
     The per-read-base Phred scores are delivered to the HMM hardware accelerator  8  as 6-bit values. The equations to derive the Priors, then, have 64 possible outcomes for the “match” case and an additional 64 possible outcomes for the “don&#39;t match” case. This may be efficiently implemented in the hardware as a 128 word look-up-table, where the address into the look-up-table is a 7-bit quantity formed by concatenating the Phred value with a single bit that indicates whether, or not, the read base matches the hypothesis haplotype base. 
     Further, with respect to determining the match to insert and/or match to delete probabilities, in various implementations of the architecture for the HMM hardware accelerator  8 , separate gap open penalties (GOP) can be specified for the Match-to-Insert state transition, and the Match-to-Delete state transition, as indicated above. This equates to the M2I and M2D values in the state transition diagram of  FIG. 12  being different. As the GOP values are delivered to the HMM hardware accelerator  8  as 6-bit Phred-like values, the gap open transition probabilities can be computed in accordance with the following equations: M2I transition probability=10̂(−(read GOP(I)/10)) and M2D transition probability=10̂(−(read GOP(D)/10)). Similar to the Priors derivation in hardware, a simple 64 word look-up-table can be used to derive the M2I and M2D values. If GOP(I) and GOP(D) are inputted to the HMM hardware  8  as potentially different values, then two such look-up-tables (or one resource-shared look-up-table, potentially clocked at twice the frequency of the rest of the circuit) may be utilized. 
     Furthermore, with respect to determining match to match transition probabilities, in various instances, the match-to-match transition probability may be calculated as: M2M transition probability=1−(M2I transition probability+M2D transition probability). If the M2I and M2D transition probabilities can be configured to be less than or equal to a value of ½, then in various embodiments the equation above can be implemented in hardware in a manner so as to increase overall efficiency and throughput, such as by reworking the equation to be: M2M transition probability=(0.5−M2I transition probability)+(0.5−M2D transition probability). This rewriting of the equation allows M2M to be derived using two 64 element look-up-tables followed by an adder, where the look-up-tables store the results. 
     Further still, with respect to determining the Insert to Insert and/or Delete to Delete transition probabilities, the I2I and D2D transition probabilities are functions of the gap continuation probability (GCP) values inputted to the HMM hardware accelerator  8 . In various instances, these GCP values may be 6-bit Phred-like values given on a per-read-base basis. The I2I and D2D values may then be derived as shown: I2I transition probability=10̂(−(read GCP(I)/10)), and D2D transition probability=10̂(−(read GCP(D)/10)). Similar to some of the other transition probabilities discussed above, the I2I and D2D values may be efficiently implemented in hardware, and may include two look-up-tables (or one resource-shared look-up-table), such as having the same form and contents as the Match-to-Indel look-up-tables discussed previously. That is, each look-up-table may have 64 words. 
     Additionally, with respect to determining the Inset and/or Delete to Match probabilities, the I2M and D2M transition probabilities are functions of the gap continuation probability (GCP) values and may be computed as: I2M transition probability=1−I2I transition probability, and D2M transition probability=1−D2D transition probability, where the I2I and D2D transition probabilities may be derived as discussed above. A simple subtract operation to implement the equations above may be more expensive in hardware resources than simply implementing another 64 word look-up-table and using two copies of it to implement the I2M and D2M derivations. In such instances, each look-up-table may have 64 words. Of course, in all relevant embodiments, simple or complex subtract operations may be formed with the suitably configured hardware. 
       FIG. 13  provides the circuitry  17   a  for a simplified calculation for HMM transition probabilities and Priors, as described above, which supports the general state transition diagram of  FIG. 12 . As can be seen with respect to  FIG. 13 , in various instances, a simple HMM hardware accelerator architecture  17   a  is presented, which accelerator may be configured to include separate GOP values for Insert and Delete transitions, and/or there may be separate GCP values for Insert and Delete transitions. In such an instance, the cost of generating the seven unique transition probabilities and one Prior each clock cycle may be configured as set forth below: eight 64 word look-up-tables, one 128 word look-up-table, and one adder. 
     Further, in various instances, the hardware  2 , as presented herein, may be configured so as to fit as many HMM engine instances  13  as possible onto the given chip target (such as on an FPGA, sASIC, or ASIC). In such an instance, the cost to implement the transition probabilities and priors generation logic  17   a  can be substantially reduced relative to the costs as provided by the below configurations. Firstly, rather than supporting a more general version of the state transitions, such as set forth in  FIG. 13 , e.g., where there may be separate values for GOP(I) and GOP(D), rather, in various instances, it may be assumed that the GOP values for insert and delete transitions are the same for a given base. This results in several simplifications to the hardware, as indicated above. 
     In such instances, only one 64 word look-up-table may be employed so as to generate a single M2Indel value, replacing both the M2I and M2D transition probability values, whereas two tables are typically employed in the more general case. Likewise, only one 64 word look-up-table may be used to generate the M2M transition probability value, whereas two tables and an add may typically be employed in the general case, as M2M may now be calculated as 1−2×M2Indel. 
     Secondly, the assumption may be made that the sequencer-dependent GCP value for both insert and delete are the same AND that this value does not change over the course of an HMM job  20 . This means that: a single Indel2Indel transition probability may be calculated instead of separate  121  and D2D values, using one 64 word look-up-table instead of two tables; and single Indel2Match transition probability may be calculated instead of separate I2M and D2M values, using one 64 word look-up-table instead of two tables. 
     Additionally, a further simplifying assumption can be made that assumes the Inset2Insert and Delete2Delete (I2I and D2D) and Insert2Match and Delete2Match (I2M and D2M) values are not only identical between insert and delete transitions, but may be static for the particular HMM job  20 . Thus, the four look-up-tables associated in the more general architecture with I2I, D2D, I2M, and D2M transition probabilities can be eliminated altogether. In various of these instances, the static Indel2Indel and Indel2Match probabilities could be made to be entered via software or via an RTL parameter (and so would be bitstream programmable in an FPGA). In certain instances, these values may be made bitstream-programmable, and in certain instances, a training mode may be implemented employing a training sequence so as to further refine transition probability accuracy for a given sequencer run or genome analysis. 
       FIG. 14  sets forth what the new state transition  17   b  diagram may look like when implementing these various simplifying assumptions. Specifically,  FIG. 22  sets forth the simplified HMM state transition diagram depicting the relationship between GOP, GCP, and transition probabilities with the simplifications set forth above. 
     Likewise,  FIG. 15  sets forth the circuitry  17   a,b  for the HMM transition probabilities and priors generation, which supports the simplified state transition diagram of  FIG. 14 . As seen with respect to  FIG. 15 , a circuit realization of that state transition diagram is provided. Thus, in various instances, for the HMM hardware accelerator  8 , the cost of generating the transition probabilities and one Prior each clock cycle reduces to: Two 64 word look-up-tables, and One 128 word look-up-table. 
     As set forth above, the engine control logic  15  is configured for generating the virtual matrix and/or traversing the matrix so as to reach the edge of the swath, e.g., via high-level engine state machines, where result data may be finally summed, e.g., via final sum control logic  19 , and stored, e.g., via put/get logic. 
     Accordingly, as can be seen with respect to  FIG. 16 , in various embodiments, a method for producing and/or traversing an HMM cell matrix  30  is provided. Specifically,  FIG. 16  sets forth an example of how the HMM accelerator control logic  15  goes about traversing the virtual cells in the HMM matrix. For instance, assuming for exemplary purposes, a 5 clock cycle latency for each multiply and each add operation, the worst-case latency through the M, I, D state update calculations would be the 20 clock cycles it would take to propagate through the M update calculation. There are half as many operations in the I and D state update calculations, implying a 10 clock cycle latency for those operations. 
     These latency implications of the M, I, and D compute operations can be understood with respect to  FIG. 16 , which sets forth various examples of the cell-to-cell data dependencies. In such instances, the M and D state information of a given cell feed the D state computations of the cell in the HMM matrix that is immediately to the right (e.g., having the same read base as the given cell, but having the next haplotype base). Likewise, the M and I state information for the given cell feed the I state computations of the cell in the HMM matrix that is immediately below (e.g., having the same haplotype base as the give cell, but having the next read base). So, in particular instances, the M, I, and D states of a given cell feed the D and I state computations of cells in the next diagonal of the HMM cell matrix. 
     Similarly, the M, I, and D states of a given cell feed the M state computation of the cell that is to the right one and down one (e.g., having both the next haplotype base AND the next read base). This cell is actually two diagonals away from the cell that feeds it (whereas, the I and D state calculations rely on states from a cell that is one diagonal away). This quality of the I and D state calculations relying on cells one diagonal away, while the M state calculations rely on cells two diagonals away, has a beneficial result for hardware design. 
     Particularly, given these configurations, I and D state calculations may be adapted to take half as long (e.g., 10 cycles) as the M state calculations (e.g., 20 cycles). Hence, if M state calculations are started 10 cycles before I and D state calculations for the same cell, then the M, I, and D state computations for a cell in the HMM matrix  30  will all complete at the same time. Additionally, if the matrix  30  is traversed in a diagonal fashion, such as having a swath  35  of about 10 cells each within it (e.g., that spans ten read bases), then: The M and D states produced by a given cell at (hap, rd) coordinates (i, j) can be used by cell (i+1, j) D state calculations as soon as they are all the way through the compute pipeline of the cell at (i, j). 
     The M and I states produced by a given cell at (hap, rd) coordinates (i, j) can be used by cell (i, j+1) I state calculations one clock cycle after they are all the way through the compute pipeline of the cell at (i, j). Likewise, the M, I and D states produced by a given cell at (hap, rd) coordinates (i, j) can be used by cell (i+1, j+1) M state calculations one clock cycle after they are all the way through the compute pipeline of the cell at (i, j). Taken together, the above points establish that very little dedicated storage is needed for the M, I, and D states along the diagonal of the swath path that spans the swath length, e.g., of ten reads. In such an instance, just the registers required to delay cell (i, j) M, I, and D state values one clock cycle for use in cell (i+1, j+1) M calculations and cell (i, j+1) I calculations by one clock cycle). Moreover, there is somewhat of a virtuous cycle here as the M state computations for a given cell are begun 10 clock cycles before the I and D state calculations for that same cell, natively outputting the new M, I, and D states for any given cell simultaneously. 
     In view of the above, and as can be seen with respect to  FIG. 16 , the HMM accelerator control logic  15  may be configured to process the data within each of the cells of the virtual matrix  30  in a manner so as to traverse the matrix. Particularly, in various embodiments, operations start at cell (0,0), with M state calculations beginning 10 clock cycles before I and D state calculations begin. The next cell to traverse should be cell (1,0). However, there is a ten cycle latency after the start of I and D calculations before the results from cell (0,0) will be available. The hardware, therefore, inserts nine “dead” cycles into the compute pipeline. These are shown as the cells with haplotype index less than zero in  FIG. 16 . 
     After completing the dead cycle that has an effective cell position in the matrix of (−9,−9), the M, I, and D state values for cell (0,0) are available. These (e.g., the M and D state outputs of cell (0,0)) may now be used straight away to start the D state computations of cell (0,1). One clock cycle later, the M, I, and D state values from cell (0,0) may be used to begin the I state computations of cell (0,1) and the M state computations of cell (1,1). 
     The next cell to be traversed may be cell (2,0). However, there is a ten cycle latency after the start of I and D calculations before the results from cell (1,0) will be available. The hardware, therefore, inserts eight dead cycles into the compute pipeline. These are shown as the cells with haplotype index less than zero, as in  FIG. 16  along the same diagonal as cells (1,0) and (0,1). After completing the dead cycle that has an effective cell position in the matrix of (−8, −9), the M, I, and D state values for cell (1,0) are available. These (e.g., the M and D state outputs of cell (1,0)) are now used straight away to start the D state computations of cell (2,0). 
     One clock cycle later, the M, I, and D state values from cell (1,0) may be used to begin the I state computations of cell (1,1) and the M state computations of cell (2,1). The M and D state values from cell (0,1) may then be used at that same time to start the D state calculations of cell (1,1). One clock cycle later, the M, I, and D state values from cell (0,1) are used to begin the I state computations of cell (0,2) and the M state computations of cell (1,2). 
     Now, the next cell to traverse may be cell (3,0). However, there is a ten-cycle latency after the start of I and D calculations before the results from cell (2,0) will be available. The hardware, therefore, inserts seven dead cycles into the compute pipeline. These are again shown as the cells with haplotype index less than zero in  FIG. 16  along the same diagonal as cells (2,0), (1,1), and (0,2). After completing the dead cycle that has an effective cell position in the matrix of (−7,−9), the M, I, and D state values for cell (2,0) are available. These (e.g., the M and D state outputs of cell (2,0)) are now used straight away to start the D state computations of cell (3,0). And, so, computation for another ten cells in the diagonal begins. 
     Such processing may continue until the end of the last full diagonal in the swath  35   a , which, in this example (that has a read length of 35 and haplotype length of 14), will occur after the diagonal that begins with the cell at (hap, rd) coordinates of (13,0) is completed. After the cell (4,9) in  FIG. 16  is traversed, the next cell to traverse should be cell (13,1). However, there is a ten-cycle latency after the start of the I and D calculations before the results from cell (12,1) will be available. 
     The hardware may be configured, therefore, to start operations associated with the first cell in the next swath  35   b , such as at coordinates (0, 10). Following the processing of cell (0, 10), then cell (13, 1) can be traversed. The whole diagonal of cells beginning with cell (13, 1) is then traversed until cell (5, 9) is reached. Likewise, after the cell (5, 9) is traversed, the next cell to traverse should be cell (13, 2). However, as before there may be a ten cycle latency after the start of I and D calculations before the results from cell (12, 2) will be available. Hence, the hardware may be configured to start operations associated with the first cell in the second diagonal of the next swath  35   b , such as at coordinates (1, 10), followed by cell (0, 11). 
     Following the processing of cell (0, 11), the cell (13, 2) can be traversed, in accordance with the methods disclosed above. The whole diagonal  35  of cells beginning with cell (13,2) is then traversed until cell (6, 9) is reached. Additionally, after the cell (6, 9) is traversed, the next cell to be traversed should be cell (13, 3). However, here again there may be a ten-cycle latency period after the start of the I and D calculations before the results from cell (12, 3) will be available. The hardware, therefore, may be configured to start operations associated with the first cell in the third diagonal of the next swath  35   c , such as at coordinates (2, 10), followed by cells (1, 11) and (0, 12), and likewise. 
     This continues as indicated, in accordance with the above until the last cell in the first swath  35   a  (the cell at (hap, rd) coordinates (13, 9)) is traversed, at which point the logic can be fully dedicated to traversing diagonals in the second swath  35   b , starting with the cell at (9, 10). The pattern outlined above repeats for as many swaths of 10 reads as necessary, until the bottom swath  35   c  (those cells in this example that are associated with read bases having index  30 , or greater) is reached. 
     In the bottom swath  35 , more dead cells may be inserted, as shown in  FIG. 16  as cells with read indices greater than 35 and with haplotype indices greater than 13. Additionally, in the final swath  35   c , an additional row of cells may effectively be added. These cells are indicated at line  35  in  FIG. 16 , and relate to a dedicated clock cycle in each diagonal of the final swath where the final sum operations are occurring. In these cycles, the M and I states of the cell immediately above are added together, and that result is itself summed with a running final sum (that is initialized to zero at the left edge of the HMM matrix  30 ). 
     Taking the discussion above as context, and in view of  FIG. 16 , it is possible to see that, for this example of read length of 35 and haplotype length of 14, there are 102 dead cycles, 14 cycles associated with final sum operations, and 20 cycles of pipeline latency, for a total of 102+14+20=146 cycles of overhead. It can also be seen that, for any HMM job  20  with a read length greater than 10, the dead cycles in the upper left corner of  FIG. 16  are independent of read length. It can also be seen that the dead cycles at the bottom and bottom right portion of  FIG. 16  are dependent on read length, with fewest dead cycles for reads having mod(read length, 10)=9 and most dead cycles for mod(read length, 10)=0. It can further be seen that the overhead cycles become smaller as a total percentage of HMM matrix  30  evaluation cycles as the haplotype lengths increase (bigger matrix, partially fixed number of overhead cycles) or as the read lengths increase (note: this refers to the percentage of overhead associated with the final sum row in the matrix being reduced as read length−row-count−increases). Using such histogram data from representative whole human genome runs, it has been determined that traversing the HMM matrix in the manner described above results in less than 10% overhead for the whole genome processing. 
     Further methods may be employed to reduce the amount of overhead cycles including: Having dedicated logic for the final sum operations rather than sharing adders with the M and D state calculation logic. This eliminates one row of the HMM matrix  30 . Using dead cycles to begin HMM matrix operations for the next HMM job in the queue. 
     Each grouping of ten rows of the HMM matrix  30  constitutes a “swath”  35  in the HMM accelerator function. It is noted that the length of the swath may be increased or decreased so as to meet the efficiency and/or throughput demands of the system. Hence, the swatch length may be about five rows or less to about fifty rows or more, such as about ten rows to about forty-five rows, for instance, about fifteen or about twenty rows to about forty rows or about thirty five rows, including about twenty five rows to about thirty rows of cells in length. 
     With the exceptions noted in the section, above, related to harvesting cycles that would otherwise be dead cycles at the right edge of the matrix of  FIG. 16 , the HMM matrix may be processed one swath at a time. As can be seen with respect to  FIG. 16 , the states of the cells in the bottom row of each swath  35   a  feed the state computation logic in the top row of the next swath  35   b . Consequently, there may be a need to store (put) and retrieve (get) the state information for those cells in the bottom row, or edge, of each swath. 
     The logic to do this may include one or more of the following: when the M, I, and D state computations for a cell in the HMM matrix  30  complete for a cell with mod(read index, 10)=9, save the result to the M, I, D state storage memory. When M and I state computations (e.g., where D state computations do not require information from cells above them in the matrix) for a cell in the HMM matrix  30  begin for a cell with mod(read index, 10)=0, retrieve the previously saved M, I, and D state information from the appropriate place in the M, I, D state storage memory. Note in these instances that M, I, and D state values that feed row  0  (the top row) M and I state calculations in the HMM matrix  30  are simply a predetermined constant value and do not need to be recalled from memory, as is true for the M and D state values that feed column 0 (the left column) D state calculations. 
     As noted above, the HMM accelerator may or may not include a dedicated summing resource in the HMM hardware accelerator such that exist simply for the purpose of the final sum operations. However, in particular instances, as described herein, an additional row may be added to the bottom of the HMM matrix  30 , and the clock cycles associated with this extra row may be used for final summing operations. For instance, the sum itself may be achieved by borrowing (e.g., as per  FIG. 13 ) an adder from the M state computation logic to do the M+I operation, and further by borrowing an adder from the D state computation logic to add the newly formed M+I sum to the running final sum accumulation value. In such an instance, the control logic to activate the final sum operation may kick in whenever the read index that guides the HMM traversing operation is equal to the length of the inputted read sequence for the job. These operations can be seen at line  34  toward the bottom of the sample HMM matrix  30  of  FIG. 16 . 
     Hence, as can be seen above, in one implementation, the variant caller may make use of the mapper and/or aligner engines to determine the likelihood as to where various reads originated, such as with respect to a given location. In such instances, the variant caller may be configured to detect the underlying sequence at that location, such as independently of other regions not immediately adjacent to it. This is particularly useful and works well when the region of interest does not resemble any other region of the genome over the span of a single read (or a pair of reads for paired-end sequencing). However, a significant fraction of the human genome does not meet this criterion, which can make variant calling, e.g., the process of reconstructing a subject&#39;s genome from the reads that an NGS produces, challenging. 
     Particularly, though DNA sequencing has improved dramatically, variant calling remains a difficult problem, largely due to the genome&#39;s redundant structure. As disclosed herein, however, the complexities presented by the genome&#39;s redundancy may be overcome, at least in part, from a perspective driven by short read data. More particularly, the devices, systems, and methods of employing the same as disclosed herein may be configured in such a manner so as to focus on Homologous or Similar regions that may otherwise have been characterized by low variant calling accuracy. In certain instances, such low variant calling accuracy may stem from difficulties observed in read mapping and alignments such as in homologous regions that typically may result in very low read MAPQs. Accordingly, presented herein are strategic implementations that accurately call variants (SNPs, INDELs, and the like) in homologous regions by jointly considering the information present in the homologous regions. 
     For instance, many regions of the genome are homologous, e.g., they have near-identical copies elsewhere, and as a result, the true source location of a read may be subject to considerable uncertainty. Specifically, if a group of reads is mapped with low confidence, a typical variant caller may ignore the reads, even though they contain useful information. In other instances, if a read is mismapped (e.g., the primary alignment is not the true source of the read), it can result in detection errors. More specifically, previously implemented short-read sequencing technologies have been susceptible to these problems, and conventional detection methods often leave large regions of the genome in the dark. In some instances, long-read sequencing can mitigate these problems, but it typically has much higher cost and/or higher error rates, takes longer, and/or suffers from other shortcomings. Therefore, in various instances, instead of considering each region in isolation and/or instead of performing and analyzing ling read sequencing, multi-region joint detection (MRJD) methodologies may be employed, such as where the MRJD considers multiple, e.g., all, locations from which a group of reads may have originated and attempts to detect the underlying sequences together, e.g., jointly, using all available information, which may be regardless of confidence and/or certainty scores. 
     For instance, for a diploid organism with statistically uniform coverage, a brute force Bayesian calculation may be performed. However, in such a brute force MLRD computation, the complexity of the calculation grows rapidly with the number of regions N and the number of candidate haplotypes K to be considered. Particularly, to consider all combinations of candidate haplotypes, the number of candidate solutions for which to calculate probabilities is exponential. For example, as described in greater detail below, in a brute force implementation, the number of candidate haplotypes includes the number of active positions, which if a graph-assembly technique is used to generate the list of candidate haplotypes, such as in a De Brujin graph disclosed herein, then the number of active positions is the number of independent “bubbles” in the graph. Hence, such a brute-force calculation can be prohibitively expensive to implement, and as such brute force Bayesian calculations can be prohibitively complex. 
     Accordingly, in one aspect, as set forth in  FIG. 17 , a method to reduce the complexity of such brute force calculations is herein provided. For instance, as disclosed above, though the speed and accuracy of DNA sequencing has improved dramatically, especially with respect to the methods disclosed herein, variant calling, e.g., the process of reconstructing a subject&#39;s genome from the reads a sequencer produces, remains a difficult problem, largely due to the genome&#39;s redundant structure. The devices, systems, and methods disclosed herein therefore are configured to reduce the complexities presented by the genome&#39;s redundancy from a perspective driven by short read data in contrast to long read sequencing. In particular, provided herein are methods for performing very long read detection that accounts for homologous and/or similar regions of the genome that are usually characterized by low variant calling accuracy without necessarily having to perform long read sequencing. 
     Specifically, as can be seen with respect to  FIG. 17 , a high-level processing chain is provided, such as where the processing chain may include one or more of the following steps: Identifying and inputting homologous regions, performing pre-processing of the input homologous regions, performing a pruned very long read (VLRD) or multi region joint detection (MJRD), and outputting a variant call file. Particularly with respect to identifying homologous regions, a mapped, aligned, and/or sorted SAM and/or BAM file, e.g., a CRAM, may be used as the primary input to a multi-region joint detection processing engine implementing an MRJD algorithm, as described herein. The MJRD processing engine may be part of an integrated circuit such as a CPU and/or GPU and/or Quantum computing platform, running software, e.g., a quantum algorithm, or implemented within an FPGA, ASIC, or the like. For instance, the above disclosed mapper and/or aligner may be used to generate a CRAM file, e.g., with settings to output N secondary alignments for each read along with the primary alignments. These primary and secondary reads may then be used to identify a list of homologous regions, which homologous regions may be computed based on a user defined similarity threshold between the N regions of the reference genome. This list of identified homologous regions may then be fed to the pre-processing stage of a suitably configured MRJD module. 
     Accordingly, in the pre-processing stage, for every set of homologous regions, a joint-pileup may first be generated such as by using the primary alignments from one or more, e.g., every, region in the set. See, for instance,  FIG. 19 . Using this joint pileup, a list of active/candidate variant positions (SNPS/INDELs) may then be generated whereby each of these candidate variants may be processed and evaluated by the MRJD pre-processing engine(s). To reduce computation complexity, a connection matrix may be computed that may be used to define the order of processing of the candidate variants. 
     In such implementations, the multi-region joint detection algorithm evaluates each identified candidate variant based on the processing order defined in the generated connection matrix. Firstly, one or more candidate joint diplotypes (G i ) may be generated and given a candidate variant. Next, the a-posteriori probabilities of each of the joint diplotypes (P(G i |R)) may be calculated. From these a-posteriori probabilities a genotype matrix may be computed. Next, N diplotypes with the lowest a-posteriori probabilities may be pruned so as to reduce the computational complexity of the calculations. Then the next candidate variant that provides evidence for the current candidate variant being evaluated may be included and the above process repeated. Having included information such as from one or more, e.g., all, the candidate variants from one or more, e.g., all, regions in the homologous region set for the current variant, a variant call may be made from the final genotyping matrix. Each of the active positions, therefore, may all be evaluated in the manner above thereby resulting in a final VCF file. 
     Particularly, as can be seen with respect to  FIG. 18 , a MJRD preprocessing step may be implemented, such as including one or more of the following steps or blocks: The identified and assembled joint pile-up is loaded, a candidate variant list is then created from the assembled joint pile up, and a connection matrix is computed. Particularly, in various instances, a preprocessing methodology may be performed, such as prior to performing one or more variant call operations, such as a multiple read joint detection operation. Such operations may include one or more preprocessing blocks, including: steps pertaining to the loading of joint pile-ups, generating a list of variant candidates from the joint pileups, and computing a connection matrix. Each of the blocks and potential steps associated therewith will now be discussed in greater detail. 
     Specifically, a first joint pile up pre-processing block may be included in the analysis procedure. For example, various reference regions for an identified span may be extracted, such as from the mapped and/or aligned reads. Particularly, using the list of homologous regions, a joint pileup for each set of homologous regions may be generated. Next, a user-defined span may be used to extract the N reference regions corresponding to N homologous regions within a set. Subsequently, one or more, e.g., all, of the reference regions may be aligned, such as by using a Smith-Waterman alignment, which may be used to generate a universal coordinate system of all the bases in the N reference regions. Further, all the primary reads corresponding to each region may then be extracted from the input SAM or BAM file and be mapped to the universal coordinates. This mapping may be done, as described herein, such as by using the alignment information (CIGAR) present in a CRAM file for each read. In the scenario where some reads pairs were not previously mapped, the reads may be mapped and/or aligned, e.g., Smith-Waterman aligned, to its respective reference region. 
     More particularly, once a joint pile up has been generated and loaded, see for instance,  FIG. 19 , a candidate variant list may be created, such as from the joint pile up. For instance, a De Bruijn graph (DBG) or other assembly graph may be produced so as to extract various candidate variants (SNPs/Indels) that may be identified from the joint pileup. Once the DBG is produced the various bubbles in the graph can be mined so as to derive a list of variant candidates. 
     Particularly, given all the reads, a graph may be generated using each reference region as a backbone. All of the identified candidate variant positions can then be aligned to universal coordinates. A connection matrix may then be computed, where the matrix defines the order of processing of the active positions, which may be a function of the read length and/or insert size. As referenced herein,  FIG. 19  shows an example of a joint pileup of two homologous regions in chromosome 1. Although this pileup is with reference to two homologous regions of chromosome 1, this is for exemplary purposes only as the production of the pileup process may be used for any and all homologous regions regardless of chromosome. 
     As can be seen with respect to  FIG. 20 , a candidate variant list may be created as follows. First, a joint pileup may be formed and a De Bruijn graph (DBG) or other assembly graph may be constructed, in accordance with the methods disclosed herein. The DBG may then be used to extract the candidate variants from the joint pileups. The construction of the DBG is performed in such a manner as to generate bubbles, indicating variations, representing alternate pathways through the graph where each alternate path is a candidate haplotypes. See, for instance,  FIGS. 20 and 21 . 
     Accordingly, the various bubbles in the graph represent the list of candidate variant haplotype positions. Hence, given all of the reads, the DBG may be generated using each reference region as a backbone. Then all of the candidate variant positions can be aligned to universal coordinates. Specifically,  FIG. 20  illustrates a flow chart setting forth the process of generating a DBG and using the same to produce candidate haplotypes. More specifically, the De Bruijn graph may be employed in order to create the candidate variant list of SNPs and INDELs. Given that there are N regions that are being jointly processed by MRJD, N de-bruijn graphs may be constructed. In such an instance, every graph may use one reference region as a backbone and all of the reads corresponding to the N regions. 
     For instance, in one methodological implementation, after the DBG is constructed, the candidate haplotypes may be extracted from the De Bruijn graph based on the candidate events. However, when employing an MRJD pre-processing protocol, as described herein, N regions may be jointly processed, such as where the length of the regions can be a few thousand bases or more, and the number of haplotypes to be extracted can grow exponentially very quickly. Accordingly, in order to reduce the computational complexity, instead of extracting entire haplotypes, only the bubbles need be extracted from the graphs that are representative of the candidate variants. 
     An example of bubble structures formed in a De Bruijn graph is shown in  FIG. 21 . A number of regions to be processed jointly are identified. This determines one of two processing pathways that may be followed. If joint regions are identified all the reads may be used to form a DBG. Bubbles showing possible variants may be extracted so as to identify the various candidate haplotypes. Specifically, for each bubble a SW alignment may be performed on the alternate paths to the reference backbone. From this the candidate variants may be extracted and the events from each graph may be stored. 
     However, in other instances, once the first process has been performed, so as to generate one or more DBGs, and/or i is now equal to 0, then the union of all candidate events from all of the DBGs may be generated, where any duplicates may be removed. In such an instance, all candidate variants may be mapped, such as to a universal coordinate system, so as to produce the candidate list, and the candidate variant list may be sent as an input to a pruning module, such as the MJRD module. An example of only performing bubble extraction, instead of extracting the entire haplotypes, is shown in  FIG. 22 . In this instance, it is only the bubble region showing possible variants that is extracted and processed, as described herein. 
     Specifically, once the representative bubbles have been extracted, the global alignment, e.g., Smith-Waterman alignment, of the bubble path and the corresponding reference backbone may be performed to get the candidate variant(s) and its position in the reference. This may be done for all extracted bubbles in all of the De Bruijn graphs. Next, the union of all the extracted candidate variants may be taken from the N graphs, the duplicate candidates, if any, may be removed, and the unique candidate variant positions may be mapped to the universal coordinate system obtained from the joint pile-up. This results in a final list of candidate variant positions for the N regions that may act as an input to a “Pruned” MRJD algorithm. 
     In particular preprocessing blocks, as described herein above, a connection matrix may be computed. For instance, a connection matrix may be used to define the order of processing of active, e.g., candidate, positions, such as a function of read length and insert size. For example, to further reduce computational complexity, a connection matrix may be computed so as to define the order of processing of identified candidate variants that are obtained from the De Bruijn graph. This matrix may be constructed and employed in conjunction with or as a sorting function to determine which candidate variants to process first. This connection matrix, therefore, may be a function of the mean read length and the insert size of the paired-end reads. Accordingly, for a given candidate variant, other candidate variant positions that are at integral multiples of the insert size or within the read length have higher weights compared to the candidate variants at other positions. This is because these candidate variants are more likely to provide evidence for the current variant being evaluated. An exemplary sorting function, as implemented herein, is shown in  FIG. 23  for mean read length of 101 and insert-size of 300. 
     With respect to a MJRD pruning function, exemplary steps of a pruned MRJD algorithm, as referenced above, is set forth in  FIG. 24 . For instance, the input to the MRJD platform and algorithm is the joint pileup of N regions, e.g., all the candidate variants (SNPs/INDELs), the a-priori probabilities based on a mutation model, and the connection matrix. Accordingly, the input into the pruned MRJD processing platform may be the joint pile-up, the identified active positions, the generated connection matrix, and the a-posteriori probability model, and/or the results thereof. 
     Next, each candidate variant in the list can be processed and other variants can be successively added as evidence for a current candidate being processed using the connection matrix. Accordingly, given the current candidate variant and any supporting candidates, candidate joint diplotypes may be generated. For instance, a joint diplotype is a set of 2N haplotypes, where N is the number of regions being jointly processed. The number of candidate joint diplotypes M is a function of the number of regions being jointly processed, number of active/candidate variants being considered, and the number of phases. An example for generating joint diplotypes is shown below. 
     For: P=1, Number of active/candidate variant positions being considered;
 
N=2, Number of regions being jointly processed;
 
M=2 2.N.P =2 4 =16 candidate joint-diplotypes
 
     Hence, for a single candidate active position, given all the reads and both the reference regions, let the two haplotypes be ‘A’ and ‘G’. 
     Unique haplotypes=‘A’ and ‘G’
 
Candidate Diplotypes=‘AA’, ‘AG’, ‘GA’ and ‘GG’, (4 candidates for 1 region).
 
     
       
         
           
             
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     Accordingly, using the candidate joint diplotypes, the read likelihoods can be calculated given a haplotype for each haplotype in every candidate joint diplotype set. This may be done using a HMM algorithm, as described herein. However, in doing so the HMM algorithm may be modified from its standard use case so as to allow for candidate variants (SNPs/INDELs) in the haplotype, which have not yet been processed, to be considered. Subsequently, the read likelihoods can be calculated given a joint diplotype (P(r i |Gm)) using the results from the modified HMM. This may be done using the formula below. 
     For the case of 2-region joint detection: 
     G m =[ϑ 11,m , ϑ 12,m , ϑ 21,m , ϑ 22,m ], wherein ϑ ij,m , i is the region and j is the phase 
     
       
         
           
             
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     P(R|G m )=Π i P(ri|Gm). Given P(r i |Gm), it is straightforward to calculate P(R|Gm) for all the reads. Next, using Bayes&#39; formula, the a-posteriori probability (P(G i |R)) may be computed from P(R|G i ) and the a-priori probabilities (P(G i )). 
         P ( G   i   |R )= P ( R|G   i ) P ( G   i )/Σ k   P ( R|Gk ) P ( Gk ).
 
     Further, an intermediate genotype matrix may be calculated for each region given the a-posteriori probabilities for all the candidate joint diplotypes. For each event combination in the genotype matrix the a-posteriori probabilities of all joint diplotypes supporting that event may be summed up. At this point, the genotype matrix may be considered as “intermediate” because not all the candidate variants supporting the current candidate have been included. However, as seen earlier, the number of joint diplotype candidates grows exponentially with the number of candidate variant positions and number of regions. This in-turn exponentially increases the computation required to calculate the a-posteriori probabilities. Therefore, in order to reduce the computational complexity, at this stage, the number of joint diplotypes based on the a-posteriori probabilities may be pruned so that the number of joint diplotypes to keep may be user defined and programmable. Finally, the final genotype matrix may be updated based on a user-defined confidence metric of variants which is computed using the intermediate genotype matrix. The various steps of these processes are set forth in the process flow diagram of  FIG. 24 . 
     The process above may be repeated until all the candidate variants are included as evidence for the current candidates being processed using the connection matrix. Once all of the candidates have been included, the processing of the current candidate is done. Other stopping criteria for processing candidate variants are also possible. For example, the process may be stopped when the confidence has stopped increasing as more candidates variants are added. This analysis, as exemplified in  FIG. 24 , may be restarted and repeated in the same manner for all other candidate variants in the list thereby resulting in a final variant call file at the output of MRJD. Accordingly, instead of considering each region in isolation, a Multi-Region Joint Detection protocol, as described herein, may be employed so as to consider all locations from which a group of reads may have originated as it attempts to detect the underlying sequences jointly using all available information. 
     Accordingly, for Multi-Region Joint Detection, an exemplary MRJD protocol may employ one or more of the following equations in accordance with the methods disclosed herein. Specifically, instead of considering each region to be assessed in isolation, MRJD considers a plurality of locations from which a group of reads may have been originated and attempts to detect the underlying sequences jointly, such as by using as much as, e.g., all, the available information that is useful. For instance, in one exemplary embodiment: 
     Let N be the number of regions to be jointly processed. And let H k  be a candidate haplotype, k=1 . . . K, each of which may include various SNPs, insertions and/or deletions relative to a reference sequence. Each haplotype H k  represents a single region along a single strand (or “phase”, e.g., maternal or paternal), and they need not be contiguous (e.g., they may include gaps or “don&#39;t care” sequences). 
     Let G m  be a candidate solution for both phases Φ=1, 2 (for a diploid organism) and all regions n=1 . . . N: 
     
       
         
           
             
               G 
               m 
             
             = 
             
               [ 
               
                 
                   
                     
                       Gm 
                       , 
                       1 
                       , 
                       
                         1 
                          
                         … 
                       
                     
                   
                   
                     
                       Gm 
                       , 
                       1 
                       , 
                       N 
                     
                   
                 
                 
                   
                     
                       Gm 
                       , 
                       2 
                       , 
                       
                         1 
                          
                         … 
                       
                     
                   
                   
                     
                       Gm 
                       , 
                       2 
                       , 
                       N 
                     
                   
                 
               
               ] 
             
           
         
       
     
     where each element G m,Φ,n  is a haplotype chosen from the set of candidates {H 1  . . . H k }. 
     First, the probability of each read may be calculated for each candidate haplotype P(r i |H k ), for example, by using a Hidden Markov Model (HMM). In the case of datasets with paired reads, r i  indicates the pair {r i,1 , r i,2 }, and P(r i |H k )=P(r i,1 |H k )P(r i,2 |H k ). In the case of datasets with linked reads (e.g., barcoded reads), r i  indicates the group of reads {R i,1  . . . r i,NL } that came from the same long molecule, and P(r i |H k )=Π n=1   NL  P(ri, n|Hk). 
     Next, for each candidate solution G m , m=1 . . . M, we calculate the conditional probability of each read 
     
       
         
           
             
               P 
               ( 
               
                 
                   r 
                   i 
                 
                 | 
                 
                   G 
                   m 
                 
               
               ) 
             
             = 
             
               
                 1 
                 
                   2 
                    
                   N 
                 
               
                
               
                 
                   ∑ 
                   
                     n 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   
 
                 
                  
                 
                   
                     ∑ 
                     
                       Φ 
                       = 
                       1 
                     
                     2 
                   
                    
                   
                       
                   
                    
                   
                     P 
                      
                     
                       ( 
                       
                         
                           ri 
                           | 
                           Gm 
                         
                         , 
                         Φ 
                         , 
                         n 
                       
                       ) 
                     
                   
                 
               
             
           
         
       
     
     and conditional probability of the entire pileup R={r 1  . . . r NR }: P(R|G m )=Π i=1   NR  P(ri|Gm). 
     Next, the a-posteriori probability is calculated of each candidate solution given the observed pileup: P(G m |R)=P(R|Gm)P(Gm)/Σ i=1   M  P(R|Gi)P(Gi) where P(G m ) indicates the a-priori probability of the candidate solution, which is set forth in detail here below. 
     Finally, the relative probability of every candidate variant V j  is calculated 
     
       
         
           
             
               
                 
                   P 
                    
                   
                     ( 
                     
                       Vj 
                       | 
                       R 
                     
                     ) 
                   
                 
                 
                   P 
                    
                   
                     ( 
                     
                       ref 
                       | 
                       R 
                     
                     ) 
                   
                 
               
               = 
               
                 
                   Σ 
                   
                     
                       n 
                       | 
                       Gm 
                     
                     = 
                     
                       &gt; 
                       vj 
                     
                   
                 
                  
                 
                   
                     P 
                      
                     
                       ( 
                       
                         Gm 
                         | 
                         R 
                       
                       ) 
                     
                   
                   / 
                   
                     Σ 
                     
                       
                         m 
                         | 
                         Gm 
                       
                       = 
                       
                         &gt; 
                         ref 
                       
                     
                   
                 
                  
                 
                   P 
                    
                   
                     ( 
                     
                       Gm 
                       | 
                       R 
                     
                     ) 
                   
                 
               
             
             , 
           
         
       
     
     such as where G m →V j  indicates that G m  supports variant V j , and G m →ref indicates that G m  supports the reference. In a VCF file, this may be reported as a quality score on 
     
       
         
           
             
               QUAL 
                
               
                 ( 
                 
                   V 
                   j 
                 
                 ) 
               
             
             = 
             
               
                 - 
                 10 
               
                
               
                 log 
                 10 
               
                
               
                 
                   
                     P 
                      
                     
                       ( 
                       
                         Vj 
                         | 
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                       ( 
                       
                         ref 
                         | 
                         R 
                       
                       ) 
                     
                   
                 
                 . 
               
