Gene sequencing system and method for multi-gene repetitive sequence

Abstract

The invention discloses a gene sequencing system and method for polygene repetitive sequences, and belongs to the technical field of gene sequencing. The gene sequencing system comprises a high-throughput sequencing module, a nested multiplex PCR amplification module, a data processing module, a central processing module, an in-storage calculation module, a storage calculation unit, a process control module, a target function module and an anomaly detection module. The problems that in the prior art, the gene sequencing efficiency is low, and particularly, multi-gene repetitive sequence gene sequencing cannot be met are solved. Through a parallel processing mechanism, the data handling overhead is reduced, special hardware acceleration, efficient process management, anomaly detection and correction and flexible programmability are realized, and the processing efficiency and accuracy are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gene sequencing, and in particular to a gene sequencing system and method for multi-gene repeat sequences. Background Art

[0002] In the field of gene sequencing, especially when it comes to the analysis of multi-gene repetitive sequences, existing technologies face many challenges. Traditional gene sequencing processes usually rely on a single central processor for control, resulting in low processing efficiency and a long time for single gene sequencing. In addition, with the continuous increase in the amount of sequencing data, the handling and processing of data has become a bottleneck, and frequent data movement increases processing delays and energy consumption. At the same time, abnormal amplification products produced during PCR amplification often affect the accuracy of sequencing results, and the existing technologies have limited means of detecting and correcting these abnormal products, which can easily introduce deviations. In addition, the existing process control mechanism is not flexible enough, and it is difficult to optimize and adjust according to different sequencing requirements, resulting in a complex and difficult to manage processing flow. Summary of the invention

[0003] The purpose of the present invention is to provide a gene sequencing system and method for multi-gene repetitive sequences, which significantly improves processing efficiency and accuracy through parallel processing mechanism, reduced data handling overhead, dedicated hardware acceleration, efficient process management, anomaly detection and correction, and flexible programmability. The dedicated hardware acceleration improves the algorithm execution efficiency; efficient process management reduces the time overhead of system control; anomaly detection and correction ensures data quality; flexible programmability allows the process to be adjusted according to different needs. These advantages together ensure the efficiency, accuracy and flexibility of multi-gene repetitive sequence processing and solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A gene sequencing system for multi-gene repeat sequences, comprising: High-throughput sequencing module, used to obtain raw sequencing data of target DNA fragments; A nested multiplex PCR amplification module for specifically amplifying repetitive sequences within a target region, wherein the module reduces nonspecific amplification by designing a specific primer combination; Data processing module: The data processing module is based on machine learning algorithms and is used to automatically identify and correct sequencing errors caused by repeated sequences; The central processing module is used to call and execute the central sequencing operator; In-memory computing module, used to cache genetic data and execute in-memory sequencing operators; A storage computing unit, used to call and execute the storage sequencing operator; A process control module is used to receive process management information and execute different steps in the gene sequencing process according to management commands; The objective function module is used to obtain independent components and noise components through ICA decomposition, and to construct an objective function according to the fluorescence intensity distribution to obtain the trend term component of the PCR reaction; The abnormal detection module is used to detect abnormal amplification cycles, and obtain and delete abnormally amplified DNA samples according to the abnormal amplification cycles.

[0005] Preferably, the nested multiplex PCR amplification module comprises a set of optimized primer pairs and a customized thermal cycle program, each of the primer pairs being designed to match different variants of the target repetitive sequence; the thermal cycle program performs PCR reactions under multiple temperature gradients by dynamically adjusting the extension time and annealing temperature.

[0006] Preferably, the data processing module includes a deep learning module and an algorithm module. The deep learning module is based on a convolutional neural network and a long short-term memory network, and is used to learn the pattern of repetitive sequences from large-scale sequencing data and distinguish between real variation signals and technical errors; the algorithm module uses statistical methods to detect and correct read segment alignment errors caused by repetitive sequences, identifies potential error sites by calculating the Hamming distance between adjacent read segments, and uses Bayesian statistical methods for probability correction, thereby improving the accuracy of sequencing results.

[0007] Preferably, the central processing module includes: A general computing unit is used to execute the algorithm part executed by the non-accelerator array unit, split the instructions of the dynamic programming algorithm, and distribute the split instruction information to the instruction parsing unit; An instruction parsing unit, used to parse instruction information, distribute the parsed instruction information to the accelerator array unit, and support efficient interaction between the general computing unit and the accelerator array unit; The accelerator array unit is used to perform accelerated calculations based on the results of the analysis; it supports computing tasks of different granularities and flexibly adjusts the allocation of computing resources; Configure storage units to store necessary computing data such as reference sequences, read sequences, and result sequences; provide parameters required for the dynamic programming algorithm; and supply required data to general computing units and accelerator array units in a timely manner.

