Machine learning-based intelligent device for DNA sequence matching and mutation detection

Abstract

A machine learning-based system for DNA sequence alignment and mutation detection, consisting of: a sequence alignment processing unit comprising a plurality of field-programmable gate arrays (FPGAs), each FPGA being configured to execute pre-trained neural networks for recognizing DNA sequence patterns, wherein the sequence alignment processing unit can be operated to perform real-time alignment of a target DNA sequence relative to a reference genome; a mutation detection unit operationally connected to the sequence alignment processing unit, wherein the mutation detection unit is configured to analyze aligned sequences to identify genetic variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, wherein the mutation detection unit uses a machine learning-based model to detect the genetic variations with a confidence value for each detected variation, an input / output interface suitable for receiving DNA sequence data from external data sources, wherein the input / output interface includes a high-speed data transfer protocol to enable fast loading of genomic datasets into the system; a memory management unit that is operationally connected to the sequence alignment processing unit and the mutation detection unit, wherein the memory management unit is configured to store and retrieve DNA sequence data during alignment and mutation detection processes and to enable simultaneous read / write operations for the parallel processing of multiple DNA samples; a machine learning execution unit configured to dynamically update the machine learning models used in the sequence alignment processing unit and the mutation detection unit based on feedback from the mutation detection process, thereby improving the accuracy of both sequence alignment and mutation detection over time; and A power management system configured to monitor and control the performance of the sequence alignment processing unit, the mutation detection unit, and the machine learning execution unit based on the computational load, with the power management system optimizing energy efficiency while maintaining system performance during high-intensity processing tasks.

Description

Field of the invention:

[0001] The present invention relates to a method for aligning DNA sequences and detecting mutations using a smart device. It utilizes advanced machine learning algorithms running on a custom hardware platform to rapidly and accurately detect the presence of genetic alterations (such as single nucleotide polymorphisms or SNPs, as well as larger structural variations) with high sensitivity. The invention is particularly useful in genetic diagnostics, bioinformatics studies, and precision medicine, as the efficiency of bioinformatics analysis of today's typically very large genomic datasets is essential for clinically safe use. Background of the invention:

[0002] DNA sequence alignment is a central task in genomics to identify similarities and differences between them. The traditional way we align DNA sequences and identify mutations requires significant computational power as well as manual intervention, which can prolong the process. As the amount of genomic data grows rapidly, we urgently need further refinement of how to process this massive information here and now at high speed without compromising accuracy. Although many DNA sequence analysis techniques have benefited from machine learning (ML), these approaches do not fully utilize its potential because they are limited by the hardware on which ML models run.Therefore, there is a need for an intelligent device that combines machine learning models with real-time hardware to improve both the throughput and accuracy of DNA type alignment and decoding.

[0003] DNA sequence alignment is the essential process in the genome if we want to understand the genetic information that guides us and also compare DNA sequences belonging to different organisms, species, or individuals. Good alignment of these sequences is crucial for accurately detecting mutations, such as single nucleotide polymorphisms (SNPs), insertions, deletions, or other genetic variations. These variants play an important role in several fields, such as disease diagnosis, precision medicine, evolutionary biology, and drug discovery. DNA sequence alignment is performed in various ways to compare DNA sequences, and many computational tools and techniques have been developed for this purpose over the years. However, due to the rapid increase in genomic data volume, existing solutions are limited in terms of execution speed and scalability.

[0004] Until now, DNA sequence alignment has involved the use of bioinformatics tools with heuristic or probabilistic techniques to determine an optimal sequence match. One commonly used program is BLAST (Basic Local Alignment Search Tool), which can quickly align sequences. This is because BLAST finds the short, highly scored sequence pairs between a query and reference sequences and then expands these matches to perform alignments. While BLAST has certainly found the optimal solution for many scenarios, scenarios involving large-scale genomic data pose a significant challenge to BLAST's performance when it comes to processing "big data" genomics. Furthermore, due to its heuristic nature, MetaBat must trade accuracy for speed, making it less suitable than other methods when high precision is required (e.g.,clinical diagnostics and personalized medicine).