             
           
         
       
     
     An exemplary process for performing various variant calling operations is set forth herein with respect to  FIG. 25  where a conventional and MRJD detection process are compared. Specifically,  FIG. 25  illustrates a joint pileup of paired reads for two regions whose reference sequences differ by only 3 bases over the range of interest. All the reads are known to come from either region #1 or region #2, but it is not known with certainty from which region any individual read originated. Note, as described above, that the bases are only shown for the positions where the two references differ, e.g., bubble regions, or where the reads differ from the reference. These regions are referred to as the active positions. All other positions can be ignored, as they don&#39;t affect the calculation. 
     Accordingly, as can be seen with respect to  FIG. 25 , in a conventional detector, the read pairs 1-16 would be mapped to region #2, and these alone would be used for variant calling in region #2. All of these reads match the reference for region #2, so no variants would be called. Likewise, read pairs 17-23 would be mapped to region #1, and these alone would be used for variant calling in region #1. As can be seen, all of these reads match the reference for region #1, so no variants will be called. However, read pairs 24-32 map equally well to region #1 and region #2 (each has a one-base difference to ref #1 and to ref #2), so the mapping is indeterminate, and a typical variant caller would simply ignore these reads. As such, a conventional variant caller would make no variant calls for either region, as seen in  FIG. 25 . 
     However, with MRJD,  FIG. 25  illustrates that the result is completely different than that received employing conventional methods. The relevant calculations are set forth below. In this instance N=2 regions. Additionally, there are three positions, each with 2 candidate bases (one can safely ignore bases whose count is sufficiently low, and in this example the count is zero on all but 2 bases in each position). If all combinations are considered, this will yield K=2 3 =8 candidate haplotypes: H 1 =CAT, H 2 =CAA, H 3 =CCT, H 4 =CCA, H 5 =GAT, H 6 =GAA, H 7 =GCT, H 8 =GCA. 
     In a brute-force calculation where all combinations of all candidate haplotypes are considered, the number of candidate solutions is M=K 2N =8 2.2 =4096, and P(G m /R) may be calculated for each candidate solution G m . The following illustrates this calculation for two candidate solutions: 
     
       
         
           
             
               
                 G 
                 m1 
               
               = 
               
                 [ 
                 
                   
                     
                       CAT 
                     
                     
                       GCA 
                     
                   
                   
                     
                       CAT 
                     
                     
                       GCA 
                     
                   
                 
                 ] 
               
             
             , 
             
               
                 G 
                 
                   m 
                    
                   
                       
                   
                    
                   2 
                 
               
               = 
               
                 [ 
                 
                   
                     
                       CAT 
                     
                     
                       GCA 
                     
                   
                   
                     
                       CCT 
                     
                     
                       GCA 
                     
                   
                 
                 ] 
               
             
           
         
       
     
     Where G m1  has no variants (this is the solution found by a conventional detector), and G m2  has a single heterozygous SNP A→C in position #2 of region #1. 
     The probability P(r i |H k ) depends on various factors including the base quality and other parameters of the HMM. It may be assumed that only base call errors are present and all base call errors are equally likely, so P(r i |H k )=(1−p e ) Np(i)−Ne(i) (p e /3) Ne(i) , where p e  is the probability of a base call error, N p (i) is the number of active base position(s) overlapped by read i, and Ne(i) is the number of errors for read i, assuming haplotype H k . Accordingly, it may be assumed that p e =0.01, which corresponds to a base quality of phred 20. The table set forth in  FIG. 26  shows P(r i |H k ), for all read pairs and all candidate haplotypes. The two far right columns show P(r i |G m1 ) and P(r i |G m2 ), with the product at the bottom.  FIG. 26  shows that P(R|G m1 )=3.5 −30  and P(R|G m2 )=2.2 −15 , a difference of 15 orders of magnitude in favor of G m2 . 
     The a-posteriori probabilities P(G m |R) depend on the a-priori probabilities P(G m ). To complete this example, a simple independent identically distributed (IID) model may be assumed, such that the a-priori probability of a candidate solution with Nv variants is (1−p v ) N.Np-Nv(p   v /9) Nv , where N p  is the number of active positions (3 in this case) and Pv is the probability of a variant, assumed to be 0.01 in this example. This yields P(G m )=7.22e-13, and P(G m2 )=0.500. It is noted that G m2  is heterozygous over region #1, and all heterozygous pairs of haplotypes have a mirror-image representation with the same probability (obtained by simply swapping the phases). In this case, the sum of the probabilities for G m2  and its mirror image sum to 1.000. Calculating probabilities of individual variants, a heterozygous A→C SNP at position #2 of region #1, with quality score of phred 50.4 can be seen. 
     Accordingly, as can be seen, there is an immense computational complexity for performing a brute force variant calling operation, which complexity can be reduced by performing multiple region joint detection, as described herein. For instance, the complexity of the above calculations grows rapidly with the number of regions N and the number of candidate haplotypes K. To consider all combinations of candidate haplotypes, the number of candidate solutions for which to calculate probabilities is M=K 2N . In a brute force implementation, the number of candidate haplotypes is K=2 Np , where N p  is the number of active positions (e.g., as exemplified above, if graph-assembly techniques are used to generate the list of candidate haplotypes, then Np is the number of independent bubbles in the graph). Hence, a mere brute-force calculation can be prohibitively expensive to implement. For example, if N=3 and Np=10, the number of candidate solutions is M=2 3.2.10 =2 60 =10 18 . However, in practice, it&#39;s not uncommon to have values of N p  much higher than this. 
     Consequently, because a brute force Bayesian calculation can be prohibitively complex, the following description sets forth further methods for reducing the complexity of such calculations. For instance, in a first step of another embodiment, starting with a small number of positions N p   j  (or even a single position N p   j =1), the Bayesian calculation may be performed over those positions. At the end of the calculation, the candidates whose probability falls below a predefined threshold may be eliminated, such as in a pruning of the tree function, as described above. In such an instance, the threshold may be adaptive. 
     Next, in a second step, the number of positions N p   j  may be increased by a small number ΔN p  (such as one: N p   j+1 =N p   j ΔN p ), and the surviving candidates can be combined with one or more, e.g., all, possible candidates at the new position(s), such as in a growing the tree function. These steps of (1) performing the Bayesian calculation, (2) pruning the tree, and (3) growing the tree, may then be repeated, e.g., sequentially, until a stopping criteria is met. The threshold history may then be used to determine the confidence of the result (e.g., the probability that the true solution was or was not found). This process is illustrated in the flow chart set forth in  FIG. 27 . 
     It is to be understood that there are a variety of possible variations to this approach. For instance, as indicated, the pruning threshold may be adaptive, such as based on the number of surviving candidates. For instance, a simple implementation may set the threshold to keep the number of candidates below a fixed number, while a more sophisticated implementation may set the threshold based on a cost-benefit analysis of including additional candidates. Further, a simple stopping criteria may be that a result has been found with a sufficient level of confidence, or that the confidence on the initial position has stopped increasing as more positions are added. Further still, a more sophisticated implementation may perform some type of cost-benefit analysis of continuing to add more positions. Additionally, as can be seen with respect to  FIG. 27 , the order in which new positions are added may depend on several criteria, such as the distance to the initial position(s) or how highly connected these positions are to the already-included positions (e.g., the amount of overlap with the paired reads). 
     A useful feature of this algorithm is that the probability that the true solution wasn&#39;t found can be quantified. For instance, a useful estimate is obtained by simply summing the probabilities of all pruned branches at each step: P pruned =P pruned +Σ mεpruned set  P(G m   j |R). Such an estimate is useful for calculating the confidence of the resulting variant calls: 
     
       
         
           
             
               
                 P 
                  
                 
                   ( 
                   
                     vj 
                     | 
                     R 
                   
                   ) 
                 
               
               
                 P 
                  
                 
                   ( 
                   
                     ref 
                     | 
                     R 
                   
                   ) 
                 
               
             
             = 
             
               
                 
                   Σ 
                   
                     
                       m 
                       | 
                       Gm 
                     
                     = 
                     
                       &gt; 
                       vj 
                     
                   
                 
                  
                 
                   P 
                    
                   
                     ( 
                     
                       Gm 
                       | 
                       R 
                     
                     ) 
                   
                 
               
               + 
               
                 
                   Ppruned 
                   / 
                   
                     Σ 
                     
                       
                         m 
                         | 
                         Gm 
                       
                       = 
                       
                         &gt; 
                         ref 
                       
                     
                   
                 
                  
                 
                   P 
                    
                   
                     ( 
                     
                       Gm 
                       | 
                       R 
                     
                     ) 
                   
                 
               
               + 
               
                 Ppruned 
                 . 
               
             
           
         
       