[0008] Preferably, the in-memory computing module includes: A main memory unit, used to cache gene data in the gene sequencing process; The logic computing unit is used to call the gene data in the main memory module, call the preset in-memory sequencing operator, and execute the in-memory sequencing operator to process the called gene data.

[0009] Preferably, the in-memory computing module includes: An operating parameter monitoring module, used for real-time monitoring of the operating parameters of the main memory unit; wherein the operating parameters include main memory capacity, main memory capacity change rate, storage bandwidth and main memory access speed; A change rate comparison module, used for comparing the main memory capacity change rate with a preset main memory capacity change rate threshold; A first data retrieval module, configured to retrieve the current main memory capacity of the main memory unit when the main memory capacity change rate exceeds a preset main memory capacity change rate threshold; A capacity stability evaluation coefficient acquisition module, used to acquire a capacity stability evaluation coefficient by using the main memory capacity change rate and the current main memory capacity of the main memory unit; The capacity stability evaluation coefficient is obtained by the following formula:

[0010] Where S represents the capacity stability evaluation coefficient; n represents the number of unit times of the main memory unit operation, and the unit time is 1s; C d Indicates the current main memory capacity of the main memory unit; C y Indicates the maximum remaining main memory capacity allowed for the normal operation of the main memory unit cache; C i represents the main memory capacity corresponding to the main memory unit in the i-th unit time; R i represents the rate of change of main memory capacity per unit time; s i represents the proportion of redundant data cached in the main memory unit corresponding to the i-th unit time; α represents the first adjustment coefficient, which is used to control the strength of the relationship between the main memory capacity of the main memory unit and the main memory capacity change rate, and the value range of the first adjustment coefficient is 2.31-4.68; β represents the second adjustment coefficient, which controls the negative impact of the main memory capacity change rate on the cache stability of the main memory unit, and the value range of the second adjustment coefficient is 0.18-0.87; R b R represents the standard deviation of the rate of change of main memory capacity corresponding to n unit time; fmax It represents the maximum value of the change rate of main memory capacity between two adjacent units of time in n units of time; A coefficient comparison module, used for comparing the capacity stability evaluation coefficient with a preset coefficient threshold; The abnormality determination module is used to determine whether the main memory unit has an operation abnormality by using the storage bandwidth and the main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold.

[0011] Preferably, the abnormality determination module includes: A second data retrieval module, configured to retrieve storage bandwidth and main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold; An operation evaluation coefficient acquisition module, used to acquire the operation evaluation coefficient of the main memory unit by using the storage bandwidth and the main memory access speed; The operation evaluation coefficient of the main memory unit is obtained by the following formula:

[0012] Wherein, G represents the operation evaluation coefficient of the main memory unit; n represents the number of unit time of the main memory unit operation, and the unit time is 1s; B i R represents the storage bandwidth corresponding to the main memory unit in the i-th unit time; i V represents the change rate of main memory capacity per unit time i; i represents the main memory access speed corresponding to the main memory unit in the i-th unit time; P Bi represents the storage bandwidth change rate corresponding to the main memory unit in the i-th unit time; P Vi represents the change rate of the main memory access speed corresponding to the main memory unit in the i-th unit time; A comprehensive operation evaluation coefficient acquisition module, used to integrate the operation evaluation coefficient of the main storage unit with the capacity stability evaluation coefficient to acquire the comprehensive operation evaluation coefficient of the main storage unit; The comprehensive operation evaluation coefficient is obtained by the following formula:

[0013] Wherein, H represents the comprehensive operation evaluation coefficient; w 01 and w 02 Indicates the weight value corresponding to the capacity stability evaluation coefficient and the operation evaluation coefficient of the main memory unit; S represents the capacity stability evaluation coefficient; G represents the operation evaluation coefficient of the main memory unit; A comprehensive coefficient comparison module, used for comparing the comprehensive operation evaluation coefficient with a preset comprehensive coefficient threshold; The abnormality determination and alarm module is used to determine that the operation of the main memory unit is abnormal and issue an abnormality alarm when the comprehensive operation evaluation coefficient is lower than a preset comprehensive coefficient threshold.