[0005] Another popular aligner is the Burrows-Wheeler Aligner (BWA), which offers a much more memory-efficient way to map DNA sequences to reference genomes. We prefer BWA because it effectively supports large datasets with the least memory footprint. Its drawback is poor alignment accuracy, particularly for highly repetitive sequences or larger indels. Furthermore, the use of a fixed reference genome in BWA can lead to a bias in the detection of novel genetic variants. This is particularly problematic in clinical and research areas where the identification of novel mutations can have important implications for understanding disease mechanisms or developing treatment strategies.

[0006] While these approaches have been relatively successful, the increasing volume of genomic data due to advances in next-generation sequencing (NGS) has revealed several significant limitations of available alignment tools. A major challenge is the computational cost of aligning large datasets, which may depend in part on the tolerance for false positives. Genomic data is vast, often in the terabyte range (e.g., whole genome sequences), meaning that significant computational resources are required to successfully process such a volume of information. Even tools such as BLAST and BWA, which are quite common in bioinformatics, cannot handle such large data sets at the level expected in a real-time application.This results in long turnaround times for analyzing DNA sequences studied by genomic researchers and clinicians, leading to delays in important discoveries or diagnostic reports.

[0007] Another problem with traditional alignment algorithms is that there has always been a trade-off between speed and accuracy. Worker tools like BLAST use heuristic methods to maximize the speed of sequence alignment and thus achieve the best possible alignment, leading to mutation errors, especially in regions that are similar or highly repetitive. This problem can lead to false positives or negatives, which is particularly problematic in clinical cases where precision is critical. For example, in the case of cancer diagnostics, where the discovery of a single genetic mutation can have profound consequences for the best treatment for a patient, these tools are not accurate enough.

[0008] In addition to speed and accuracy, scalability is a crucial consideration. Continuing this spectrum, the question arises how to handle the alignment of larger and even ever-growing genomic datasets, which in turn necessitates scalability support from various existing short-read aligners. 3-7 However, the bottlenecks typically lie in their inability to process multiple large datasets simultaneously, which significantly slows down data analysis pipelines.

[0009] This is particularly evident in high-throughput sequencing (HTS) technologies used in research, which have the inherent property of generating large amounts of data very quickly, resulting in batch processing. These tools are often not scalable, and large-scale analyses are performed on cloud computing or high-performance computing (HPC) platforms that are out of reach for most researchers. While these infrastructures immediately reduce scaling issues, they are often expensive and cumbersome to implement in most laboratory or clinical settings.

[0010] Current solutions also rely heavily on predefined reference genomes for alignment. BWA and BLAST are good tools for aligning sequences to known reference genomes, but this is difficult for new or highly divergent genomes. This is particularly challenging when studying non-model organisms, for which a complete reference genome may not yet be available or may not be available at all. This poses a problem because using a reference genome means that genetic mapping data can be biased, potentially missing some mutations or misaligning them. Furthermore, it limits the detection power for large insertions, deletions, or inversions, known as structural variants (SVs), which cannot be detected by conventional alignment tools.

[0011] This also means that traditional alignment tools do not incorporate machine learning or new data-driven approaches that could lead to higher accuracy in stream alignments and mutation detection. ML has been heavily invested in other areas of genomics, such as variant detection and gene expression analysis

[36] , but its wider use for sequence alignment applications is quite limited. Machine learning algorithms can learn from thousands of examples provided during training and recognize patterns as they process new cases. This makes them particularly good at detecting rare or complex genetic variants that a classic heuristic-based tool might miss. Nevertheless, the majority of current alignment tools use static algorithms that do not learn from recent data, limiting their growth potential.