     
     Good confidence estimates are essential for producing good Receiver Operating Characteristic (ROC) curves. This is a key advantage of this pruning method over other ad hoc complexity reductions. 
     Returning to the example pileup of  FIG. 25 , and starting from the left-most position (position #1) and working toward the right one base position at a time, using a pruning threshold of phred 60 on each iteration: Let {G m   j , m=1 . . . M j } represent the candidate solutions on the j-th iteration.  FIG. 28  shows the candidate solutions on the first iteration, representing all combinations of bases C and G, listed in order of decreasing probability. For any solution with equivalent mirror-image representations (obtained by swapping the phases), only a single representation is shown here. The probabilities for all candidate solutions can be calculated, and those probabilities beyond the pruning threshold (indicated by the solid line in the  FIG. 28 ) can be dropped. As can be seen with respect to  FIG. 28 , as a result of the pruning methods disclosed herein, six candidates survive. 
     Next, as can be seen with respect to  FIG. 29 , the tree can be grown by finding all combinations of the surviving candidates from iteration #1 and candidate bases (C and A) in the position #2. A partial list of the new candidates is shown in  FIG. 29 , again shown in order of decreasing probability. Again, the probabilities can be calculated and compared to the pruning threshold, and in this instance 5 candidates survive. 
     Finally, all combinations of the surviving candidates from iteration #2 and the candidate bases in position #3 (A and T) can be determined. The final candidates and their associated probabilities are shown in  FIG. 30 . Accordingly, when calculating the probabilities of individual variants, it is determined that a heterozygous A→C SNP at position #2 of region #1, with quality score of phred 50.4, which is the same result found in the brute-force calculation. In this example, pruning had no significant effect on the end result, but in general pruning may affect the calculation, often resulting in a more confidence score. 
     There are many possible variations to the implementations of this approach, which may affect the performance and complexity of the system, and different variations may be appropriate for different scenarios. For instance, there can be variations in deciding which regions to include. For example, prior to running a Multi-Region Joint Detection, the variant caller may be configured to determine whether a given active region should be processed individually or jointly with other regions, and if jointly, it may then determine which regions to include. In other instances, some implementations may rely on a list of secondary alignments provided by the mapper so as to inform or otherwise make this decision. Other implementations may use a database of homologous regions, computed offline, such as based on a search of the reference genome. 
     Accordingly, a useful step in such operations is in deciding which positions to include. For instance, it is to be noted that various regions of interest may not be self-contained and/or isolated from adjacent regions. Hence, information in the pileup can influence the probability of bases separated by far more than the total read length (e.g., the paired read length or long molecule length). As such, it must be decided which positions to include in the MRJD calculation, and the number of positions is not unconstrained (even with pruning). For example, some implementations may process overlapping blocks of positions and update the results for a subset of the positions based on the confidence levels at those positions, or the completeness of the evidence at those positions (e.g., positions near the middle of the block typically have more complete evidence than those near the edge). 
     Another determining factor may be the order in which new positions may be added. For instance, for pruned MRJD, the order of adding new positions may affect performance. For example, some implementations may add new positions based on the distance to the already-included positions, or the degree of connectivity with these positions (e.g., the number of reads overlapping both positions). Additionally, there are also many variations on how pruning may be performed. In the example set forth above, the pruning was based on a fixed probability threshold, but in general the pruning threshold may be adaptive or based on the number of surviving candidates. For instance, a simple implementation may set the threshold to keep the number of candidates below a fixed number, while a more sophisticated implementation may set the threshold based on a cost-benefit analysis of including additional candidates. 
     Various implementations may perform pruning based on probabilities P(R|G m ) instead of the a-priori probabilities P(G m |R). This has the advantage of allowing the elimination of equivalent mirror-image representations across regions (in addition to phases). This advantage is at least partially offset by the disadvantage of not pruning out candidates with very low a-priori probabilities, which in various instances may be beneficial. As such, a useful solution may depend on the scenario. If pruning is done, such as based on the P(R|G m ), then the bayesian calculation would be performed once after the final iteration. 
     Further in the example above, the process was stopped after processing all base positions in the pileup shown, but other stopping criteria are also possible. For instance, if only a subset of the base positions (e.g. when processing overlapping blocks) is being solved for, the process may stop when the result for the subset has been found with a sufficient level of confidence, or when the confidence has stopped increasing as more positions are added. A more sophisticated implementation, however, may perform some type of cost-benefit analysis, weighing the computational cost against the potential value of adding more positions. 
     A-priori probabilities may also be useful. For instance, in the examples above, a simple IID model was used, but other models may also be used. For example, it is to be noted that clusters of variants are more common than would be predicted by an IID model. It is also to be noted that variants are more likely to occur at positions where the references differ. Therefore, incorporating such knowledge into the a-priori probabilities P(Gm) can improve the detection performance and yield better ROC curves. Particularly, it is to be noted that the a-priori probabilities for homologous regions are not well-understood in the genomics community, and this knowledge is still evolving. As such, some implementations may update the a-priori models as better information becomes available. This may be done automatically as more results are produced. Such updates may be based on other biological samples or other regions of the genome for the same sample, which learnings can be applied to the methods herein to further promote a more rapid and accurate analysis. 
     Accordingly, in some instance, an iterative MJRD process may be implemented. Specifically, the methodology described herein can be extended to allow message passing between related regions so as to further reduce the complexity and/or increase the detection performance of the system. For instance, the output of the calculation at one location can be used as an input a-priori probability for the calculation at a nearby location. Additionally, some implementations may use a combination of pruning and iterating to achieve the desired performance/complexity tradeoff. 
     Further, sample preparation may be implemented to optimize the MRJD process. For instance, for paired-end sequencing, it may be useful to have a tight distribution on the insertion size when using conventional detection. However, in various instances, introducing variation in the insertion size could significantly improve the performance for MRJD. For example, the sample may be prepared to intentionally introduce a bimodal distribution, a multi-modal distribution, or bell-curve-like distribution with a higher variance than would typically be implemented for conventional detection. 
       FIG. 31  illustrates the ROC curves for MRJD and a conventional detector for human sample NA12878 over selected regions of the genome with a single homologous copy, such that N=2, with varying degrees of reference sequence similarity. This dataset used paired-end sequencing with a read length of 101 and a mean insertion size of approx. 400. As can be seen with respect to  FIG. 31 , MRJD offers dramatically improved sensitivity and specificity over these regions than conventional detection methods.  FIG. 32  illustrates the same results displayed as a function of the sequence similarity of the references, measured over a window of 1000 bases (e.g. if the references differ by 10 bases out of 1000, then the similarity is 99.0 percent). For this dataset, it may be seen that conventional detection starts to perform badly at a sequence similarity ˜0.98, while MRJD performs quite well up to 0.995 and even beyond. 
     Additionally, in various instances, this methodology may be extended to allow message passing between related regions to further reduce the complexity and/or increase the detection performance. For instance, the output of the calculation at one location can be used as an input a-priori probability for the calculation at a nearby location, and in some implementations may use a combination of pruning and iterating to achieve the desired performance/complexity tradeoff. In particular instances, as indicated above, prior to running multi-region joint detection, the variant caller may determine whether a given active region should be processed individually or jointly with other regions. Additionally, as indicated above, some implementations may rely on a list of secondary alignments provided by the mapper to make such a decision. Other implementations may use a database of homologous regions, computed offline based on a search of the reference genome. 
     In view of the above, a Pair-Determined Hidden Markov Model (PD-HMM may be implemented in a manner so as to take advantage of the benefits of MRJD. For instance, MRJD can separately estimate the probability of observing a portion or all of the reads given each possible joint diplotype, which comprises one haplotype per ploidy per homologous reference region, e.g., for two homologous regions in diploid chromosomes, each joint diplotype will include four haplotypes. In such instances, all or a portion of the possible haplotypes may be considered, such as by being constructed, for instance, by modifying each reference region with every possible subset of all the variants for which there is nontrivial evidence. However, for long homologous reference regions, the number of possible variants is large, so the number of haplotypes (combinations of variants) becomes exponentially large, and the number of joint diplotypes (combinations of haplotypes) may be astronomical. 
     Consequently, to keep MRJD calculations tractable, it may not be useful to test all possible joint diplotypes. Rather, in some instances, the system may be configured in such a manner that only a small subset of “most likely” joint diplotypes is tested. These “most likely” joint diplotypes may be determined by incrementally constructing a tree of partially-determined joint diplotypes. In such an instance, each node of the tree may be a partially determined joint diplotype that includes a partially determined haplotype per ploidy per homologous reference region. In this instance, a partially determined haplotype may include a reference region modified by a partially determined subset of the possible variants. Accordingly, a partially determined subset of the possible variants may include an indication, for each possible variant, of one of three states: that the variant is determined and present, or the variant is determined and absent, or the variant is not yet determined, e.g., it may be present or absent. At the root of the tree, all variants are undetermined in all haplotypes; tree nodes branching successively further from the root have successively more variants determined as present or absent in each haplotype of each node&#39;s joint diplotype. 
     Further, in the context of this joint diplotype tree, as described above, the amount of MRJD calculations is kept limited and tractable by trimming branches of the tree in which all joint diplotype nodes are unlikely, e.g., moderately to extremely unlikely, relative to other more likely branches or nodes. Accordingly, such trimming may be performed on branches at nodes that are still only partially determined; e.g., several or many variants are still not determined as present or absent from the haplotypes of a trimmed node&#39;s joint diplotype. Thus, in such an instance, it is useful to be able to estimate or bound the likelihood of observing each read assuming the truth of a partially determined haplotype. A modified pair hidden Markov model (pHMM) calculation, denoted “PD-HMM” for “partially determined pair hidden Markov model” is useful to estimate the probability P(R|H) of observing read R assuming the true haplotype H* is consistent with partially determined haplotype H. Consistent in this context means that some specific true haplotype H* agrees with partially determined haplotype H with respect to all variants whose presence or absence are determined in H, but for variants undetermined in H, H* may agree with the reference sequence either modified or unmodified by each undetermined variant. 
     Note that it is not generally adequate to run an ordinary pHMM calculation for some shorter sub-haplotype of H chosen to encompass only determined variant positions. It is generally important to build the joint diplotype tree with undetermined variants being resolved in an efficient order, which is generally quite different than their geometric order, so that a partially determined haplotype H will typically have many undetermined variant positions interleaved with determined ones. To properly consider PCR indel errors, it is useful to use a pHMM-like calculation spanning through all determined variants and significant radius around them, which may not be compatible with attempts to avoid undetermined variant positions. 
     Accordingly, the inputs to PD-HMM may include the called nucleotide sequence of read R, the base quality scores (e.g., phred scale) of the called nucleotides of R, a baseline haplotype H0, and a list of undetermined variants (edits) from H0. The undetermined variants may include single-base substitutions (SNPs), multiple-base substitutions (MNPs), insertions, and deletions. Advantageously, it may be adequate to support undetermined SNPs and deletions. An undetermined MNP may be imperfectly but adequately represented as multiple independent SNPs. An undetermined insertion may be represented by first editing the insertion into the baseline haplotype, then indicating the corresponding undetermined deletion which would undo that insertion. 
     Restrictions may be placed on the undetermined deletions, to facilitate hardware engine implementation with limited state memory and logic, such as that no two undetermined deletions may overlap (delete the same baseline haplotype bases). If a partially determined haplotype must be tested with undetermined variants violating such restrictions, this may be resolved by converting one or more undetermined variants into determined variants in a larger number of PD-HMM operations, covering cases with those variants present or absent. For example, if two undetermined deletions A and B violate by overlapping each other in baseline haplotype H0, then deletion B may be edited into H0 to yield HOB, and two PD-HMM operations may be performed using undetermined deletion A only, one for baseline haplotype H0, and the other for baseline haplotype H0B, and the maximum probability output of the two PD-HMM operations may be retained. 
     The result of a PD-HMM operation may be an estimate of the maximum P(R|H*) among all haplotypes H* that can be formed by editing H0 with any subset of the undetermined variants. The maximization may be done locally, contributing to the pHMM-like dynamic programming in a given cell as if an adjacent undetermined variant is present or absent from the haplotype, whichever scores better, e.g., contributes the greater partial probability. Such local maximization during dynamic programming may result in larger estimates of the maximum P(R|H*) than true maximization over individual pure H* haplotypes, but the difference is generally inconsequential. 
     Undetermined SNPs may be incorporated into PD-HMM by allowing one or more matching nucleotide values to be specified for each haplotype position. For example, if base  30  of H0 is ‘C’ and an undetermined SNP replaces this ‘C’ with a ‘T’, then the PD-HMM operation&#39;s haplotype may indicate position  30  as matching both bases ‘C’ and ‘T’. In the usual pHMM dynamic programming, any transition to an ‘M’ state results in multiplying the path probability by the probability of a correct base call (if the haplotype position matches the read position) or by the probability of a specific base call error (if the haplotype position mismatches the read position); for PD-HMM this is modified by using the correct-call probability if the read position matches either possible haplotype base (e.g. ‘C’ or ‘T’), and the base-call-error probability otherwise. 
     Undetermined haplotype deletions may be incorporated into PD-HMM by flagging optionally-deleted haplotype positions, and modifying the dynamic programming of pHMM to allow alignment paths to skip horizontally across undetermined deletion haplotype segments without probability loss. This may be done in various manners, but with the common property that probability values in M, I, and/or D states can transmit horizontally (along the haplotype axis) over the span of an undetermined deletion without being reduced by ordinary gap-open or gap-extend probabilities. 
     In one particular embodiment, haplotype positions where undetermined deletions begin are flagged “F1”, and positions where undetermined deletions end are flagged “F2”. In addition to the M, I, and D “states” (partial probability representations) for each cell of the HMM matrix (haplotype horizontal/read vertical), each PD-HMM cell may further include BM, BI, and BD “bypass” states. In F1-flagged haplotype columns, BM, BI, and BD states receive values copied from M, I, and D states of the cell to the left, respectively. In non-F2-flagged haplotype columns, particularly columns starting with an F1 flagged column end extending into the interior of an undetermined deletion, BM, BI, and BD states transmit their values to BM, BI, and BD states of the cell to the right, respectively. In F2-flagged haplotype columns, in place of M, I, and D states used to calculate states of adjacent cells, the maximum of M and BM is used, and the maximum of I and BI is used, and the maximum of D and BD is used, respectively. This is exemplified in an F2 column as multiplexed selection of signals from M and BM, from I and BI, and from D and BD registers. 
     Note that although BM, BI, and DB state registers may be represented in F1 through F2 columns, and maximizing M/BM, I/BI, and D/BD multiplexers may be shown in an F2 column, these components may be present for all cell calculations, enabling an undetermined deletion to be handled in any position, and enabling multiple undetermined deletions with corresponding F1 and F2 flags throughout the haplotype. Note also that F1 and F2 flags may be in the same column, for the case of a single-base undetermined deletion. It is further to be noted that the PD-HMM matrix of cells may be depicted as a schematic representation of the logical M, I, D, BM, BI, and BD state calculations, but in a hardware implementation, a smaller number of cell calculating logic elements may be present, and pipelined appropriately to calculate M, D, I, BM, BI, and BD state values at high clock frequencies, and the matrix cells may be calculated with various degrees of hardware parallelism, in various orders consistent with the inherent logical dependencies of the PD-HMM calculation. 
     Thus, in this embodiment, the pHMM state values in one column may be immediately left of an undetermined deletion which may be captured and transmitted rightward, unchanged, to the rightmost column of this undetermined deletion, where they substitute into pHMM calculations whenever they beat normal-path scores. Where these maxima are chosen, the “bypass” state values BM, BI, and BD represent the local dynamic programming results where the undetermined deletion is taken to be present, while “normal” state values M, I, and D represent the local dynamic programming results where the undetermined deletion is taken to be absent. 
     In another embodiment, a single bypass state may be used, such as a BM state receiving from an M state in F1 flagged columns, or receiving a sum of M, D, and/or I states. In another embodiment, rather than using “bypass” states, gap-open and/or gap-extend penalties are eliminated within columns of undetermined deletions. In another embodiment, bypass states contribute additively to dynamic programming rightward of undetermined deletions, rather than local maximization being used. In a further embodiment, more or fewer or differently defined or differently located haplotype position flags are used to trigger bypass or similar behavior, such as a single flag indicating membership in an undetermined deletion. In an additional embodiment, two or more overlapping undetermined deletions may participate, such as with the use of additional flags and/or bypass states. Additionally, undetermined insertions in the haplotype are supported, rather than, or in addition to, undetermined deletions. Likewise, undetermined insertions and/or deletions on the read axis are supported, rather than or in addition to undetermined deletions and/or insertions on the haplotype axis. In another embodiment, undetermined multiple-nucleotide substitutions are supported as atomic variants (all present or all absent). In a further embodiment, undetermined length-varying substitutions are supported as atomic variants. In another embodiment, undetermined variants are penalized with fixed or configurable probability or score adjustments. 
     This PD-HMM calculation may be implemented as a hardware engine, such as in FPGA or ASIC technology, by extension of a hardware engine architecture for “ordinary” pHMM calculation or may be implemented by one or more quantum circuits in a quantum computing platform. In addition to an engine pipeline logic to calculate, transmit, and store M, I, and D state values for various or successive cells, parallel pipeline logic can be constructed to calculate, transmit, and store BM, BI, and BD state values, as described herein and above. Memory resources and ports for storage and retrieval of M, I, and D state values can be accompanied by similar or wider or deeper memory resources and ports for storage and retrieval of BM, BI, and BD state values. Flags such as F1 and F2 may be stored in memories along with associated haplotype bases. 
     Multiple matching nucleotides for e.g. undetermined SNP haplotype positions may be encoded in any manner, such as using a vector of one bit per possible nucleotide value. Cell calculation dependencies in the pHMM matrix are unchanged in PD-HMM, so order and pipelining of multiple cell calculations can remain the same for PD-HMM. However, the latency in time and/or clock cycles for complete cell calculation increases somewhat for PD-HMM, due to the requirement to compare “normal” and “bypass” state values and select the larger ones. Accordingly, it may be advantageous to include one or more extra pipeline stages for PD-HMM cell calculation, resulting in additional clock cycles of latency. Additionally, it may further be advantageous to widen each “swath” of cells calculated by one or more rows, to keep the longer pipeline filled without dependency issues. 
     This PD-HMM calculation tracks twice as many state values (BM, BI, and BD, in addition to M, I, and D), as an ordinary pHMM calculation, and may require about twice the hardware resources for an equivalent throughput engine embodiment. However, a PD-HMM engine has exponential speed and efficiency advantages for increasing numbers of undetermined variants, versus an ordinary pHMM engine run once for each haplotype representing a distinct combination of the undetermined variants being present or absent. For example, if a partially determined haplotype has 30 undetermined variants, each of which may be independently present or absent, there are 2̂30, or more than 1 billion, distinct specific haplotypes that pHMM would otherwise need to process. 
     Accordingly, in view of the above, for embodiments involving FPGA-accelerated mapping, alignment, sorting, and/or variant calling applications, one or more of these functions may be implemented in one or both of software and hardware (HW) processing components, such as software running on a traditional CPU, and/or firmware such as may be embodied in an FPGA, ASIC, sASIC, and the like. In such instances, the CPU and FPGA need to be able to communicate so as to pass results from one step on one device, e.g., the CPU or FPGA, to be processed in a next step on the other device. For instance, where a mapping function is run, the building of large data structures, such as an index of the reference, may be implemented by the CPU, where the running of a hash function with respect thereto may be implemented by the FPGA. In such an instance, the CPU may build the data structure, store it in an associated memory, such as a DRAM, which memory may then be accessed by the processing engines running on the FPGA. 
     For instance, in some embodiments, communications between the CPU and the FPGA may be implemented by any suitable interconnect such as a peripheral bus, such as a PCIe bus, USB, or a networking interface such as Ethernet. However, a PCIe bus may be a comparatively loose integration between the CPU and FPGA, whereby transmission latencies between the two may be relatively high. Accordingly, although one device e.g., (the CPU or FPGA) may access the memory attached to the other device (e.g., by a DMA transfer), the memory region(s) accessed are non-cacheable, because there is no facility to maintain cache coherency between the two devices. As a consequence, transmissions between the CPU and FPGA are constrained to occur between large, high-level processing steps, and a large amount of input and output must be queued up between the devices so they don&#39;t slow each other down waiting for high latency operations. This slows down the various processing operations disclosed herein. Furthermore, when the FPGA accesses non-cacheable CPU memory, the full load of such access is imposed on the CPU&#39;s external memory interfaces, which are bandwidth-limited compared to its internal cache interfaces. 
     Accordingly, because of such loose CPU/FPGA integrations, it is generally necessary to have “centralized” software control over the FPGA interface. In such instances, the various software threads may be processing various data units, but when these threads generate work for the FPGA engine to perform, the work must be aggregated in “central” buffers, such as either by a single aggregator software thread, or by multiple threads locking aggregation access via semaphores, with transmission of aggregated work via DMA packets managed by a central software module, such as a kernel-space driver. Hence, as results are produced by the HW engines, the reverse process occurs, with a software driver receiving DMA packets from the HW, and a de-aggregator thread distributing results to the various waiting software worker threads. However, this centralized software control of communication with HW FPGA logic is cumbersome and expensive in resource usage, reduces the efficiency of software threading and HW/software communication, limits the practical HW/software communication bandwidth, and dramatically increases its latency. 
     Additionally, as can be seen with respect to  FIG. 33A , a loose integration between the CPU  1000  and FPGA  7  may require each device to have its own dedicated external memory, such as DRAMs  1014 ,  14 . As depicted in  FIG. 33A , the CPU(s)  1000  has its own DRAM  1014  on the system motherboard, such as DDR3 or DDR4 DIMMs, while the FPGA  7  has its own dedicated DRAMs  14 , such as four 8 GB SODIMMs, that may be directly connected to the FPGA  7  via one or more DDR3 busses  6 , such as a high latency PCIe bus. Likewise, the CPU  1000  may be communicably coupled to its own DRAM  1014 , such as by a suitably configured bus  1006 . As indicated above, the FPGA  7  may be configured to include one or more processing engines  13 , which processing engines may be configured for performing one or more functions in a bioinformatics pipeline as herein described, such as where the FPGA  7  includes a mapping engine  13   a , an alignment engine  13   b , and a variant call engine  13   c . Other engines as described herein may also be included. In various embodiments, one or both of the CPU may be configured so as to include a cache  1014   a ,  14   a  respectively, that is capable of storing data, such as result data that is transferred thereto by one or more of the various components of the system, such as one or more memories and/or processing engines. 
     Many of the operations disclosed herein, to be performed by the FPGA  7  for genomic processing, require large memory accesses for the performance of the underlying operations. Specifically, due to the large data units involved, e.g. 3+ billion nucleotide reference genomes, 100+ billion nucleotides of sequencer read data, etc., the FPGA  7  may need to access the host memory  1014  a large number of times such as for accessing an index, such as a 30 GB hash table or other reference genome index, such as for the purpose of mapping the seeds from a sequenced DNA/RNA query to a 3 Gbp reference genome, and/or for fetching candidate segments, e.g., from the reference genome, to align against. 
     Accordingly, in various implementations of the system herein disclosed, many rapid random memory accesses may need to occur by one or more of the hardwired processing engines  13 , such as in the performance of a mapping, aligning, and/or variant calling operation. However, it may be prohibitively impractical for the FPGA  7  to make so many small random accesses over the peripheral bus  3  or other networking link to the memory  1014  attached to the host CPU  1000 . For instance, in such instances, latencies of return data can be very high, bus efficiency can be very low, e.g., for such small random accesses, and the burden on the CPU external memory interface  1006  may be prohibitively great. 
     Additionally, as a result of each device needing its own dedicated external memory, the typical form factor of the full CPU  1000 +FPGA  7  platform is forced to be larger than may be desirable, e.g., for some applications. In such instances, in addition to a standard system motherboard for one or more CPUs  1000  and supporting chips  7  and memories,  1014  and/or  14 , room is needed on the board for a large FPGA package (which may even need to be larger so as to have enough pins for several external memory busses) and several memory modules,  1014 ,  14 . Standard motherboards, however, do not include these components, nor would they easily have room for them, so a practical embodiment may be configured to utilize an expansion card  2 , containing the FPGA  7 , its memory  14 , and other supporting components, such as power supply, e.g. connected to the PCIe expansion slot on the CPU motherboard. To have room for the expansion card  2 , the system may be fabricated to be in a large enough chassis, such as a 1U or 2U or larger rack-mount server. 
     In view of the above, in various instances, as can be seen with respect to  FIG. 33B , to overcome these factors, it may be desirable to configure the CPU  1000  to be in a tight coupling arrangement with the FPGA  7 . Particularly, in various instances, the FPGA  7  may be tightly coupled to the CPU  1000 , such as by a low latency interconnect  3 , such as a quick path interconnect (QPI). Specifically, to establish a tighter CPU+FPGA integration, the two devices may be connected by any suitable low latency interface, such as a “processor interconnect” or similar, such as INTELS® Quick Path Interconnect (QPI) or HyperTransport (HT). 
     Accordingly, as seen with respect to  FIG. 33B , a system  1  is provided wherein the system includes both a CPU  1000  and a processor, such as an FPGA  7 , wherein both devices are associated with one or more memory modules. For instance, as depicted, the CPU  1000  may be coupled, such as via a suitably configured bus  1006 , to a DRAM  1014 , and likewise, the FPGA  7  is communicably coupled to an associated memory  14  via a DDR3 bus  6 . However, in this instance, instead of being coupled to one another such as by a typical high latency interconnect, e.g., PCIe interface, the CPU  1000  is coupled to the FPGA  7  by a low latency, hyper transport interconnect  3 , such as a QPI. In such an instance, due to the inherent low latency nature of such interconnects, the associated memories  1014 ,  14  of the CPU  1000  and the FPGA  7  are readily accessible to one another. Additionally, in various instances, due to this tight coupling configuration, one or more cashes  1114   a / 14   a  associated with the devices may be configured so as to be coherent with respect to one another. 
     Some key properties of such a tightly coupled CPU/FPGA interconnect include a high bandwidth, e.g., 12.8 GB/s; low latency, e.g., 100-300 ns; an adapted protocol designed for allowing efficient remote memory accesses, and efficient small memory transfers, e.g., on the order of 64 bytes or less; and a supported protocol and CPU integration for cache access and cache coherency. In such instances, a natural interconnect for use for such tight integration with a given CPU  1000  may be its native CPU-to-CPU interconnect  1003 , which may be employed herein to enable multiple cores and multiple CPUs to operate in parallel in a shared memory  1014  space, thereby allowing the accessing of each other&#39;s cache stacks and external memory in a cache-coherent manner. 
     Accordingly, as can be seen with respect to  FIGS. 34A and 34B , a board  2  may be provided, such as where the board may be configured to receive one or more CPUs  1000 , such as via a plurality of interconnects  1003 , such as native CPU-CPU interconnects  1003   a  and  1003   b . However, in this instance, as depicted in  FIG. 34A , a CPU  1000  is configured so as to be coupled to the interconnect  1003   a , but rather than another CPU being coupled therewith via interconnect  1003   b , an FPGA  7  of the disclosure is configured so as to be coupled therewith. Additionally, the system  1  is configured such that the CPU  1000  may be coupled to the associated FPGA  7 , such as by a low latency, tight coupling interconnect  3 . In such instances, each memory  1014 ,  14  associated with the respective devices  1000 ,  7  may be made so as to accessible to each other, such as in a high-bandwidth, cache coherent manner. 
     Likewise, as can be seen with respect to  FIG. 34B , the system can also be configured so as to receive packages  1002   a  and/or  1002   b , such as where each of the packages include one or more CPUs  1000   a ,  1000   b  that are tightly coupled, e.g., via low latency interconnects  3   a  and  3   b , to one or more FPGAs  7   a ,  7   b , such as where given the system architecture, each package  2   a  and  2   b  may be coupled one with the other such as via a tight coupling interconnect  3 . Further, as can be seen with respect to  FIG. 35 , in various instances, a package  1002   a  may be provided, wherein the package  1002   a  includes a CPU  1000  that has been fabricated in such a manner so as to be closely coupled with an integrated circuit such as an FPGA  7 . In such an instance, because of the close coupling of the CPU  1000  and the FPGA  7 , the system may be constructed such that they are able to directly share a cache  1014   a  in a manner that is consistent, coherent, and readily accessible by either device, such as with respect to the data stored therein. 
     Hence, in such instances, the FPGA  7 , and or package  2   a / 2   b , can, in effect, masquerade as another CPU, and thereby operate in a cache-coherent shared-memory environment with one or more CPUs, just as multiple CPUs would on a multi-socket motherboard  1002 , or multiple CPU cores would within a multi-core CPU device. With such an FPGA/CPU interconnect, the FPGA  7  can efficiently share CPU memory  1014 , rather than having its own dedicated external memory  14 , which may or may not be included or accessed. Thus, in such a configuration, rapid, short, random accesses are supported efficiently by the interconnect  3 , such as with low latency. This makes it practical and efficient for the various processing engines  13  in the FPGA  7  to access large data structures in CPU memory  1000 . 
     For instance, as can be seen with respect to  FIG. 37 , a system for performing a method is provided, such as where the method includes one or more steps for performing a function of the disclosure, such as a mapping function, as described herein, in a shared manner. Particularly, in one step (1) a data structure may be generated or otherwise provided, such as by a CPU  1000 , which data structure may then be stored in an associated memory (2), such as a DRAM  1014 . The data structure may be any data structure, such as with respect to those described herein, but in this instance may be a reference genome or an index of the reference genome, such as for the performance of a mapping and/or aligning or variant calling function. In a second step (2), such as with respect to a mapping function, an FPGA  7  associated with the CPU  1000 , such as by a tight coupling interface  3 , may access the CPU associated memory  1014 , so as to perform one or more actions with respect to the reference genome and/or an index thereof. Particularly, in a step (3) the FPGA  7  may access the data structure so as to produce one or more seeds thereof, which seeds may be employed for the purposes of performing a hash function with respect thereto, such as to produce one or more reads that have been mapped to one or more positions with respect to the reference genome. 
     In a further step (3), the mapped result data may be stored, e.g., in either the host memory  1014  or in an associated DRAM  14 . In such an instance, the FPGA  7 , more particularly, a processing engine  13  thereof, e.g., an alignment engine, may then access the stored mapped data structure so as to perform an aligning function thereon, so as to produce one or more reads that have been aligned to the reference genome. In an additional step (4), the host CPU may then access the mapped and/or aligned data so as to perform one or more functions thereon, such as for the production of a De Brujin Graph, which DBG may then be stored in its associated memory. Likewise, in one or more additional steps, the FPGA  7  may once again access the host CPU memory  1014  so as to access the DBG and perform an HMM analysis thereon so as to produce one or more variant call files. 
     In particular instances, the CPU  1000  and/or FPGA  7  may have one or more memory cache&#39;s which due to the tight coupling of the interface between the two devices will allow the separate caches to be coherent, such as with respect to the transitionary data, e.g., results data, stored thereon, such as results from the performance of one or more functions herein. In a manner such as this, data may be shared substantially seamlessly between the tightly coupled devices, thereby allowing a pipeline of functions to be weaved together such as in a bioinformatics pipeline. Thus, in such an instance, it may no longer be necessary for the FPGA  7  to have its own dedicated external memory  14  attached, and hence, due to such a tight coupling configuration, the reference genome and/or reference genomic index, as herein described, may be intensively shared, e.g., in a cache coherent manner, such as for read mapping and alignment, and other genomic data processing operations. 
     Additionally, the low latency and cache coherency, as well as other components discussed herein, allow smaller, lower-level operations to be performed in one device (e.g., in a CPU or FPGA) before handing a data unit or processing thread  20  back to the other device, such as for further processing. For example, rather than a CPU thread  20   a  queuing up large amounts of work for the FPGA hardware logic  13  to perform, and the same or another thread  20   b  processing a large queue of results at a substantially later time; a single CPU thread  20  might make a blocking “function call” to an FPGA hardware engine  13 , resuming software execution as soon as the hardware function completes. Hence, rather than packaging up data structures in packets to stream by DMA  14  into the FPGA  7 , and unpacking results when they return, a software thread  20  could simply provide a memory pointer to the FPGA engine  13 , which could access and modify the shared memory  1014 / 14  in place, in a cache-coherent manner. 
     Particularly, given the relationship between the structures provided herein, the granularity of the software/hardware cooperation can be much finer, with much smaller, lower level operations being allocated so as to be performed by various hardware engines  13 , such as function calls from various allocated software threads  20 . For example, in a loose CPU/FPGA interconnect platform, for efficient acceleration of DNA/RNA read mapping, alignment, and/or variant calling, a full mapping/aligning/variant calling pipeline may be constructed as one or more FPGA engines, with unmapped and unaligned reads streamed from software to hardware, and the fully mapped and aligned reads streamed from the hardware back to the software, where the process may be repeated, such as for variant calling. With respect to the configurations herein described, this can be very fast, however, in various instances, it may suffer from limitations of flexibility, complexity, and/or programmability, such because the whole map/align and/or variant call pipeline is implemented in hardware circuitry, which although reconfigurable in an FPGA, is generally much less flexible and programmable than software, and may therefore be limited to less algorithmic complexity. 
     By contrast, using a tight CPU/FPGA interconnect, such as a QPI or other interconnect in the configurations disclosed herein, several resource expensive discrete operations, such as seed generation and/or mapping, rescue scanning, gapless alignment, gapped, e.g., Smith-Waterman, alignment, etc., can be implemented as distinct separately accessible hardware engines  13 , e.g., see  FIG. 38  and the overall mapping/alignment and/or variant call algorithms can be implemented in software, with low-level acceleration calls to the FPGA for the specific expensive processing steps. This framework allows full software programmability, outside the specific acceleration calls, and enables greater algorithmic complexity and flexibility, than standard hardware implemented operations. 
     Furthermore, in such a framework of software execution accelerated by discrete low-level FPGA hardware acceleration calls, hardware acceleration functions may more easily be shared for multiple purposes. For instance, when hardware engines  13  form large, monolithic pipelines, the individual pipeline subcomponents may generally be specialized to their environment, and interconnected only within one pipeline, which unless tightly coupled may not generally be accessible for any purpose. But many genomic data processing operations, such as Smith-Waterman alignment, gapless alignment, De Bruijn or assembly graph construction and other such operations, can be used in various higher level parent algorithms. For example, as described herein, Smith-Waterman alignment may be used in DNA/RNA read mapping such as with respect to a reference genome, but may also be configured so as to be used by haplotype-based variant callers, to align candidate haplotypes to a reference genome, or to each other, or to sequenced reads, such as in a HMM analysis. Hence, exposing various discrete low-level hardware acceleration functions via general software function calls may enable the same acceleration logic, e.g.,  13 , to be leveraged throughout a genomic data processing application, such as in the performance of both alignment and variant calling, e.g. HMM, operations. 
     It is also practical, with tight CPU/FPGA interconnection, to have distributed rather than centralized CPU  1000  software control over communication with the various FPGA hardware engines  13  described herein. In widespread practices of multi-threaded, multi-core, and multi-CPU software design, many software threads and processes communicate and cooperate seamlessly, without any central software modules, drivers, or threads to manage intercommunication. In such a format, this is practical because of the cache-coherent shared memory, which is visible to all threads in all cores in all of the CPUs; while physically, coherent memory sharing between the cores and CPUs occurs by intercommunication over the processor interconnect, e.g., QPI or HT. 
     In a similar manner, as can be seen with respect to  FIGS. 36 and 38  with the tight CPU/FPGA interconnect disclosed herein, many threads  20   a, b, c , and processes running on one or multiple cores and/or CPUs  1000   a ,  100   b , and  1000   c  can communicate and cooperate in a distributed manner with the various different FPGA hardware acceleration engines, such as by the use of cache-coherent memory sharing between the various CPU(s) and FPGA(s). For instance, as can be seen with respect to  FIG. 36 , a multiplicity of CPU cores  1000   a ,  1000   b , and  1000   c  can be coupled together in such a manner so as to share one or more memories, e.g., DRAMs, and/or one or more caches having one or more layers or levels associated therewith. Likewise, with respect to  FIG. 38 , in another embodiment, a single CPU may be configured to include multiple cores  1000   a ,  1000   b , and  1000   c  that can be coupled together in such a manner so as to share one or more memories, e.g., DRAMs, and/or one or more caches having one or more layers or levels associated therewith. 
     Hence, in either embodiment, data to be passed from one or more software threads  20  from one or more CPU cores  1000  to a hardware engine  13  or vice versa may simply be updated in the shared memory  1014 , or a cache thereof, visible to both devices. Even requests to process data in shared memory  1014 , or notification of results updated in shared memory, can be signaled between the software and hardware, such as over a DDR4 bus  1014 , in queues implemented within the shared memory itself. Standard software mechanisms for control transfer and data protection, such as semaphores, mutexes, and atomic integers, can also be implemented similarly for software/hardware coordination. 
     Consequently, in some embodiments, with no need for the FPGA  7  to have its own dedicated memory  14  or other external resources, due to cache coherent memory-sharing over a tight CPU/FPGA interconnect, it becomes much more practical to package the FPGA  7  more compactly and natively within traditional CPU  1000  motherboards, without the use of expansion cards. See, for example  FIGS. 34A and 34B  and  FIG. 35 . Several packaging alternatives are available. Specifically, an FPGA  7  may be installed onto a multi-CPU motherboard in a CPU socket, as shown in  FIGS. 34A and 34B , such as by use of an appropriate interposer, such as a small PC board  2 , or alternative wire-bond packaging of an FPGA die within a CPU chip package  2   a , to route CPU socket pins to FPGA pins, including power and ground, the processor interconnect  3  (QPI, HT, etc.), and system connections. Additionally, an FPGA die and CPU die may be included in the same multi-chip package (MCP) with necessary connections, including power, ground, and CPU/FPGA interconnect, made within the package  2   a . Inter-die connections may be made by die-to-die wire-bonding, or by connection to a common substrate or interposer, or by bonded pads or through-silicon vias between stacked dice. 
     Further, FPGA and CPU cores may be fabricated on a single die, see  FIG. 35 , using system-on-a-chip (SOC) methodology. In any of these cases, custom logic, e.g.,  17 , may be instantiated inside the FPGA  7  to communicate over the CPU/FPGA interconnect  3  by its proper protocol, and to service and convert memory access requests from internal FPGA engines  13  to the CPU/FPGA interconnect  3  protocols. Alternatively, some or all of this logic may be hardened into custom silicon, to avoid using up FPGA logic real estate for this purpose, such as where the hardened logic may reside on the CPU die, and/or the FPGA die, or a separate die. Also, in any of these cases, power supply and heat dissipation requirements may be obeyed appropriately; such as within a single package (MCP or SOC), the FPGA size and CPU core count may be chosen to stay within a safe power envelope, or dynamic methods (clock frequency management, clock gating, core disabling, power islands, etc.) may be used to regulate power consumption according to changing the FPGA and/or the CPU computation demands. 
     All of these packaging options share several advantages. The tightly-integrated CPU/FPGA platform becomes compatible with standard motherboards and/or system chassis, of a variety of sizes. If the FPGA is installed via an interposer (not shown) in a CPU socket, see  FIGS. 26A and 26B , then at least a dual-socket motherboard  1002  may be employed, and e.g. a quad-socket motherboard may be required to allow 3 CPUs+1 FPGA, 2 CPUs+2 FPGAs, or 1 CPU+3 FPGAs, etc. If each FPGA resides in the same chip package as a CPU (either MCP or SOC), see  FIG. 34B , then even a single-socket motherboard is adequate, potentially in a very small chassis (although a dual socket motherboard is depicted); this also scales upward very well, e.g. 4 FPGAs and 4 multi-core CPUs on a 4-socket server motherboard, which nevertheless could operate in a compact chassis, such as a 1U rack-mount server. 
     In various instances, therefore, there may be no need for an expansion card to be installed so as to integrate the CPU and FPGA acceleration, because the FPGA  7  may be integrated in to the CPU  1000  socket. This implementation avoids the extra space and power requirements of an expansion card, as well as the additional failure point, expansion cards sometimes being relatively low-reliability components. Furthermore, standard CPU cooling solutions (head sinks, heat pipes, and/or fans), which are efficient yet low-cost since they are manufactured in high volumes, can be applied to FPGAs or CPU/FPGA packages in CPU sockets, whereas cooling for expansion cards can be expensive and inefficient. 
     Likewise, an FPGA/interposer or CPU/FPGA package may include the full power supply of a CPU socket, e.g. 150 W, whereas a standard expansion card may be power limited, e.g. 25 W or 75 W from the PCIe bus. In various instances, for genomic data processing applications, all these packaging options may facilitate easy installation of a tightly-integrated CPU+FPGA compute platform, such as within a DNA sequencer. For instance, typical modern “next-generation” DNA sequencers contain the sequencing apparatus (sample and reagent storage, fluidics tubing and control, sensor arrays, primary image and/or signal processing) within a chassis that also contains a standard or custom server motherboard, wired to the sequencing apparatus for sequencing control and data acquisition. A tightly-integrated CPU+FPGA platform, as herein described, may be achieved in such a sequencer such as by simply installing one or more FPGA/interposer or FPGA/CPU packages in CPU sockets of its existing motherboard, or alternatively by installing a new motherboard with both CPU(s) and FPGA(s). 
     Further, all of these packaging options may be configured to facilitate easy deployment of the tightly-integrated CPU+FPGA platform such as into a cloud or datacenter server rack, which require compact/dense servers, and very high reliability/availability. Hence, in accordance with the teachings herein, there are many processing stages for data from DNA (or RNA) sequencing to mapping and aligning to variant calling, which can vary depending on the primary and/or secondary and/or tertiary processing technologies and the application. Such processing steps may include one or more: signal processing on electrical measurements from a sequencer, an image processing on optical measurements from the sequencer, base calling using processed signal or image data to determine the most likely nucleotide sequence and confidence scores, filtering sequenced reads with low quality or polyclonal clusters, detecting and trimming adapters, key sequences, barcodes, and low quality read ends, as well as De novo sequence assembly, generating and/or utilizing De Bruijn graphs and/or sequence graphs, e.g., De Bruijn and sequence graph construction, editing, trimming, cleanup, repair, coloring, annotation, comparison, transformation, splitting, splicing, analysis, subgraph selection, traversal, iteration, recursion, searching, filtering, import, export, including mapping reads to a reference genome, aligning reads to candidate mapping locations in the reference genome, local assembly of reads mapped to a reference region, sorting reads by aligned position, marking duplicate reads, including PCR or optical duplicates, re-alignment of multiple overlapping reads for indel consistency, base quality score recalibration, variant calling (single sample or joint), structural variant analysis, copy number variant analysis, somatic variant calling (e.g., tumor sample only, matched tumor/normal, or tumor/unmatched normal, etc.), RNA splice junction detection, RNA alternative splicing analysis, RNA transcript assembly, RNA transcript expression analysis, RNA differential expression analysis, RNA variant calling, DNA/RNA difference analysis, DNA methylation analysis and calling, variant quality score recalibration, variant filtering, variant annotation from known variant databases, sample contamination detection and estimation, phenotype prediction, disease testing, treatment response prediction, custom treatment design, ancestry and mutation history analysis, population DNA analysis, genetic marker identification, encoding genomic data into standard formats (e.g. FASTA, FASTQ, SAM, BAM, VCF, BCF), decoding genomic data from standard formats, querying, selecting or filtering genomic data subsets, general compression and decompression for genomic files (gzip, BAM compression), specialized compression and decompression for genomic data (CRAM), genomic data encryption and decryption, statistics calculation, comparison, and presentation from genomic data, genomic result data comparison, accuracy analysis and reporting, genomic file storage, archival, retrieval, backup, recovery, and transmission, as well as genomic database construction, querying, access management, data extraction, and the like. 