[0014] Preferably, the objective function module is further used for: The sine wave and its frequency and phase in the residual curve are obtained by short-time Fourier transform; Obtain component estimation factors for each type of phase, obtain the optimal phase and a number of corresponding error sinusoidal waves, and form error components; Get the overall Gaussian kurtosis of the error component and construct the objective function based on the Gaussian kurtosis.

[0015] Preferably, the anomaly detection module includes: An isolated forest tree construction unit is used to construct an isolated forest tree, identify abnormal amplification cycles, and recursively divide all fluorescence intensity amplitudes into left and right subsets until further division is impossible; An outlier calculation unit is used to calculate the number of paths of each node and use it as an outlier. The node with fewer paths is more likely to represent an abnormal amplification cycle. The abnormal threshold setting unit is used to set the threshold of the abnormal score to identify the abnormal amplification cycle, preset an abnormal threshold, and perform linear normalization processing on the abnormal value provided by the abnormal value calculation unit to obtain the abnormal score. If the abnormal score of a certain amplification cycle is lower than the threshold, it is identified as an abnormal amplification cycle; A differential image analysis unit is used to analyze abnormal amplification cycles by using a frame difference method, using a real-time fluorescence PCR instrument to capture cumulative images in each amplification cycle, and to differentiate the cumulative image corresponding to the abnormal amplification cycle from the cumulative image of the previous cycle by using the frame difference method to identify abnormally amplified DNA samples; The abnormal sample deletion unit is used to delete abnormally amplified DNA samples according to the abnormally amplified sample information provided by the differential image analysis unit and delete these samples.

[0016] A gene sequencing method for multi-gene repetitive sequences is implemented based on a gene sequencing system for multi-gene repetitive sequences, comprising the following steps: Step 1: Use a high-throughput sequencing module to obtain raw sequencing data of the target DNA fragment; Step 2: amplify the target repetitive sequence using a nested multiplex PCR amplification module; Step 3: Analyze sequencing data using a machine learning-based data processing module to identify and correct sequencing errors; Step 4: Processing the gene data in parallel through the central processing unit, the in-memory computing unit and the storage computing unit; Step 5: Apply the objective function module to detect and correct abnormal amplification; Step 6: Use the anomaly detection module to obtain and delete abnormally amplified DNA samples; Step 7: Compare the corrected sequencing results with the known reference genome to determine the specific location and variation of the repeated sequences.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention improves the processing efficiency of the gene sequencing process through the parallel processing mechanism of the central processing unit, the in-memory computing unit and the storage computing unit. This parallel processing capability helps to speed up the sequencing of multi-gene repetitive sequences when processing large amounts of data.

[0018] 2. The present invention reduces the distance and time cost of data transportation through the design of the in-memory computing module and the storage computing unit, and improves the performance of data processing. In particular, for data that needs to be accessed frequently, the in-memory computing module can realize fast data processing and avoid the delay caused by multiple data readings.

[0019] 3. The present invention provides hardware acceleration for specific algorithms in gene sequencing through the use of accelerator array modules and customized computing function modules, thereby improving the efficiency of algorithm execution, and can significantly reduce the calculation time when processing multi-gene repetitive sequences.

[0020] 4. The anomaly detection module in the present invention can effectively identify and correct anomalies in the PCR amplification process to ensure the quality of sequencing data. In addition, the anomaly detection module can also obtain and delete abnormally amplified DNA samples to prevent these samples from interfering with subsequent analysis.

[0021] 5. The present invention uses programmable gene sequencing process control, so that the gene sequencing process can be adjusted according to different types of multi-gene repeat sequences, increasing the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the data flow structure of the present invention; Figure 2 It is a schematic diagram of the control flow structure of the present invention; Figure 3 It is a schematic diagram of the information flow structure of the present invention; Figure 4 The flowchart of the sequencing system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In order to solve the problem of low efficiency of gene sequencing in existing technologies, especially the inability to meet the needs of gene sequencing of multiple gene repetitive sequences, please refer to Figure 1-4 , this embodiment provides the following technical solutions: A gene sequencing system for multi-gene repeat sequences, comprising: High-throughput sequencing module, used to obtain raw sequencing data of target DNA fragments; A nested multiplex PCR amplification module for specifically amplifying repetitive sequences within a target region, wherein the module reduces nonspecific amplification by designing a specific primer combination; Data processing module: The data processing module is based on machine learning algorithms and is used to automatically identify and correct sequencing errors caused by repeated sequences; The central processing module is used to call and execute the central sequencing operator; In-memory computing module, used to cache genetic data and execute in-memory sequencing operators; A storage computing unit, used to call and execute the storage sequencing operator; A process control module is used to receive process management information and execute different steps in the gene sequencing process according to management commands; The objective function module is used to obtain independent components and noise components through ICA decomposition, and to construct an objective function according to the fluorescence intensity distribution to obtain the trend term component of the PCR reaction; The abnormal detection module is used to detect abnormal amplification cycles, and obtain and delete abnormally amplified DNA samples according to the abnormal amplification cycles.