[0012] Finally, another issue arises regarding the UI / UX elements of current alignment tools and their accessibility. Furthermore, tools like BLAST or BWA, while very powerful, have a steep learning curve. Conventional tools like feature counting or alignment applications are typically based on a command-line interface, and therefore, the user must have a strong knowledge of bioinformatics to set parameters and interpret results. This is a major obstacle for researchers and clinicians with minimal computer literacy who nevertheless require DNA sequence analysis as part of their work. Furthermore, the lack of real-time feedback in these tools has resulted in users having to wait several hours for feedback, which in turn contributes to slower analysis.

[0013] Therefore, current DNA sequence alignment tools such as BLAST 8 and BWA [9] have played an important role in the development of genomic research; however, they prove inadequate when confronted with the numerous facets associated with modern genomic applications. The increasing volume of genomic data, the demand for higher accuracy and faster results, and challenges such as novel sequences or complex genetic variants that must be processed, all indicate that a new wave of solutions is urgently needed. In this article, we explore how the introduction of machine learning into DNA sequence alignment can be part of a solution to many of these challenges.Using machine learning algorithms that can adapt and improve, you can build systems to align sequences faster and more accurately over time, while also performing genetic mutation detection with greater specificity. Finally, the application of hardware accelerators optimized for the computational aspects required for alignment and mutation detection will play a critical role in scaling the process without compromising accuracy. Summary of the invention:

[0014] The present invention describes an intelligent machine learning-based device for real-time DNA sequence alignment and mutation detection. The device combines custom hardware with machine learning algorithms optimized on a variety of real DNA sequences to solve the problem. FPGA accelerator: The system integrates dedicated processing units and field-programmable gate arrays (FPGAs) that handle computationally intensive sequence alignment tasks, thus achieving unprecedented performance and energy efficiency compared to conventional methods. The device has a key feature: the ability to continuously update its machine learning model, making mutation detection more accurate when processing large amounts of data.The device is multiplexed, allowing it to process many DNA samples simultaneously, making it highly scalable for large genomic datasets; a feature that is particularly advantageous in clinical diagnostics and genome research applications.

[0015] This invention consists of several interconnected components: a DNA sequence alignment processing unit that offers an option for aligning your DNA sequences using machine learning models; a mutation detection module that analyzes aligned sequences and detects genetic variations, etc.; and an input / output interface for loading data with genomic information. Its high-speed memory management system allows the device to quickly manage and transfer data between processing units, making it suitable for efficient operation with large datasets. Furthermore, this system's machine learning model is constantly updated based on feedback from the mutation detection process, further improving its quality and accuracy.

[0016] According to a first aspect, the present invention provides an ML-oriented intelligent device for DNA sequence alignment and mutation detection that is significantly more accurate, faster, and more scalable than existing solutions. This invention is specifically tailored to solve the shortcomings of current DNA alignment tools by combining state-of-the-art machine learning algorithms with dedicated hardware units to maximize the performance of both aspects of DNA alignment and mutation detection. The invention utilizes machine learning to identify complicated patterns in massive genomic datasets, resulting in improved sequence alignment and higher reliability in mutation detection for clinical or research purposes.

[0017] The present invention also provides a device capable of processing large amounts of genomic data in real time, making it ideal for whole-genome sequencing and molecular diagnostics in the era of high-throughput rates. This device is capable of processing multiple DNA samples in parallel without reducing the precision of sequencing alignment. This increases efficiency at every level, resulting in less time spent analyzing large data sets and fast turnaround times in both genetic diagnostics and research.

[0018] The invention also offers a flexible and modifiable solution that can be applied to different DNA sequences or genomic datasets. One goal of the invention is that the device itself can process new DNA sequences, even in situations where a wide range of reference genomes may not yet exist or may be incomplete, thereby reducing the dependence on predefined reference sequences. This is particularly crucial for non-model organisms or for identifying new mutations not found in current genome databases.

[0019] Another goal of the invention is to provide a more user-friendly device that can be easily used by medical professionals and researchers without extensive bioinformatics knowledge. The invention achieves this by providing an intuitive interface with real-time feedback and very few, yet modifiable, parameters, enabling a wider range of users to more effectively utilize DNA sequence alignment and mutation detection in clinical diagnostics and research laboratories.