     All of these operations can be quite slow and expensive when implemented on traditional compute platforms. The sluggishness of such exclusively software implemented operations may be due in part to the complexity of the algorithms, but is typically due to the very large input and output datasets that results in high latency with respect to moving the data. The devices and systems disclosed herein overcome these problems, in part due to the configuration of the various hardware processing engines and/or in part due to the CPU/FPGA coupling configurations. Accordingly, as can be seen with respect to  FIG. 39 , one or more, e.g., all of these operations, may be accelerated by cooperation of CPUs  1000  and FPGAs  7 , such as in a distributed processing model, as described herein. For instance, in some cases (encryption, general compression, read mapping, and/or alignment), a whole operational function may be substantially or entirely implemented in custom FPGA logic (such as by hardware design methodology, e.g. RTL), such as where the CPU software mostly serves the function of compiling large data packets for preprocessing via worker threads  20 , such as aggregating the data into various jobs to be processed by one or more hardware implemented processing engines, and feeding the various data inputs, such as in a first in first out format, to one or more of the FPGA engine(s)  13 , and/or receives results therefrom. 
     For instance, as can be seen with respect to  FIG. 39 , in various embodiments, a worker thread generates various packets of job data that may be compiled and/or streamed into larger job packets that may be queued up and/or further aggregated in preparation for transfer, e.g., via a DDR3 to the FPGA  7 , such as over a high bandwidth, low latency, point to point interconnect protocol, e.g., QPI  3 . In particular instances, the data may be buffered in accordance with the particular data sets being transferred to the FPGA. Once the packaged data is received by the FPGA  7 , such as in a cache coherent manner, it may be processed and sent to one or more specialized clusters  11  whereby it may further be directed to one or more sets of processing engines for processing thereby in accordance with one or more of the pipeline operations herein described. 
     Once processed, results data may then be sent back to the cluster and queued up for being sent back over the tight coupling point to point interconnect to the CPU for post processing. In certain embodiments, the data may be sent to a de-aggregator thread prior to post processing. Once post processing has occurred, the data may be sent back to the initial worker thread  20  that may be waiting on the data. Such distributed processing is particularly beneficial for the functions herein disclosed above. Particularly, these functions are distinguishable by the facts that their algorithmic complexity (although having a very high net computational burden) are pretty limited, and they each may be configured so as to have a fairly uniform compute cost across their various sub-operations. 
     However, in various cases, rather than processing the data in large packets, smaller sub-routines or discrete function protocols or elements may be performed, such as pertaining to one or more functions of a pipeline, rather than performing the entire processing functions for that pipeline on that data. Hence, a useful strategy may be to identify one or more critical compute-intensive sub-functions in any given operation, and then implement that sub-function in custom FPGA logic (hardware acceleration), such as for the intensive sub-function(s), while implementing the balance of the operation, and ideally much or most of the algorithmic complexity, in software to run on CPUs, as described herein, such as with respect to  FIG. 39 . 
     Generally, it is typical of many genomic data processing operations that a small percentage of the algorithmic complexity accounts for a large percentage of the overall computing load. For instance, as a typical example, 20% of the algorithmic complexity for the performance of a given function may account for 90% of the compute load, while the remaining 80% of the algorithmic complexity may only account for 10% of the compute load. Hence, in various instances, the system components herein described may be configured so as to implement the high, e.g., 20% or more, complexity portion so as to be run very efficiently in custom FPGA logic, which may be a tractable and maintainable in a hardware design, and thus, may be configured for executing this in FPGA; which in turn may reduce the CPU compute load by 90%, thereby enabling 10× overall acceleration. Other typical examples may be even more extreme, such as where 10% of the algorithmic complexity may account for 98% of the compute load, in which case applying FPGA acceleration, as herein described, to the 10% complexity portion be even easier, but may also enable up to 50× net acceleration. 
     However, such a “piecemeal” or distributed processing acceleration approaches may be more practical when implemented in a tightly integrated CPU+FPGA platform, rather than on a loosely integrated CPU+FPGA platform. Particularly, in a loosely integrated platform, the portion, e.g., the functions, to be implemented in FPGA logic may be selected so as to minimize the size of the input data to the FPGA engine(s), and to minimize the output data from the FPGA engine(s), such as for each data unit processed, and additionally may be configured so as to keep the software/hardware boundary tolerant of high latencies. In such instances, the boundary between the hardware and software portions may be forced, e.g., on the loosely-integrated platform, to be drawn through certain low-bandwidth/high-latency cut-points, which divisions may not otherwise be desirable when optimizing the partitioning of the algorithmic complexity and computational loads. This may often result either in enlarging the boundaries of the hardware portion, encompassing an undesirably large portion of the algorithmic complexity in the hardwired format, or in shrinking the boundaries of the hardware portion, undesirably excluding portions with dense compute load. 
     By contrast, on a tightly integrated CPU+FPGA platform, due to the cache-coherent shared memory and the high-bandwidth/low-latency CPU/FPGA interconnect, the low-complexity/high-compute-load portions of a genomic data processing operation can be selected very precisely for implementation in custom FPGA logic (e.g., via the hardware engine(s) described herein), with optimized software/hardware boundaries. In such an instance, even if a data unit is large at the desired software/hardware boundary, it can still be efficiently handed off to an FPGA hardware engine for processing, just by passing a pointer to the particular data unit. Particularly, in such an instance, as per  FIG. 33B , the hardware engine  13  of the FPGA  7 , may not need to access every element of the data unit stored within the DRAM  1014 ; rather, it can access the necessary elements, e.g., within the cache  1014   a , with efficient small accesses over the low-latency interconnect  3 ′ serviced by the CPU cache, thereby consuming less aggregate bandwidth than if the entire data unit had to be accessed and/or transferred to the FPGA  7 , such as by DMA of the DRAM  1014 , over a loose interconnect  3 , as per  FIG. 33A . 
     In such instances, the hardware engine  13  can annotate processing results into the data unit in-place in CPU memory  1014 , without streaming an entire copy of the data unit by DMA to CPU memory. Even if the desired software/hardware boundary is not appropriate for a software thread  20  to make a high-latency, non-blocking queued handoff to the hardware engine  13 , it can potentially make a blocking function call to the hardware engine  13 , sleeping for a short latency until the hardware engine completes, the latency being dramatically reduced by the cache-coherent shared memory, the low-latency/high-bandwidth interconnect, and the distributed software/hardware coordination model, as in  FIG. 33B . 
     In particular instances, because the specific algorithms and requirements of signal/image processing and base calling vary from one sequencer technology to another, and because the quantity of raw data from the sequencer&#39;s sensor is typically gargantuan (this being reduced to enormous after signal/image processing, and to merely huge after base calling), such signal/image processing and base calling may be efficiently performed within the sequencer itself, or on a nearby compute server connected by a high bandwidth transmission channel to the sequencer. However, DNA sequencers have been achieving increasingly high throughputs, at a rate of increase exceeding Moore&#39;s Law, such that existing Central Processing Unit (“CPU”) and/or Graphics Processing Unit “GPU” based signal/image processing and base calling, when implemented individually and alone, have become increasingly inadequate to the task. Nevertheless, since a tightly integrated CPU+FPGA and/or a GPU+FPGA and/or a GPU/CPU+FPGA platform can be configured to be compact and easily instantiated within such a sequencer, e.g., as CPU and/or GPU and/or FPGA chip positioned on the sequencer&#39;s motherboard, or easily installed in a server adjacent to the sequencer, or a cloud-based server system accessible remotely from the sequencer, such a sequencer may be an ideal platform to offer the massive compute acceleration offered by the custom FPGA/ASIC hardware engines described herein. 
     For instance, a system may be provided so as to perform primary, secondary, and/or tertiary processing, or portions thereof, as herein described, so as to be implemented by a CPU, GPU, and/or FPGA; a CPU+FPGA; a GPU+FPGA; and/or a GPU/CPU+FPGA platform. Further, such accelerated platforms, e.g., including one or more FPGA hardware engines, are useful for implementation in cloud-based systems, as described herein. For example, signal/image processing, base calling, mapping, aligning, sorting, and/or variant calling algorithms, or portions thereof, generally require large amounts of floating point and/or fixed-point math, notably additions and multiplications. These functions can also be configured so as to be performed by one or more quantum processing circuits such as to be implemented in a quantum processing platform. 
     Particularly, large modern FPGAs/quantum circuits contain thousands of high-speed multiplication and addition resources, and custom engines implemented on or by them can perform parallel arithmetic operations at rates far exceeding the capabilities of simple general CPUs. Likewise, simple GPUs, have more comparable parallel arithmetic resources, but they often have awkward architectural limitations and programming restrictions that may prevent them from being fully utilized; whereas FPGA arithmetic resources, as implemented herein, can be wired up or otherwise configured by design to operate in exactly the designed manner with near 100% efficiency, such as for performing the calculations necessary to perform the functions herein. Accordingly, GPU cards may be added to expansion slots on a motherboard with a tightly integrated CPU and/or FPGA, thereby allowing all three processor types to cooperate, although the GPU may still cooperate with all of its own limitations and the limitations of loose integration. 
     More particularly, in various instances, with respect to Graphics Processing Units (GPUs), a GPU can be configured so as to implement one or more of the functions, as herein described, so as to accelerate the processing speed of the underlying calculations necessary for preforming that function, in whole or in part. More particularly, a GPU may be configured to perform one or more tasks in a mapping, aligning, sorting, and/or variant calling protocol, such as to accelerate one or more of the computations, e.g., the large amounts of floating point and/or fixed-point math, such as additions and multiplications involved therein, so as to work in conjunction with a server&#39;s CPU and/or FPGA to accelerate the application and processing performance and shorten the computational cycles required for performing such functions. Cloud servers, as herein described, with GPU/CPU/FPGA cards may be configured so as to easily handle compute-intensive tasks and deliver a smoother user experience when leveraged for virtualization. 
     Accordingly, if a tightly integrated CPU+FPGA or GPU+FPGA and/or CPU/GPU/FPGA with shared memory platform is employed within a sequencer or attached server for signal/image processing, base calling, mapping, aligning, sorting, and/or variant calling functions, there may be an advantage achieved such as in an incremental development process. For instance, initially, a limited portion of the compute load, such as a dynamic programming function for base calling, mapping, aligning, and/or variant calling may be implemented in one or more FPGA engines, where as other work may be done in the CPU and/or GPU expansion cards. However, the tight CPU/GPU/FPGA integration and shared memory model may be further configured, later, so as to make it easy to incrementally select additional compute-intensive functions for GPU and/or FPGA acceleration, which may then be implemented as FPGA hardware engines, and various of their functions may be offloaded for execution into the FPGA(s) thereby accelerating signal/image/base calling/mapping/aligning/variant processing. Such incremental advances can be implemented as needed to keep up with the increasing throughput of various primary and/or secondary and/or tertiary processing technologies. 
     Hence, read mapping and alignment, e.g., of one or more reads to a reference genome, as well as sorting and/or variant calling may be benefited from such FPGA and/or GPU acceleration. Specifically, mapping and alignment and/or variant calling, or portions thereof, may be implemented partially or entirely as custom FPGA logic, such as with the “to be aligned” reads streaming from the CPU/GPU memory into the FPGA map/align engines, and mapped and/or aligned read records streaming back out, which may further be streamed back on-board, such as in the performance of sorting and/or variant calling. This type of FPGA acceleration works on a loosely-integrated CPU/GPU+FPGA platform, and in the configurations described herein may be extremely fast. Nevertheless, there are some additional advantages that may be gained by moving to a tightly-integrated CPU+FPGA platform. 
     Accordingly, with respect to mapping and aligning and variant calling, in some embodiments, a shared advantage of a tightly-integrated CPU/GPU+FPGA and/or quantum processing platform, as described herein, is that the map/align/variant calling acceleration, e.g., hardware acceleration, can be efficiently split into several discrete compute-intensive operations, such as seed generation and/or mapping, seed chain formation, paired end rescue scans, gapless alignment, and gapped alignment (Smith-Waterman or Needleman-Wunsch), De Bruijn graph formation, performing a HMM computation, and the like, such as where the CPU and/or GPU and/or quantum computing software performs lighter (but not necessarily less complex) tasks, and may make acceleration calls to discrete hardware and/or other quantum computing engines as needed. Such a model may be less efficient in a typical loosely-integrated CPU/GPU+FPGA platform, e.g., due to large amounts of data to transfer back and forth between steps and high latencies, but may be more efficient in a tightly-integrated CPU+FPGA, GPU+FPGA, and/or quantum computing platform with cache-coherent shared memory, high-bandwidth/low-latency interconnect, and distributed software/hardware coordination model. Additionally, such as with respect to variant calling, both Hidden Markov model (HMM) and/or dynamic programming (DP) algorithms, including Viterbi and forward algorithms, may be implemented in association with a base calling/mapping/aligning operation, such as to compute the most likely original sequence explaining the observed sensor measurements, in a configuration so as to be well suited to the parallel cellular layout of FPGAs and quantum circuits described herein. 
     Specifically, an efficient utilization of hardware and/or software resources in a distributed processing configuration can result from reducing hardware and/or quantum computing acceleration to discrete compute-intensive functions. In such instances, several of the functions disclosed herein may be performed in a monolithic pure-hardware engine so as to be less compute intensive, but may nevertheless still be algorithmically complex, and therefore may consume large quantities of physical FPGA resources (lookup-tables, flip-flops, block-RAMs, etc.). In such instances, moving a portion or all of various discrete functions to software could take up available CPU cycles, in return for relinquishing substantial amounts of FPGA area. In certain of these instances, the freed FPGA area can be used for establishing greater parallelism for the compute intensive map/align/variant call sub-functions, thus increasing acceleration, or for other genomic acceleration functions. Such benefits may also be achieved by implementing compute intensive functions in one or more dedicated quantum circuits for implementation by a quantum computing platform. 
     Hence, in various embodiments, the algorithmic complexity of the one or more functions disclosed herein may be somewhat lessened by being configured in a pure hardware or pure quantum computing implementation. However, some operations, such as comparing pairs of candidate alignments for paired-end reads, and/or performing subtle mapping quality (MAPQ) estimations, represent very low compute loads, and thus could benefit from more complex and accurate processing in CPU/GPU and/or quantum computing software. Hence, in general, reducing the hardware processing to specific compute-intensive operations would allow more complex and accurate algorithms to be employed in the CPU/GPU portions. 
     Furthermore, the whole map/align/variant call operation could be configured so as to employ more algorithmic complexity at high levels, such as by calling compute-intensive hardware functions in a dynamic order or iteratively, whereas a monolithic pure-hardware/quantum processing design may be implemented in a manner so as to function more efficiently as a linear pipeline. For example, if during processing one Smith-Waterman alignment displayed evidence of the true alignment path escaping the scoring band, e.g., swath as described above, another Smith-Waterman alignment could be called to correct this. Hence, these configurations could essentially reduce the FPGA hardware/quantum acceleration to discrete functions, such as a form of procedural abstraction, which would allow higher level complexity to be built easily on top of it. 
     Additionally, in various instances, flexibility within the map/align/variant calling algorithms and features thereof may be improved by reducing hardware and/or quantum acceleration to discrete compute-intensive functions, and configuring the system so as to perform other, e.g., less intensive parts, in the software of the CPU and/or GPU. For instance, although hardware algorithms can be modified and reconfigured in FPGAs, generally such changes to the hardware designs, e.g., via firmware, may require several times as much design effort as similar changes to software code. In such instances, the compute-intensive portions of mapping and alignment and/or variant calling, such as seed mapping, seed chain formation, paired end rescue scans, gapless alignment, gapped alignment, and HMM, which are relatively well-defined, are thus stable functions and do not require frequent algorithmic changes. These functions, therefore, may be suitably optimized in hardware, whereas other functions, which could be executed by CPU/GPU software, are more appropriate for incremental improvement of algorithms, which is significantly easier in software. However, once fully developed could be implemented in hardware. One or more of these functions may also be configured so as to be implemented in one or more quantum circuits of a quantum processing machine. 
     Accordingly, in various instances, variant calling (with respect to DNA or RNA, single sample or joint, germ line or somatic, etc.) may also benefit from FPGA and/or quantum acceleration, such as with respect to its various compute intensive functions. For instance, haplotype-based callers, which call bases on evidence derived from a context provided within a window around a potential variant, as described above, is often the most compute-intensive operation. These operations include comparing a candidate haplotype (e.g., a single-strand nucleotide sequence representing a theory of the true sequence of at least one of the sampled strands at the genome locus in question) to each sequencer read, such as to estimate a conditional probability of observing the read given the truth of the haplotype. 
     Such an operation may be performed via one or more of an MRJD, Pair Hidden Markov Model (pair-HMM), and/or a Pair-Determined Hidden Markov Model (PD-HMM) calculation that sums the probabilities of possible combinations of errors in sequencing or sample preparation (PCR, etc.) by a dynamic programming algorithm. Hence, with respect thereto, the system can be configured such that a pair-HMM or PD-HMM calculation may be accelerated by one or more, e.g., parallel, FPGA hardware or quantum processing engines, whereas the CPU/GPU/QPU software may be configured so as to execute the remainder of the parent haplotype-based variant calling algorithm, either in a loosely-integrated or tightly-integrated CPU+FPGA, or GPU+FPGA or CPU and/or GPU+FPGA and/or QPU platform. For instance, in a loose integration, software threads may construct and prepare a De Bruijn and/or assembly graph from the reads overlapping a chosen active region (a window or contiguous subset of the reference genome), extract candidate haplotypes from the graph, and queue up haplotype-read pairs for DMA transfer to FPGA hardware engines, such as for pair-HMM or PD-HMM comparison. The same or other software threads can then receive the pair-HMM results queued and DMA-transferred back from the FPGA into the CPU/GPU memory, and perform genotyping and Bayesian probability calculations to make final variant calls. Of course, one or more of these functions can be configured so as to be run on one or more quantum computing platforms. 
     For instance, as can be seen with respect to  FIG. 38 , the CPU/GPU  1000  may include one or more, e.g., a plurality, of threads  20   a ,  20   b , and  20   c , which may each have access to an associated DRAM  1014 , which DRAM has work space  1014   a ,  1014   b , and  1014   c , within which each thread  20   a ,  20   b , and  20   c , may have access, respectively, so as to perform one or more operations on one or more data structures, such as large data structures. These memory portions and their data structures may be accessed, such as via respective cache portions  1014   a ′, such as by one or more processing engines  13   a ,  13   b ,  13   c  of the FPGA  7 , which processing engines may access the referenced data structures such as in the performance of one or more of the operations herein described, such as for mapping, aligning, sorting, and/or variant calling. Because of the high bandwidth, tight coupling interconnect  3 , data pertaining to the data structures and/or related to the processing results may be shared substantially seamlessly between the CPU and/or GPU and/or QPU and/or the associated FPGA, such as in a cache coherent manner, so as to optimize processing efficiency. 
     Accordingly, in one aspect, as herein disclosed, a system may be provided wherein the system is configured for sharing memory resources amongst its component parts, such as in relation to performing some computational tasks or sub-functions via software, such as run by a CPU and/or GPU and/or QPU, and performing other computational tasks or sub functions via firmware, such as via the hardware of an associated chip, such as an FPGA and/or ASIC or structured ASIC. This may be achieved in a number of different ways, such as by a direct loose or tight coupling between the CPU/GPU/QPU and the chip, e.g., FPGA. Such configurations may be particularly useful when distributing operations related to the processing of large data structures, as herein described, that have large functions or subfunctions to be used and accessed by both the CPU and/or GPU and/or QPU and the integrated circuit. Particularly, in various embodiments, when processing data through a genomics pipeline, as herein described, such as to accelerate overall processing function, timing, and efficiency, a number of different operations may be run on the data, which operations may involve both software and hardware processing components. 
     Consequently, data may need to be shared and/or otherwise communicated, between the software component running on the CPU and/or GPU and/or the QPU and the hardware component embodied in the chip, e.g., an FPGA or ASIC. Accordingly, one or more of the various steps in the processing pipeline, or a portion thereof, may be performed by one device, e.g., the CPU/GPU/QPU, and one or more of the various steps may be performed by the other device, e.g., the FPGA or ASIC. In such an instance, the CPU and the FPGA need to be communicably coupled, such as by a point to point interconnect, in such a manner to allow the efficient transmission of such data, which coupling may involve the shared use of memory resources. To achieve such distribution of tasks and the sharing of information for the performance of such tasks, the CPU and/or GPU and/or QPU may be loosely or tightly coupled to the FPGA, or other chip set. 
     Hence, in particular embodiments, a genomics analysis platform is provided. For instance, the platform may include a motherboard, a memory, and plurality of integrated circuits, such as forming one or more of a CPU/GPU/QPU, a mapping module, an alignment module, a sorting module, and/or a variant call module. Specifically, in particular embodiments, the platform may include a first integrated circuit, such as an integrated circuit forming a central processing unit (CPU) and/or a graphics processing unit (GPU) that is responsive to one or more software algorithms that are configured to instruct the CPU/GPU to perform one or more sets of genomics analysis functions, as described herein, such as where the CPU/GPU includes a first set of physical electronic interconnects to connect with the motherboard. In particular embodiments, a quantum processing unit is provided, wherein the QPU includes one or more quantum circuits that are configured for performing one or more of the functions disclosed herein. In various instances, the memory may also be attached to the motherboard and may further be electronically connected with the CPU and/or GPU and/or QPU, such as via at least a portion of the first set of physical electronic interconnects. In such instances, the memory may be configured for storing a plurality of reads of genomic data, and/or at least one or more genetic reference sequences, and/or an index, e.g., such as a hash table, of the one or more genetic reference sequences. 
     Additionally, the platform may include one or more of a second integrated circuit(s), such as where each second integrated circuit forms a field programmable gate array (FPGA) having a second set of physical electronic interconnects to connect with the CPU and the memory, such as via a point-to-point interconnect protocol. In such an instance, the FPGA may be programmable by firmware to configure a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomics analysis functions, e.g., mapping, aligning, sorting, variant calling, e.g., an HMM function, etc. Particularly, the hardwired digital logic circuits of the FPGA may be arranged as a set of processing engines to perform one or more pre-configured steps in a sequence analysis pipeline of the genomics analysis, such as where the set(s) of processing engines include one or more of a mapping and/or aligning and/or sorting and/or variant call module, which modules may be formed of the separate or the same subsets of processing engines. 
     For instance, with respect to variant calling, a pair-HMM or PD-HMM calculation is one of the most compute-intensive steps of a haplotype-based variant calling. Hence, variant calling speed may be greatly improved by accelerating this step in one or more FPGA or quantum processing engines, as herein described. However, there may be additional benefit in accelerating other compute-intensive steps in additional FPGA and/or QP engines, to achieve a greater speed-up of variant calling, or a portion thereof, or reduce CPU/GPU load and the number of CPU/GPU cores required, or both, as seen with respect to  FIG. 38 . 
     Additional compute-intensive functions, with respect to variant calling, that may be implemented in FPGA and/or quantum processing engines include: callable-region detection, where reference genome regions covered by adequate depth and/or quality of aligned reads are selected for processing; active-region detection, where reference genome loci with nontrivial evidence of possible variants are identified, and windows of sufficient context around these loci are selected as active regions for further processing; De-Bruijn or other assembly graph construction, where reads overlapping an active region and/or K-mers from those reads are assembled into a graph; assembly graph preparation, such as trimming low-coverage or low-quality paths, repairing dangling head and tail paths by joining them onto a reference backbone in the graph, transformation from K-mer to sequence representation of the graph, merging similar branches and otherwise simplifying the graph; extracting candidate haplotypes from the assembly graph; as well as aligning candidate haplotypes to the reference genome, such as by Smith-Waterman alignment, e.g., to determine variants (SNPs and/or indels) from the reference represented by each haplotype, and synchronize its nucleotide positions with the reference. 
     All of these functions may be implemented as high-performance hardware engines within the FPGA, and/or by one or more quantum circuits of a quantum computing platform. However, calling such a variety of hardware acceleration functions from many integration points in the variant calling software may become inefficient on a loosely-coupled CPU/GPU/QPU+FPGA platform, and therefore a tightly-integrated CPU/GPU/QPU+FPGA platform may be desirable. For instance, various stepwise processing methods such as: constructing, preparing, and extracting haplotypes from a De Bruijn, or other assembly graph, could strongly benefit from a tightly-integrated CPU/GPU/QPU+FPGA platform. Additionally, assembly graphs are large and complex data structures, and passing them repeatedly between the CPU and/or GPU and the FPGA could become resource expensive and inhibit significant acceleration. 
     Hence, an ideal model for such graph processing, employing a tightly-integrated CPU/GPU/QPU and/or FPGA platform, is to retain such graphs in cache-coherent shared memory for alternating processing by CPU and/or GPU and/or QPU software and FPGA hardware functions. In such an instance, a software thread processing a given graph may iteratively command various compute-intensive graph processing steps by a hardware engine, and then the software could inspect the results and determine the next steps between the hardware calls. This processing model, may be configured to correspond to software paradigms such as a data-structure API or an object-oriented method interface, but with compute intensive functions being accelerated by custom hardware engines, which is made practical by being implemented on a tightly-integrated CPU and/or GPU and/or QPU+FPGA platform, with cache-coherent shared memory and high-bandwidth/low-latency CPU/GPU/QPU/FPGA interconnects. 
     Accordingly, in addition to mapping and aligning sequencer reads to a reference genome, reads may be assembled “de novo,” e.g., without a reference genome, such as by detecting apparent overlap between reads, e.g., in a pileup, where they fully or mostly agree, and joining them into longer sequences, contigs, scaffolds, or graphs. This assembly may also be done locally, such as using all reads determined to map to a given chromosome or portion thereof. Assembly in this manner may also incorporate a reference genome, or segment of one, into the assembled structure. 
     In such an instance, due to the complexity of joining together read sequences that do not completely agree, a graph structure may be employed, such as where overlapping reads may agree on a single sequence in one segment, but branch into multiple sequences in an adjacent segment, as explained above. Such an assembly graph, therefore, may be a sequence graph, where each edge or node represents one nucleotide or a sequence of nucleotides that is considered to adjoin contiguously to the sequences in connected edges or nodes. In particular instances, such an assembly graph may be a k-mer graph, where each node represents a k-mer, or nucleotide sequence of (typically) fixed length k, and where connected nodes are considered to overlap each other in longer observed sequences, typically overlapping by k−1 nucleotides. In various methods there may be one or more transformations performed between one or more sequence graphs and k-mer graphs. 
     Although assembly graphs are employed in haplotype-based variant calling, and some of the graph processing methods employed are similar, there are important differences. De novo assembly graphs are generally much larger, and employ longer k-mers. Whereas variant-calling assembly graphs are constrained to be fairly structured and simple, such as having no cycles and flowing source-to-sink along a reference sequence backbone, de novo assembly graphs tend to be more unstructured and complex, with cycles, dangling paths, and other anomalies not only permitted, but subjected to special analysis. De novo assembly graph coloring is sometimes employed, assigning “colors” to nodes and edges signifying, for example, which biological sample they came from, or matching a reference sequence. Hence, a wider variety of graph analysis and processing functions need to be employed for de novo assembly graphs, often iteratively or recursively, and especially due to the size and complexity of de novo assembly graphs, processing functions tend to be extremely compute intensive. 
     Hence, as set forth above, an ideal model for such graph processing, on a tightly-integrated CPU/GPU/QPU+FPGA platform, is to retain such graphs in cache-coherent shared memory for alternating processing between the CPU/GPU/QPU software and FPGA hardware functions. In such an instance, a software thread processing a given graph may iteratively command various compute-intensive graph processing steps to be performed by a hardware engine, and then inspect the results to thereby determine the next steps to be performed by the hardware, such as by making appropriate hardware calls. Like above, this processing model, is greatly benefitted by implementation on a tightly-integrated CPU+FPGA platform, with cache-coherent shared memory and high-bandwidth/low-latency CPU/FPGA interconnect. 
     Additionally, as described herein below, tertiary analysis includes genomic processing that may follow variant calling, which in clinical applications may include variant annotation, phenotype prediction, disease testing, and/or treatment response prediction, as described herein. Reasons it is beneficial to perform tertiary analysis on such a tightly-integrated CPU/GPU/QPU+FPGA platform are that such a platform configuration enables efficient acceleration of primary and/or secondary processing, which are very compute intensive, and it is ideal to continue with tertiary analysis on the same platform, for convenience and reduced turnaround time, and to minimize transmission and copying of large genomic data files. Hence, either a loosely or tightly-integrated CPU/GPU/QPU+FPGA platform is a good choice, but a tightly coupled platform may include additional benefits because tertiary analysis steps and methods vary widely from one application to another, and in any case where compute-intensive steps slow down tertiary analysis, custom FPGA acceleration of those steps can be implemented in an optimized fashion. 
     For instance, a particular benefit to tertiary analysis on a tightly-integrated CPU/GPU/QPU+FPGA platform is the ability to re-analyze the genomic data iteratively, leveraging the CPU/GPU/QPU and/or FPGA acceleration of secondary processing, in response to partial or intermediate tertiary results, which may benefit additionally from the tight integration configuration. For example, after tertiary analysis detects a possible phenotype or disease, but with limited confidence as to whether the detection is true or false, focused secondary re-analysis may be performed with extremely high effort on the particular reads and reference regions impacting the detection, thus improving the accuracy and confidence of relevant variant calls, and in turn improving the confidence in the detection call. Additionally, if tertiary analysis determines information about the ancestry or structural variant genotypes of the analyzed individual, secondary analysis may be repeated using a different or modified reference genome, which is more appropriate for the specific individual, thus enhancing the accuracy of variant calls and improving the accuracy of further tertiary analysis steps. 
     However, if tertiary analysis is done on a CPU-only platform after primary and secondary processing (possibly accelerated on a separate platform), then re-analysis with secondary processing tools is likely to be too slow to be useful on the tertiary analysis platform itself, and the alternative is transmission to a faster platform, which is also prohibitively slow. Thus, in the absence of any form of hardware or quantum acceleration on the tertiary analysis platform, primary and secondary processing must generally be completed before tertiary analysis begins, without the possibility of easy re-analysis or iterative secondary analysis. But on an FPGA-accelerated platform, and especially a tightly-integrated CPU and/or GPU and/or QPU and/or FPGA platform where secondary processing is maximally efficient, iterative analysis becomes practical and useful. 
     Accordingly, as indicated above, the modules herein disclosed may be implemented in the hardware of the chip, such as by being hardwired therein, and in such instances their implementation may be such that their functioning may take place at a faster speed, with greater accuracy, as compared to when implemented in software, such as where there are minimal instructions to be fetched, read, and/or executed. Additionally, in various instances, the functions to be performed by one or more of these modules may be distributed such that various of the functions may be configured so as to be implemented by the host CPU and/or GPU and/or QPU software, whereas in other instances, various other functions may be performed by the hardware of an associated FPGA, such as where the two or more devices perform their respective functions with one another such as in a seamless fashion. For such purposes, the CPU, GPU, QPU, and/or FPGA may be tightly coupled, such as via a low latency, high bandwidth interconnect, such as a QPI, CCVI, CAPI, and the like. Accordingly, in some instances, the high computationally intensive functions to be performed by one or more of these modules may be performed by a quantum processor implemented by one or more quantum circuits. 
     Hence, given the unique hardware and/or quantum processing implementation, the modules of the disclosure may function directly in accordance with their operational parameters, such as without needing to fetch, read, and/or execute instructions, such as when implemented solely in CPU software. Additionally, memory requirements and processing times may be further reduced, such as where the communications within chip is via files, e.g., stored locally in the FPGA/CPU/GPU/QPU cache, such as a cache coherent manner, rather than through extensive accessing an external memory. Of course, in some instances, the chip and/or card may be sized so as to include more memory, such as more on board memory, so as to enhance parallel processing capabilities, thereby resulting in even faster processing speeds. For instance, in certain embodiments, a chip of the disclosure may include an embedded DRAM, so that the chip does not have to rely on external memory, which would therefore result in a further increase in processing speed, such as where a Burrows-Wheeler algorithm or De Brujin Graph may be employed, instead of a hash table and hash function, which may in various instances, rely on external, e.g., host memory. In such instances, the running of a portion or an entire pipeline can be accomplished in 6 or 10 or 12 or 15 or 20 minutes or less, such as from start to finish. 
     As indicated above, there are various different points where any given module can be positioned on the hardware, or be positioned remotely therefrom, such as on a server accessible on the cloud. Where a given module is positioned on the chip, e.g., hardwired into the chip, its function may be performed by the hardware, however, where desired, the module may be positioned remotely from the chip, at which point the platform may include the necessary instrumentality for sending the relevant data to a remote location, such as a server accessible via the cloud, so that the particular module&#39;s functionality may be engaged for further processing of the data, in accordance with the user selected desired protocols. Accordingly, part of the platform may include a web-based interface for the performance of one or more tasks pursuant to the functioning of one or more of the modules disclosed herein. For instance, where mapping, alignment, and/or sorting are all modules that may occur on the chip, in various instances, one or more of local realignment, duplicate marking, base quality core recalibration, and/or variant calling may take place on the cloud. 
     Particularly, once the genetic data has been generated and/or processed, e.g., in one or more primary and/or secondary processing protocols, such as by being mapped, aligned, and/or sorted, such as to produce one or more variant call files, for instance, to determine how the genetic sequence data from a subject differs from one or more reference sequences, a further aspect of the disclosure may be directed to performing one or more other analytical functions on the generated and/or processed genetic data such as for further, e.g., tertiary, processing, as depicted in  FIG. 40 . For example, the system may be configured for further processing of the generated and/or secondarily processed data, such as by running it through one or more tertiary processing pipelines  700 , such as one or more of a genome pipeline, an epigenome pipeline, metagenome pipeline, joint genotyping, a MuTect2 pipeline, or other tertiary processing pipeline, such as by the devices and methods disclosed herein. 
     For instance, in various instances, an additional layer of processing  800  may be provided, such as for disease diagnostics, therapeutic treatment, and/or prophylactic prevention, such as including NIPT, NICU, Cancer, LDT, AgBio, and other such disease diagnostics, prophylaxis, and/or treatments employing the data generated by one or more of the present primary and/or secondary and/or tertiary pipelines. For example, particular bioanalytic pipelines include genome pipelines, epigenome pipelines, meta genome pipelines, joint genotyping pipelines, GATK/MuTect2, and other such pipelines. Hence, the devices and methods herein disclosed may be used to generate genetic sequence data, which data may then be used to generate one or more variant call files and/or other associated data that may further be subject to the execution of other tertiary processing pipelines in accordance with the devices and methods disclosed herein, such as for particular and/or general disease diagnostics as well as for prophylactic and/or therapeutic treatment and/or developmental modalities. See, for instance,  FIGS. 41B and 43 . 
     As described above, the methods and/or system herein presented may include the generating and/or the otherwise acquiring of genetic sequence data. Such data may be generated or otherwise acquired from any suitable source, such as by a NGS or “sequencer on a chip technology.” Once generated and/or acquired, the methods and systems herein may include subjecting the data to further processing such as by one or more secondary processing protocols. The secondary processing protocols may include one or more of mapping, aligning, and sorting of the generated genetic sequence data, such as to produce one or more variant call files, for example, so as to determine how the genetic sequence data from a subject differs from one or more reference sequences or genomes. A further aspect of the disclosure may be directed to performing one or more other analytical functions on the generated and/or processed genetic data, e.g., secondary result data, such as for additional, e.g., tertiary, processing, which processing may be performed on or in association with the same chip or chipset as that hosting the aforementioned sequencer technology. 
     Accordingly, in a first instance, such as with respect to the generation, acquisition, and/or transmission of genetic sequence data, as set forth in  FIGS. 38-40 , such data may be produced either locally or remotely and/or the results thereof may then be directly processed, such as by a local computing resource  100 , or may be transmitted to a remote location, such as to a remote computing resource  300 , for further processing, e.g. for secondary and/or tertiary processing. For instance, the generated genetic sequence data may be processed locally, and directly, such as where the sequencing and secondary processing functionalities are housed on the same chipset and/or within the same device on-site  10 . Likewise, the generated genetic sequence data may be processed locally, and indirectly, such as where the sequencing and secondary processing functionalities occur separately by distinct apparatuses that share the same facility or location but may be separated by a space albeit communicably connected, such as via a local network  10 . In a further instance, the genetic sequence data may be derived remotely, such as by a remote NGS, and the resultant data may be transmitted over a cloud based network  50  to a remote location  300 , such as separated geographically from the sequencer. 
     Specifically, as illustrated in  FIG. 40 , in various embodiments, a data generation apparatus, e.g., nucleotide sequencer  110 , may be provided on site, such as where the sequencer is a “sequencer on a chip” or a NGS, wherein the sequencer is associated with a local computing resource  100  either directly or indirectly such as by a local network connection  10 . The local computing resource  100  may include or otherwise be associated with one or more of a data generation  110  and/or a data acquisition  120  mechanism(s). Such mechanisms may be any mechanism configured for either generating and/or otherwise acquiring data, such as analog, digital, and/or electromagnetic data related to one or more genetic sequences of a subject or group of subjects, such as where the genetic sequence data is in a BCL or FASTQ file format. 
     For example, such a data generating mechanism  110  may be a primary processor such as a sequencer, such as a NGS, a sequencer on a chip, or other like mechanism for generating genetic sequence information. Further, such data acquisition mechanisms  120  may be any mechanism configured for receiving data, such as generated genetic sequence information; and/or together with the data generator  110  and/or computing resource  100  is capable of subjecting the same to one or more secondary processing protocols, such as a secondary processing pipeline apparatus configured for running a mapper, aligner, sorter, and/or variant caller protocol on the generated and/or acquired sequence data as herein described. In various instances, the data generating  110  and/or data acquisition  120  apparatuses may be networked together such as over a local network  10 , such as for local storage  200 ; or may be networked together over a local and/or cloud based network  30 , such as for transmitting and/or receiving data, such as digital data related to the primary and/or secondary processing of genetic sequence information, such as to or from a remote location, such as for remote processing  300  and/or storage  400 . In various embodiments, one or more of these components may be communicably coupled together by a hybrid network as herein described. 
     The local computing resource  100  may also include or otherwise be associated with a compiler  130  and/or a processor  140 , such as a compiler  130  configured for compiling the generated and/or acquired data and/or data associated therewith, and a processor  140  configured for processing the generated and/or acquired and/or compiled data and/or controlling the system  1  and its components, as herein described, such as for performing primary, secondary, and/or tertiary processing. For instance, any suitable compiler may be employed, however, in certain instances, further efficiencies may be achieved not only by implementing a tight-coupling configuration, such as discussed above, for the efficient and coherent transfer of data between system components, but may further be achieved by implementing a just-in-time (JIT) computer language compiler configuration. 
     Specifically, as used herein just-in-time (JIT) refers to a device, system, and/or method for converting acquired and/or generated file formats from one form to another. In a broad usage structure, the JIT system disclosed herein may include a compiler  130 , or other computing architecture, e.g., a processing program, that may be implemented in a manner so as to convert various code from one form into another. For instance, in one implementation, a JIT compiler may function to convert bytecode, or other program code that contains instructions that must be interpreted, into instructions that can be sent directly to an associated processor  140  for near immediate execution, such as without the need for interpretation of the instructions by the particular machine language. Particularly, after a coding program, e.g., a Java program, has been written, the source language statements may be compiled by the compiler, e.g., Java compiler, into bytecode, rather than compiled into code that contains instructions that match any given particular hardware platform&#39;s processing language. This bytecode compiling action, therefore, is platform-independent code that can be sent to any platform and run on that platform regardless of its underlying processor. Hence, a suitable compiler may be a compiler that is configured so as to compile the bytecode into platform-specific executable code that may then be executed immediately. In this instance, the JIT compiler may function to immediately convert one file format into another, such as “on the fly”. 
     Hence, a suitably configured compiler, as herein described, is capable of overcoming various deficiencies in the art. Specifically, past compiling programs that were written in a specific language had to be recompiled and/or re-written dependent on each specific computer platform on which it was to be implemented. In the present compiling system, the compiler may be configured so as to only have to write and compile a program once, and once written in a particular form, may be converted into one or more other forms nearly immediately. More specifically, the compiler may be a JIT, or other similar dynamic translation compiler format, which is capable of writing instructions in a platform agnostic language that does not have to be recompiled and/or re-written dependent on the specific computer platform on which it is implemented. For instance, in a particular use model, the compiler may be configured for interpreting compiled bytecode, and/or other instructions, into instructions that are understandable by a given particular processor for the conversion of one file format into another, regardless of computing platform. Principally, the JIT system herein is capable of receiving one genetic file, such as representing a genetic code, for example, where the file is a BCL or FASTQ file, e.g., generated from a genetic sequencer, and rapidly converting it into another form, such as into a SAM, BAM, and/or CRAM file, such as by using the methods disclosed herein. 
     Particularly, in various instances, the system herein disclosed may include a first and/or a second compiler  130   a  and  130   b , such as a virtual compiling machine, that handles one or a plurality of bytecode instruction conversions at a time. For instance, using a Java type just-in-time compiler, or other suitably configured second compiler, within the present system platform, will allow for the compiling of instructions into bytecode that may then be converted into the particular system code, e.g., as though the program had been compiled initially on that platform. Accordingly, once the code has been compiled and/or (re-)compiled, such as by the JIT compiler(s)  130 , it will run more quickly in the computer processor  140 . Hence, in various embodiments, just-in-time (JIT) compilation, or other dynamic translation compilation, may be configured so as to be performed during execution of a given program, e.g., at run time, rather than prior to execution. In such an instance, this may include the step(s) of translation to machine code or translation into another format, which may then be executed directly, thereby allowing for one or more of ahead-of-time compilation (AOT) and/or interpretation. 
     More particularly, as implemented within the present system, a typical genome sequencing dataflow generally produces data in one or more file formats, derived from one or more computing platforms, such as in a BCL, FASTQ, SAM, BAM, CRAM, and/or VCF file format, or their equivalents. For instance, a typical DNA sequencer  110 , e.g., an NGS, produces raw signals representing called bases that are designated herein as reads, such as in a BCL and/or FASTQ file, which may optionally be further processed, e.g., enhanced image processing, and/or compressed  150 . Likewise, the reads of the generated BCL/FASTQ files may then be further processed within the system, as herein described, so as to produce mapping and/or alignment data, which produced data, e.g., of the mapped and aligned reads, may be in a SAM or BAM file format, or alternatively a CRAM file format. Further, the SAM or BAM file may then be processed, such as through a variant calling procedure, so as to produce a variant call file, such as a VCF file or gVCF file. Accordingly, all of these produced BCL, FASTQ, SAM, BAM, CRAM, and/or VCF files, once produced are (extremely) large files that all need to be stored such as in system memory architecture locally  200  or remotely  400 . The storage of any one of these files is expensive. The storage of one or more of them, e.g., all of them, is extremely expensive. 