[0025] The nested multiplex PCR amplification module includes a set of optimized primer pairs and a customized thermal cycle program, each of which is designed to match different variants of the target repetitive sequence to improve amplification efficiency and specificity; the thermal cycle program dynamically adjusts the extension time and annealing temperature to perform PCR reactions under multiple temperature gradients to improve the amplification efficiency of repetitive sequences in complex templates.

[0026] The operation flow of the primer pair is as follows: Use the bioinformatics tool Primer3 to design a pair of primers based on the sequence information of the target region. The primer length is usually between 18 and 30 bases, and the Tm value is approximately between 55 and 65°C. Ensure the specificity and stability of the primers by adjusting parameters such as primer length, GC content, and Tm value. Use the BLAST tool to check the possibility of primer binding to non-target regions to reduce nonspecific amplification. Set up positive and negative controls to observe whether there are nonspecific amplification bands, and select primer pairs with no nonspecific amplification bands and high amplification efficiency. Perform PCR amplification experiments by gradient dilution of the primer solution to find the optimal concentration, which ranges from 0.2 μM to 1.3 μM.

[0027] The operation process of the thermal cycle program includes the initial denaturation process, the cyclic denaturation process, the annealing and extension process, and the cooling process. During the operation of the thermal cycle program, a known amplification product is set as a positive control, and a reaction without template DNA is set as a negative control. The PCR product is added to 1% agarose gel, and the electrophoresis analysis is performed to observe whether there is a band of the expected size. The DNA concentration is measured using Qubit to ensure that the concentration of the amplification product meets the requirements and better evaluate the amplification efficiency.

[0028] A set of optimized primer pairs and a customized thermal cycle program work together to significantly improve the amplification efficiency and specificity of multi-gene repetitive sequences through specific primer design and optimized PCR conditions, reduce non-specific amplification products, and ensure the accuracy of subsequent sequencing analysis.

[0029] The data processing module includes a deep learning module and an algorithm module. The deep learning module is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM), and is used to learn the pattern of repetitive sequences from large-scale sequencing data and distinguish between real variation signals and technical errors. The algorithm module uses statistical methods to detect and correct read segment alignment errors caused by repetitive sequences, identifies potential error sites by calculating the Hamming distance between adjacent read segments, and uses Bayesian statistical methods for probability correction, thereby improving the accuracy of sequencing results.

[0030] The central processing module includes: A general computing unit is used to execute the algorithm part executed by the non-accelerator array unit, split the instructions of the dynamic programming algorithm, and distribute the split instruction information to the instruction parsing unit; An instruction parsing unit is used to parse instruction information, distribute the parsed instruction information to the accelerator array unit, and support efficient interaction between the general computing unit and the accelerator array unit to ensure the correct execution of instructions; The accelerator array unit is used to perform accelerated calculations based on the results of the analysis, improving the execution efficiency of the dynamic programming algorithm; it supports computing tasks of different granularities and flexibly adjusts the allocation of computing resources; Configure storage units to store necessary computing data such as reference sequences, read sequences, and result sequences; provide the parameters required by the dynamic programming algorithm to ensure the correctness of the calculation; and supply the required data to the general computing unit and accelerator array unit in a timely manner.

[0031] The central processing module achieves efficient execution of algorithms and effective management of data through the collaborative work between general computing units, instruction parsing units, accelerator array units and configuration storage units, ensuring that key steps in the gene sequencing process can be completed quickly and accurately.

[0032] Specifically, the in-memory computing module includes: A main memory unit is used to cache gene data in the gene sequencing process, and uses a volatile memory device such as SDRAM to temporarily store intermediate data used in the gene sequencing process; The logic computing unit is used to call the gene data in the main memory module, call the preset in-memory sequencing operator, and execute the in-memory sequencing operator to process the called gene data; the operator type includes FM index operator or Hash index operator, which is used to process intermediate data.