[0020] The invention generally aims at a cost-effective and less massive system that consumes relatively low power and can run continuously for extended periods in high-performance environments without overheating or requiring complex cooling mechanisms. The device's small form factor, with state-of-the-art power management and thermal cooling design, enables use in a variety of applications and meets the required performance. SHORT DESCRIPTION OF THE FIGURE

[0021] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a machine learning-based intelligent device for DNA sequence alignment and mutation detection.

[0022] In the Fig. A block diagram of a machine learning-based intelligent device for DNA sequence alignment and mutation detection is shown. The system 100 includes: a sequence alignment processing unit 102 comprising a plurality of field-programmable gate arrays (FPGAs) 102(a), each FPGA configured to execute pre-trained neural networks for DNA sequence pattern recognition, the sequence alignment processing unit capable of performing real-time alignment of a target DNA sequence against a reference genome;a mutation detection unit 104 operatively connected to the sequence alignment processing unit, wherein the mutation detection unit is configured to analyze aligned sequences to identify genetic variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations. The mutation detection unit uses a machine learning-based model to detect the genetic variations with a confidence score for each detected variation; an input / output interface 106 for receiving DNA sequence data from external data sources, wherein the input / output interface comprises a high-speed data transfer protocol to enable rapid loading of genomic datasets into the system;a memory management unit 108 operatively connected to the sequence alignment processing unit and the mutation detection unit, the memory management unit configured to store and retrieve DNA sequence data during alignment and mutation detection processes and to enable concurrent read / write operations for parallel processing of multiple DNA samples; a machine learning model execution unit 110 configured to dynamically update the machine learning models 110(a) used in the sequence alignment processing unit and the mutation detection unit based on feedback from the mutation detection process, thereby improving the accuracy of both sequence alignment and mutation detection over time;and a power management system 112 configured to monitor and regulate the power supplied to the sequence alignment processing unit, the mutation detection unit, and the machine learning model execution unit based on the computational load, wherein the power management system optimizes power efficiency while maintaining system performance during high-intensity processing tasks.;

[0023] In one embodiment, each field-programmable gate array (FPGA) 102(a) within the sequence alignment processing unit 102 is individually programmable to process different types of DNA sequences, including highly repetitive regions, by dynamically adjusting the neural network configurations to optimize the accuracy of the sequence alignment with respect to specific sequence properties.

[0024] In one embodiment, the machine learning-based model 110(a) in the mutation detection unit is configured to perform a multi-step analysis of genetic variations, the first step comprising identifying potential mutation sites by comparing aligned sequences to the reference genome, and the second step comprising validating the detected mutations using a deep learning algorithm trained on genomic datasets with known mutations, thereby minimizing false positives and improving the accuracy of mutation detection.

[0025] In one embodiment, the input / output interface 106 further includes a wireless communication module that enables the direct transfer of DNA sequence data from external sequencing machines or laboratory information systems (LIS), wherein the wireless communication module supports multiple protocols including Wi-Fi and Bluetooth for seamless integration with existing genomic data infrastructures.

[0026] In one embodiment, memory management unit 108 is configured with a high-throughput parallel access architecture that enables the simultaneous processing of multiple DNA samples by allocating different memory blocks to each sample. This ensures that processing one data set does not interfere with or delay the processing of another data set.

[0027] In one embodiment, the machine learning model execution unit 110 is further configured to optimize the execution of the machine learning models based on the complexity of the DNA sequences being processed, with more computationally intensive sequences, such as those with structural variations or large insertions, triggering the use of deeper neural network architectures, while less complex sequences use simpler models to maximize system performance.

[0028] In one embodiment, the power management system 112 includes real-time monitoring circuitry that adjusts the voltage and frequency supplied to each component based on the intensity of the computational tasks, and wherein the system enters a power-saving mode during idle times, thereby reducing overall power consumption without impacting system responsiveness during DNA analysis.