     As indicated, just-in-time (JIT) or other dual compiling or dynamic translation compilation analysis, may be configured and deployed herein so as to reduce such high storage costs. For instance, a JIT analysis scheme may be implemented herein so as to store data in only one format (e.g., a compressed FASTQ or BAM, etc., file format), while providing access to one or more file formats (e.g., BCL, FASTQ, SAM, BAM, CRAM, and/or VCF, etc.). This rapid file conversion process may be effectuated by rapidly processing the genomic data utilizing the herein disclosed respective hardware and/or quantum acceleration platforms, e.g., such as for mapping, aligning, sorting, and/or variant calling (or component functions thereof, such as HMM and Smith-Waterman, compression and decompression, and the like), in hardware engines on an integrated circuit, such as an FPGA, or by a quantum processor. Hence, by implementing JIT or similar analysis along with such acceleration, the genomic data can be processed in a manner so as to generate desired file formats on the fly, at speeds comparable to normal file access. Thus, considerable storage savings may be realized by JIT-like processing with little or no loss of access speed. 
     Particularly, two general options are useful for the underlying storage of the genomic data produced herein so as to be accessible for JIT-like processing, these include the storage of unaligned reads (e.g., that may include compressed FASTQ, or unaligned compressed SAM, BAM, or CRAM files), and the storage of aligned reads (e.g., that may include compressed BAM or CRAM files). However, since the accelerated processing disclosed herein allows any of the referenced file formats to be derived rapidly, the underlying file format for storage may be selected so as to achieve the smallest compressed file size, thereby decreasing the expense of storage. Hence, because of the comparatively smaller file size for unprocessed, e.g., raw un-aligned, read data, there is an advantage to storing unaligned reads so that the data fields are minimized. 
     More particularly, in view of the rapid processing speeds achievable by the devices, systems, and methods disclosed herein, in many instances, there may be no need to store mapped and/or alignment information for each and every read, because this information may be rapidly derived upon need, such as on the fly. Further, although a compressed FASTQ (e.g. FASTQ.gz) file format is commonly used for storage of genetic sequence data, such unaligned reads may be stored in more advanced compressed formats as well, such as SAM, BAM, or CRAM files, which may further reduce the file size, such as by use of compact binary representation and/or more targeted compression methods. Hence, these file formats may be compressed prior to storage, be decompressed after storage, and processed rapidly, such as on the fly, so as to convert one file format from another. 
     However, an advantage to storing aligned reads is that much or all of each read&#39;s sequence content can be omitted. Specifically, system efficiency can be enhanced and storage space saved by only storing the differences between the read sequences and the selected reference genome, such as at indicated variant alignment positions of the read. More specifically, since differences from the reference are usually sparse, the aligned position and list of differences can often be more compactly stored than the original read sequence. Therefore, in various instances, the storage of an aligned read format, e.g., when storing data related to the differences of aligned reads, may be preferable to the storage of unaligned read data. In such an instance, if an aligned read format is used as the underlying storage format, such as in a JIT procedure, other formats, such as a SAM, BAM, and/or CRAM, compressed file formats, may also be used. 
     Along with the aligned and/or unaligned read file data to be stored, a wide variety of other data, such as metadata derived from the various computations determined herein, may also be stored. Such computated data may include read mapped, alignment and/or subsequent processing data, such as alignment scores, mapping confidence, edit distance from the reference, etc. In certain instances, such metadata and/or other extra information need not be retained in the underlying storage for JIT analysis, such as in those instances where it can be reproduced on the fly, such as by the accelerated data processing herein described. 
     With respect to meta-data, this data may be a small file that instructs the system as to how to go backwards or forwards from one file format into conversion to another file format. Hence, the meta-data file allows the system to create a bit-compatible version of any other file type. For instance, proceeding forward from an originating data file, the system need only access and implement the instructions of the meta-data. Along with rapid file format conversion, JIT also enables rapid compression and/or decompression and/or storage, such as in a genomics dropbox memory cache. 
     As discussed in greater detail below, once sequence data is generated  110 , it may be stored locally  200 , and/or may be made accessible for storage remotely, such as in a cloud accessible dropbox memory cahce  400 . For example, once in the dropbox, the data may appear as accessible on the cloud  50 , and may then be further processed, e.g., substantially immediately. This is particularly useful when there is a mapping/aligning/sorting/variant calling system  100 / 300  of the disclosure on either side of the cloud  50  interface facilitating the automatic uploading and processing of the data, which can be further processed such as using the JIT technology herein described. 
     For instance, an underlying storage format for JIT compiling and/or processing may contain only minimal data fields, such as read name, base quality scores, alignment position, and/or orientation in the reference, and a list of differences from the reference, such as where each field may be compressed in an optimal manner for its data type. Various other metadata may be included and/or otherwise associated with the storage file. In such an instance, the underlying storage for JIT analysis may be in a local file system,  200  such as on hard disk drives and solid state drives, or a network storage resource such as NAS or object or Dropbox like storage system  400 . Particularly, when various file formats, such as BCL, FASTQ, SAM, BAM, CRAM, VCF, etc., have been produced for a genomic dataset, which may be submitted for JIT processing and/or storage, the JIT or other similar compiling and/or analysis system may be configured so as to convert the data to a single underlying storage format for storage. Additional data, such as metadata and/or other information (which may be small) necessary to reproduce all other desired formats by accelerated genomic data processing, may also be associated with the file and stored. Such additional information may include one or more of: a list of file formats to be reproduced, data processing commands to reproduce each format, unique ID (e.g., URL or MD5/SHA hash) of reference genome, various parameter settings, such as for mapping, alignment, sorting, variant calling, and/or any other processing, as described herein, randomization seeds for processing steps, e.g., utilizing pseudo-randomization, to deterministically reproduce the same results, user Interface, and the like. 
     In various instances, the data to be stored and/or retrieved in a JIT or similar dynamic translation processing and/or analysis system may be presented to the user, or other applications, in a variety of manners. For instance, one option is to have the JIT analysis storage in a standard or custom “JIT object” file format, such as SAM, BAM, CRAM, or other custom format, and provide user tools to rapidly convert the JIT object into the desired format (e.g., in a local temporary storage) using the accelerated processing disclosed herein. Another option is to present the appearance of multiple file formats, such as BCL, FASTQ, SAM, BAM, CRAM, VCF, etc. to the user, and the user applications, in such a manner that the file-system access to various file formats utilizes a JIT procedure. A further option is to make user tools that otherwise accept specific file formats (BCL, FASTQ, SAM, BAM, CRAM, VCF, etc.) that are able to be presented as a JIT object instead, and may automatically call for JIT analysis to obtain the data in the desired data format, e.g., BCL, FASTQ, SAM, BAM, CRAM, VCF, etc. 
     Accordingly, JIT procedures are useful for providing access to multiple file formats, e.g., BCL, FASTQ, SAM, BAM, CRAM, VCF, and the like, from a single file format by rapidly processing the underlying stored compressed filed format. Additionally, it remains useful even if only a single file format is to be accessed, because compression is still achieved relative to storing the accessed format directly. In such an instance, the underlying file storage format may be different than the accessed file format, and/or may contain less metadata, and/or may be compressed more efficiently than the accessed format. 
     In various instances, the methods of JIT analysis, as provided herein, may also be used for transmission of genomic data, over the internet or another network, to minimize transmission time and lessen consumed network bandwidth. Particularly, in the storage application, a single compressed underlying file format may be stored, and/or one or more formats may be accessed via accelerated genomic data processing. Similarly, in the transmission application, only a single compressed underlying file format may be transmitted from a source network node to a destination network node, such as where the underlying format may be chosen primarily for smallest compressed file size, and/or where all desired file formats may be generated at the destination node by or for genomic data processing, such as on the fly. In this many, only one compressed data file format need be used for storage and/or transfer, from which file format the other various file formats may be derived. 
     For instance, hardware and/or quantum accelerated genomic data processing, as herein described, may be utilized in (or by) both the source network node, to generate and/or compress the underlying format for transmission, and the destination network node, to decompress and/or generate other desired file formats by accelerated genomic data processing. Nevertheless, JIT or other dynamic translation analysis continues to be useful in the transmission application even if only one of the source node or the destination node utilizes hardware and/or quantum accelerated genomic data processing. For example, a data server that sends large amounts of genomic data may utilize hardware and/or quantum accelerated genomic data processing so as to generate the compressed underlying format for transmission to various destinations. In such instances, each destination may use slower software genomic data processing to generate other desired data formats. Hence, although the speed advantage of JIT analysis is lessened at the destination node, transmission time, and network utilization are still usefully reduced, and the source node is able to service many such transmissions efficiently due to its hardware and/or quantum accelerated genomic data processing. 
     Further, in another example, a data server that receives uploads of large amounts of genomic data, e.g., from various sources, may utilize hardware and/or quantum accelerated genomic data processing and/or storage, while the various source nodes may use slower software run on a CPU/GPU to generate the compressed underlying file format for transmission. Alternatively, hardware and/or quantum accelerated genomic data processing may be utilized by one or more intermediate network nodes, such as a gateway server, between the source and destination nodes, to transmit and/or receive genomic data in a compressed underlying file format, according to the JIT or other dynamic translation analysis methods, thus gaining the benefits of reduced transmission time and network utilization without overburdening the said intermediate network nodes with excessive software processing. 
     Hence, as can be seen with respect to  FIG. 40 , in certain instances, the local computing resource  100  may include a compiler  130 , such as a JIT compiler, and may further include a compressor unit  150  that is configured for compressing data, such as generated and/or acquired primary and/or secondary processed data, which data may be compressed, such as prior to transfer over a local  10  and/or cloud  30  and/or hybrid cloud based  50  network, such as in a JIT analysis procedure, and which may be decompressed subsequent to transfer and/or prior to use. 
     As described above, in various instances, the system may include a first integrated and/or quantum circuit  100  such as for performing a mapping, aligning, sorting, and/or variant calling operation, so as to generate one or more of mapped, aligned, sorted, and/or variant called results data. Additionally, the system may include a further integrated and/or quantum circuit  300  such as for employing the results data in the performance of one or more genomics and/or bioinformatics pipeline analyses, such as for tertiary processing. For instance, the result data generated by the first integrated and/or quantum circuit  100  may be used, e.g., by the first or a second integrated and/or quantum circuit  300 , in the performance of a further genomics and/or bioinformatics pipeline processing procedure. Specifically, secondary processing of genomics data may be performed by a first hardware and/or quantum accelerated processor  100  so as to produce results data, and tertiary processing may be performed on that results data, such as where the further processing is performed by a CPU and/or GPU and/or QPU  300  that is operatively coupled to the first integrated circuit. In such an instance, the second circuit  300  may be configured for performing tertiary processing of the genomics variation data produced by the first circuit  100 . Accordingly, the results data derived from the first integrated server acts as an analysis engine driving the further processing steps described herein with reference to tertiary processing, such as by the second integrated and/or quantum processing circuit  300 . 
     However, the data generated in each of these primary and/or secondary and/or tertiary process steps may be immense, requiring very high resource and/or memory costs such as for storage, either locally  200  or remotely  400 . For instance, in a first primary processing step, generated nucleic acid sequence data  110 , such as in a BCL and/or FASTQ file format, may be received  120 , such as from an NGS  110 . Regardless of the file format of this sequence data, the data may be employed in a secondary processing protocol as described herein. The ability to receive and process primary sequence data directly from an NGS, such as in a BCL and/or FASTQ file format, is very useful. Particularly, instead of converting the sequence data file from the NGS, e.g., BCL, to a FASTQ file, the file may be directly received from the NGS, e.g., as a BCL file, and may be processed, such as by being received and converted by the JIT system, e.g., on the fly, into a FASTQ file that may then be processed, as described herein, such as to produce a mapped, aligned, sorted, and/or variant called results data that may then be compressed, such as into a SAM, BAM, and/or CRAM file, and/or may be subjected to further processing, such as by one or more of the disclosed genomics tertiary processing pipelines. 
     Accordingly, such data once produced needs to be stored in some manner. However, such storage is not only resource intensive, it is also costly. Specifically, in a typical genomics protocol, the sequenced data once generated is stored as a large FASTQ file. Then, once processed such as by being subjected to a mapping and/or aligning protocol, a BAM file is created, which file is also typically stored, increasing the expense of genomic data storage, such as by having to store both a FASTQ and a BAM file. Further, once the BAM file is processed, such as by being subjected to variant calling protocol, a VCF file is produced, which VCF also typically needs to be stored. In such an instance, in order to adequately provide and make use of the generated genetic data, all three of the FASTQ, BAM, and VCF files may need to be stored, either locally  200  or remotely  400 . Additionally, the original BCL file may also be stored. Such storage is inefficient as well as being memory resource intensive and expensive. 
     However, the computational power of the hardware and/or quantum processing architectures implemented herein, along with the JIT compilation, compression, and storage, greatly ameliorates these inefficiencies, resource costs, and expenses. For instance, in view of the methods implemented and the processing speeds achieved by the present accelerated integrated circuits, such as for the conversion of a BCL file to a FASTQ file, and then the conversion of a FASTQ file to a SAM or BAM file, and then the conversion of a BAM file to a CRAM and/or VCF file, and back again, the present system greatly reduces the number of computing resources and/or file sizes needed for the efficient processing and/or storage of such data. The benefits of these systems and methods are further enhanced by the fact that only one file format, e.g., a BCL, FASTQ, SAM, BAM, CRAM, and/or VCF, need be stored, from which all the other file formats may be derived and processed. Particularly, only one file format needs to be saved and from such file any of the other file formats may be generated rapidly, e.g., on the fly, in accordance with the methods disclosed herein, such as in a just in time, or JIT, compiling format. 
     For example, in accordance with typical prior methods, a large amount of computing resources, e.g., server farms and large memory banks, is needed for the processing and storage of FASTQ files being generated by a NGS sequencer. Particularly, in a typical instance, once the NGS produces the large FASTQ file, the server farm would then be employed to receive and convert the FASTQ file to a BAM and/or CRAM file, which processing may take up to a day or more. However, once produced, the BAM file itself must then be stored, requiring further time and resources. Likewise, the BAM or CRAM file may be processed in such a manner to generate a VCF, which may also take up another day or more, and which file will also need to be stored, thereby incurring further resource costs and expenses. More particularly, in a typical instance, the FASTQ file for a human genome consumes about 90 GB of storage, per file. Likewise, a typical human genome BAM file may consume about 160 GB. The VCF file may also need to be stored, albeit such files are quite smaller than the FASTQ and/or BAM files. SAM and CRAM files may also be generated throughout the secondary processing procedures, and these too may need to be stored. 
     Prior to the technologies provided herein, it has been computationally intensive to go from one step to another, e.g., from one file format to another, and hence, all of the data for these file formats would typically have to be stored. This is in part due to the fact that if a user ever wanted to go back and regenerate one or more of the files, it would require a large amount of computing resources and time to re-do the processes involved to regenerate the various files thereby incurring a high monetary expense. Further, where these files are compressed before storage, such compression may take from about 2 to about 5 to about 10 or more hours, with about the same amount of time required for decompression, prior to reuse. Because of these high expenses, typical users would not compress such files prior to storage, and would also typically store all two, three or more file formats, e.g., BCL, FASTQ, BAM, VCF, incurring increased costs over increased time. 
     Accordingly, the JIT protocols employed herein make use of the accelerated processing speeds achieved by the present hardware and/or quantum accelerators, so as to realize enhanced efficiency, at reduced time and costs both for processing as well as for storage. Instead of storing 2, 3, or more copies of the same general data in different file formats, only one file format needs to be stored, and on the fly, any of the other file types can be regenerated, such as using the accelerated processing platforms discussed herein. Particularly, from storing a FASTQ file, the present devices and systems make it easy to go backwards to a BCL file, or forwards to a BAM file, and then further to a VCF, such as in under 30 minutes, such as within 20 minutes, or about within 15 or 10 minutes, or less. 
     Hence, using the pipelines and the speed of processing offered by the hardwired/quantum processing engines herein disclosed, only a single file format need be stored, while the other file formats may easily and rapidly be generated therefrom. So instead of needing to store all three file formats, a single file format need be stored from which any other file format may be regenerated such as on the fly, just in time for the further processing steps desired by the user. Consequently, the system may be configured for ease of use such that if a user simply interacts with a graphical user interface, such as presented at an associated display of the device, e.g., the user clicks on the FASTQ, BAM, VCF, etc. button presented in the GUI, the desired file format may be presented, while in the background, one or more of the processing engines of the system may be performing the accelerated processing steps necessary for regenerating the requested file in the requested file format from the stored file. 
     Typically, one or more of a compressed version of a BCL, FASTQ, SAM, BAM, CRAM, and/or VCF file will be saved, along with a small metafile that includes all of the configurations of how the system was run to create the compressed and/or stored file. Such metafile data details how the particular file format, e.g., FASTQ and/or BAM file, was generated and/or what steps would be necessary for going backwards or forwards so as to generate any of the other file formats. In a manner such as this the process can proceed forwards or be reversed going backwards using the configuration stored in the metafile. This can be about an 80% or more reduction in storage and economic cost if the computing function is bundled with the storage functions. 
     As can be seen with respect to  FIG. 40 , these files may be stored  200 / 400  on a server that is accessible via the cloud  30 / 50 . As such, the system is set up such that with respect to a user storing generated sequence data on the cloud, the user of the system may log on and access the cloud based server and thereby store the generated data in a single file format, such as on a hybrid cloud  400 . Further, with the click of a button the user can access all of the other file formats, which would then be processed and generated behind the scene, e.g., on the fly, thus cutting down on both processing time and burden as well as storage costs, such as where the computing and the storage functions are bundled together. 
     Accordingly, there are two parts of this process that are enabled by the speed of performing the accelerated mapping, aligning, sorting, and/or variant calling functions in the hardwired and/or quantum processing configuration, so as to enable seamless compression and storing of only a single file type with on-the-fly regeneration of any of the other file types. In particular embodiments, it would be the BAM file, or a compressed SAM or CRAM file associated therewith, which would be stored, and from that file the others may be generated, e.g., in a forward or a reverse direction, such as to reproduce a VCF or FASTQ or BCL file, respectively. For instance, when a FASTQ file is originally stored, when going in the forward direction, a checksum of the file may be taken. Likewise, when going backward, a checksum may be generated on the file that is being recreated going backward, and these checksums may then be used to ensure that the recreated files match identically to one another and/or their compressed file formats. In a manner such as this it may be ensured that all of the data is stored, the system knows exactly where the data is stored, in what file format it is stored, what the original file format was in, and from this data the system can regenerate any file format in an identical manner going forwards or backwards between file formats (once the template is originally generated). 
     Hence, the speed advantage of the “just in time” compiling is enabled in part by the hardware and/or quantum implemented generation of the relevant files, such as in generating a BAM file from a previously generated FASTQ file. Particularly, compressed BAM files, including SAM and CRAM files, are not typically stored within a database because of the increased time it takes prior to processing to decompress the compressed stored file. However, the JIT system allows this to be done without substantial penalties. More particularly, implementing the devices and processes disclosed herein, not only can generated sequence data be compressed and decompressed rapidly, e.g., almost instantaneously, it may also be stored efficiently. Additionally, from the stored file, in whatever file format it is stored, any of the other file formats may be regenerated in mere moments. 
     Hence, as can be seen with reference to  FIG. 41A , when the accelerated hardware and/or quantum processing performs various secondary processing procedures, such as mapping and aligning, sorting, and variant calling, a further step of compression may also be performed, such as in an all in one process, prior to storage in the compressed form. Then when the user desires to analyze or otherwise use the compressed data, the file may be retrieved, decompressed, and/or converted from one file format to another, and/or be analyzed, such as by the JIT engine(s) being loaded into the hardwired processor, or configured within the quantum processor, and subjecting the compressed file to one or more procedures of the JIT pipeline. 
     Accordingly, in various instances, the FPGA can be fully or partially reconfigured, and/or a quantum processing engine may be organized, so as to perform a JIT procedure. Particularly, the JIT module can be loaded into the system and/or configured as one or more engines, which engines may include one or more compression engines  150  that are configured for working in the background. Hence, when a given file format is called, the JIT-like system may perform the necessary operations on the requested data so as to produce a file in the requested format. These operations may include compression and/or decompression as well as conversion so as to derive the requested data in the identified file format. 
     For instance, when genetic data is generated, it is usually produced in a raw data format, such as a BCL file, which then may get converted into a FASTQ file, e.g., by the NGS that generates the data. However, with the present system, the raw data files, such as in BCL or other raw file format, may be streamed or otherwise transmitted into the JIT module, which can then convert the data into a FASTQ file and/or into another file format. For example, once a FASTQ file is generated, the FASTQ file may then be processed, as disclosed herein, and a corresponding BAM file may be generated. And likewise, from the BAM file a corresponding VCF may be generated. Additionally, SAM and CRAM files may also be generated during appropriate steps. Each one of these steps may be performed very rapidly, especially once the appropriate file format has once been generated. Hence, once the BCL file is received, e.g., straight from the sequencer, the BCL can be converted into a FASTQ file or be directly converted into a SAM, BAM, CRAM, and/or VCF file, such as by a hardware and/or quantum implemented mapping/aligning/sorting/variant calling procedure. 
     For example, in one use model, on a typical sequencing instrument, a large number of different subject&#39;s genomes may be loaded into individual lanes of a single sequencing instrument to be run in parallel. Consequently, at the end of the run, a large number of diverse BCL files, derived from all the different lanes and representing the whole genomes of each of the different subjects, are generated in a multiplex complex. Accordingly, these multiplexed BCL files may then be de-multiplexed, and respective FASTQ files may be generated representing the genetic code for each individual subject. For instance, if in one sequencing run N BCL files are generated, these files will need to be de-multiplexed, layered, and stitched together for each subject. This stitching is a complex process where each subject&#39;s genetic material is converted to BCL files, which may then be converted to a FASTQ file or used directly for mapping, aligning, and/or sorting, variant calling, and the like. This process may be automated so as to greatly speed up the various steps of the process. 
     As can be seen with respect to  FIG. 40 , once this data has been generated  110 , it may then be stored in a password protected and/or encrypted memory cache, such as in a dedicated genomics dropbox-like memory  400 . Accordingly, as the generated and/or processed genetic data comes off of the sequencer, the data may be processed and/or stored and made available to other users on other systems, such as in a dropbox-like cache  400 . In such an instance, the automated bioinformatics analysis pipeline system may then access the data in the cache and automatically begin processing it. For example, as can be seen with respect to  FIG. 41B , the system may include a management system having a controller, such as a microprocessor or other intelligence, e.g., artificial intelligence, that manages the retrieving of the BCL and/or FASTQ files, e.g., from the memory cache, and then directs the processing of that information, so as to generate a BAM, CRAM, SAM, and/or VCF, thereby automatically generating and outputting the various processing results and/or storing the same in the dropbox memory  400 . 
     A unique benefit of JIT processing, as implemented within this use model, is that JIT allows the various genetic files produced to be compressed, e.g., prior to data storage, and to be decompressed rapidly prior to usage. Hence, JIT processing can compile and/or compress and/or store the data as it is coming off the sequencer, where such storage is in a secure genomic dropbox memory cache. This genomic dropbox cache  400  may be a cloud  50  accessible memory cache that is configured for the storing of genomics data received from one or more automated sequencers  110 , such as where the sequencer(s) are located remotely from the memory cache  400 . 
     Particularly, once the sequence data has been generated  110 , e.g., by a remote NGS, it may be compressed  150  for transmission and/or storage  400 , so as to reduce the amount of data that is being uploaded to and stored in the cloud  50 . Such uploading, transmission, and storage may be performed rapidly because of the data compression  150  that takes place in the system, such as prior to transmission. Additionally, once uploaded and stored in the cloud based memory cache  400 , the data may then be retrieved, locally  100  or remotely  300 , so as to be processed in accordance with the devices, systems, and methods of the BiolT pipeline disclosed herein, so as to generate a mapping, aligning, sorting, and/or variant call file, such as a SAM, BAM, and/or CRAM file, which may then be stored, along with a meta-file that sets forth the information as to how the generated file, e.g., SAM, BAM, CRAM, etc. file, was produced. 
     Hence, when taken together with the metadata, the compressed SAM, BAM, and/or CRAM file may then be processed to produce any of the other file formats, such as FASTQ and/or VCF files. Accordingly, as discussed above, on the fly, JIT can be used to regenerate the FASTQ file or VCF from the compressed BAM file and vice versa. The BCL file can also be regenerated in like manner. It is to be noted that SAM and CRAM files can likewise be compressed and/or stored and can be used to produce one or more of the other file formats. For instance, a CRAM file, which can be un-CRAMed, can be used to produce a variant call file, and likewise for the SAM file. Hence, only the SAM, BAM and/or CRAM file need be saved and from these files, the other file formats, e.g., VCF, FASTQ, BCL files, can be reproduced. 
     Accordingly, as can be seen with respect to  FIG. 40 , a mapping and/or aligning and/or sorting and/or variant calling instrument  110  may be on-site  100  and/or another second corresponding instrument  300  may be located remotely and made accessible in the cloud  30 / 50 . This configuration, along with the devices and methods disclosed herein, is configured to enable a user to rapidly perform a BiolT analysis, as herein disclosed, so as to produce results data, which results data may then be processed so as to be compressed, and once compressed the data may be uploaded and made accessible via a cloud based interface. In such an instance, the compressed and uploaded data may be stored  400 , e.g., “in the cloud,” such as a SAM, BAM, and/or CRAM file. 
     Further, when desired, the second mapping and/or aligning and/or sorting and/or variant calling instrument  300 , e.g., associated with the cloud  50 , may then access the stored and/or compressed file(s) and may process those files so as to rapidly generate a BCL, FASTQ, SAM, BAM, VCF or other file format from the stored and/or compressed files, e.g., on the fly, using JIT processing. This configuration thereby alleviates the typical transfer speed bottleneck. Hence, in various embodiments, the system may include, a first mapping and/or aligning and/or sorting and/or variant calling instrument  100 , which may be positioned locally, such as for local data production, compression  150 , and/or storage  200 ; and a second instrument  300  may be positioned remotely and associated in the cloud  50 , whereby the second instrument  300  is configured for receiving the generated and compressed data and storing it, e.g., via an associated storage device  400 . Once stored, the data may be accessed, e.g., by the first and/or second instrument, for decompression and conversion of the stored files into one or more of the other file formats. 
     Therefore, in one implementation of the system as exemplified in  FIG. 40 , data e.g., raw sequence data such as in a BCL or FASTQ file format, which is generated by a data generating apparatus, e.g., a sequencer  110 , may be uploaded and stored in the cloud  30 / 50 , such as in an associated genomics dropbox-like memory cache  400 . This data may then be accessed by a first mapping and/or aligning and/or sorting and/or variant calling instrument  100 , as described herein, which may then process the sequence data to produce mapped, aligned, sorted, and/or variant results data. This result data may then be compressed and/or stored in the genomics dropbox cache  400 , such as in a SAM, BAM, CRAM and/or VCF file. It is to be noted that the first instrument  100  may be local and associated with the sequencing instrument  110  itself, or may be remote and associated with a local cloud  30  and/or a local  200  or remote memory cache  400 . A second mapping and/or aligning and/or sorting and/or variant calling instrument  300 , e.g., a cloud based instrument, with the proper authorities, may then connect with the genomics drop box  400 , so as to access the files, e.g., compressed files, and may then decompress those files to make the results available for further, e.g., secondary or tertiary, processing. 
     Accordingly, in various instances, the system may be streamlined such that as data is generated and comes off of the sequencer  110 , such as in raw data format, it may either be immediately uploaded into the cloud  50  and stored in a genomics dropbox  400 , or it may be transmitted to a BiolT processing system  300  for further processing and/or compression prior to being uploaded and stored  400 . Once stored within the memory cache  400 , the system may then immediately queue up the data for retrieval, compression, decompression, and/or for further processing such as by another associated BiolT processing apparatus  300 , which when processed into results data may then be compressed and/or stored  400  for further use later. At this point, a tertiary processing pipeline may be initiated whereby the stored results data from secondary processing may be decompressed and used such as for tertiary analysis, in accordance with the methods disclosed herein. 
     Hence, in various embodiments, the system may be pipelined such that all of the data that comes off of the sequencer  110  may either be compressed, e.g., by a local computing resource  100 , prior to transfer and/or storage  200 , or the data may be transferred directly into the genomics dropbox folder for storage  400 . Once received thereby, the stored data may then substantially immediately be queued for retrieval and compression and/or decompression, such as by a remote computing resource  300 . After being decompressed the data may substantially immediately be available for processing such as for mapping, aligning, sorting, and/or variant calling to produce secondarily processed results data that may then be re-compressed for storage. Afterward, the compressed secondary results data may then be accessed, e.g., in the genomics dropbox  400 , be decompressed, and/or be used in one or more tertiary processing procedures. As the data may be compressed when stored and substantially immediately decompressed when retrieved, it is available for use by many different systems and in many different bioanalytical protocols at different times, simply by accessing the dropbox storage cache  400 . 
     Therefore, in such manners as these, the Bio-IT platform pipelines presented herein may be configured so as to offer incredible flexibility of data generation and/or analysis, and are adapted to handle the input of particular forms of genetic data in multiple formats so as to process the data and produce output formats that are compatible for various downstream analysis. Accordingly, as can be seen with respect to  FIG. 40 , presented herein are devices, systems, and methods for performing genetic sequencing analysis, which may include one or more of the following steps. First, a file input is received, the input may be in one or more of a FASTQ or BCL file format, which file may then be decompressed, and/or processed herein so as to generate a VCF/gVCF. Such compression and/or decompression may occur at any suitable time throughout the process. 
     Accordingly, in certain instances, the file to be received by the system may be streamed or otherwise transferred to the system directly from the sequencing apparatus, e.g., NGS, and as such the transferred file may be in a BCL file format. Where the received file is in a BCL file format it may be converted, and/or otherwise demultiplexed, into a FASTQ file for processing by the system, or the BCL file may be processed directly. For instance, the platform pipeline processors can be configured to receive BCL data that is streamed directly from the sequencer, or it may receive data in a FASTQ file format. However, receiving the sequence data directly as it is streamed off of the sequencer is useful because it enables the data to go directly from raw sequencing data to being processed into a VCF for output. 
     Accordingly, once the BCL or the FASTQ file is received, it may be mapped and/or aligned, which mapping and/or aligning, may be performed on single end or paired end reads, such as with read lengths that may range from about 10 or about 20, such as 26 bp or less up to about 1K, or about 2.5K, or about 5K, even about 10K bp or more. Once mapped and/or aligned the sequence may then be sorted, such as position sorted, such as through binning by reference range and/or sorting of the bins by reference position. Additionally, the sequence data may be processed via duplicate marking, such as based on the starting position and CIGAR string so as to generate a high quality duplicate report. At this point, a SAM file may be generated, which when compressed may form a BAM file, such as for storage and/or further processing. Further, once the BAM file has been retrieved, the sequence data may be forwarded to a variant calling module of the system, such as a haplotype variant caller with reassembly, which in some instances, may employ one or more of a Hidden Markov Model and/or Smith-Waterman Alignment that may be implemented, in either in software and/or hardware, so as to generate a VCF. 
     Hence, the system and/or one or more of its components may be configured so as to be able to convert BCL data to FASTQ or SAM/BAM/CRAM data formats, which may then be sent throughout the system for further processing and/or data reconstruction. For instance, once the sequence data is mapped or aligned, e.g., to produce a SAM file, the SAM file may then be compressed into one or more BAM files, which may then be transmitted to a VCF engine so as to be converted throughout the processing of the system to a VCF/gVCF, which may then be compressed into a CRAM file. Consequently, the files to be output along the system may be a Gzip and/or CRAM file. 
     Particularly, as can be seen with respect to  FIG. 40 , one or more of the files, once generated may be compressed and/or transferred from one system component to another, and once received may then be decompressed, e.g., if previously compressed, or converted/demultiplexed. More particularly, once a BCL file is received, it may be converted into a FASTQ file that may then be processed by the integrated circuit(s) of the system, so as to be mapped and/or aligned. Once mapped and/or aligned, the resulting sequence data, e.g., in a SAM file format, may be processed further such as by being compressed one or more times, e.g., into a BAM file, which data may then be processed by position sorting, duplicate marking, and/or variant calling the results of which, e.g., in a VCF format, may then be compressed once more. Particularly, the system may be adapted so as to process BCL data directly, thereby eliminating a FASTQ file conversion step. Likewise, the BCL data may be fed directly to the pipeline to produce a unique output VCF file per sample. Intermediate SAM/BAM/CRAM files can then be generated on demand. The system, therefore, may be configured for receiving and/or transmitting one or more data files, such as a BCL or FASTQ data file containing sequence information, and processing the same so as to produce a data file that has been compressed, such as a SAM/BAM/CRAM data file. 
     Accordingly, as can be seen with respect to  FIG. 41A , a user may want to access the compressed file and convert it to an original version of the generated BCL  111   c  and/or FASTQ file  111   d , such as for subjecting the data to further, e.g., more advanced, signal processing  111   b , such as for error correction. Alternatively, the user may access the raw sequence data, e.g., in a BCL or FASTQ file format  111 , and subject that data to further processing, such as for mapping  112  and/or aligning  113 . The results data from these procedures may then be compressed and/or stored  114 . The same or another user may then want to access the compressed form of the mapped and/or aligned results data and then run another analysis on the data, such as to produce one or more VCFs  115  that may then be compressed and/or stored. An additional user of the system may then access the compressed VCF file  116 , decompress it, and subject the data to one or more tertiary processing protocols. 
     Further, a user may want to do a pipeline compare. The mapping/aligning/sorting/variant calling is useful for preforming various genomic analysis. For instance, if a further DNA or RNA analysis, or some other kind of analysis, is afterward desired, a user may want to run the data through another pipeline, and hence having access to the original data file so as to be regenerated is very useful. Likewise, this process may be useful such as where a different SAM/BAM/CRAM file may be desired to be created, or recreated, such as where there is a new or different reference genome generated, and hence it may be desired to re-do the mapping and aligning to the new reference genome. 
     Storing the compressed SAM/BAM/CRAM files is further useful because it allows a user of the system to take advantage of the fact that a reference genome forms the backbone of the results data. In such an instance, it is not the data that agrees with the reference that is important, but rather how the data disagrees with the reference. Hence, only that data that disagrees with the reference is essential for storage. Consequently, the system can take advantage of this fact by storing only what is important and/or useful to the users of the system. Thus, the entire genomic file (showing agreement and disagreement with the reference), or a sub-portion of it (showing only agreement or disagreement with the reference), may be configured for being compressed and stored. It may be seen, therefore, that as only the differences and/or variations between the reference and the genome being examined are the most useful to examine, in various embodiments, only these differences need be stored, as anything that is the same as the reference need not be reviewed again. Accordingly, since any given genome differs only slightly from a reference, e.g., 99% of human genomes are typically identical, after the BAM file is created, it is only the variations between the reference genome that need be reviewed and/or saved. 
     Additionally, another useful component of the system is a workflow management controller, which may be used to automate the system flow. Such system animation may include utilizing the various system componentry to access data, either locally or remotely, as and/or where it becomes available and then substantially automatically subjecting the data to further processing steps, such with respect to the BiolT pipelines disclosed herein. Accordingly, the workflow management controller is a core automation technology for directing the various pipelines of the system, and in various instances may employ an artificial intelligence component, See  FIG. 41B . 
     Specifically, the workflow management controller allows the system to receive inputs from multiple sequencing instruments, e.g.,  110   a ,  110   b ,  110   c , etc., and/or multiple inputs from a single sequencing instrument  110 , where the data being received represents the genomes of multiple subjects. In such instances, the workflow management controller not only keeps track of all of the incoming data, but it also efficiently organizes and facilitates the secondary and/or tertiary processing of the received data. Accordingly, the workflow management controller allows the system to seamlessly connect to both small and large sequencing centers, where all kinds of genetic material may be coming through one or more sequencing instruments at the same time, all of which may be transferred into the system, such as over the cloud  50 . 
     More specifically, as can be seen with respect to  FIG. 41 , in various instances, one or a multiplicity of samples may be received within the system, and hence the system may be configured for receiving and efficiently processing the samples, either sequentially or in parallel, such as in a multi sample processing regime. Accordingly, to streamline and/or automate multi sample processing, the system may be controlled by a comprehensive Workflow Management System (WMS) or LIMS (laboratory information management system). The WMS enables users to easily schedule multiple workflow runs for any pipeline, as well as to adjust or accelerate NGS analysis algorithms, platform pipelines, and their attendant applications. 
     In such an instance, each run sequence may have a bar code on it indicating the type of sequence it is, the file format, and/or what processing steps have been performed, and what processing steps need to be performed. For instance, the bar code may include a manifest indicating “this is a genome run, of subject X, in file format Y, so this data has to go through pipeline Z,” or likewise may indicate “this is A&#39;s result data that needs to go in this reporting system.” Accordingly, as the data is received, processed, and transmitted through the system, the bar codes and results will get loaded into the workflow management system, such as LIMS (laboratory information management system). LIMS, in this instance, may be a standard tool that is employed for the management of laboratories, or it may be a specifically designed tool used for managing process flow. 
     In any instance, the workflow management controller tracks a barcoded sample from when it arrives in a given site, e.g., for storage and/or processing, until the results are sent out to the user. Particularly, the workflow management controller is configured to track all data as it flows through the system end-to-end. More particularly, as the sample comes in, the bar code associated with the sample is read, and based on that reading the system determines what the requested work flows are and prepares the sample for processing. Such processing may be simple, such as being run through a single genome pipeline, or it may be more complex, such as by being run through multiple, e.g., five pipelines, that need to be stitched together. In one particular model it may be run through the system, it then may be run through a GATK equivalent module, the results may be compared, and then the sample may be transmitted to another pipeline for further, e.g., tertiary processing. 
     Hence, the system as a whole can be run in accordance with several different processing pipelines. In fact, many of the system processes can be interconnected, where the workflow manager is notified or otherwise determines that a new job is pending, quantifies the job matrices, identifies available resources for performing the required analyses, loads the job into the system, receives the data coming in, e.g., off the sequencer, loads it in, and then processes it. Particularly, once the workflow is set up, it can be saved, and then a modified bar code gets assigned to that workflow, and the automated process takes place in accordance with the directives of the workflow. 
     Prior to the present automated workflow management system, it would take a number of Bioinformaticians a long period of time to configure and set up the system, and its component parts, and it would then require further time for actually running the analysis. To make matters more complicated, the system would have to be reconfigured prior to receiving the next sample to analyze, requiring even more time to reconfigure the system for analyzing the new sample set. With the technology disclosed herein the system can be entirely automated. The present system, particularly, is configured so as to automatically receive multiple samples, map them to multiple different workflows and pipelines, and run them on the same or multiple different system cards. 
     Accordingly, the workflow management system reads the job requirements of the bar codes, allocates resources for performing the jobs, e.g., regardless of location, updates the sample barcode, and directs the samples to the allocated resources, e.g., processing units, for processing. Hence, it is the workflow manager that determines the secondary and/or tertiary analyses protocols that will be run on the received samples. These processing units are resources that are available for delineating and performing the operations allocated to each data set. Particularly, the work flow controller controls the various operations associated with receiving and reading the sample, determining jobs, allocating resources for the performance of those jobs, connecting all system components, and advancing the sample set through the system from component to component. The controller, therefore, acts to manage the overall system from start to finish, e.g., from sample receipt to VCF generation, and/or through to tertiary processing. 
     Hence, the system may include a display device having a graphic user interface for allowing a potential user of the system to transmit sample data for entry into one or more of the BiolT pipelines disclosed herein. The GUI is configured for allowing the user to manage the system components, e.g., via a suitably configured web portal, and to track sample processing progress, regardless of whether the computing resources to be engaged are available locally or remotely. Accordingly, the GUI may list a set of jobs that may be performed and/or a set of resources for performing the jobs, and the user may self-select which jobs they want run and by which resources. Hence, in an instance such as this, each individual user may build thereon a unique, or may use a predetermined, analysis workflow, such as by clicking on, dragging, or otherwise selecting whatever work projects they desire to be run. 
     For instance, in one use model, a dashboard is presented with a GUI interface that may include a plurality of icons representing the various processes that may be implemented and run on the system. In such an instance, a user can click on or drag the selected work process icons into a workflow interface, so as to build a desired workflow process, which once built may be saved and used to establish the control instructions for the sample set barcodes. Once the desired work projects have been selected, the work flow management controller may configure the desired workflow processes, and then identify and select the resources for performing the selected analysis. 
     Once the workflow analysis process begins, the dashboard may be viewed so as to track progress through the system. For example, the dashboard may indicate how much data is running through the system, what processes are being run on the data, how much has been accomplished, how much processing remains, what workflows have been completed, and which still need to be accessed, the latest projects to be run, and which runs have been completed. Essentially, full access to everything that&#39;s running on the system, or a sub-portion thereof, may be provided to the desktop. 
     Further, in various instances, the desktop may include various different user interfaces that may be accessible via one or more tabs. For instance, one tab for accessing the system controls may be a “local resources tab,” which when selected allows a user to select control functions that are capable of being implemented locally. Another tab may be configured for accessing “cloud resources,” which when selected allows a user to select other control functions that are capable of being implemented remotely. Accordingly, in interacting with the dashboard, a user can select which resources to perform which tasks, and as such can increase or decrease resource usage as required so as to meet the project requirements. 
     Hence, as the computational complexity increases, and/or increased speed is desired, the user (or the system itself, e.g., WMS) can bring more and more resources online, as needed, such as by the mere click of a button, instructing the workflow manager to bring additional local and/or cloud based resources online, as needed to complete the task within the desired timeframe. In this manner, although the system is automated and/or controlled by the workflow manager controller, a user of the system can still set the control parameters, and when needed can bring cloud based resources on line. Accordingly, the controller can expand to the cloud as needed to bring on line additional processing and/or storage resources. 