[0033] Specifically, the in-memory computing module includes: An operating parameter monitoring module, used for real-time monitoring of the operating parameters of the main memory unit; wherein the operating parameters include main memory capacity, main memory capacity change rate, storage bandwidth and main memory access speed; A change rate comparison module, used for comparing the main memory capacity change rate with a preset main memory capacity change rate threshold; A first data retrieval module, configured to retrieve the current main memory capacity of the main memory unit when the main memory capacity change rate exceeds a preset main memory capacity change rate threshold; A capacity stability evaluation coefficient acquisition module, used to acquire a capacity stability evaluation coefficient by using the main memory capacity change rate and the current main memory capacity of the main memory unit; The capacity stability evaluation coefficient is obtained by the following formula:

[0034] Where S represents the capacity stability evaluation coefficient; n represents the number of unit times of the main memory unit operation, and the unit time is 1s; C d Indicates the current main memory capacity of the main memory unit; C y Indicates the maximum remaining main memory capacity allowed for the normal operation of the main memory unit cache; C i represents the main memory capacity corresponding to the main memory unit in the i-th unit time; R i represents the rate of change of main memory capacity per unit time; s i represents the proportion of redundant data cached in the main memory unit corresponding to the i-th unit time; α represents the first adjustment coefficient, which is used to control the strength of the relationship between the main memory capacity of the main memory unit and the main memory capacity change rate, and the value range of the first adjustment coefficient is 2.31-4.68; β represents the second adjustment coefficient, which controls the negative impact of the main memory capacity change rate on the cache stability of the main memory unit, and the value range of the second adjustment coefficient is 0.18-0.87; R b R represents the standard deviation of the rate of change of main memory capacity corresponding to n unit time; fmaxIt represents the maximum value of the change rate of main memory capacity between two adjacent units of time in n units of time; A coefficient comparison module, used for comparing the capacity stability evaluation coefficient with a preset coefficient threshold; The abnormality determination module is used to determine whether the main memory unit has an operation abnormality by using the storage bandwidth and the main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold.

[0035] The technical effect of the above technical solution is: by using a volatile storage device (such as SDRAM) to cache the gene data in the gene sequencing process through the main memory unit, the intermediate data can be temporarily stored, thereby improving the data processing efficiency of the gene sequencing process. This is particularly important for large-scale and highly complex gene data processing tasks. The logic calculation unit can call the gene data in the main memory module and execute the preset in-memory sequencing operator (such as FM index operator or Hash index operator) to process these data. This in-memory calculation method reduces the data transmission overhead and improves the processing speed. The operating parameter monitoring module can monitor the operating parameters of the main memory unit in real time, including the main memory capacity, the main memory capacity change rate, the storage bandwidth and the main memory access speed. This provides the possibility of timely discovery and handling of potential problems. By comparing the main memory capacity change rate with the preset main memory capacity change rate threshold, and using the above formula to calculate the capacity stability evaluation coefficient, the technical solution can comprehensively evaluate the capacity stability of the main memory unit. This helps to prevent performance degradation or data loss caused by insufficient or unstable capacity. When the capacity stability evaluation coefficient is lower than the preset coefficient threshold, the abnormality determination module can use the storage bandwidth and main memory access speed to further determine whether the main memory unit has an operational abnormality. This intelligent determination method improves the reliability and stability of the system. Although not directly mentioned in the technical solution, based on the results of real-time monitoring and evaluation, the system can dynamically adjust resource configuration (such as increasing main memory capacity, optimizing storage structure, etc.) to cope with changing data processing needs.

[0036] In summary, this technical solution improves the data processing efficiency of the gene sequencing process and the reliability and stability of the system by real-time monitoring, evaluation and optimization of the operating status of the main memory unit. This is of great significance for promoting the development and application of gene sequencing technology.

[0037] Specifically, the abnormality determination module includes: A second data retrieval module, configured to retrieve storage bandwidth and main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold; An operation evaluation coefficient acquisition module, used to acquire the operation evaluation coefficient of the main memory unit by using the storage bandwidth and the main memory access speed; The operation evaluation coefficient of the main memory unit is obtained by the following formula:

[0038] Wherein, G represents the operation evaluation coefficient of the main memory unit; n represents the number of unit time of the main memory unit operation, and the unit time is 1s; B i R represents the storage bandwidth corresponding to the main memory unit in the i-th unit time; i V represents the change rate of main memory capacity per unit time i; i represents the main memory access speed corresponding to the main memory unit in the i-th unit time; P Bi represents the storage bandwidth change rate corresponding to the main memory unit in the i-th unit time; P Vi represents the change rate of the main memory access speed corresponding to the main memory unit in the i-th unit time; A comprehensive operation evaluation coefficient acquisition module, used to integrate the operation evaluation coefficient of the main storage unit with the capacity stability evaluation coefficient to acquire the comprehensive operation evaluation coefficient of the main storage unit; The comprehensive operation evaluation coefficient is obtained by the following formula:

[0039] Wherein, H represents the comprehensive operation evaluation coefficient; w 01 and w 02 Indicates the weight value corresponding to the capacity stability evaluation coefficient and the operation evaluation coefficient of the main memory unit; S represents the capacity stability evaluation coefficient; G represents the operation evaluation coefficient of the main memory unit; A comprehensive coefficient comparison module, used for comparing the comprehensive operation evaluation coefficient with a preset comprehensive coefficient threshold; The abnormality determination and alarm module is used to determine that the operation of the main memory unit is abnormal and issue an abnormality alarm when the comprehensive operation evaluation coefficient is lower than a preset comprehensive coefficient threshold.

[0040] The technical effect of the above technical solution is: the technical solution obtains the storage bandwidth and main memory access speed through the second data retrieval module, and then uses these parameters to calculate the operation evaluation coefficient of the main memory unit. This not only takes into account the capacity stability of the main memory unit, but also comprehensively evaluates its data transmission and processing capabilities, thereby providing a more comprehensive performance monitoring and evaluation. Through the calculation of the comprehensive operation evaluation coefficient (H), the technical solution integrates the capacity stability evaluation coefficient (S) and the operation evaluation coefficient (G) of the main memory unit. This integration method enables the system to more accurately reflect the overall operating status of the main memory unit, providing a more reliable basis for subsequent abnormality judgment. In the calculation of the comprehensive operation evaluation coefficient, w 01 and w 02The weight values ​​corresponding to the capacity stability evaluation coefficient and the operation evaluation coefficient of the main memory unit can be flexibly set according to actual needs. This weight setting method enables the system to allocate targeted weights to various evaluation indicators according to different application scenarios and performance requirements, thereby improving the accuracy and practicality of the evaluation. When the comprehensive operation evaluation coefficient is lower than the preset comprehensive coefficient threshold, the abnormal judgment and alarm module can automatically determine that there is an abnormality in the operation of the main memory unit and issue an abnormal alarm. This intelligent judgment method not only improves the response speed of the system, but also can promptly remind management personnel to troubleshoot and handle faults, thereby avoiding problems such as performance degradation or data loss caused by faults. By real-time monitoring and evaluation of the operating status of the main memory unit, and making abnormal judgments and alarms based on the evaluation results, the technical solution can promptly discover and handle potential performance problems. This helps to improve the reliability and stability of the system and ensure that key tasks such as gene sequencing processes can proceed smoothly.

[0041] In summary, this technical solution provides strong support for key tasks such as gene sequencing processes through comprehensive performance monitoring and evaluation, introduction of comprehensive operation evaluation coefficients, flexible weight setting, intelligent abnormality judgment and alarm, and improved system reliability and stability.

[0042] The operation process of the in-memory computing module is as follows: S01. Data storage The main memory unit receives the gene data transmitted from the central processing unit or other units; and temporarily stores the gene data in the SDRAM for subsequent processing.

[0043] S02, data call The logic computing unit initiates a data request to the main memory module according to the needs of the current processing task; the main memory unit transfers the required gene data to the logic computing unit.

[0044] S03. Data Processing The logic computing unit calls the preset in-memory sequencing operator according to the current task requirements; the logic computing unit executes the corresponding operator to process the gene data obtained from the main memory unit.

[0045] S04. Data storage and return The processed gene data is stored back to the main memory unit; if necessary, the data in the main memory unit can be returned to the central processing module or other computing modules through the DIMM interface.

[0046] The in-memory computing module stores genetic data in volatile memory devices and uses a dedicated hardware structure that supports hash computing to execute high-concurrency sequencing operators, reducing the overhead of data movement and improving data processing performance in the genetic sequencing process. Through the close cooperation between the main memory unit and the logical computing unit, efficient processing and management of genetic data is achieved.

[0047] The central processing module, in-memory computing module and storage computing unit form a parallel processing mechanism, which realizes the efficient execution of different steps in the gene sequencing process. The central processing module is responsible for global control, task scheduling and execution of advanced algorithms. The in-memory computing module is used for fast processing of intermediate data and supports high-concurrency index calculations, while the storage computing unit focuses on the storage of long-term data and data compression and decompression. The three realize the efficient execution of the gene sequencing process through parallel operation and division of labor, reduce the time overhead of data movement, and improve the overall processing performance.