[0029] In one embodiment, the sequence alignment processing unit 102 includes a fault-tolerant architecture that enables real-time detection and correction of errors in FPGA operation during DNA alignment tasks, thereby ensuring system reliability and minimizing the risk of data corruption during high-throughput processing of large genomic datasets.

[0030] In one embodiment, the mutation detection unit 104 further comprises a mutation classification module, wherein the classification module uses machine learning techniques to categorize detected genetic variations into different mutation types, including synonymous and non-synonymous mutations, based on the potential impact of each mutation on gene expression or protein function.

[0031] In one embodiment, the machine learning model execution unit 110 is capable of integrating external genomic databases into the model training process, allowing the system's mutation detection accuracy to be continuously improved as new mutation data becomes available, with the system regularly updating its machine learning models by retrieving and processing data from cloud-based genomic resources.

[0032] Here, we present an advanced DNA sequence alignment and mutation detection system that integrates state-of-the-art machine learning algorithms with specialized hardware for fast and accurate genome analysis. At the heart of the system is a sequence alignment processing unit that uses field-programmable gate arrays (FPGAs) programmed to run pre-trained neural networks. These neural networks find patterns in DNA sequences and compare them to the reference genome. The alignment algorithm used by the system works by splitting the target DNA sequence into smaller fragments and then aligning them to the reference genome using a hierarchical alignment pattern. This allows the system to perform both global and local sequence alignment according to the complexity of the input dataset.

[0033] These FPGAs are configured to allow the neural network's parameters to be adjusted on the fly based on features in the input sequence. For example, regions containing highly repetitive DNA elements or sequences with complex structural variants use deeper layers of the neural network to capture long-range dependencies and subtle variations in the input sequences. In the less complex parts of the genome, we use shallower layers in the neural network to ensure computation times remain low without losing accuracy during alignment. In this way, parallel processing ensures a streamlined alignment process, as multiple FPGAs can work on different regions of the sequence simultaneously ( Fig. , which significantly reduces the time to complete an alignment task.

[0034] The mutation detection unit analyzes the aligned sequences to detect genetic variations after alignment. The algorithm detects mutations using a machine learning model trained on large genomic datasets containing various known mutation types. The model then proceeds in several steps: First, it scans the aligned sequence and locates possible mutation sites by aligning target sequences between reference genomes. In this step, the algorithm then detects areas where differences occur between alignments of paired sequences (e.g., mismatches or insertions and deletions).

[0035] The mutations are then subjected to in-depth analysis for these sites, with the algorithm next performing a machine learning-based validation process. This second validation step consists of running the identified variants through a secondary neural network trained on patterns indicative of specific mutation types, such as SNPs, indels, and larger structural rearrangements. Each detected variation is assigned a confidence score by the deep learning model—this estimates the probability that a putative mutation is not due to sequencing errors or alignment artifacts. This multi-step validation process results in a low rate of false positives, making this mutation detection system highly reliable for precise clinical applications.

[0036] The mutation detection unit also provides a classification algorithm that classifies detected mutations according to their potential effects. For example, the system can distinguish between synonymous and non-synonymous mutants (synonymous mutations of gene sequences do not change which protein they code for, whereas non-synonymous mutations actually result in a different amino acid sequence). It can also identify compound heterozygous mutations, which involve two different affected loci on both alleles of a gene and are important for many recessive hereditary diseases. This is achieved by heliotyping the aligned DNA sequences, detecting mutations that have occurred on each parental allele.

[0037] The unit that executes the machine learning models available to the system is crucial for resolving performance issues that arise during sequence alignment and mutation detection. The implementation of the machine learning models used in the alignment and mutation detection units is continuously updated by the execution unit. The system then obtains new genomic data, either from external databases or directly from user-provided datasets, and incorporates it into the iterative training process. This allows the system to learn from emerging mutations and refine its detection level over time. The model execution unit dynamically selects the most appropriate model for a given task, depending on the complexity of the DNA sequence being processed. For example, when processing sequences with significant structural variability (e.g.,connections) are analyzed, the unit opts to use deeper convolutional or more sophisticated neural network architectures; on the contrary, it uses lighter models, which are simpler solutions that can process less structured data more quickly while minimizing computational effort.