     In various instances, the desktop interface may be configured as a mobile application or “app” that is accessible via a mobile device and/or desktop computer. Consequently, in one aspect, a genomics market place, or cohort, may be provided so as to allow a plurality of users to collaborate in one or more research projects, so as to form an electronic cohort market place that is accessible via the dashboard app, e.g., a web based browser interface. As such, the system may provide an online forum for performing collaborative research and/or a market place for developing various analytical tools for analyzing genetic data, which system may be accessible directly via the system interface, or via the app, to allow remote control of the system by a user. 
     For instance, as can be seen with reference to  FIG. 43 , in one aspect, an online app store is provided to allow users to develop, sell, and use genomics tools that can be incorporated into the system and be employed to analyze the genomic data transmitted to and entered into the system. Particularly, the genomic app store enables customers that desire to develop genetic tests, e.g., like a NICU test, and once developed may be uploaded on to the system, e.g., genetic marketplace, for purchase and running as a platform thereon, so that anyone running the newly developed system platform, can deploy the uploaded tests via the web portal. More particularly, a user can browse the web portal “app” store, find a desired test, e.g., the NICU test, download it, and/or configure the system to implement it, such as on their uploadable genetic data. The online “cohort” marketplace, therefore, presents a rapid and efficient way to deploy new genetic analytic applications, which applications allow for identical results to be obtained from any of the present system platforms that runs the downloaded application. More particularly, the online market place provides a mechanism for anyone to work with the system to develop genetic analysis applications that remote users can download and configure for use in accordance with the present workflow models. 
     Another aspect of the cohort marketplace disclosed herein is that it allows for the secure sharing of data. For instance, presently, genomic data is highly protected. Often such genetic data is large and difficult to transfer in a secure and protected manner, such as where the subject&#39;s identity is restricted. However, the present genetics market place allows cohort participants to share genetic data without having to identify the subject. In such a market place, cohort participants can share questions and processes so as to advance their research in a protected and secure environment, without risking the identity of their respective subject&#39;s genomes. Additionally, a user can enlist the help of other researchers in the analysis of their sample sets without identifying to whom those genomes belong. 
     For instance, a user can identify subjects having a specific genotype and/or phenotype, such as stage  3  breast cancer, and/or having been treated with a particular drug. A cohort can be formed to see how these drugs affect cancerous cell growth on a genetic level. Therefore, these characteristics, amongst others, may form a cohort selection criteria that will allow other researchers, e.g., remotely located, to perform standard genetic analyses on the genetic data, using uniform analytic procedures, on subjects they have access to that fit within the cohort criteria. In this manner, a given researcher need not be responsible for identifying and securing all members of a sample set, e.g., subjects fitting within the criteria, to substantiate his or her scientific inquiry. 
     Particularly, Researcher A may set up a research cohort within the marketplace, and identify the appropriate selection criteria for subjects, the genomic test(s) to be run, and the parameters by which the test is to be run. Researchers B and C, located remotely from Researcher A, may then sign up for the cohort, identify and select subjects matching the criteria, and then run the specified tests on their subjects, using the uniform procedures disclosed herein, so as to help Researcher A achieve or better accomplish his or her research goals in an expeditious manner. This is beneficial because only a portion of genetic data is being transmitted, subject identity is protected, and as the data is being analyzed using the same genetic analysis system employing the same parameters, the results data will be the same regardless of where and on what machine the test(s) are run. Consequently, the cohort market place allows users to form and build cohorts simply by posting the selection criteria and run parameters on the dashboard. Compensation rates may also be posted and payments rendered by employing a suitably configured commerce, e.g., monetary exchange, program. 
     Anyone that accepts participation in the cohort can then download the criteria and data file(s) and/or use genetic data of subjects they have already generated and/or stored in performing the requested analyses. For instance, each cohort participant will have, or be able to generate, a database of BCL and/or FASTQ files that are stored in their individual servers. These genetic files will have been derived from subjects who happen to meet the selection criteria. Specifically, this stored genetic and/or other data of the subject may be scanned so as to determine suitability for inclusion within the cohort selection criteria. Such data may have been generated for a number of purposes, but regardless of the reasons for the generation, once generated it may be selected and subjected to the requested pipeline analyses and used for inclusion within the cohort. 
     Accordingly, in various embodiments, the cohort system may be a forum for connecting researchers, so as to allow them to pool their resources and data, e.g., genetic sequence data. For example, engaging a cohort would allow a first researcher to introduce a project requiring genetic data analyses requiring the mining and/or examination of a number of genomes from various subjects, such as with respect to mapping, aligning, variant calling, and/or the like. Therefore, instead of having to gather subjects and collect sample sets individually, the cohort initiator can advertise the need for a prescribed analyses procedure to be run on sample sets previously or to be collected by others, and as such a collective approach to generating sample sets and analyzing the same is provided for by the cohort organization herein. Particularly, the cohort initiator can set up the cohort selection, create a configuration file to be shared with the potential cohort participants, create the workflow parameters, e.g., within a workflow folder, and can thereby automate data generation and analyses, e.g., via the workflow management system. The system may also enable the commercial aspect of the transaction, e.g., the payment processing for compensating the cohort participants for their provision of genetic data sets that may be analyzed, such as with respect to mapping, aligning, variant calling, and/or with respect to tertiary analyses. 
     In various embodiments, the cohort structured analyses may be directed to primary processing, e.g., of either DNA or RNA, such as with respect to image processing and/or base quality score recalibration, methylation analysis, and the like; and/or may be directed to the performance of secondary analysis, such as with respect to mapping, aligning, sorting, variant calling, and the like; and/or may be directed to tertiary analysis, such as with respect to genomic, epigenomic, metagenomic, joint genotyping, GATK, and/or other forms of tertiary analyses. Additionally, it is to be understood that although many of the pipelines and analyses performed thereby may involve primary and/or secondary processing, various analysis platforms herein may not be directed to primary or secondary processing. For instance, in certain instances, an analysis platform may be exclusively directed to performing tertiary analysis, such as on genetic data, or other forms of genomics and/or bioinformatics analyses. 
     For example, in particular embodiments, with respect to the particular analytical procedures to be run, the analyses to be performed may include one or more of mapping, aligning, sorting, variant calling, and the like, so as to produce results data that may be subjected to one or more other secondary and/or tertiary analyses procedures, depending on the specific pipelines selected to be run. The workflow may be simple or it may be complex, e.g., it may require the performance of one pipeline module, e.g., mapping, or multiple modules, such as mapping, aligning, sorting, variant calling, and/or others, but an important parameter is that the workflow should be identical for each person that takes part of the cohort. Particularly, a unique feature of the system is that the requester establishing the cohort sets forth the control parameters so as to ensure that the analysis to be performed are performed in the same manner, regardless of where those procedures are performed and on what machines. 
     Consequently, when setting up the cohort the requester will upload both selection criteria along with a configuration file. Other cohort participants will then view the selection criteria to determine if they have data sets of genetic information falling within the set forth criteria, and if so will perform the requested analysis on the data, based on the settings of the configuration file. Researches may sign up to be selected as a cohort participant, and if subscription is great a lottery or competition can be held to select the participants. In various instances, a bidding system could be initiated. The results data generated by the cohort participants may be processed onsite or on the cloud, and as long as the configuration file is followed, the processing of the data will be the same. Particularly, the configuration file sets forth how the BiolT analytics device is to be configured, and once the device is set up in accordance with the prescribed configuration, a device associated with the system will perform the requested genetic analyses in the same manner regardless of where located, e.g., locally or remotely. The results data may then be uploaded onto the cohort market place, and payment tendered and received in view of the received results data. 
     For instance, the analysis of the genetic data may be performed locally, and the results uploaded onto the cloud, or the genetic data itself may be uploaded and the analyses run on the cloud, e.g., a server or server network associated with the cloud. In various instances, it may be useful to only upload the results data, so as to better protect the subjects&#39; identities. Particularly, by uploading only results data, not only is security protected, but large amounts of data need not be transferred, thereby enhancing system efficiency. 
     More particularly, in various instances, a compressed file containing results data from one or more of the pipelines may be uploaded, and in some instances, only a file containing a description of variations need be uploaded. In some instances, only an answer need be given, such as a text answer, e.g., a “yes” or “no” answer. Such answers are preferable as they do not set forth the identity of the subject. However, if the analyses need to be performed online, e.g., in the cloud, selected BCL and/or FASTQ files may be uploaded, the analyses performed, and the results data may then be pushed back to the initial submitter, who can then upload the results data at the cohort interface. The original raw data may then be deleted from the online memory. In this and other such manners, the cohort requester will not have access to the identities of the subjects. 
     Compression, such as that employed in “just in time analysis” (JIT), is particularly useful in enhancing cohort efficiency. For instance, using typical procedures, the movement of data into and out of the cohort system is very expensive. Accordingly, although in various configurations, raw and/or uncompressed data uploaded to the system may be stored there, in particular instances, the data can be compressed prior to being uploaded, the data may then be processed within the system, and the results can then be compressed prior to being transmitted out of the system, such as where the compression is effectuated in accordance with a JIT protocol. In this instance, storage of such data, such as in a compressed form is less expensive, and therefore the cohort system is very cost efficient. 
     Additionally, in various instances, a plurality of cohorts may be provided within an online marketplace, and given the compression processes herein described, data may be transmitted from one cohort to another, so as to allow researches of various different cohorts to share data between them, which without the compression methods disclosed herein could be prohibitively costly. Particularly, without the speed and efficiency of JIT compression data once transmitted into the cloud, would typically stay in the cloud, albeit it would be accessible therein for review and manipulation. However, JIT allows data to be quickly transmitted to and from the cloud for both local and/or cloud based processing. Further, as can be seen with respect to  FIG. 41B , in particular instances, the system  1  may be configured for subjecting the generated and/or secondarily processed data to further processing, e.g., via a local  100  and/or a remote  300  computing resource, such as by running it through one or more tertiary processing pipelines, such as one or more of a genome pipeline, for instance, an epigenome pipeline, metagenome pipeline, joint genotyping, a GATK/MuTect2 pipeline, or other tertiary processing pipeline. The results data from such processing may then be compressed and/or stored locally  200  and/or be transferred so as to be stored remotely  400 . 
     In additional instances, as can be seen with respect to  FIG. 41C , the system  1  may include a further tier of processing modules, such as configured for rendering additional processing, e.g., of the secondary and/or tertiary processing results data, such as for diagnosis, disease and/or therapeutic discovery, and/or prophylaxis thereof. For instance, in various instances, an additional layer of processing may be provided, such as for disease diagnostics, therapeutic treatment, and/or prophylactic prevention, such as including NIPT, NICU, Cancer, LDT, AgBio, and other such disease diagnostics, prophylaxis, and/or treatments employing the data generated by one or more of the present primary and/or secondary and/or tertiary pipelines. 
     Accordingly, herein presented is a system  1  for producing and using a local and/or global hybrid cloud network  30 / 50 . For instance, presently, the local cloud  30  is used primarily for storage, such as at a remote storage location  400 . In such an instance, the computing of data is performed locally  100  by a local computing resource  140 , and where storage needs are extensive, the cloud  30  is accessed so as to store the data generated by the local computing resource  140 , such as by use of a remote storage resource  400 . Hence, generated data is typically either wholly managed on site locally  100 , or it is totally managed off site  300 , on the cloud  30 . 
     Particularly, in a general implementation of a bioinformatics analysis platform, the local computing  140  and/or storage  200  functions are maintained locally on site  100 , and where storage needs exceed local storage capacity, or where there is a need for stored data to be made available to other remote users, such data may be transferred via internet  30  to a global cloud  50  for remote storage  400  thereby. In such an instance, where the computing resources  140  required for performance of the computing functions are minimal, but the storage requirements extensive, the computing function  140  may be maintained locally  100 , while the storage function  400  may be maintained remotely, with the fully processed data being transferred back and forth between the local processing function  140 , such as for local processing only, and the storage function  400 , such as for the remote storage  400  of the processed data, such as by employing the JIT protocols disclosed herein above. 
     For instance, this may be exemplified with respect to the sequencing function, such as with a typical NGS, where the data generation and/or computing resource  100  is configured for performing the functions required for the sequencing of the genetic material so as to produce genetic sequenced data, e.g., reads, which data is produced onsite  100  and/or transferred onsite locally. These reads, once generated, such as by the onsite NGS, may then be transferred, e.g., as a BCL or FASTQ file, over the cloud network  30 , such as for storage  400  at a remote location  300  in a manner so as to be recalled from the cloud  30  when necessary, such as for further processing. For example, once the sequence data has been generated and stored, e.g.,  400 , the data may then be recalled, e.g. for local usage, such as for the performance of one or more of secondary and/or tertiary processing functions, that is at a location remote from the storage facility  400 , e.g., locally  100 . In such an instance, the local storage resource  200  serves merely as a storage cache where data is placed while waiting transfer to or from the cloud  30 , such as to or from the remote storage facility  400 . 
     Likewise, where the computing function is extensive, such as requiring one or more remote computing servers or computing cluster cores  300  for processing the data, and where the storage demands for storing the processed data  200  are relatively minimal, as compared to the computing resources  300  required to process the data, the data to be processed may be sent, such as over the cloud  30 , so as to be processed by a remote computing resource  300 , which resource may include one or more cores or clusters of computing resources, e.g., one or more super computing resources. In such an instance, once the data has been processed by the cloud based computer core  300 , the processed data may then be transferred over the cloud network  30  so as to be stored local  200  and readily available for use by the local computing resource  140 , such as for local analysis and/or diagnostics. Of course, the remotely generated data  300  may also be stored remotely  400 . 
     This may be exemplified with respect to a typical secondary processing function, such as where the pre-processed sequenced, e.g., read, data that is stored locally  200  is accessed, such as by the local computing resource  100 , and transmitted over the cloud internet  30  to a remote computing facility  300  so as to be further processed thereby, e.g., in a secondary or tertiary processing function, to obtain processed results data that may then be sent back to the local facility  100  for storage  200  thereby. This may be the case where a local practitioner generates sequenced read data using a local data generating resource  100 , e.g., automated sequencer, so as to produce a BCL or FASTQ file, and then sends that data over the network  30  to a remote computing facility  300 , which then runs one or more functions on that data, such as a Burrows-Wheeler transform or Needlemen-Wunsch and/or Smith-Waterman alignment function on that sequence data, so as to generate results data, e.g., in a SAM file format, that may then be compressed and transmitted over the internet  30 , e.g., as a BAM file, to the local computing resource  100  so as to be examined thereby in one or more local administered processing protocols, such as for producing a VCF, which may then be stored locally  200 . In various instances the data may also be stored remotely  400 . 
     What is needed, however, is a seamless integration between the engagement between local  100  and remote  300  computer processing as well as between local  200  and remote  400  storage, such as in the hybrid cloud  50  based system presented herein. In such an instance, the system can be configured such that local  100  and remote  300  computing resources are configured so as to run seamlessly together, such that data to be processed thereby can be allocated real time to either the local  200  or the remote  300  computing resource without paying an extensive penalty due to transfer rate and/or in operational efficiency. This may be the case, for instance, where the software and/or hardware and/or quantum processing to be deployed or otherwise run by the computing resources are configured so as to correspond to one another and/or are the same or functionally similar, e.g., the hardware and/or software is configured in the same manner so as to run the same algorithms in the same manner on the generated and/or received data. 
     For instance, as can be seen with respect to  FIG. 41A  a local computing resource  100  may be configured for generating or for receiving generated data, and therefore may include a data generating mechanism  110 , such as for primary data generation and/or analysis, e.g., so as to produce a BCL and/or a FASTQ sequence file. This data generating mechanism  110  may be or may be associated with a local computer  100 , as described herein throughout, having a processor  140  that may be configured to run one or more software applications and/or may be hardwired so as to perform one or more algorithms such as in a wired configuration on the generated and/or acquired data. For example, the data generating mechanism  110  may be configured for one or more of generating data, such as sequencing data  111 . In various embodiments, the generated data may be sensed data  111   a , such as data that is detectable as a change in voltage, ion concentration, electromagnetic radiation, and the like; and/or the data generating mechanism  110  may be configured for generating and/or processing signal, e.g., analog or digital signal data, such as data representing one or more nucleotide identities in a sequence or chain of associated nucleotides. In such an instance, the data generating mechanism  110 , e.g., sequencer  111 , may further be configured for preliminarily processing the generated data so as for signal processing  111   b  or to perform one or more base call operations  111   c , such as on the data so as to produce sequence identity data, e.g., a BCL and/or FASTQ file. 
     It is to be noted that in this instance, the data  111  so produced may be generated locally and directly, such as by a local data generating  110  and/or computing resource  140 , e.g., a sequencer on a chip. Alternatively, the data may be produced locally and indirectly, e.g., by a remote computing and/or generating resource, such as a remote NGS. The data, e.g., in BCL and/or FASTQ file format, once produced may then be transferred indirectly over the local cloud  30  to the local computing resource  100  such as for secondary processing  140  and/or storage thereby in a local storage resource  200 , such as while awaiting further local processing  140 . In such an instance, where the data generation resource is remote from the local processing  100  and/or storage  200  resources, the corresponding resources may be configured such that the remote and/or local storage, remote and local processing, and/or communicating protocols employed by each resource may be adapted to smoothly and/or seamlessly integrate with one another, e.g., by running the same, similar, and/or equivalent software and/or by having the same, similar, and/or equivalent hardware configurations, and/or employing the same communications and/or transfer protocols, which, in some instances, may have been implemented at the time of manufacture or later thereto. 
     Specifically, in one implementation, these functions may be implemented in a hardwired configuration such as where the sequencing function and the secondary processing function are maintained upon the same or associated chip or chipset, e.g., such as where the sequencer and secondary processor are directly interconnected on a chip, as herein described. In other implementations, these functions may be implemented on two or more separate devices via software, e.g., on a quantum processor, CPU, or GPU that has been optimized to allow the two remote devices to communicate seamlessly with one another. In other implementations, a combination of optimized hardware and software implementations for performing the recited functions may also be employed. 
     More specifically, the same configurations may be implemented with respect to the performance of the mapping, aligning, sorting, variant calling, and/or other functions that may be deployed by the local  100  and/or remote  300  computing resources. For example, the local computing  100  and/or remote  300  resources may include software and/or hardware configured for performing one or more secondary  600  or tertiary  700  tiers of processing functions  112 - 115  on locally and/or remotely generated data, such as genetic sequence data, in a manner that the processing and results thereof may be seamlessly shared with one another and/or stored thereby. Particularly, the local computing function  100  and/or the remote computing function  300  may be configured for generating and/or receiving primary data, such as genetic sequence data, e.g., in a BCL and/or a FASTQ file format, and running one or more secondary  600  or tertiary  700  processing protocols on that generated and/or acquired data. In such an instance, one or more of these protocols may be implemented in a software, hardware, or combinational format, such as run on a quantum processor, a CPU, and/or a GPU. For instance, the data generating  110  and/or the local  140  and/or the remote  300  processing resource may be configured for performing one or more of a mapping operation  112 , an alignment operation  113 , or other related function  114  on the acquired or generated data in software and/or in hardware. 
     Accordingly, in various embodiments, the data generating resource, such as the sequencer  111 , e.g., NGS or sequencer on a chip, whether implemented in software and/or in hardware, or a combination of the same, may further be configured to include an initial tier of processors  500  such as a scheduler, various analytics, comparers, graphers, releasers, and the like, so as to assist the data generator  111 , e.g., sequencer, in converting biological information into raw read data, such as in a BCL or FASTQ file format  111   d . Further, the local computing  100  resource, whether implemented in software and/or in hardware, or a combination of the same, may further be configured to include a further tier of processors  600  such as may include a mapping engine  112 , or may otherwise include programming for running a mapping algorithm on the genetic sequence data, such as for performing a Burrows-Wheeler transform and/or other algorithms for building a hash table and/or running a hash function  112   a  on said data, such as for hash seed mapping, so as to generate mapped sequence data. Further still, the local computing  100  resource whether implemented in software and/or in hardware, or a combination of the same, may further be configured to include an initial tier of processors  600  such as may also include an alignment engine  113 , as herein described, or may otherwise include programming for running an alignment algorithm on the genetic sequence data, e.g., mapped sequenced data, such as for performing a gapped and/or gapless Smith-Waterman alignment, and/or Needleman-Wunsch, or other like scoring algorithm  113   a  on said data, so as to generate aligned sequence data. 
     The local computing  100  and/or data generating resource  110  may also be configured to include one or more other modules  114 , whether implemented in software and/or in hardware, or a combination of the same, which may be adapted to perform one or more other processing functions on the genetic sequence data, such as on the mapped and/or aligned sequence data. Thus, the one or more other modules may include a suitably configured engine  114 , or otherwise include programming, for running the one or more other processing functions such as a sorting  114   a , deduplication  114   b , recalibration  114   c , local realignment  114   d , duplicate marking  114   f , Base Quality Score Recalibration  114   g  function(s) and/or a compression function (such as to produce a SAM, Reduced BAM, and/or a CRAM compression and/or decompression file)  114   e , in accordance with the methods herein described. In various instances, one or more of these processing functions may be configured as one or more pipelines of the system  1 . 
     Likewise, the system  1  may be configured to include a module  115 , whether implemented in software and/or in hardware, or a combination of the same, which may be adapted for processing the data, e.g., the sequenced, mapped, aligned, and/or sorted data in a manner such as to produce a variant call file  116 . Particularly, the system  1  may include a variant call module  115  for running one or more variant call functions, such as a Hidden Markov Model (HMM) and/or GATK function  115   a  such as in a wired configuration and/or via one or more software applications, e.g., either locally or remotely, and/or a converter  115   b  for the same. In various instances, this module may be configured as one or more pipelines of the system  1 . 
     In particular embodiments, as set forth in  FIG. 41B , the system  1  may include a local computing function  100  that may be configured for employing a computer processing resource  140  for performing one or more further processing functions on data, e.g., BCL and/or FASTQ data, generated by the system generator  110  or acquired by the system acquisition mechanism  120  (as described below), such as by being transferred thereto, for instance, by a third party  121 , such as via a cloud  30  or hybrid cloud network  50 . For example, a third party analyzer  121  may deploy a remote computing resource  300  so as to generate relevant data in need of further processing, such as genetic sequence data or the like, which data may be communicated to the system  1  over the network  30 / 50  so as to be further processed. This may be useful, for instance, where the remote computing resource  300  is a NGS, configured for taking raw biological data and converting it to a digital representation thereof, such as in the form of one or more FASTQ files containing reads of genetic sequence data, and where further processing is desired, such as to determine how the generated sequence of an individual differs from that of one or more reference sequences, as herein described, and/or it is desired to subject the results thereof to furthered, e.g., tertiary, processing. 
     In such an instance, the system  1  may be adapted so as to allow one or more parties, e.g., a primary and/or secondary and/or third party user, to access the associated local processing resources  100 , and/or a suitably configured remote processing resource  300  associated therewith, in a manner so as to allow the user to perform one or more quantitative and/or qualitative processing functions  152  on the generated and/or acquired data. For instance, in one configuration, the system  1  may include, e.g., in addition to primary and/or secondary  600  processing pipelines, a third tier of processing modules  700 , which processing modules may be configured for performing one or more processing functions on the generated and/or acquired primary and/or secondary processed data. 
     Particularly, in one embodiment, the system  1  may be configured for generating and/or receiving processed genetic sequence data  111  that has been either remotely or locally mapped  112 , aligned  113 , sorted  114   a , and/or further processed  114  so as to generate a variant call file  116 , which variant call file may then be subjected to further processing such as within the system  1 , such as in response to a second and/or third party analytics requests  121 . More particularly, the system  1  may be configured to receive processing requests from a third party  121 , and further be configured for performing such requested secondary  600  and/or tertiary processing  700  on the generated and/or acquired data. Specifically, the system  1  may be configured for producing and/or acquiring genetic sequence data  111 , may be configured for taking that genetic sequence data and mapping  112 , aligning  113 , and/or sorting  114   a  it to produce one or more variant call files (VCFs)  116 , and additionally the system  1  may be configured for performing a tertiary processing function  700  on the data, e.g., with respect to the one or more VCFs. 
     The system  1  may be configured so as to perform any form of tertiary processing  700  on the generated and/or acquired data, such as by subjecting it to one or more pipeline processing functions  700  such as to generate genome data  122   a , epigenome data  122   b , metagenome data  122   c , and the like, including joint genotyping  122   d , GATK  122   e  and/or MuTect2  122   f  analysis pipelines, among other potential data analytic pipelines. Further, the system  1  may be configured for performing an additional tier of processing  800  on the generated and/or processed data, such as including one or more of non-invasive prenatal testing (NIPT)  123   a , N/P ICU  123   b , cancer related diagnostics and/or therapeutic modalities  123   c , various laboratory developed tests (LDT)  123   d , agricultural biological (Ag Bio) applications  123   e , or other such health care related  123   f  processing function. 
     Hence, in various embodiments, where a primary user may access and/or configure the system  1  and its various components directly, such as through direct access therewith, such as through the local computing resource  100 , as presented herein, the system  1  may also be adapted for being accessed by a secondary party, such as is connected to the system  1  via a local network or intranet connection  10  so as to configure and run the system  1  within the local environment. Additionally, in certain embodiments, the system may be adapted for being accessed and/or configured by a third party  121 , such as over an associated hybrid-cloud network  50  connecting the third party  121  to the system  1 , such as through an application program interface (API), accessible as through one or more graphical user interface (GUI) components. Such a GUI may be configured to allow the third party user to access the system  1 , and using the API to configure the various components of the system, the modules, associated pipelines, and other associated data generating and/or processing functionalities so as to run only those system components necessary and/or useful to the third party and/or requested or desired to be run thereby. 
     Accordingly, in various instances, the system  1  as herein presented may be adapted so as to be configurable by a primary, secondary, or tertiary user of the system. In such an instance, the system  1  may be adapted to allow the user to configure the system  1  and thereby to arrange its components in such a manner as to deploy one, all, or a selection of the analytical system resources, e.g.,  152 , to be run on data that is either generated, acquired, or otherwise transferred to the system, e.g., by the primary, secondary, or third party user, such that the system  1  runs only those portions of the system necessary or useful for running the analytics requested by the user to obtain the desired results thereof. For example, for these and other such purposes, an API may be included within the system  1  wherein the API is configured so as to include or otherwise be operably associated with a graphical user interface (GUI) including an operable menu and/or a related list of system function calls from which the user can select and/or otherwise make so as to configure and operate the system and its components as desired. 
     In such an instance, the GUI menu and/or system function calls may direct the user selectable operations of one or more of a first tier of operations  600  including: sequencing  111 , mapping  112 , aligning  113 , sorting  114   a , variant calling  115 , and/or other associated functions  114  in accordance with the teachings herein, such as with relation to the primary and/or secondary processing functions herein described. Further, where desired the GUI menu and/or system function calls may direct the operations of one or more of a second tier of operations  700  including: a genome pipeline  122   a , epigenome pipeline  122   b , metagenome pipeline  122   c , a joint genotyping pipeline  122   d , GATK  122   e  and/or MuTect2  122   f  analysis pipelines. Furthermore, where desired the GUI menu and system function calls may direct the user selectable operations of one or more of a third tier of operations  800  including: non-invasive prenatal testing (NIPT)  123   a , N/P ICU  123   b , cancer related diagnostics and/or therapeutic modalities  123   c , various laboratory developed tests (LDT)  123   d , agricultural biological (Ag Bio) applications  123   e , or other such health care related  123   f  processing functions. 
     Accordingly, the menu and system function calls may include one or more primary, secondary, and/or tertiary processing functions, so as to allow the system and/or its component parts to be configured such as with respect to performing one or more data analysis pipelines as selected and configured by the user. In such an instance, the local computing resource  100  may be configured to correspond to and/or mirror the remote computing resource  300 , and/or likewise the local storage resource  200  may be configured to correspond and/or mirror the remote storage resource  400  so that the various components of the system may be run and/or the data generated thereby may be stored either locally or remotely in a seamless distributed manner as chosen by the use of the system  1 . Additionally, in particular embodiments, the system  1  may be made accessible to third parties, for running proprietary analysis protocols  121   a  on the generated and/or processed data, such as by running through an artificial intelligence interface designed to find correlations there between. 
     The system  1  may be configured so as to perform any form of tertiary processing on the generated and/or acquired data. Hence, in various embodiments, a primary, secondary, or tertiary user may access and/or configure any level of the system  1  and its various components either directly, such as through direct access with the computing resource  100 , indirectly, such as via a local network connection  30 , or over an associated hybrid-cloud network  50  connecting the party to the system  1 , such as through an appropriately configured API having the appropriate permissions. In such an instance, the system components may be presented as a menu, such as a GUI selectable menu, where the user can select from all the various processing and storage options desired to be run on the user presented data. Further, in various instances, the user may upload their own system protocols so as to be adopted and run by the system so as to process various data in a manner designed and selected for by the user. In such an instance, the GUI and associated API will allow the user to access the system  1  and using the API add to and configure the various components of the system, the modules, associated pipelines, and other associated data generating and/or processing functionalities so as to run only those system components necessary and/or useful to the party and/or requested or desired to be run thereby. 
     Where the above with respect to  FIGS. 41A and 41B  are directed to data generation  110  such as local data generation  100 , employing a local computing resource  140 . As indicated above, and with respect to  FIG. 41C , one or more of the above demarcated modules, and their respective functions and/or associated resources, may be configured for being performed remotely, such as by a remote computing resource  300 , and further be adapted to be transmitted to the system  1 , such as in a seamless transfer protocol over a global cloud based internet connection  50 , such as via a suitably configured data acquisition mechanism  120 . 
     Accordingly, in such an instance, the local computing resource  100  may include a data acquisition mechanism  120 , such as configured for transmitting and/or receiving such acquired data and/or associated information. For instance, the system  1  may include a data acquisition mechanism  120  that is configured in a manner so as to allow the continued processing and/or storage of data to take place in a seamless and steady manner, such as over a cloud or hybrid based network  30 / 50  where the processing functions are distributed both locally  100  and/or remotely  300 , and likewise where one or more of the results of such processing may be stored locally  200  and/or remotely  400 , such that the system seamlessly allocates to which local or remote resource a given job is to be sent for processing and/or storage regardless of where the resource is physically positioned. Such distributed processing, transferring, and acquisition may include one or more of sequencing  111 , mapping  112 , aligning  113 , sorting  114   a , duplicate marking  114   c , deduplication, recalibration  114   d , local realignment  114   e , Base Quality Score Recalibration  114   f  function(s) and/or a compression function  114   g , as well as a variant call function  116 , as herein described. Where stored locally  200  or remotely  400 , the processed data, in whatever state it is in in the process may be made available to either the local  100  or remote processing  300  resources, such as for further processing prior to re-transmission and/or re-storage. 
     Specifically, the system  1  may be configured for producing and/or acquiring genetic sequence data  111 , may be configured for taking that genetic sequence data and processing it locally  140 , or transferring the data over a suitably configured cloud  30  or hybrid cloud  50  network such as to a remote processing facility for remote processing  300 . Further, once processed the system  1  may be configured for storing the processed data remotely  400  or transferring it back for local storage  200 . Accordingly, the system  1  may be configured for either local or remote generation and/or processing of data, such as where the generation and/or processing steps may be from a first tier of primary and/or secondary processing functions  600 , which tier may include one or more of: sequencing  111 , mapping  112 , aligning  113 , and/or sorting  114   a  so as to produce one or more variant call files (VCFs)  116 . 
     Likewise, the system  1  may be configured for either local or remote generation and/or processing of data, such as where the generation and/or processing steps may be from a second tier of tertiary processing functions  700 , which tier may include one or more of generating and/or acquiring data pursuant to a genome pipeline  122   a , epigenome pipeline  122   b , metagenome pipeline  122   c , a joint genotyping pipeline  122   d , GATK  122   e  and/or MuTect2  122   f  analysis pipeline. Additionally, the system  1  may be configured for either local or remote generation and/or processing of data, such as where the generation and/or processing steps may be from a third tier of tertiary processing functions  800 , which tier may include one or more of generating and/or acquiring data related to and including: non-invasive prenatal testing (NIPT)  123   a , N/P ICU  123   b , cancer related diagnostics and/or therapeutic modalities  123   c , various laboratory developed tests (LDT)  123   d , agricultural biological (Ag Bio) applications  123   e , or other such health care related  123   f  processing functions. 
     In particular embodiments, as set forth in  FIG. 41C , the system  1  may further be configured for allowing one or more parties to access the system and transfer information to or from the associated local processing  100  and/or remote  300  processing resources as well as to store information either locally  200  or remotely  400  in a manner that allows the user to choose what information get processed and/or stored where on the system  1 . In such an instance, a user can not only decide what primary, secondary, and/or tertiary processing functions get performed on generated and/or acquired data, but also how those resources get deployed, and/or where the results of such processing gets stored. For instance, in one configuration, the user may select whether data is generated either locally or remotely, or a combination thereof, whether it is subjected to secondary processing, and if so, which modules of secondary processing it is subjected to, and/or which resource runs which of those processes, and further may determine whether the then generated or acquired data is further subjected to tertiary processing, and if so, which modules and/or which tiers of tertiary processing it is subjected to, and/or which resource runs which of those processes, and likewise, where the results of those processes are stored for each step of the operations. 
     Particularly, in one embodiment, the user may configure the system  1  of  FIG. 41A  so that the generating of genetic sequence data  111  takes place remotely, such as by an NGS, but the secondary processing  600  of the data occurs locally  100 . In such an instance, the user can then determine which of the secondary processing functions occur locally  100 , such as by selecting the processing functions, such as mapping  112 , aligning  113 , sorting  111 , and/or producing a VCF  116 , from a menu of available processing options. The user may then select whether the locally processed data is subjected to tertiary processing, and if so which modules are activated so as to further process the data, and whether such tertiary processing occurs locally  100  or remotely  300 . Likewise, the user can select various options for the various tiers of tertiary processing options, and where any generated and/or acquired data is to be stored, either locally  200  or remotely  400 , at any given step or time of operation. 
     More particularly, a primary user may configure the system to receive processing requests from a third party, where the third party may configure the system for performing such requested primary, secondary, and/or tertiary processing on generated and/or acquired data. Specifically, the user or second and/or third party may configure the system  1  for producing and/or acquiring genetic sequence data, either locally  100  or remotely  200 . Additionally, the user may configure the system  1  for taking that genetic sequence data and mapping, aligning, and/or sorting it, either locally or remotely, so as to produce one or more variant call files (VCFs). Additionally, the user may configure the system for performing a tertiary processing function on the data, e.g., with respect to the one or more VCFs, either locally or remotely. 
     More particular still, the user or other party may configure the system  1  so as to perform any form of tertiary processing on the generated and/or acquired data, and where that processing is to occur in the system. Hence, in various embodiments, the first, second, and/or third party  121  user may access and/or configure the system  1  and its various components directly such as by directly accessing the local computing function  100 , via a local network connection  30 , or over an associated hybrid-cloud network  50  connecting the party  121  to the system  1 , such as through an application program interface (API), accessible as through one or more graphical user interface (GUI) components. In such an instance, the third party user may access the system  1  and use the API to configure the various components of the system, the modules, associated pipelines, and other associated data generating and/or processing functionalities so as to run only those system components necessary and/or useful to the third party and/or requested or desired to be run thereby, and further allocate which computing resources will provide the requested processing, and where the results data will be stored. 
     Accordingly, in various instances, the system  1  may be configurable by a primary, secondary, or tertiary user of the system who can configure the system  1  so as to arrange its components in such a manner as to deploy one, all, or a selection of the analytical system resources to be run on data that the user either directly generates, causes to be generated by the system  1 , or causes to be transferred to the system  1 , such as over a network associated therewith, such as via the data acquisition mechanism  120 . In such a manner, the system  1  is configurable so as to only run those portions of the system necessary or useful for the analytics desired and/or requested by the requesting party. For example, for these and other such purposes, an API may be included wherein the API is configured so as to include a GUI operable menu and/or a related list of system function calls that from which the user can select so as to configure and operate the system as desired. 
     Additionally, in particular embodiments, the system  1  may be made accessible to third parties, such as governmental regulators, such as the Federal Drug Administration (FDA)  70   b , or allow third parties to collate, compile, and/or access a data base of genetic information derived or otherwise acquired and/or compiled by the system  1  so as to form an electronic medical records (EMR) database  70   a  and/or to allow governmental access and/or oversight of the system, such as the FDA for Drug Development Evaluation. The system  1  may also be set up to conglomerate, compile, and/or annotate the data  70   c  and/or allow other high level users access thereto. 
     Accordingly, in various embodiments, as can be seen with respect to  FIG. 42A , a hybrid cloud  50  is provided wherein the hybrid cloud is configured for connecting a local computing  100  and/or storage resource  200  with a remote computing  300  and/or storage  400  resource, such as where the local and remote resources are separated one from the other distally, spatially, geographically, and the like. In such an instance, the local and distal resources may be configured for communicating with one another in a manner so as to share information, such as digital data, seamlessly between the two. Particularly, the local resources may be configured for performing one or more types of processing on the data, such as prior to transmission across the hybrid network  50 , and the remote resources may be configured for performing one or more types of further processing of the data. 
     For instance, in one particular configuration, the system  1  may be configured such that a generating and/or analyzing function  152  is configured for being performed locally  100  by a local computing resource, such as for the purpose of performing a primary and/or secondary processing function, so as to generate and/or process genetic sequence data, as herein described. Additionally, in various embodiments, the local resources may be configured for performing one or more tertiary processing functions on the data, such as one or more of genome, exome, and/or epigenome analysis, or a cancer, microbiome, and/or other DNA/RNA processing analysis. Further, where such processed data is meant to be transferred, such as to a remote computing  300  and/or storage  400  resource, the data may be transformed such as by a suitably configured transformer  151 , which transformer  151  may be configured for indexing, converting, compressing, and/or encrypting the data, such as prior to transfer over the hybrid network  50 . 
     In particular instances, such as where the generated and processed data is transferred to a remote computing resource  300  for further processing, such processing may be of a global nature and may include receiving data from a plurality of local computing resources  100 , collating such pluralities of data, annotating the data, and comparing the same, such as to interpret the data, determine trends thereof, analyzing the same for various biomarkers, and aiding in the development of diagnostics, therapeutics, and/or prophylactics. Accordingly, in various instances, the remote computing resource  300  may be configured as a data processing hub, such as where data from a variety of sources may be transferred, processed, and/or stored while waiting to be transformed and/or transferred, such as by being accessed by the local computing resource  100 . More particularly, the remote processing hub  300  may be configured for receiving data from a plurality of resources  100 , processing the same, and distributing the processed data back to the variety of local resources  100  so as to allow for collaboration amongst researchers and/or resources  100 . Such collaboration may include various data sharing protocols, and may additionally include preparing the data to be transferred, such as by allowing a user of the system  1  to select amongst various security protocols and/or privacy settings so as to control how the data will be prepared for transfer. 
     In one particular instance, as presented in  FIG. 42B , a local computing  100  and/or storage  200  resource is provided, such as on-site at a user&#39;s location. The computing resource  100  and/or storage  200  resource may be coupled to a data generating resource  121 , such as an NGS or sequencer on a chip, as herein described, such as over a direct or an intranet connection  10 , where the sequencer  121  is configured for generating genetic sequencing data, such as BCL and/or FASTQ files. For instance, the sequencer  121  may be part of and/or housed in the same apparatus as that of the computing resource  100  and/or storage unit  200 , so as to have a direct communicable and/or operable connection therewith, or the sequencer  121  and computing resource  100  and/or storage resource  200  may be part of separate apparatuses from one another, but housed in the same facility, and thus connected over a cabled or intranet  10  connection. In some instances, the sequencer  121  may be housed in a separate facility than that of the computing  100  and/or storage  200  resource and thus may be connected over an internet  30  or hybrid cloud connection  50 . 
     In such instances, the genetic sequence data may be processed  100  and stored locally  200 , prior to being transformed, by a suitably configured transformer  151 , or the generated sequence data may be transmitted directly to one or more of the transformer  151  and/or analyzer  152 , such as over a suitably configured local connection  10 , intranet  30 , or hybrid cloud connection  50 , as described above such as prior to being processed locally. Particularly, like the data generating resource  121 , the transformer  151  and/or analyzer  152  may be part of and/or housed in the same apparatus as that of the computing resource  100  and/or storage unit  200 , so as to have a direct communicable and/or operable connection therewith, or the transformer  151  and/or analyzer  152  and computing resource  100  and/or storage resource  200  may be part of separate apparatuses from one another, but housed in the same facility, and thus connected over a cabled or intranet  10  connection. In some instances, the transformer  151  and/or analyzer  152  may be housed in a separate facility than that of the computing  100  and/or storage  200  resource and thus may be connected over an internet  30  or hybrid cloud connection  50 . 