[0048] The process control module is mainly used to manage the gene sequencing process in the in-memory computing unit and the storage computing unit. Specifically, the main functions of the process detection module include: Receive process management information from external sources such as process management software; Arbitrate and parse the received information, obtain the message queue number, and write the management command into the corresponding management message queue; Call the management command in the management message queue according to the data information, and execute the delay controlled by the data information according to the command; After the management command is called, the command is deleted, so that the command executed by the next gene sequencing process is placed at the top of the management message queue, which is convenient for quick calling.

[0049] In this way, the process detection module realizes efficient control of the gene sequencing process, reduces the system's time overhead in gene sequencing process control, and improves the performance of the gene sequencing processing process.

[0050] The objective function module optimizes the extraction of independent components through a series of complex calculation steps, and finally obtains the trend term components of the PCR reaction. The specific process of the objective function module is as follows: S11. Calculation of the overall Gaussian kurtosis of the error component: The module first obtains the frequency of each error sine wave, and divides the error sine waves into several frequency categories according to the frequency. For each frequency category, the ratio of the number of error sine waves in the category to the total number of error sine waves, that is, the distribution probability of the frequency category, is calculated. Based on these distribution probabilities, the Gaussian kurtosis is calculated, which reflects the overall characteristics of the error components.

[0051] S12, ICA decomposition of residual curve: The residual curve is decomposed using the FASTICA algorithm. Each decomposition will produce two independent components. The module classifies all data points according to their fluorescence intensity to form several amplitude categories, calculates the ratio of the number of data points in this category to the total number of data points in the independent component, that is, the fluorescence intensity distribution probability corresponding to the amplitude category, and calculates the Gaussian kurtosis of the independent component. The independent component with the smallest Gaussian kurtosis is selected as the independent component, and the one with the largest Gaussian kurtosis is selected as the noise component.

[0052] S13. Construct objective function: According to the fluorescence intensity distribution probability corresponding to the amplitude category in the independent component, the overall Gaussian kurtosis of the error component and the residual curve, the objective function is constructed. The objective function takes into account the number of amplitude categories in the independent component, the distribution probability corresponding to the amplitude category, the mean and standard deviation of the distribution probability, the overall Gaussian kurtosis of the error component and the mean square error between the independent component and the residual curve.

[0053] S14, determine the optimal decomposition result: The module will calculate the output value of the objective function corresponding to each decomposition result, and finally select the decomposition result corresponding to the minimum output value of the objective function as the optimal decomposition result, and use the independent components in the result as the trend item components of the PCR reaction.

[0054] Through the above steps, the objective function module can effectively identify and remove abnormal amplification cycles in the PCR reaction, ensuring that the trend item components finally obtained are accurate, thereby improving the accuracy of the gene sequencing results.

[0055] The anomaly detection module includes: An isolated forest tree construction unit is used to construct an isolated forest tree, identify abnormal amplification cycles, and recursively divide all fluorescence intensity amplitudes into left and right subsets until further division is impossible; An outlier calculation unit is used to calculate the number of paths of each node and use it as an outlier. The node with fewer paths is more likely to represent an abnormal amplification cycle. The abnormal threshold setting unit is used to set the threshold of the abnormal score to identify the abnormal amplification cycle, preset an abnormal threshold, and perform linear normalization processing on the abnormal value provided by the abnormal value calculation unit to obtain the abnormal score. If the abnormal score of a certain amplification cycle is lower than the threshold, it is identified as an abnormal amplification cycle; A differential image analysis unit is used to analyze abnormal amplification cycles by using a frame difference method, using the cumulative images taken by the real-time fluorescence PCR instrument at each amplification cycle, and to differentiate the cumulative image corresponding to the abnormal amplification cycle from the cumulative image of the previous cycle by using the frame difference method, so as to identify abnormally amplified DNA samples; The abnormal sample deletion unit is used to delete abnormally amplified DNA samples. According to the abnormally amplified sample information provided by the differential image analysis unit, these samples are deleted to ensure the accuracy of subsequent whole genome resequencing.