[0038] The system enables users to load data at high speed by implementing an input / output interface for loading large DNA sequence datasets. Wired and wireless connection options (USB, Ethernet ports) ensure easy integration into laboratory information systems (LIS) or genomic data storage. A memory management unit stores and delivers DNA sequence data in an ordered sequence during processing. The memory contains multiple blocks dedicated to use with only one sample or part of the sequence, enabling parallel processing without bottlenecks. This parallelization is crucial for the efficient processing of large data sets, such as those found in high-throughput genomic applications.

[0039] It also features a powerful power management system that manages power well wherever it's consumed. The power management system continuously monitors the resource utilization of all FGPAs and other processing parts, monitoring all conditions in real time to ensure an appropriate supply. When switching to low-computational load times, such as when the system is idle or when processing simple sequences, the power management system reduces the voltage and frequency supplied to the components. However, in high-intensity scenarios (such as when attempting to analyze a series of complex genomic reads), the system increases power to keep everything running.This approach not only reduces energy consumption through better power management, but also helps prevent overheating that could impair system functionality during activities that require constant high-performance processing.

[0040] The system features a touchscreen interface that allows users to set sequence alignment parameters such as the reference genome and genomic regions of interest and monitor progress in real time against the aligned data. It is configured with a simple interface for user convenience, allowing even non-experts to use it efficiently. Furthermore, the interface tracks confidence values ​​and classifies each detected mutation for unambiguous interpretation by clinicians or researchers.

[0041] The system also combines read / write copies of all user data, which are always encrypted to the highest possible level during transmission and storage within the platform itself. A secure data encryption module is located within the input / output interface, so if you want (or need) to transfer your DNA sequence information, this sensitive material is protected from misuse and can only be accessed by authorized personnel. This is especially critical in clinical settings where patient privacy and data protection are paramount.

[0042] We therefore conclude that the system represents a remarkable innovation for DNA sequence alignment mapping and mutation detection due to its integration of machine learning algorithms with dedicated hardware components, as well as optimal performance and memory management mechanisms. It offers customers unmatched accuracy, speed, and scalability and is a must-have in genetic diagnostics, precision medicine, including complex diseases such as cardiovascular disease, which affects nearly 1.5 million Americans each year, and genomic research facilities.

[0043] It offers an all-in-one system for improving the alignment accuracy and efficiency of mutation detection of DNA sequences through machine learning. At the heart of the device is a sequence alignment processing unit consisting of multiple FPGAs. These FPGAs are preconfigured with custom-developed neural networks that have already been trained on the patterns of DNA sequences and their correspondence to a reference genome. Collaborative processing by Density IOS: Despite huge genomic datasets, the system is capable of realigning DNA sequences at any time. Connected to the mutation detection unit is a hardware component called the sequence alignment processing unit, which checks these aligned sequences for consistencies in genetic variations.

[0044] The Mutation Detection Unit uses machine learning models updated with genomic data from new species to increase detection accuracy. The module, known as the Unit, can be used to discover SNPs, insertions and deletions (InDels), and structural variants by detecting local misalignments when comparing aligned sequences with known reference sequences. When a mutation is detected, the module assigns a confidence score to the variant, indicating how certain this change is based on its training dataset. By continuously learning in this way, the system can improve its mutation detection capability over time and keep pace with advances in genetic research.

[0045] The large datasets are stored on a high-speed input / output interface, allowing for rapid loading of DNA sequences into the system. The interface offers multiple connection options, including USB, Ethernet, and Wi-Fi, for transferring data directly from external storage devices or network servers. This system also utilizes a memory management unit to more effectively store and retrieve DNA sequences during the alignment process, enabling optimized mutation detection. This allows all incoming data to be processed more quickly, helping you avoid problems and increase throughput.