     For instance, the transformer  151  may be configured for preparing the data to be transmitted either prior to analysis or post analysis, such as by a suitably configured computing resource  100  and/or analyzer  152 . For instance, the analyzer  152  may perform a secondary and/or tertiary processing function on the data, as herein described, such as for analyzing the generated sequence data with respect to determining its genomic and/or exomic characteristics  152   a , its epigenomic features  152   b , any various DNA and/or RNA markers of interests and/or indicators of cancer  152   c , and its relationships to one or more microbiomes  152   d , as well as one or more other secondary and/or tertiary processes as described herein. 
     As indicated, the generated and/or processed data may be transformed, such as by a suitably configured transformer  151  such as prior to transmission throughout the system  1  from one component thereof to another, such as over a direct, local  10 , internet  30 , or hybrid cloud  50  connection. Such transformation may include one or more of conversion  151   d , such as where the data is converted from one form to another; comprehension  151   c , including the coding, decoding, and/or otherwise taking data from an incomprehensible form and transforming it to a comprehensible form, or from one comprehensible form to another; indexing  151   b , such as including compiling and/or collating the generated data from one or more resources, and making it locatable and/or searchable, such as via a generated index; and/or encryption  151   a , such as creating a lockable and unlockable, password protected dataset, such as prior to transmission over an internet  30  and/or hybrid cloud  50 . 
     Hence, as can be seen with respect to  FIG. 42C , in these and/other such instances, the hybrid cloud  50  may be configured for allowing seamless and protected transmission of data throughout the components of the system, such as where the hybrid cloud  50  is adapted to allow the various users of the system to configure its component parts and/or the system itself so as to meet the research, diagnostic, therapeutic and/or prophylactic discovery and/or development needs of the user. Particularly, the hybrid cloud  50  and/or the various components of the system  1  may be operably connected with compatible and/or corresponding API interfaces that are adapted to allow a user to remotely configure the various components of the system  1  so as to deploy the resources desired in the manner desired, and further to do so either locally, remotely, or a combination of the same, such as based on the demands of the system and the particulars of the analyses being performed, all the while being enabled to communicate in a secured, encryptable environment. 
     In particular instances, the system  1  may include a processing architecture  310 , such as an interpreter, that is configured for performing an interpreting function  310 . The interpreter  310  may perform one or a series of analytic functions on generated data, such as annotation  311 , interpretation  312 , diagnostics  313 , and/or a detection and/or an analysis function for determining the presence of one or more biomarkers, such as in the genetic data. The interpreter  313  may be part of or separate from the local computing resource  100 , such as where the interpreter  310  is coupled to the computing resource  100  via a cloud interface, such as a hybrid cloud  50 . 
     Further an additional processing architecture  320  may be included, such as where the architecture  320  is configured as a collaborator. The collaborator  320  may be configured for performing one or more functions directed to ensuring the security and/or privacy of data to be transmitted. For instance, the collaborator may be configured for securing the data sharing process  321 , for ensuring the privacy of transmission  322 , setting control parameters  323 , and/or for initiating a security protocol  324 . The collaborator  313  is configured for allowing for the sharing of data, such as for facilitating the collaboration of processing, as such the collaborator  320  may be part of or separate from the local computing resource  100 , such as where the collaborator  320  is coupled to the computing resource  100  via a cloud interface, such as a hybrid cloud  50 . The interpreter  310 , collaborator  320 , and/or the local computing resource  100  may further be coupled to a remote computing resource  300 , such as for enhancing system efficiency by offloading computing  300  and/or storage  400  functions into the cloud  50 . In various instance, the system  1  may be configured for allowing secure third party analysis  121  to take place, such as where the third party can connect with and engage the system such as through a suitably configured API. 
     As can be seen with respect to  FIG. 43 , the system  1  may be a multi-tiered and/or multiplexed bioanalytical processing platform that includes layers of data generating and/or data processing units each having one or more processing pipelines that may be deployed in a systematic and concurrent or sequential manner so as to process genetic information from its primary processing stage to a secondary and/or tertiary processing stage. Particularly, presented herein are devices configured for performing bioanalysis in one or more of hardware and/or software implementations, as well as methods of their use, and systems including the same. For instance, in one embodiment, a genomics processing platform may be provided and configured as a multiplicity of integrated circuits, which integrated circuits may be adapted as, or otherwise be included within, one or more of a central or graphics processing unit, such as a general purpose CPU and/or GPU, a hardwired implementation, and/or a quantum processing unit. Particularly, in various embodiments, one or more pipelines of the genomics processing platform may be configured by one or more quantum circuits of a quantum processing unit. 
     Accordingly, the platforms herein presented may be configured so as to harnesses the tremendous power of optimized software and/or hardware and/or quantum processing implementations for the performance of the various genetic sequencing and/or secondary processing functions, herein disclosed, which may be run on one or more integrated circuits. Such integrated circuits may be seamlessly coupled together and may further be seamlessly coupled to various other integrated circuits, e.g., CPUs and/or GPUs and/or QPUs, of the system that are configured for running the various software and/or hardwired based applications of tertiary bioanlytical functions. 
     Particularly, in various embodiments, these processes may be performed by optimized software run on a CPU, GPU and/or QPU, and/or may be implemented as a firmware configured integrated circuit, which may be part of the same device or separate devices that may be positioned on the same motherboard, different PCIe cards within the same device, separate devices in the same facility, and/or located at different facilities. Accordingly, the one or more integrated circuits may be directly coupled together, such as by being physically incorporated into the same mother board, or separate mother boards positioned within the same housing and/or otherwise coupled together, or they may be positioned on separate motherboards or pCIE cards that are capable of communicating with one another remotely, such as wirelessly and/or via a networked interface, such as via the cloud. In particular instances, the integrated circuit(s) forming or being a part of the CPU, GPU, and/or QPU which integrated circuit(s) may be arranged as and/or be a part of the secondary and/or tertiary analytics platform may be configured so as to form a pipeline of analyses where the various data generated may be fed into and out of, back and forth, between the various integrated circuits, such as in a seamless and/or streaming fashion, such as to expedite the analyses herein. 
     For instance, in some instances, the various devices for use in accordance with the methods disclosed herein may include, or otherwise be associated with, one or more sequencing devices, for performing a sequencing protocol, which sequencing protocol may be performed by software run on a remote sequencer, such as by a Next Gen sequencer, e.g., IIlumina&#39;s HiSeq Ten, located in a core sequencing facility, such as made accessible via a cloud based interface. In other instances, the sequencing may be performed in a hardwired configuration run on a sequencing chip, such as implemented by Thermo Fisher&#39;s Ion Torrent or other sequencer a chip technologies, where sequencing is performed by use of a semiconductor technology that delivers benchtop next gen sequencing, and/or by an integrated circuit configured as, or to otherwise include, a field effect transistor employing a graphene channel layer. In such instances, where the sequencing is performed by one or more integrated circuits configured as, or to include, a semiconducting sequencing microchip, the chip(s) may be positioned remotely from the one or more other integrated circuits disclosed herein and configured for performing secondary and/or tertiary analytics on the sequenced data, or they may be positioned relatively close to one another so as to be directly coupled together or at least within the same general proximity of one another, such as within the same facility. In such instances, a sequencing and/or BiolT analytics pipeline may be formed such that the raw sequencing data generated by the sequencer may be rapidly communicated to the other analytic components of the pipeline for direct analysis, such as in a streaming manner. 
     Further, once the raw sequencing or read data is produced by the sequencing instrument, this data may be transmitted to and be received by an integrated circuit configured for performing various bioanalytic functions on genetic and/or protein sequences, such as with respect to analyzing the generated and/or received DNA, RNA, and/or protein sequence data. This sequence analysis may involve the comparing of a generated or received nucleic acid or protein sequence to one or more databases of known sequences, such as for performing secondary analysis on the received data, and/or in some instances, for performing disease diagnostics, such as where the database of known sequences for performing the comparison may be a database containing morphologically distinct and/or abhorrent sequence data, that is data of genetic samples pertaining to or believed to pertain to one or more diseased states. 
     Accordingly, in various instances, once isolated and sequenced, the genetic data may be subjected to secondary analysis, which may be performed on the received data, such as for the performance of mapping, aligning, sorting, variant calling, and/or the like, so as to generate mapped and/or aligned data that may then be used to derive one or more VCF detailing the difference between the mapped and/or aligned genetic sequence and a reference sequence. Particularly, once secondary processing has occurred, the genetic information may then be passed onto one or more tertiary processing modules of the system, such as for further processing thereby, such as to derive therapeutically and/or prophylactic results. More particularly, after variant calling, the mapper/aligner/variant caller may output a standard VCF file that is ready for and may be communicated to an additional integrated circuit for performing tertiary analysis, such as analyses related to whole genome analysis pipeline, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, an epigenome analysis, a metagenome analysis, a joint genotyping analysis, a variance analysis, e.g., a GATK analysis, structural variants analysis, somatic variants analysis, and the like, as well as an RNA-sequencing or other genomics analysis. 
     Hence, the bioanalytic, e.g., the BiolT, platform herein presented may include highly optimized algorithms for mapping, aligning, sorting, duplicate marking, haplotype variant calling, compression and/or decompression, such as in a software, hardwired, and/or a quantum processing configuration. For example, although one or more of these functions may be configured to be performed entirely or partially in a hardwired configuration, in particular instances, the tertiary processing platform may be configured for running one or more software and/or quantum processing applications, such as one or more programs directed at performing one or more bioanalytics functions, such as one or more of the functions disclosed herein below. Particularly, the sequenced and/or mapped and/or aligned and/or other processed data may then be further processed by one or more other highly optimized algorithms for one or more of whole genome analysis, genotyping analysis, micro-array analysis, exome analysis, microbiome analysis, epigenome analysis, metagenome analysis, joint genotyping, and/or a variant, e.g., GATK analysis, such as implemented by software being run on a general purpose CPU and/or GPU and/or QPU. 
     Accordingly, as can be seen with reference to  FIG. 43 , in various embodiments, the multiplexed bioanalytical processing platforms are configured for performing one or more of primary, secondary, and/or tertiary processing. For example, the primary processing stage produces genetic sequence data, such as in one or more BCL and/or FASTQ files for transfer into the system  1 . Once within the system  1  the sequenced genetic data, including any associated metadata, may be advanced to a secondary processing stage  600 , so as to produce one or more variant call files. Hence, the system may also be configured to take the one or more variant call files along with any associated metadata, and/or or other associated processed data, and in one or more tertiary processing stages, may perform one or more other operations thereon, such as for the purposes of performing one or more diagnostics and/or prophylactic and/or therapeutic procedures there with. 
     Particularly, an analysis of the data may be initiated, e.g., in response to a third-party request  121 , and/or in response to data submitted by the third party  121 , and/or data automatically retrieved from a local  200  and/or remote  400  storage facility. Such further processing may include a first tier of processing wherein various pipeline run protocols  700  are configured to perform analytics on the determined genetic, e.g., variation, data of one or more subjects. For instance, a first tier of tertiary processing units may include a genomics processing platform that is configured to perform genome, epigenome, metagenome, genotyping, and/or various variant analysis, and/or other bioinformatics based analysis. Additionally, in a second tertiary processing tier, various disease diagnostic, research, and/or analysis protocols  800  may be performed, which analysis may include one or more of NIPT, NICU, cancer, LDT, biological, AgBio applications and the like. 
     The system  1  may further be adapted so as to receive and/or transmit various data  900  related to the procedures and processes herein disclosed such as related to electronic medical records (EMR) data, Federal Drug Administration testing and/or structuring data, data relevant to annotation, and the like. Such data may be useful so as to allow a user to make and/or allow access to generated medical, diagnostic, therapeutic, and/or prophylactic modalities developed through use of the system  1  and/or made accessible thereby. Accordingly, in various instances, the devices, methods, and systems presented herein allow for the secure performance of genetic and bioanalytic analysis, as well as for the secure transfer of the results thereof, in a forum that may be easily usable for downstream processing. 
     Particularly, the first tertiary processing tier  700  may include one or more genomics processing platforms, such as for performing genetics analysis, such as on mapped and/or aligned data, e.g., in a SAM or BAM file format, and/or for processing variant data, such as in a VCF format. For instance, the first tertiary processing platform may include one or more of a genome pipeline, epigenome pipeline, a metagenome pipeline, a joint genotyping pipeline, as well as one/or more variant analysis pipelines, including: a GATK pipeline, structural variant pipeline, somatic variant calling pipeline, and in some instances, may include an RNA-sequencing analysis pipeline. One or more other genomic analysis pipelines may also be included. 
     More specifically, with reference to  FIG. 43 , in various instances, the multi-tiered and/or multiplexed bioanalytical processing platform includes a further layer of data generation and/or processing units. For instance, in certain instances, the bioanalytical processing platform incorporates one or more processing pipelines, in one or more of software and/or hardware implementations, that are directed to performing one or more tertiary processing protocols. For example, in particular instances, a platform of tertiary processing pipelines  700  may include one or more of a genome pipeline, an epigenome pipeline, a metagenome pipeline, a joint genotyping pipeline, a variance pipeline, such as a GATK pipeline, and/or other pipelines, such as an RNA pipeline. 
     It is to be noted that with respect to  FIGS. 40 and 43 , one or more, e.g., all, of these functions therefore may be performed locally, e.g., on site  10 , on the cloud  30 , or via controlled access through the hybrid cloud  50 . In such an instance, a developer environment is created that allows a user to control the functionality of the system  1  to meet his or her individual needs and/or to allow access thereto for others seeking the same or similar results. Consequently, the various components, processes, procedures, tools, tiers, and hierarchies of the system may be configurable such as via a GUI interface that allows the user to select which components of the system to be run, on which data, at what time, and in what order in accordance with the user determined desires and protocols, so as to generate relevant data and connections between data that may be securely communicated throughout the system whether locally or remotely. As indicated, these components can be made to communicate seamlessly together, e.g., regardless of location and/or how connected, such as by being in a tightly coupled configuration and/or a seamless cloud based coupling, and/or by being configurable, e.g., via a JIT protocol, so as to run the same or similar processes in the same or similar manner, such as by employing corresponding API interfaces dispersed throughout the system, the employment of which allows the various users to configure the various components to run the various procedures in like manner. 
     For instance, an API may be defined in a header file with respect to the processes to be run by each particular component of the system  1 , wherein the header describes the functionality and determines how to call a function, such as the parameters that are passed, the inputs received and outputs transmitted, and the manner in which this occurs, what comes in and how, what goes out and how, and what gets returned, and in what manner. For example, in various embodiments, one or more of the components and/or elements thereof, which may form one or more pipelines of one or more tiers of the system may be configurable such as by instructions entered by a user and/or one or more second and/or third party applications. These instructions may be communicated to the system via the corresponding APIs which communicate with one or more of the various drivers of the system, instructing the driver(s) as to which parts of the system, e.g., which modules and/or which processes thereof are to be activated, when, and in what order, given a preselected parameter configuration, which may be determined by a user selectable interface, e.g., GUI. 
     Particularly, the one or more DMA drivers of the system  1  may be configured to run in corresponding fashion, such as at the kernel level of each component and the system  1  as a whole. In such an instance, one or more of the provided kernel&#39;s may have their own very low level, basic API that provides access to the hardware and functions of the various components of the system  1  so as to access applicable registers and modules so as to configure and direct the processes and the manners in which they are run on the system  1 . Particularly, on top of this layer, a virtual layer of service functions may be built so as to form the building blocks that are used for a multiplicity of functions that send files down to the kernel(s) and get results back, encodes, encrypts, and/or transmits the relevant data and further performs more higher level functions thereon. On top of that layer an additional layer may be built that uses those service functions, which may be an API level that a user may interface with, which may be adapted to function primarily for configuration of the system  1  as a whole or its component parts, downloading files, and uploading results, which files and/or results may be transmitted throughout the system either locally or globally. 
     Such configuration may include communicating with registers and also performing function calls. For example, as described herein above, one or more function calls necessary and/or useful to perform the steps, e.g., sequentially, to execute a mapping and/or aligning and/or sorting and/or variant call, or other secondary and/or tertiary function as herein described may be implemented in accordance with the hardware operations and/or related algorithms so as to generate the necessary processes and perform the required steps. 
     Specifically, because in certain embodiments one or more of these operations may be based on one or more structures, the various structures needed for implementing these operations may need to be constructed. There will therefore be a function call that performs this function, which function call will cause the requisite structure to be built for the performance of the operation, and because of this a call will accept a file name of where the structure parameter files are stored and will then generate one or more data files that contain and/or configure the requisite structure. Another function call may be to load the structure that was generated via the respective algorithm and transfer that down to the memory on the chip and/or system  1 , and/or put it at the right spot where the hardware is expecting them to be. Of course, various data will need to be downloaded onto the chip and/or otherwise be transferred to the system generator, as well for the performance of the various other selected functions of the system  1 , and the configuration manager can perform these functions, such as by loading everything that needs to be there in order for the modules of pipelines of the tiers of the platforms of the chip and/or system as a whole to perform their functions, into a memory on, attached, or otherwise associated with the chip and/or system. 
     Additionally, the API may be configured to allow one or more chips of the system  1  to interface with the circuit board of the sequencer  121 , the computing resource  100 / 300 , transformer  151 , analyzer  152 , interpreter  310 , collaborator  320 , or other system component, when included therewith, so as to receive the FASTQ and/or other generated and/or processed genetic sequencing files directly from the sequencer or other processing component such as immediately once they have been generated and/or processed and then transfers that information to the configuration manager which then directs that information to the appropriate memory banks in the hardware and/or software that makes that information available to the pertinent modules of the hardware, software, and/or system as a whole so that they can perform their designated functions on that information so as to call bases, map, align, sort, etc. the sample DNA/RNA with respect to the reference genome, and or to run associated secondary and/or tertiary processing operations thereon. 
     Accordingly, in various embodiments, a client level interface (CLI) may be included wherein the CLI may allow the user to call one or more of these functions directly. In various embodiments, the CLI may be a software application, e.g., having a GUI, that is adapted to configure the accessibility and/or use of the hardware and/or various other software applications of the system. The CLI, therefore, may be a program that accepts instructions, e.g., arguments, and makes functionality available simply by calling an application program. As indicated above, the CLI can be command line based or GUI (graphical user interface) based. The line based commands happen at a level below the GUI, where the GUI includes a windows based file manager with click on function boxes that delineate which modules, which pipelines, which tiers, of which platforms will be used and the parameters of their use. For example, in operation, if instructed, the CLI will locate the reference, will determine if a hash table and/or index needs to be generated, or if already generated locate where it is stored, and direct the uploading of the generated hash table and/or index, etc. These types of instructions may appear as user options at the GUI that the user can select the associated chip(s)/system  1  to perform. 
     Furthermore, a library may be included wherein the library may include pre-existing, editable, configuration files, such as files orientated to the typical user selected functioning of the hardware and/or associated software, such as with respect to a portion or whole genome and/or protein analysis, for instance, for various analyses, such as personal medical histories and ancestry analysis, or disease diagnostics, or drug discovery, therapeutics, and/or one or more of the other analytics, etc. These types of parameters may be preset, such as for performing such analyses, and may be stored in the library. For example, if the platform herein described is employed such as for NIPT, NICU, Cancer, LDT, AgBio, and related research on a collective level, the preset parameters may be configured differently than if the platform were directed simply to researching genomic and/or genealogy based research, such as on an individual level. 
     More particularly, for specific diagnosis of an individual, accuracy may be an important factor, therefore, the parameters of the system may be set to ensure increased accuracy albeit in exchange for possibly a decrease in speed. However, for other genomics applications, speed may be the key determinant and therefore the parameters of the system may be set to maximize speed, which however may sacrifice some accuracy. Accordingly, in various embodiments, often used parameter settings for performing different tasks can be preset into the library to facilitate ease of use. Such parameter settings may also include the necessary software applications and/or hardware configurations employed in running the system  1 . For instance, the library may contain the code that executes the API, and may further include sample files, scripts, and any other ancillary information necessary for running the system  1 . Hence, the library may be configured for compiling software for running the API as well as various of the executables. 
     Additionally, as can be seen with respect to  FIGS. 42C and 43 , the system may be configured such that one or more of the system components may be performed remotely, such as where the system component is adapted to run one or more comparative functions on the data, such as an interpretive function  310  and/or collaborative function  320 . For instance, where an interpretive protocol is employed on the data, the interpretive protocol  312  may be configured to analyze and draw conclusions about the data and/or determine various relationships with respect thereto, one or more other analytical protocols may also be performed and include annotating the data  311 , performing a diagnostic  313  on the data, and/or analyzes the data, so as to determine the presence or absence of one or more biomarkers  314 . 
     Additionally, where a collaborative protocol is performed, the system  1  may be configured for providing an electronic forum where data sharing  321  may occur, which data sharing protocol may include user selectable security  324  and/or privacy  322  settings that allow the data to be encrypted and/or password protected, so that the identity and sources of the data may be hidden from a user of the system  1 . In particular instances, the system  1  may be configured so as to allow a 3 rd  party analyzer  121  to run virtual simulations on the data. Further, one generated, the interpreted data and/or the data subjected to one or more collaborative analyses may be stored either remotely  400  or locally  200  so as to be made available to the remote  300  or local  100  computing resources, such as for further processing and/or analysis. 
     In another aspect, as can be seen with respect to  FIG. 44 , a method for using the system to generate one or more data files upon which one or more secondary and/or tertiary processing protocols may be run is provided. For instance, the method may include providing a genomic infrastructure such as for one or more of onsite, cloud-based, and/or hybrid genomic and/or bioinformatics generation and/or processing and/or analysis. 
     In such an instance, the genomic infrastructure may include a bioinformatics processing platform having one or more memories that are configured to store one or more configurable processing structures for configuring the system so as to be able to perform one or more analytical processing functions on data, such as data including a genomic sequence of interest or processed result data pertaining thereto. The memory may include the genomic sequence of interest to be processed, e.g., once generated and/or acquired, one or more genetic reference sequences, and/or may additionally include an index of the one or more genetic reference sequences and/or a list of splice junctions pertaining thereto. The system may also include an input having a platform application programming interface (API) for selecting from a list of options one or more of the configurable processing structures, such as for configuring the system, such as by selecting which processing functions of the system will be run on the data, e.g., the pre- or processed genomic sequences of interest. A graphical user interface (GUI) may also be present, such as operably associated with the API, so as to present a menu by which a user can select which of the available options he or she desires to be run on the data. 
     The system may be implemented on one or more integrated circuits that may be formed of one or more sets of configurable, e.g., preconfigured and/or hardwired, digital logic circuits that may be interconnected by a plurality of physical electrical interconnects. In such an instance, the integrated circuit may have an input, such as a memory interface, for receiving one or a plurality of the configurable structure protocols, e.g., from the memory, and may further be adapted for implementing the one or more structures on the integrated circuit in accordance with the configurable processing structure protocols. The memory interface of the input may also be configured for receiving the genomic sequence data, which may be in the form of a plurality of reads of genomic data. The interface may also be adapted for accessing the one or more genetic reference sequences and the index(es). 
     In various instances, the digital logic circuits may be arranged as a set of processing engines that are each formed of a subset of the digital logic circuits. The digital logic circuits and/or processing engines may be configured so as to perform one or more pre-configurable steps of a primary, secondary, and/or tertiary processing protocol so as to generate the plurality of reads of genomic sequence data, and/or for processing the plurality of reads of genomic data, such as according to the genetic reference sequence(s) or other genetic sequence derived information. The integrated circuit may further have an output so as to output result data from the primary, secondary, and/or tertiary processing, such as according to the platform application programming interface (API). 
     Particularly, in various embodiments, the digital logic circuits and/or the sets of processing engines may form a plurality of genomic processing pipelines, such as where each pipeline may have an input that is defined according to the platform application programming interface so as to receive the result data from the primary and/or secondary processing by the bioinformatics processing platform, and for performing one or more analytic processes thereon so as to produce result data. Additionally, the plurality of genomic processing pipelines may have a common pipeline API that defines a secondary and/or tertiary processing operation to be run on the result data from the primary and/or secondary processed data, such as where each of the plurality of genomic processing pipelines is configured to perform a subset of the secondary and/or tertiary processing operations and to output result data of the secondary and/or tertiary processing according to the pipeline API. 
     In such instances, a plurality of the genomic analysis applications may be stored in the memory and/or an associated searchable application repository, such as where each of the plurality of genomic analysis applications are accessible via an electronic medium by a computer such as for execution by a computer processor, so as to perform a targeted analysis of the genomic pre- or post processed data from the result data of the primary, secondary, and/or tertiary processing, such as by one or more of the plurality of genomic processing pipelines. In particular instances, each of the plurality of genomic analysis applications may be defined by the API and may be configured for receiving the result data of the primary, secondary, and/or tertiary processing, and/or for performing the target analysis of the pre- or post processed genomic data, and for outputting the result data from the targeted analysis to one of one or more genomic databases. 
     The method may additionally include, selecting, e.g., from the menu of the GUI, one or more genomic processing pipelines from a plurality of the available genomic processing pipelines of the system; selecting one or more genomic analysis applications from the plurality of genomic analysis applications that are stored in an application repository; and executing, using a computer processor, the one or more selected genomic analysis applications to perform a targeted analysis of genomic data from the result data of the primary, secondary, and/or tertiary processing. 
     Additionally, in various embodiments, all of mapping, aligning, and sorting, may take place on the chip, and local realignment, duplicate marking, base quality score recalibration may, and/or one or more of the tertiary processing protocols and/or pipelines, in various embodiments, also take place on the chip, and in various instances, various compression protocols, such as SAM and/or BAM and/or CRAM, may also take place on the chip. However, once the primary, secondary, and/or tertiary processed data has been produced, it may be compressed, such as prior to being transmitted, such as by being sent across the system, being sent up to the cloud, such as for the performance of the variant calling module, a secondary, tertiary, and/or other processing platform, such as including an interpretive and/or collaborative analysis protocol. This might be useful especially given the fact that variant calling, including the tertiary processing thereof, can be a moving target, e.g., there is not one standardized agreed upon algorithm that the industry uses. 
     Hence, different algorithms can be employed, such as by remote users, so as to achieve a different type of result, as desired, and as such having a cloud based module for the performance of this function may be useful for allowing the flexibility to select which algorithm is useful at any particular given moment, and also as for serial and/or parallel processing. Accordingly, any one of the modules disclosed herein can be implemented as either hardware, e.g., on the chip, or software, e.g., on the cloud, but in certain embodiments, all of the modules may be configured so that their function may be performed on the chip, or all of the modules may be configured so that their function may be performed remotely, such as on the cloud, or there will be a mixture of modules wherein some are positioned on one or more chips and some are positioned on the cloud. Further, as indicated, in various embodiments, the chip(s) itself may be configured so as to function in conjunction with, and in some embodiments, in immediate operation with a genetic sequencer, such as an NGS and/or sequencer on a chip. 
     More specifically, in various embodiments, an apparatus of the disclosure may be a chip, such as a chip that is configured for processing genomics data, such as by employing a pipeline of data analysis modules. Accordingly, as can be seen with respect to  FIG. 45 , a genomics pipeline processor chip  100  is provided along with associated hardware of a genomics pipeline processor system  10 . The chip  100  has one or more connections to external memory  102  (at “DDR3 Mem Controller”), and a connection  104  (e.g., PCIe or QPI Interface) to the outside world, such as a host computer  1000 , for example. A crossbar  108  (e.g., switch) provides access to the memory interfaces to various requestors. DMA engines  110  transfer data at high speeds between the host and the processor chip&#39;s  100  external memories  102  (via the crossbar  108 ), and/or between the host and a central controller  112 . The central controller  112  controls chip operations, especially coordinating the efforts of multiple processing engines  13 . The processing engines are formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects, and are organized into engine clusters  11 / 114 . In some implementations, the engines  13  in one cluster  11 / 114  share one crossbar port, via an arbiter  115 . The central controller  112  has connections to each of the engine clusters. Each engine cluster  11 / 114  has a number of processing engines  13  for processing genomic data, including a mapper  120  (or mapping module), an aligner  122  (or aligning module), and a sorter  124  (or sorting module), one or more processing engines for the performance of other functions, such as variant calling, may also be provided. Hence, an engine cluster  11 / 114  can include other engines or modules, such as a variant caller module, as well. 
     In accordance with one data flow model consistent with implementations described herein, the host CPU/GPU  1000  sends commands and data via the DMA engines  110  to the central controller  112 , which load-balances the data to the processing engines  13 . The processing engines return processed data to the central controller  112 , which streams it back to the host via the DMA engines  110 . This data flow model is suited for mapping and alignment and variant calling. As indicated, in various instances, communication with the host CPU/GPU may be through a relatively loose or tight coupling, such as a low latency, high bandwidth interconnect, such as a QPI, such as to maintain cache coherency between associated memory elements of the two or more devices. It is to be noted, in various instances, the host device may be a Quantum Processing Unit, such as for the sending of instructions and data, as well as for the running or processes consistent with the methods disclosed herein. 
     For instance, in various instances, due to various power and/or space constraints, such as when performing big data analytics, such as mapping/aligning/variant calling in a hybrid software/hardware accelerated environment, as described herein, where data needs to be moved both rapidly and seamlessly between system devices, a cache coherent tight coupling interface may be useful for performing such data transmissions throughout the system to and from the coupled devices, such as to and from the sequencer, DSP (digital signal processor), CPU and/or GPU or CPU/GPU hybrid, accelerated integrated circuit, e.g., FPGA, ASIC (on network card), a quantum processing unit, as well as other Smart Network Accelerators in a rapid, cache-coherent manner. In such instances, a suitable cache coherent, tight-coupling interconnect may be one or more of a single interconnect technology specification that is configured to ensure that processing, such as between a multiplicity of processing platforms, using different instruction set architectures (ISA), can coherently share data between the different platforms and/or with one or more associated accelerators, e.g., such as a hardwired FPGA implemented accelerator, so as to enable efficient heterogeneous computing, and thereby significantly improve the computing efficiency of the system, which in various instances may be configured as a cloud-based server system. Hence, in certain instances, a high bandwidth, low latency, cache coherent interconnect protocol, such as a QPI, Coherent Processor Accelerator Interface (CAPI), NVLink/GPU, or other suitable interconnect protocol may be employed so as to expedite various data transmissions between the various components of the system, such as pertaining to the mapping, aligning, and/or variant calling compute functions that may involve the use of acceleration engines the functioning of which requires the need to access, process, and move data seamlessly among various system components irrespective of where the various data to be processed resides in the system. And, where such data is retained within an associated memory device, such as a RAM or DRAM, the transmission activities may further involve expedited and coherent search and in-memory database processing. 
     Particularly, in particular embodiments, such heterogeneous computing may involve a multiplicity of processing and/or acceleration architectures that may be interconnected in a reduced instruct set computing format. In such an instance, such an interconnect device may be a coherent connect interconnect six (CCVI) device, which is configured to allow all computing componentry within the system to address, read, and/or write to one or more associated memories in a single, consistent, and coherent manner. More particularly, a CCVI interconnect may be employed so as to connect various of the devices of the system, such as the CPU and/or GPU, or CPU/GPU hybrid, FPGA/ASIC, QPU, and/or associated memories, etc. one with the other, such as in a high bandwidth manner that is configured to increase transfer rates between the various components while evidencing extremely reduced latency rates. Specifically, a CCVI interconnect may be employed and configured so as to allow components of the system to access and process data irrespective of where the data resides, and without the need for complex programing environments that would otherwise need to be implemented to make the data coherent. Other such interconnects that may be employed so as to speed up, e.g., decrease, processing time and increase accuracy include QPI, CAPI, NVLink, or other interconnect that may be configured to interconnect the various components of the system and/or to ride on top of an associated PCI-express peripheral interconnect. 
     Hence, in accordance with an alternative data flow model consistent with implementations described herein, the host CPU/GPU/QPU  1000  streams data into the external memory  1014 , either directly via DMA engines  110  and the crossbar  108 , or via the central controller  112 . The host CPU/GPU/QPU  1000  sends commands to the central controller  112 , which sends commands to the processing engines  13 , which instruct the processing engines as to what data to process. Because of the tight coupling, the processing engines  13  access input data directly from the external memory  1014  or a cache associated therewith, process it, and write results back to the external memory  1014 , such as over the tightly coupled interconnect  3 , reporting status to the central controller  112 . The central controller  112  either streams the result data back to the host  1000  from the external memory  1014 , or notifies the host to fetch the result data itself via the DMA engines  110 . 
       FIG. 46  illustrates a genomics pipeline processor and system  20 , showing a full complement of processing engines  13  inside an engine cluster  11 / 214 . The pipeline processor system  20  may include one or more engine clusters  11 / 214 . In some implementations, the pipeline processor system  20  includes four or more engine clusters  11 / 214 . The processing engines  13  or processing engine types can include, without limitation, a mapper, an aligner, a sorter, a local realigner, a base quality recalibrater, a duplicate marker, a variant caller, a compressor and/or a decompressor. In some implementations, each engine cluster  11 / 214  has one of each processing engine type. Accordingly, all processing engines  13  of the same type can access the crossbar  208  simultaneously, through different crossbar ports, because they are each in a different engine cluster  11 / 214 . Not every processing engine type needs to be formed in every engine cluster  11 / 214 . Processing engine types that require massive parallel processing or memory bandwidth, such as the mapper (and attached aligner(s)) and sorter, may appear in every engine cluster of the pipeline processor system  20 . Other engine types may appear in only one or some of the engine clusters  214 , as needed to satisfy their performance requirements or the performance requirements of the pipeline processor system  20 . 
       FIG. 47  illustrates a genomics pipeline processor system  30 , showing, in addition to the engine clusters  11  described above, one or more embedded central processing units (CPUs)  302 . Examples of such embedded CPUs include Snapdragons® or standard ARM® cores, or in other instances may be an FPGA. These CPUs execute fully programmable bio-IT algorithms, such as advanced variant calling, such as the building of a DBG or the performance of an HMM. Such processing is accelerated by computing functions in the various engine clusters  11 , which can be called by the CPU cores  302  as needed. Furthermore, even engine-centric processing, such as mapping and alignment, can be managed by the CPU cores  302 , giving them heightened programmability. 
       FIG. 48  illustrates a processing flow for a genomics pipeline processor system and method. In some preferred implementations, there are three passes over the data. The first pass includes mapping  402  and alignment  404 , with the full set of reads streamed through the engines  13 . The second pass includes sorting  406 , where one large block to be sorted (e.g., a substantial portion or all reads previously mapped to a single chromosome) is loaded into memory, sorted by the processing engines, and returned to the host. The third pass includes downstream stages (local realignment  408 , duplicate marking  410 , base quality score recalibration (BQSR)  412 , SAM output  414 , reduced BAM output  416 , and/or CRAM compression  418 ). The steps and functions of the third pass may be done in any combination or subcombination, and in any order, in a single pass. Hence, in this manner data is passed relatively seamlessly from the one or more processing engines, to the host CPU/GPU/QPU, such as in accordance with one or more of the methodologies described herein. Hence, a virtual pipeline architecture, such as described above, is used to stream reads from the host into circular buffers in memory, through one processing engine after another in sequence, and back out to the host. In some implementations, CRAM decompression can be a separate streaming function. In some implementations, the SAM output  414 , reduced BAM output  416 , and/or CRAM compression  418  can be replaced with variant calling, compression and decompression. 
     In various instances, a hardware implementation of a sequence analysis pipeline is described. This can be done in a number of different ways such as an FPGA or ASIC or structured ASIC implementation. The functional blocks that are implemented by the FPGA or ASIC or structured ASIC are set forth in  FIG. 49 . Accordingly, the system includes a number of blocks or modules to do sequence analysis. The input to the hardware realization can be a FASTQ file, but is not limited to this format. In addition to the FASTQ file, the input to the FPGA or ASIC or structured ASIC consists of side information, such as Flow Space Information from technology such as from the NGS. The blocks or modules may include the following blocks: Error Control, Mapping, Alignment, Sorting, Local Realignment, Duplicate Marking, Base Quality Recalibration, BAM and Side Information reduction and/or variant calling. 
     With respect to  FIG. 49 , these blocks or modules can be present inside, or implemented by, the hardware, but some of these blocks may be omitted or other blocks added to achieve the purpose of realizing a sequence analysis pipeline. Blocks  2  and  3  describe two alternatives of the sequence analysis pipeline platform. The sequence analysis pipeline platform comprising an FPGA or ASIC or structured ASIC and software assisted by a host (e.g., PC, server, cluster or cloud computing) with cloud and/or cluster storage. Blocks  4 - 7  describe different interfaces that the sequence analysis pipeline can have. In Blocks  4  and  6  the interface can be a PCIe and/or QPI/CAPI/CCVI/NVLink interface, but is not limited to a PCIe, QPI, or other interface. In Blocks  5  and  7  the hardware (FPGA or ASIC or structured ASIC) can be directly integrated into a sequencing machine. Blocks  8  and  9  describe the integration of the hardware sequence analysis pipeline integrated into a host system such as a PC, server cluster or sequencer. Surrounding the hardware FPGA or ASIC or structured ASIC are a plurality of DDR3 memory elements and a PCIe/QPI/CAPI/CCVI/NVLink interface. The board with the FPGA/ASIC/sASIC connects to a host computer, consisting of a host CPU, GPU, and/or QPU, that could be either a low power CPU such as an ARM®, Snapdragon®, or any other processor. Block  10  illustrates a hardware sequence analysis pipeline API that can be accessed by third party applications to perform tertiary analysis. 
       FIGS. 50A and 50B  depict an expansion card  104  having a processing chip  100 , e.g., an FPGA, of the disclosure, as well as one or more associated elements  105  for coupling the FPGA  100  with the host CPU/GPU/QPU, such as for the transferring of data, such as data to be processed and result data, back and forth from the CPU/GPU/QPU to the FPGA  100 .  FIG. 50B  depicts the expansion card of  FIG. 50A  having a plurality, e.g., 3, slots containing a plurality, e.g., 3, processing chips of the disclosure. 
     Specifically, as depicted in  FIGS. 50A and 50B , in various embodiments, an apparatus of the disclosure may include a computing architecture, such as embedded in a silicon field gate programmable array (FPGA) or application specific integrated circuit (ASIC)  100 . The FPGA  100  can be integrated into a printed circuit board (PCB)  104 , such as a Peripheral Component Interface-Express (PCIe) card, which can be plugged into a computing platform. In various instances, as shown in  FIG. 50A , the PCIe card  104  may include a single FPGA  100 , which FPGA may be surrounded by local memories  105 , however, in various embodiments, as depicted in  FIG. 50B , the PCIe card  104  may include a plurality of FPGAs  100 A,  100 B and  100 C. In various instances, the PCI card may also include a PCIe bus. This PCIe card  104  can be added to a computing platform to execute algorithms on extremely large data sets. In an alternative embodiment, as noted above with respect to  FIG. 34 , in various embodiments, the FPGA may be adapted so as to be directly associated with the CPU/GPU/QPU, such as via an interloper, and tightly coupled therewith, such as via a QPI, CAPI, CCVI interface. Accordingly, in various instances, the overall work flow of genomic sequencing involving the FPGA may include the following: Sample preparation, Alignment (including mapping and alignment), Variant analysis, Biological Interpretation, and/or Specific Applications. 
     Hence, in various embodiments, an apparatus of the disclosure may include a computing architecture that achieves the high performance execution of algorithms, such as mapping and alignment algorithms, that operate on extremely large data sets, such as where the data sets exhibit poor locality of reference (LOR). These algorithms are designed to reconstruct a whole genome from millions of short read sequences, from modern so-called next generation sequencers, require multi-gigabyte data structures that are randomly accessed. Once reconstruction is achieved, as described herein above, further algorithms with similar characteristics are used to compare one genome to libraries of others, do gene function analysis, etc. 
     There are three other typical architectures that in general may be constructed for the performance of one or more of the operations herein described in detail, such as including purpose multicore CPUs, general purpose Graphic Processing Units (GPGPUs), and/or a quantum processing unit. In such an instance, each CPU/GPU/QPU in a multicore system may have a classical cache based architecture, wherein instructions and data are fetched from a level 1 cache (L1 cache) that is small but has extremely fast access. Multiple L1 caches may be connected to a larger but slower shared L2 cache. The L2 cache may be connected to a large but slower DRAM (Dynamic Random Access Memory) system memory, or may be connected to an even larger but slower L3 cache which may then connected to DRAM. An advantage of this arrangement may be that applications in which programs and data exhibit locality of reference behave nearly as if they are executing on a computer with a single memory as large as the DRAM but as fast as the L1 cache. Because full custom, highly optimized CPUs operate at very high clock rates, e.g., 2 to 4 GHz, this architecture may be essential to achieving good performance. Additionally, as discussed in detail with respect to  FIG. 33 , in various embodiments the CPU/GPU may be tightly coupled to an FPGA, such as an FPGA configured for running one or more functions related to the various operations described herein, such as via a high bandwidth, low latency interconnect such as a QPI, CCVI, CAPI so as to further enhance performance as well as the speed and coherency of the data transferred throughout the system. In such an instance, cache coherency may be maintained between the two devices, as noted above. 