[0046] Machine Learning Algo Unit, which supports model execution to train and run machine learning algorithms on your device. It consists of a processing system that regularly updates the model based on new genomic data, improving prediction accuracy for both sequence alignment and mutation detection. Finally, the execution unit provides performance optimization for a machine learning model; it balances speed and accuracy with the complexity of the input data. The system can be operated by users via a touchscreen interface, including setting sequence alignment parameters, starting DNA analysis, and reviewing mutation detection results in real time. It provides detailed reports summarizing all analysis results with alignment score, mutation type, and confidence intervals.

[0047] The device's physical structure was carefully engineered to facilitate heat dissipation and power management, preventing overheating during extended use. To complement the tiny chip, Kioxia also developed a small M.2 drive, fitting all of these components into a casing made of thermally conductive materials for cooling. However, a chest-like arrangement of heatsinks and fans serves to keep the system operating at safe parameters during periods of high computing activity. It also features an adaptive power management system that optimizes the power supply to its FPGAs and other components based on the processing load, thus conserving energy when it's not fully utilized.

[0048] In summary, this intelligent device, developed using machine learning, offers a comprehensive solution for DNA sequence alignment and mutation detection. The product seamlessly combines state-of-the-art machine learning models with customized hardware for fast and precise genome analysis. It features a scalable architecture for processing large datasets and is suitable for applications such as genetic diagnostics, precision medicine, and bioinformatics research.

[0049] The unit is in Fig.It is presented as a compact, portable device encased in rugged enclosures made of thermally conductive materials to improve heat dissipation. A system of vents and heat sinks was designed for the enclosure to regulate the temperature and keep it within a reasonable range, allowing processing-intensive tasks to run 24 / 7. The interior of the enclosure consists of multiple interconnected FPGAs that run machine learning models to perform DNA sequence alignment workloads in real time for a dedicated processor designed specifically for this purpose. This system is built using FPGAs organized to provide an efficient parallel computing platform capable of easily processing large amounts of genomic data.

[0050] Within this architecture, a mutation detection unit is combined with the sequence alignment processing units to analyze aligned DNA sequences for mutations. The system uses machine learning models trained to identify a broad range of genetic variants with very high sensitivity. The system also features a high-speed I / O interface that allows users to directly load large DNA sequence datasets via USB, Ethernet, and Wi-Fi. The device features an integrated touchscreen interface for user engagement, simplifying setup and monitoring of DNA analysis in real time.

[0051] A power management system dynamically controls the amount of power supplied to each stage based on the number of computational cycles being processed. This mechanism allows for appropriate power distribution among the FPGAs and other components, ensuring no extra energy is wasted. This not only saves electricity costs but also saves on cooling systems. Cooling fans and heat sinks keep the device cool even under heavy workloads, allowing for extended operation without thermal throttling. The compact and efficient design of this box, combined with the flexibility to operate on battery power or via an external USB charger, makes it well-suited for use in clinical and research settings where space and power savings are critical.

[0052] The invention relates to the field of bioinformatics and genomics, in particular to DNA sequence alignment (e.g., indexing) and mutation detection in genetic data. The system combines machine learning algorithms with domain-specific hardware accelerators to improve the speed, accuracy, and scalability of genomic data analysis. The present invention is useful in areas where rapid and accurate detection of genetic mutations is required, such as: genetic diagnostics; precision medicine; evolutionary biology; and high-throughput genome research. The system provides solutions to problems associated with high-performance computing and large-scale genome data problems in a way that improves both the computational performance of sequence alignment and the prediction accuracy for mutation detection. REFERENCES 100 A machine learning-based intelligent device for DNA sequence alignment and mutation detection. 102 Sequence alignment processing unit 102a Field Programmable Gate Arrays (Fpgas) 104 Mutation detection unit 106 An input / output interface 108 A memory management unit 110 Execution Unit for Machine Learning Model 110a Machine Learning Models 112 Energy management system