     Further, GPGPUs may be employed to extend this architecture, such as by implementing very large numbers of small CPUs, each with their own small L1 cache, wherein each CPU executes the same instructions on different subsets of the data. This is a so called SIMD (Single Instruction stream, Multiple Data stream) architecture. Economy may be gained by sharing the instruction fetch and decode logic across a large number of CPUs. Each cache has access to multiple large external DRAMs via an interconnection network. Assuming the computation to be performed is highly parallelizable, GPGPUs have a significant advantage over general purpose CPUs due to having large numbers of computing resources. Nevertheless, they still have a caching architecture and their performance is hurt by applications that do not have a high enough degree of locality of reference. That leads to a high cache miss rate and processors that are idle while waiting for data to arrive from the external DRAM. Additionally, it is to be noted, in various instances, a Quantum Processing Unit may also be employed for the running of processes consistent with the methods disclosed herein. 
     For instance, in various instances, Dynamic RAMs may be used for system memory because they are more economical than Static RAMs (SRAM). The rule of thumb used to be that DRAMs had 4× the capacity for the same cost as SRAMs. However, due to declining demand for SRAMs in favor of DRAMs, which difference has increased considerably due to the economies of scale that favor DRAMs which are in high demand. Independent of cost, DRAMs are 4× as dense as SRAMs laid out in the same silicon area because they only require one transistor and capacitor per bit compared to 4 transistors per bit to implement the SRAM&#39;s flip-flop. The DRAM represents a single bit of information as the presence or absence of charge on a capacitor. 
     A problem with this arrangement is that the charge decays over time, so it has to be refreshed periodically. The need to do this has led to architectures that organize the memory into independent blocks and access mechanisms that deliver multiple words of memory per request. This compensates for times when a given block is unavailable while being refreshed. The idea is to move a lot of data while a given block is available. This is in contrast to SRAMs in which any location in memory is available in a single access in a constant amount of time. This characteristic allows memory accesses to be single word oriented rather than block oriented. DRAMs work well in a caching architecture because each cache miss leads to a block of memory being read in from the DRAM. The theory of locality of reference is that if just accessed word N, then probably going to access words N+1, N+2, N+3 and so on, soon. 
       FIG. 51  provides an exemplary implementation of a system  500  of the disclosure, including one or more of the expansions cards of  FIG. 50 , such as for bioinformatics processing  10 . The system includes a Bio IT processing chip  100  that is configured for performing one or more functions in a processing pipeline, such as base calling, error correction, mapping, alignment, sorting, assembly, variant calling, and the like as described herein. 
     The system  500  further includes a configuration manager that is adapted for configuring the onboard functioning of the one or more processors  100 . Specifically, in various embodiments, the configuration manager is adapted to communicate instructions to the internal controller of the FPGA, e.g., firmware, such as by a suitably configured driver over a loose or tightly coupled interconnect, so as to configure the one or more processing functions of the system  500 . For instance, the configuration manager may be adapted to configure the internal processing clusters  11  and/or engines  13  associated therewith so as to perform one or more desired operations, such as mapping, aligning, sorting, variant calling, and the like, in accordance with the instructions received. In such a manner only the clusters  11  containing the processing engines  13  for performing the requested processing operations on the data provided from the host system  1000  to the chip  100  may be engaged to process the data in accordance with the received instructions. 
     Additionally, in various embodiments, the configuration manager may further be adapted so as to itself be adapted, e.g., remotely, by a third party user, such as over an API connection, as described in greater detail herein above, such as by a user interface (GUI) presented by an App of the system  500 . Additionally, the configuration manager may be connected to one or more external memories, such as a memory forming or otherwise containing a database, such as a data base including one or more reference or individually sequenced genomes and/or an index thereof, and/or one or more previously mapped, aligned, and/or sorted genomes or portions thereof. In various instances, the database may further include one or more genetic profiles characterizing a diseased state such as for the performance of one or more tertiary processing protocols, such as upon newly mapped, aligned genetic sequences or a VCF pertaining thereto. 
     The system  500  may also include a web-based access so as to allow remote communications such as via the internet so as to form a cloud or at least a hybrid cloud  504  communications platform. In such a manner as this, the processed information generated from the Bio IT processor, e.g., results data, may be encrypted and stored as an electronic health record, such as in an external, e.g., remote, database. In various instances, the EMR database may be searchable, such as with respect to the genetic information stored therein, so as to perform one or more statistical analyses on the data, such as to determine diseased states or trends or for the purposes of analyzing the effectiveness of one or more prophylactics or treatments pertaining thereto. Such information along with the EMR data may then be further processed and/or stored in a further database  508  in a manner so as to insure the confidentiality of the source of the genetic information. 
     More particularly,  FIG. 51  illustrates a system  500  for executing a sequence analysis pipeline on genetic sequence data. The system  500  includes a configuration manager  502  that includes a computing system. The computing system of the configuration manager  502  can include a personal computer or other computer workstation, or can be implemented by a suite of networked computers. The configuration manager  502  can further include one or more third party applications connected with the computing system by one or more APIs, which, with one or more proprietary applications, generate a configuration for processing genomics data from a sequencer or other genomics data source. The configuration manager  502  further includes drivers that load the configuration to the genomics pipeline processor system  10 . The genomics pipeline processor system  10  can output result data to, or be accessed via, the Web  504  or other network, for storage of the result data in an electronic health record  506  or other knowledge database  508 . 
     As discussed in several places herein above, the chip implementing the genomics pipeline processor can be connected or integrated in a sequencer. The chip can also be connected or integrated, e.g., directly via an interloper, or indirectly, e.g., on an expansion card such as via a PCIe, and the expansion card can by connected or integrated in a sequencer. In other implementations, the chip can be connected or integrated in a server computer that is connected to a sequencer, to transfer genomic reads from the sequencer to the server. In yet other implementations, the chip can be connected or integrated in a server in a cloud computing cluster of computers and servers. A system can include one or more sequencers connected (e.g. via Ethernet) to a server containing the chip, where genomic reads are generated by the multiple sequencers, transmitted to the server, and then mapped and aligned in the chip. 
     For instance, in general next generation DNA sequencer (NGS) data pipelines, the primary analysis stage processing is generally specific to a given sequencing technology. This primary analysis stage functions to translate physical signals detected inside the sequencer into “reads” of nucleotide sequences with associated quality (confidence) scores, e.g. FASTQ format files, or other formats containing sequence and usually quality information. Primary analysis, as mentioned above, is often quite specific in nature to the sequencing technology employed. In various sequencers, nucleotides are detected by sensing changes in fluorescence and/or electrical charges, electrical currents, or radiated light. Some primary analysis pipelines often include: Signal processing to amplify, filter, separate, and measure sensor output; Data reduction, such as by quantization, decimation, averaging, transformation, etc.; Image processing or numerical processing to identify and enhance meaningful signals, and associate them with specific reads and nucleotides (e.g. image offset calculation, cluster identification); Algorithmic processing and heuristics to compensate for sequencing technology artifacts (e.g. phasing estimates, cross-talk matrices); Bayesian probability calculations; Hidden Markov models; Base calling (selecting the most likely nucleotide at each position in the sequence); Base call quality (confidence) estimation, and the like. As discussed herein above, one or more of these steps may be benefitted by implementing one or more of the necessary processing functions in hardware, such as implemented by an integrated circuit, e.g., an FPGA. Further, after such a format is achieved, secondary analysis proceeds, as described herein, to determine the content of the sequenced sample DNA (or RNA etc.), such as by mapping and aligning reads to a reference genome, sorting, duplicate marking, base quality score recalibration, local re-alignment, and variant calling. Tertiary analysis may then follow, to extract medical or research implications from the determined DNA content. 
     Accordingly, given the sequential nature of the above processing functions, it may be advantageous to integrate primary, secondary, and/or tertiary processing acceleration in a single integrated circuit, or multiple integrated circuits positioned on a single expansion card. This may be beneficial because sequencers produce data that typically requires both primary and secondary analysis so as to be useful and may further be used in various tertiary processing protocols, and integrating them in a single device is most efficient in terms of cost, space, power, and resource sharing. Hence, in one particular aspect, the disclosure is directed to a system, such as to a system for executing a sequence analysis pipeline on genetic sequence data. In various instances, the system may include an electronic data source, such as a data source that provides digital signals, for instance, digital signals representing a plurality of reads of genomic data, where each of the plurality of reads of genomic data include a sequence of nucleotides. The system may include one or more of a memory, such as a memory storing one or more genetic reference sequences and/or an index of the one or more genetic reference sequences; and/or the system may include a chip, such as an ASIC, FPGA, or sASIC. 
     One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or structured ASIC computer hardware, firmware, software, and/or combinations thereof. 
     These various aspects or features can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and/or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores. 
     Additionally, due to the immense growth in data production and acquisition in the 21 st  Century, a need has developed for increased processing power that is capable of handling the ever-growing computationally intense analyses upon which modern development is founded. Supercomputers have been introduced, and have been useful for advancing technological development over a wide range of platforms. However, although supercomputing is useful, it has proven to be insufficient for some of the very complex computing problems many of today&#39;s technology companies face. Particularly, since the sequencing of the human genome, the technological advancement in the biological arts has been exponential. Nevertheless, in view of the high rate and increased complexity of the raw data produced every day, there has evolved a problematic bottleneck in the processing and analysis of the data generated. Quantum computers have been developed therefor to help resolve this bottleneck. Quantum computing represents a new frontline in computing, providing an entirely new approach to solving the worlds most challenging computational needs. 
     Quantum computing has been known since 1982. For instance, in the International Journal of Theoretical Physics, Richard Feynman theorized a system for performing quantum computing. Specifically, Feynman proposed a quantum system that could be configured for use in simulating other quantum systems in such a manner that the conventional functions of computer processing can be performed more quickly and efficiently. See Feynman, 1982, International Journal of Theoretical Physics 21, pp. 467-488, which is hereby incorporated by reference in its entirety. Particularly, a quantum computer system can be designed so as to exhibit exponential time savings in complex computations. Such controllable quantum systems are commonly known as quantum computers, and have been successfully developed into general purpose processing computers that not only can be used to simulate quantum systems, but can also be adapted for running specialized quantum algorithms. More particularly, complex problems can be modeled in the form of an equation, such as a Hamiltonian, which may be represented in the quantum system in a manner that the behavior of the system provides information regarding the solution to the equation. See Deutsch, 1985, Proceedings of the Royal Society of London A 400, pp. 97-117, which is hereby incorporated by reference in its entirety. In such instances, solving a model for the behavior of the quantum system may be configured so as to involve solving a differential equation related to the wave-mechanical description of a particle, e.g., Hamiltonian, of the quantum system. 
     In essence, quantum computing is a computational system that uses quantum-mechanical phenomena, e.g., superposition and/or entanglement, to perform various calculations on large amounts of data extremely fast. As such, quantum computers are a vast improvement over conventional digital logic computers. Specifically, conventional digital logic circuits function by using binary digital logic gates that are formed through the hardwiring of electronic circuitry on a conductive substrate. In a digital logic circuit an “on/off” state of a transistor serves as a basic unit of information, e.g., a bit. Particularly, a common digital computer processor employs binary digits, e.g., bits, in an “on” or “off” state, e.g., as a 0 or 1, to encode data. Quantum computation, on the other hand, employs an information device that uses superpositions of entangled states, called quantum bits or qubits, to encode data. 
     The basis for performing such quantum computations is an information device, e.g., a unit, which forms the quantum bit. The qubit is analogous to the digital “bit” in traditional digital computers, except that the qubit has far more computational potential than a digital bit. Particularly, as described in greater detail herein, instead of only encoding one of two discrete states, like a “0” and a “1,” as found in a digital bit, a qubit can also be placed in a superposition of “0” and “1.” Specifically, the qubit can exist in both the “0” and “1” state at the same time. Consequently, the qubit can perform a quantum computation on both states simultaneously. In general, N qubits can be in a superposition of 2 N  states. Quantum algorithms, therefore, can make use of this superposition property to speed up certain computations. 
     A qubit, therefore, is analogous to a bit in a traditional digital computer, and is a type of information device that exhibits coherence. Particularly, a quantum computing device is built up from a plurality of information device, e.g., qubit, building blocks. For instance, the computing power of a quantum computer increases as the information devices that form its building blocks are coupled, e.g., entangled, together in a controllable manner. In such an instance, the quantum state of one information device affects the quantum state of each of the other information devices to which it is coupled. 
     Accordingly, like the bit in classic digital computing, the qubit in quantum computing serves as the basic unit for the encoding of information, such as quantum information. Similar to a bit, the qubit encodes data in a two-state system, which in this instance is a quantum-mechanical system. Specifically, for the qubit, the two quantum states involve entanglement, such as involving the polarization of a single photon. Hence, where in a classical system, a bit has to be in one state or the other, in a quantum computing platform, the qubit may be in a superposition of both states at the same time, which property is fundamental to quantum processing. Consequently, the distinguishing feature between the qubit and the classical bit is that multiple qubits exhibit quantum entanglement. Such entanglement is a nonlocal property that allows a set of qubits to express higher correlation than is possible in a classical system. 
     In order to function, such information devices, e.g., quantum bits, must fulfill several requirements. First, the information device must be reducible to a quantum two-level system. This means that the information device must have two distinguishable quantum states that may be used for performing computations. Second, the information devices must be capable of producing quantum effects like entanglement and superposition. Additionally, in certain instances, the information device may be configured for storing information, e.g., quantum information, such as in a coherent form. In such instances, the coherent device may have a quantum state that persists without significant degradation for a long period of time, such as on the order of microseconds or more. 
     Particularly, quantum entanglement is the physical phenomenon that occurs when a pair or a group of particles are generated or otherwise configured to interact in a manner that the quantum state of one particle cannot be described independently of another, despite the space that separates them. Consequently, instead of describing the state of one particle in isolation of the others, a quantum state must be described for the system as a whole. In such instances, the measurements of various physical properties, such as position, momentum, spin, and/or polarization, performed on entangled particles are correlated. For example, if a pair of particles are generated in such a way that their total spin is known to be zero, and one particle is found to have clockwise spin on a certain axis, the spin of the other particle, measured on the same axis, will be found to be counterclockwise, as to be expected due to their entanglement. 
     Hence, one particle of an entangled pair simply “knows” what measurement has been performed on the other, and with what outcome, even though there is no known means for such information to have been communicated between the particles, which at the time of measurement may be separated by arbitrarily large distances. Because of this relationship, unlike classical bits that can only have one value at a time, entanglement allows multiple states to be acted on simultaneously. It is these unique entangled relationships and quantum states that have been capitalized upon for the development of quantum computing. 
     Accordingly, there are various kinds of physical operations employing pure qubit states that can be performed. For instance, a quantum logic gate can be formed and configured to operate on the basic qubit, where the qubit undergoes a unitary transformation, such as where the unitary transformations corresponds to rotations, or other quantum phenomena, of the qubit. In fact, any two-level system can be used as a qubit, such as photons, electrons, nuclear spins, coherent light states, optical lattices, Josephson junctions, quantum dots, and the like. Specifically, a quantum gate is the basis for a quantum circuit operating on a small number of qubits. For instance, a quantum circuit is comprised of quantum gates that act on fixed numbers of qubits, such as two or three, or more. Qubits, therefore, are the building blocks of quantum circuits, like classical logic gates are for conventional digital circuits. Specifically, a quantum circuit is a model for quantum computation where the computation is a sequence of quantum gates that are reversible transformations on a quantum mechanical analog of an n-bit register. Such analogous structures are referred to as n-qubit registers. Hence, unlike classical logic gates Quantum logic gates are always reversible. 
     Particularly, as described herein, a digital logic gate is a physical, wired device that may be implemented using one or more diodes or transistors that act as electronic switches for performing logical operations, e.g., Boolean functions, on one or more binary inputs, so as to produce a single binary output. With amplification, logic gates can be cascaded in the same way that Boolean functions can be composed, allowing the construction of a physical model of all of Boolean logic, and therefore, all of the algorithms and mathematics that can be described with Boolean logic can be performed by digital logic gates. In a like manner a cascade of quantum logic gates can be formed for the performance of Boolean logic operations. 
     Quantum gates are usually represented as matrices. In various implementations, a quantum gate acts on k qubits that may be represented by a 2 k×2 k unitary matrix. In such instances, the number of qubits in the input and output of the gate should be equal, and the action of the gate on a specific quantum state is found by multiplying the vector that represents the state by the matrix representing the gate. Hence, given this configuration quantum computational operations may be executed on a very small number of quantum bits. For instance, there are quantum algorithms that are configured for running much more complex computations faster than any possible probabilistic classical algorithm. Particularly, a quantum algorithm is an algorithm that runs on a quantum circuit model of computation. 
     Where a classical algorithm is a finite sequence of step-by-step instructions or procedures that may be performed by digital logic circuits of a classic computer; a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. However, even though quantum algorithms exist, such as Shor&#39;s, Grovar&#39;s, and Simon&#39;s algorithms, all classical algorithms can also be performed on a quantum computer with the correct configurations. Quantum algorithms are usually used for those algorithms that are inherently quantum, e.g., such as involving superposition or quantum entanglement. Quantum algorithms may be stated in various models of quantum computation, such as the Hamiltonian oracle model. 
     Accordingly, as a classical computer has a memory made up of bits, where each bit is represented by either a “1” or a“0”; a quantum computer supports a sequence of qubits where a single qubit can represent a one, a zero, or any quantum superposition of those two qubit states. Consequently, a pair of qubits can be in any quantum superposition of 4 states, and three qubits can be in any superposition of 8 states. In general, a quantum computer with n qubits can be in an arbitrary superposition of up to 2 n  different states simultaneously, which compares to a normal computer that can only be in one of these 2 n  states at any one time. Therefore, qubits can hold exponentially more information than their classical counterparts. In action, a quantum computer operates by setting the qubits in a drift that solves the problem by manipulating those qubits with a fixed sequence of quantum logic gates. It is this sequence of quantum logic gates that forms the operations of quantum algorithms. The calculation ends with a measurement, collapsing the system of qubits into one of the 2 n  pure states, where each qubit is “0” or “1”, thereby decomposing into a classical state. Hence, traditional algorithms may also be performed on a quantum computing platform, where the outcome is typically n classical bits of information. 
     In standard notation, the basic states of a qubit are referred to as the “0” and “1” states. However, during quantum computation, the state of a qubit, in general, may be a superposition of the basic or basis states such that the qubit has a nonzero probability of occupying the “0” basis state and a simultaneous nonzero probability of occupying the “1” basis state. Accordingly, the quantum nature of the qubit is largely derived from its ability to exist in a coherent superposition of basis states, and for the state of the qubit to have a phase. A qubit will retain this ability to exist as a coherent superposition of basis states as long as the qubit is sufficiently isolated from sources of decoherence. 
     Consequently, to complete a computation using a qubit, the state of the qubit is measured. As indicated above, when a measurement of the qubit is done, the quantum nature of the qubit may be temporarily lost and the superposition of the basis states may collapse to either the “0” basis state or the “1” basis state. Thus, in such a manner as this, the qubit regains its similarity to a conventional digital “bit”. However, the actual state of the qubit after it has collapsed will depend on the various probability states present immediately prior to the measurement operation. Thus, qubits may be employed to form quantum circuits, which themselves may be configured to form a quantum computer. 
     There are several general approaches to the design and operation of a quantum computer. One approach that has been put forth is that of a circuit model for quantum computing. Circuit model quantum computing requires long quantum coherence, so the type of information device used in quantum computers that support such an approach may be the qubit, which by definition has long coherence times. Accordingly, the circuit model for quantum computing is based upon the premise that qubits can be formed of and be acted on by logical gates, much like bits, and can be programmed using quantum logic in order to perform calculations, such as Boolean computations. Research has been done to develop qubits that can be programmed to perform quantum logic functions in this manner. For example, see Shor, 2001, arXiv.org:quant-ph/0005003, which is hereby incorporated by reference in its entirety. Likewise, a computer processor may take the form of a quantum processor such as a superconducting quantum processor. 
     A superconducting quantum processor may include a number of qubits and associated local bias devices, for instance, two, three, or more superconducting qubits. Accordingly, although in various embodiments, a computer processor may be configured as a non-traditional superconducting processor, in other embodiments, it the computer processor may be configured as a superconducting processor. For instance, in some embodiments, a non-traditional superconducting processor may be configured so as to not focus on quantum effects such as superposition, entanglement, and/or quantum tunneling, but may rather operate by emphasizing different principles, such as those principles that govern the operation of classical computer processors. In other embodiments, the computer processor may be configured as a traditional superconducting processor such as by being adapted to process through various quantum effects, such as superposition, entanglement, and/or quantum tunneling. 
     Accordingly, in various instances, there may be certain advantages to the implementation of such superconducting processors. Particularly, due to their natural physical properties, superconducting processors in general may be capable of higher switching speeds and shorter computation times than non-superconducting processors, and therefore it may be more practical to solve certain problems on superconducting processors. Further, detail and embodiments of exemplary quantum processors that may be used in conjunction with the present devices, systems, and the methods of their use are described in U.S. Ser. Nos. 11/317,838; 12/013,192; 12/575,345; 12/266,378; 13/678,266; and Ser. No. 14/255,561; as well as the various divisionals, continuations, and/or continuation in parts thereof; including U.S. Pat. Nos. 7,533,068; 7,969,805; 9,026,574; 9,355,365; 9,405,876; and all of their foreign counterparts, which are hereby incorporated by reference in their entireties. 
     Further, in addition to the above quantum devices and systems, methods for their use in solving complex computational problems are also presented. For instance, the quantum devices and systems herein disclosed may be employed for controlling the quantum state of one or more information devices and/or systems, in a coherent manner, so as to perform one or more steps in a bioinformatics and/or genomics processing pipeline, such as for the performance of one or more operations in an image processing, base calling, mapping, aligning, sorting, variant calling, and/or other genomics and/or bioinformatics pipeline. In particular embodiments, the one or more operations may include performing a burrow-wheelers, smith-waterman, and/or an HMM operation. 
     Particularly, solving complex genomics and/or bioinformatics computational problems using a quantum computing device may include generating one or more qubits and using the same to form a quantum logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using the quantum processor. The representation may be an arithmetic and/or geometric problem for solution by an addition, subtraction, multiplication, and/or divide circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. For instance, a mathematical circuit may employ binary representations of factors, and these binary representations may be decomposed to reduce the total number of variables required to represent the mathematical circuit. Accordingly, in accordance with the teachings herein, a computer processor may take the form of a digital and/or an analog processor, for instance, a quantum processor such as a superconducting quantum processor. A superconducting quantum processor may include a number of qubits and associated local bias devices, for instance two or more superconducting qubits, which may be formed into one or more quantum logic circuit representations. 
     More particularly, in various embodiments, a superconducting integrated circuit may be provided. Specifically, in particular embodiments, such a superconducting integrated circuit may include a first superconducting current path that is disposed in a metal, e.g., first, metal layer. A dielectric, e.g., first dielectric, layer may also be included, such as where at least a portion of the dielectric layer is associated within and/or carried on the first metal layer. A second superconducting current path may also be included and disposed in a second metal layer, such as metal layer that is carried on or otherwise associated with the first dielectric layer. In such an embodiment, at least a portion of the second superconducting current path may overlay at least a portion of the first superconducting current path. Likewise, a second dielectric layer may also be included, such as where at least a portion of the second dielectric layer is associated with or carried on the second metal layer. Additionally, a third superconducting current path may be included and disposed in a third metal layer that may be associated with or carried on the second dielectric layer, such as where at least a portion of the third superconducting current path may overlay at least a portion of one or both of the first and second superconducting current paths. One or more additional metal layers, dielectric layers, and/or current paths may also be included and configured accordingly. 
     Further, a first superconducting connection may be positioned between the first superconducting current path and the third superconducting current path, such as where the first superconducting connection extends through both the first dielectric layer and the second dielectric layer. A second superconducting connection may also be included and positioned between the first superconducting current path and the third superconducting current path, such as where the second superconducting connection may extend through both the first dielectric layer and the second dielectric layer. Additionally, at least a portion of the second superconducting current path may be encircled by an outer superconducting current path that may be formed by at least a portion of one or more of the first superconducting current path, at least a portion of the second superconducting current path, and/or the first and second superconducting connections. Accordingly, in such instances, the second superconducting current path may be configured to couple, e.g., inductively couple, a signal to the outer superconducting current path. 
     In some embodiments, a mutual inductance between the second superconducting current path and the outer superconducting current path may be sub-linearly proportional to a thickness of the first dielectric layer and a thickness of the second dielectric layer. The first and the second superconducting connections may also each include at least one respective superconducting via. Further, in various embodiments, the second superconducting current path may be a portion of an input signal line and one or both the first and the third superconducting current paths may be coupled to a superconducting programmable device. In other embodiments, the second superconducting current path may be a portion of a superconducting programmable device and both the first and the third superconducting current paths may be coupled to an input signal line. In particular embodiments, the superconducting programmable device may be a superconducting qubit, which may then be coupled, e.g., quantumly coupled, to one or more other qubits so as to from a quantum circuit, such as of a quantum processing device. 
     Accordingly, provided herein are devices, systems, and methods for solving computational problems, especially problems related to resolving the genomics and/or bioinformatics bottleneck described herein above. In various embodiments, these devices, systems and methods introduce a technique whereby a logic circuit representation of a computational problem may be solved directly and/or may be encoded as a discrete optimization problem, and the discrete optimization problem may then be solved using a computer processor, such as a quantum processor. For instance, in particular embodiments, solving such discrete optimization problems may include executing the logic circuit to solve the original computational problem. 
     Hence, the devices, systems, and methods described herein may be implemented using any form of computer processor such as including traditional logic circuits and/or logic circuit representations, such as configured for use as a quantum processor and/or in super conducting processing. Particularly, various steps in performing an image processing, base calling, mapping, aligning, and/or variant calling bioinformatics pipeline may be encoded as discrete optimization problems and as such may be particularly well-suited to be solved using the quantum processors, disclosed herein. In other instances, such computations may be resolved more generally by a computer processor that harnesses quantum effects to achieve such computation; and/or in other instances, such computations may be performed using a dedicated integrated circuit, such as an FPGA, ASIC, or structured ASIC, as described herein in detail. In some embodiments, the discrete optimization problem is cast as a problem by configuring the logic circuits, qubits, and/or couplers in a quantum processor. In some embodiments, the quantum processor may be specifically adapted to facilitate solving such discrete optimization problems. 
     As disclosed throughout this specification and the appended claims, reference is often made to a “logic circuit representation”, e.g., of a computational problem. Depending on the context, a logic circuit may incorporate a set of logical inputs, a set of logical outputs, and a set of logic gates (e.g., NAND gates, XOR gates, and the like) that transform the logical inputs to the logical outputs through a set of intermediate logical inputs and intermediate logical outputs. A complete logic circuit may include a representation of the input(s) to the computational problem, a representation of the output(s) of the computational problem, and a representation of the sequence of intermediate steps in between the input(s) and the output(s). 
     Thus, for various purposes of the present devices, systems, and methods, the computational problem may be defined by its input(s), its output(s), and the intermediate steps that transform the input(s) to the output(s) and a “logic circuit representation” may include all of these elements. Those of skill in the art will appreciate that the encoding of a “logic circuit representation” of a computational problem as a discrete optimization problem, and the subsequent mapping of the discrete optimization problem to a quantum processor, may result in any number of layers involving any number of qubits per layer. Furthermore, such a mapping may implement any scheme of inter-qubit coupling to enable any scheme of inter-layer coupling (e.g., coupling between the qubits of different layers) and intra-layer coupling (e.g., coupling between the qubits within a particular layer). 
     Accordingly, as indicated, in some embodiments, the structure of a logic circuit may be stratified into layers. For example, the logical input(s) may represent a first layer, each sequential logical (or arithmetic) operation may represent a respective additional layer, and the logical output(s) may represent another layer. And as previously described, a logical operation may be executed by a single logic gate or by a combination of logic gates, depending on the specific logical operation being executed. Thus, a “layer” in a logic circuit may include a single logic gate or a combination of logic gates depending on the particular logic circuit being implemented. 
     Consequently, in various embodiments such as where the structure of a logic circuit stratifies into layers (for example, with the logical input(s) representing a first layer, each sequential logical operation representing a respective additional layer, and the logical output(s) representing another layer), each layer may be embodied by a respective set of qubits in the quantum and/or superconducting processor. For example, in one embodiment of a quantum processor, one or more, e.g., each, row of qubits may be programmed to represent a respective layer of a quantum logic circuit. That is, particular qubits may be programmed to represent the inputs to a logic circuit, other qubits may be programmed to represent a first logical operation (executed by either one or a plurality of logic gates), and further qubits may be programmed to represent a second logical operation (similarly executed by either one or a plurality of logic gates), and yet further qubits may be programmed to represent the outputs of the logic circuit. 
     Additionally, with various sets of qubits representing various layers of the problem, it can be advantageous to enable independent dynamic control of each respective set. Further, in various embodiments, various serial logic circuits may be mapped to the quantum processor, and the respective qubits mapped to facilitate the functional interactions for quantum processing in a manner suitable to enable independent control thereof. From the above, those of skill in the art will appreciate how a similar objective function may be defined for any logic gate. Thus, in some embodiments, the problem representing a logic circuit may essentially be comprised of a plurality of miniature optimization problems, where each gate in the logic circuit corresponds to a particular miniature optimization problem. 
     Hence, exemplary logic circuit representations may be generated using systems and methods that are known in the art. In one example, a logic circuit representation of the computational problem, e.g., the genomics and/or bioinformatics problem, may be generated and/or encoded using a classical digital computer processor and/or a quantum and/or superconducting processor as described herein. Accordingly, a logic circuit representation of the computational problem may be stored in at least one computer- or processor-readable storage medium, such as a computer-readable non-transitory storage medium or memory (e.g., volatile or non-volatile). Therefore, as discussed herein, the logic circuit representation of the computational problem may be encoded as a discrete optimization problem, or a set of optimization objectives, and in various embodiments, such as where a classical digital computer processing paradigm is configured to solve the problem, the system may be configured so that bit strings that satisfy the logic circuit have energy of zero and all other bit strings have energy greater than zero, where the discrete optimization problem may be solved in such a manner as to establish a solution to the original computational problem. 
     Further, in other embodiments, the discrete optimization problem may be solved using a computer processor, such as a quantum processor. In such an instance, solving the discrete optimization problem may then involve, for example, evolving the quantum processor to the configuration that minimizes the energy of the system in order to establish a bit string that satisfies the optimization objective(s). Accordingly, in some embodiments, the act of solving a discrete optimization problem may include three acts. First, the discrete optimization problem may be mapped to a computer processor. In some embodiments, the computer processor may include a quantum and/or super conducting processor and mapping the discrete optimization problem to the computer processor may include programming the elements (e.g., qubits and couplers) of the quantum and/or superconducting processor. Mapping the discrete optimization problem to the computer processor may include the discrete optimization problem in at least one computer or processor-readable storage medium, such as a computer-readable non-transitory storage medium or memory (e.g., volatile or non-volatile). 
     Accordingly, in view of the above, in various instances, a device, system, and method for executing a sequence analysis pipeline, such as on genomics material, is provided. For instance, the genomics material may include a plurality of reads of genomic data, such as in an image file, BCL, FASTQ file, and the like. In various embodiments, the device and/or system may be employed for executing a sequence analysis on genomic data, e.g., reads of genomic data, such as by using an index of one or more genetic reference sequences, e.g., stored in a memory, for example, where each read of genomic data and each reference sequence represents a sequence of nucleotides. 
     Particularly, in various embodiments, the device may be a quantum computing device, such as formed of a set of quantum logic circuits, e.g., hardwired quantum logic circuits, for instance, where the logic circuits are interconnected with one another. In various instances, the quantum logic circuits may be interconnected by one or more superconducting connections. Additionally, one or more of the superconducting connections may include a memory interface, such as for accessing the memory. Together the logic circuits and interconnects may be configured to process information represented as a quantum state that is itself represented as a set of one or more qubits. More particularly, the set of hardwired quantum logic circuits may be arranged as a set of processing engines, such as where each processing engine may be formed of a subset of the hardwired quantum logic circuits, and may be configured to perform one or more steps in the sequence analysis pipeline on the reads of genomic data. 
     For instance, the set of processing engines may be configured so as to include an image processing, base calling, mapping, aligning, sorting, variant calling, and/or other genomics and/or bioinformatics processing module. For example, in various embodiments, a mapping module, such as in a first hardwired configuration, may be included. Additionally, in further embodiments, an alignment module, such as in a second hardwired configuration, may be included. Further, a sorting module, such as in a third hardwired configuration, may be included. And, in additional embodiments, a variant calling module, such as in a fourth hardwired configuration, may be included. Further still, in various embodiments, an image processing and/or base calling module may be included in further hardwired configurations, such as where one or more of these hardwired configurations may include hardwired quantum logic circuits may be arranged as a set of processing engines. 
     More particularly, in particular instances, a quantum computing device and/or system may include a mapping module, where the mapping module comprises a set of quantum logic circuits that are arranged as a set of processing engines, one or more of which are configured for performing one or more steps of a mapping procedure. For instance, one or more quantum processing engines may be configured to receive a read of genomic data, such as via one or more of a plurality of superconducting connections. Further, the one or more quantum processing engines may be configured to extract a portion of the read to generate a seed, such as where the seed may represent a subset of the sequence of nucleotides represented by the read. Additionally, one or more of the quantum processing engines may be configured to calculate a first address within the index based on the seed, and access the address in the index in the memory, so as to receive a record from the address, such as where the record represents position information in the genetic reference sequence. Further more, the one or more quantum processing engines may be configured to determine, e.g., based on the record, one or more matching positions from the read to the genetic reference sequence; and output at least one of the matching positions to the memory via the memory interface. 
     Further still, the mapping module may include a set of quantum logic circuits that are arranged as a set of processing engines configured for calculating a second address within the index, e.g., based on both of the record and of a second subset of the sequence of nucleotides that is not contained in the first subset of the sequence of nucleotides. The processing engine(s) may then access the second address in the index in the memory so as to receive a second record from the second address, such as where the second record, or a subsequent record, includes position information in the genetic reference sequence. The processing engine may further be configured for determining, based on the position information, the one or more matching positions from the read to the genetic reference sequence. 
     Additionally, in various instances, a quantum computing device and/or system may include an alignment module, where the alignment module comprises a set of quantum logic circuits that are arranged as a set of processing engines, one or more of which are configured for performing one or more steps of an alignment procedure. For instance, one or more quantum processing engines may be configured to receive a plurality of mapped positions for the read from the memory, and to access the memory to retrieve a segment of the genetic reference sequence corresponding to each of the mapped positions. The one or more processing engines formed as an alignment module may further be configured to calculate an alignment of the read to each retrieved segment of the genetic reference sequence so as to generate a score for each alignment. Further, once one or more scores have been generated at least one best-scoring alignment of the read may be selected. In particular instances, the quantum computing device may include a set of quantum logic circuits that are arranged as a set of processing engines that are configured for performing a gapped or gapless alignment, such as a Smith Waterman alignment. 
     Further, in certain instances, a quantum computing device and/or system may include a variant calling module, where the variant calling module comprises a set of quantum logic circuits that are arranged as a set of processing engines, one or more of which are configured for performing one or more steps of a variant calling procedure. For instance, the quantum computing variant calling module may include a set of quantum logic circuits that are adapted for executing an analysis on a plurality of reads of genomic data, such as using one or more candidate haplotypes, e.g., stored in a memory, where each read of genomic data and each candidate haplotype represent a sequence of nucleotides. 
     Specifically, the set of quantum logic circuits may be formed as one or more quantum processing engines that are configured to receive one or more of the reads of genomic data and generate and/or receive the one or more candidate haplotypes, e.g., from the memory, such as via one or more of a plurality of superconducting connections. Further, the one or more quantum processing engines may be configured to receive one or more of the reads of genomic data and the one or more candidate haplotypes from the memory, as well as to compare nucleotides in each of the one or more reads to the one or more candidate haplotypes, so as to determine a probability of each candidate haplotype representing a correct variant call. Additionally, one or more of the quantum processing engines may be configured to generate an output based on the determined probability. 
     Additionally, in various instances, the set of quantum logic circuits may be formed as one or more quantum processing engines that are configured to determine a probability of observing each read of the plurality of reads based on at least one candidate haplotype being a true sequence of nucleotides, e.g., of a source organism of the plurality of reads. In particular instances, with respect to determining probability, the one or more quantum processing engines may be configured for executing a Hidden Markov Model. More particularly, in additional embodiments, the one or more quantum processing engines may be configured for merging the plurality of reads into one or more contiguous nucleotide sequences, and/or for generating the one or more candidate haplotypes from the one or more contiguous nucleotide sequences. For instance, in various embodiments, the merging of the plurality of reads includes the one or more quantum processing engines constructing a De Bruijn graph. 
     Accordingly, in light of the above, a system for performing various computations in solving problems related to genomics and/or bioinformatics processing is provided. For instance, the system may include one or more of an onsite automated sequencer, e.g., NGS, and/or a processing server either or both of which may include one or more CPUs, GPUs, QPUs, and/or other integrated circuits, such as including an FPGA, ASIC, and/or structured ASIC that are configured as herein described for performing one or more steps in a sequence analysis pipeline. Particularly, the Next Gen Sequencer may be configured for sequencing a plurality of nucleic acid sequences so as to generate one or more image, BCL, and/or FASTQ files representing the sequenced nucleic acid sequences, which nucleic acid sequences may be a DNA and/or an RNA sequence. These sequence files may be processed by the sequencer itself or by an associated server unit, such as where the sequencer and/or the associated server includes an integrated circuit, such as an FPGA or ASIC, configured as herein described for performing one or more steps in a secondary sequence analysis pipeline. 
     However, in various instances, such as where the automated sequencer and/or an associated server is not configured for performing a secondary sequence analysis on the data generated from the sequencer, the generated data may be transmitted to a remote server that is configured for performing a secondary and/or tertiary sequence analysis on the data, such as via a cloud mediated interface. In such an instance, the cloud accessible server may be configured for receiving the generated sequence data, such as in image, BCL, and/or in FASTQ form, and may further be configured for performing a primary, e.g., image processing, and/or a secondary and/or tertiary processing analysis, such as a sequence analysis pipeline, on the received data. For instance, the could accessible server may be one or more servers including a CPU and/or a GPU and/or a QPU one or more of which may be associated with an integrated circuit, such as an FPGA or ASIC, as herein described. Particularly, in certain instances, the cloud accessible server may be a quantum computing server, as herein described. 
     Specifically, the cloud accessible server may be configured for performing a primary, secondary, and/or tertiary genomics and/or bioinformatics analysis on the received data, which analyses may include performing one or more steps in one or more of an image processing, base calling, mapping, aligning, sorting, and/or variant calling protocols. In certain instances, some of the steps may be performed by one processing platform, such as a CPU or GPU or QPU, and others may be performed by another processing platform, such as an associated, e.g., tightly coupled, integrated circuit, such as an FPGA or ASIC, that is specifically configured for performing various of the steps in the sequence analysis pipeline. In such instances, where data and the results of analysis are to be transferred from one platform to another, the system and its components may be configured for compressing the data prior to transfer, and decompressing the data once transferred, and as such the system components may be configured for generating one or more of a SAM, BAM, or CRAM files, such as for transfer. Additionally, in various embodiments, the cloud accessible server may be a quantum computing platform that is configured herein to perform one or more steps in the sequence analysis pipeline, as described herein, and may include the performance of one or more secondary and/or tertiary processing steps in accordance with one or more of the methods disclosed herein. 
     Further, with respect to quantum computing, detail and embodiments of exemplary quantum processors and the methods of their use that may be employed in conjunction with the present devices, systems, and methods are described in U.S. Pat. Nos. 7,135,701; 7,533,068; 7,969,805; 8,560,282; 8,700,689; 8,738,105; 9,026,574; 9,355,365; 9,405,876; as well as the various counterparts thereto, which are hereby incorporated by reference in their entireties. 
     To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT), a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including, but not limited to, acoustic, speech, or tactile input. Other possible input devices include, but are not limited to, touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive trackpads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like. 
     The subject matter described herein can be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and/or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Other implementations may be within the scope of the following claims.