Claims

[1] A machine learning-based system for DNA sequence alignment and mutation detection, consisting of: a sequence alignment processing unit comprising a plurality of field-programmable gate arrays (FPGAs), each FPGA configured to execute pre-trained neural networks for recognizing DNA sequence patterns, the sequence alignment processing unit operable to perform real-time alignment of a target DNA sequence against a reference genome; a mutation detection unit operatively connected to the sequence alignment processing unit, wherein the mutation detection unit is configured to analyze aligned sequences to identify genetic variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variations, wherein the mutation detection unit uses a machine learning-based model to detect the genetic variations with a confidence value for each detected variation, an input / output interface suitable for receiving DNA sequence data from external data sources, the input / output interface comprising a high-speed data transfer protocol to enable rapid loading of genomic datasets into the system; a memory management unit operatively connected to the sequence alignment processing unit and the mutation detection unit, the memory management unit configured to store and retrieve DNA sequence data during alignment and mutation detection processes and to enable concurrent read / write operations for parallel processing of multiple DNA samples; a machine learning execution unit configured to dynamically update the machine learning models used in the sequence alignment processing unit and the mutation detection unit based on feedback from the mutation detection process, thereby improving the accuracy of both the sequence alignment and the mutation detection over time; and a power management system configured to monitor and regulate the performance of the sequence alignment processing unit, the mutation detection unit, and the machine learning execution unit based on the computational load, wherein the power management system optimizes power efficiency while maintaining system performance during high-intensity processing tasks. [2] The system of claim 1, wherein each field-programmable gate array (FPGA) within the sequence alignment processing unit is individually programmable to process different types of DNA sequences, including highly repetitive regions, by dynamically adjusting the neural network configurations to optimize the accuracy of the sequence alignment with respect to specific sequence properties. [3] The system of claim 1, wherein the machine learning-based model in the mutation detection unit is configured to perform a multi-step analysis of genetic variations, the first step comprising identifying potential mutation sites by comparing aligned sequences with the reference genome, and the second step comprising validating the detected mutations using a deep learning algorithm trained on genomic datasets with known mutations, thereby minimizing false positives and improving the accuracy of mutation detection. [4] The system of claim 1, wherein the input / output interface further comprises a wireless communication module that enables direct transfer of DNA sequence data from external sequencing machines or laboratory information systems (LIS), the wireless communication module supporting multiple protocols including Wi-Fi and Bluetooth for seamless integration with existing genomic data infrastructures. [5] The system of claim 1, wherein the memory management unit is configured with a high-throughput parallel access architecture that enables the simultaneous processing of multiple DNA samples by allocating different memory blocks to each sample, thereby ensuring that the processing of one data set does not interfere with or delay the processing of another data set. [6] The system of claim 1, wherein the machine learning model execution unit is further configured to optimize the execution of the machine learning models based on the complexity of the DNA sequences being processed, with more computationally intensive sequences, such as those with structural variations or large insertions, triggering the use of deeper neural network architectures, while less complex sequences use simpler models to maximize system performance. [7] The system of claim 1, wherein the power management system includes a real-time monitoring circuit that adjusts the voltage and frequency supplied to each component based on the intensity of the computational tasks, and wherein the system enters a power saving mode during idle times, thereby reducing overall power consumption without affecting system responsiveness during DNA analysis. [8] The system of claim 1, wherein the sequence alignment processing unit comprises a fault-tolerant architecture that enables real-time detection and correction of errors in FPGA operation during DNA alignment tasks, thereby ensuring system reliability and minimizing the risk of data corruption during high-throughput processing of large genomic datasets. [9] The system of claim 1, wherein the mutation detection unit further comprises a mutation classification module, the classification module using machine learning techniques to categorize detected genetic variations into different mutation types, including synonymous and non-synonymous mutations, based on the potential impact of each mutation on gene expression or protein function. [10] The system of claim 1, wherein the machine learning model execution unit is capable of integrating external genomic databases into the model training process, whereby the mutation detection accuracy of the system can be continuously improved as new mutation data becomes available, the system regularly updating its machine learning models by retrieving and processing data from cloud-based genomic resources.

Images