[0056] In order to better demonstrate the operation process of a gene sequencing system for multiple gene repetitive sequences, this embodiment proposes a gene sequencing method for multiple gene repetitive sequences, including the following steps: Step 1: Before sequencing, the quality of the DNA sample must be ensured, including purity, concentration, and integrity; DNA is fragmented and appropriate adapter sequences are added to construct a library suitable for sequencing; sequencing is performed using the Illumina MiSeq high-throughput sequencing platform to obtain raw sequencing data; a preliminary quality assessment of the sequencing data is performed to ensure data quality; Step 2: Design nested primers according to the target region to improve the specificity of amplification, use nested multiplex PCR technology to amplify the target repetitive sequence, monitor the amplification process with a real-time fluorescence PCR instrument, collect the fluorescence data generated during the amplification process, and compare it with the theoretical amplification curve to identify potential abnormal amplification; Step 3: Preprocess the raw sequencing data, including quality trimming and adapter removal, and use the DeepVariant machine learning model to train the model to identify sequencing errors; use the trained model to perform error correction on the sequencing data; Step 4: The central processing unit is responsible for scheduling and assigning tasks to the in-memory computing unit and the storage computing unit. The in-memory computing unit processes intermediate data, and the storage computing unit is responsible for long-term storage and auxiliary calculations, integrating the results processed by each unit to form a complete processing flow; Step 5: Use the objective function module to detect abnormal amplification through methods such as short-time Fourier transform, perform ICA decomposition on abnormal data, optimize independent components and correct abnormalities, and obtain the trend term components of the PCR reaction to reflect the abnormal parts in the actual process; Step 6: Use the anomaly detection module to identify abnormal amplification cycles through algorithms such as the isolated forest tree, and delete abnormally amplified DNA samples to reduce the impact on subsequent analysis and ensure data quality after removing abnormal samples; Step 7: Prepare the corrected sequencing data and convert the format before alignment. Use the BWA alignment tool to align the sequencing data with the reference genome. Annotate the SNPs and Indels in the test samples to provide information such as gene function and disease relevance. Finally, output the alignment results, including the location and type of the variant, for subsequent research.

[0057] Working principle: obtain the original sequencing data of the target DNA fragment through high-throughput sequencing technology; use nested multiplex PCR technology to amplify the target repetitive sequence to increase the copy number of the target region; use the data processing module based on machine learning to control the quality of the sequencing data, identify and correct sequencing errors, and ensure the accuracy of the data; use the central processing unit, in-memory computing unit, and storage computing unit to process gene data in parallel, and improve the speed and efficiency of data processing through efficient data management and task scheduling; use the objective function module to detect and correct possible anomalies in the PCR amplification process to ensure the authenticity and reliability of the amplification results; identify and delete abnormally amplified DNA samples through the anomaly detection module to further improve the accuracy of the sequencing results; finally, compare the corrected sequencing results with the known reference genome to determine the specific location and variation of the repetitive sequence, and provide basic data for subsequent genetic analysis. This series of steps ensures the accuracy and efficiency from data acquisition to the final analysis results.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0059] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A gene sequencing system for multi-gene repetitive sequences, characterized in that: include: High-throughput sequencing module, used to obtain raw sequencing data of target DNA fragments; A nested multiplex PCR amplification module for specifically amplifying repetitive sequences within a target region, wherein the module reduces nonspecific amplification by designing a specific primer combination; Data processing module: The data processing module is based on machine learning algorithms and is used to automatically identify and correct sequencing errors caused by repeated sequences; The central processing module is used to call and execute the central sequencing operator; In-memory computing module, used to cache genetic data and execute in-memory sequencing operators; A storage computing unit, used to call and execute the storage sequencing operator; A process control module is used to receive process management information and execute different steps in the gene sequencing process according to management commands; The objective function module is used to obtain independent components and noise components through ICA decomposition, and to construct an objective function according to the fluorescence intensity distribution to obtain the trend term component of the PCR reaction; The abnormal detection module is used to detect abnormal amplification cycles, and obtain and delete abnormally amplified DNA samples according to the abnormal amplification cycles.

2. A gene sequencing system for multiple gene repeat sequences according to claim 1, characterized in that: The nested multiplex PCR amplification module includes a set of optimized primer pairs and a customized thermal cycle program, each of which is designed to match different variants of the target repetitive sequence; the thermal cycle program performs PCR reactions under multiple temperature gradients by dynamically adjusting extension time and annealing temperature.

3. A gene sequencing system for multi-gene repeat sequences according to claim 2, characterized in that: The data processing module includes a deep learning module and an algorithm module. The deep learning module is based on a convolutional neural network and a long short-term memory network, and is used to learn the pattern of repeated sequences from large-scale sequencing data and distinguish between real mutation signals and technical errors; The algorithm module uses statistical methods to detect and correct read segment alignment errors caused by repeated sequences, identifies potential error sites by calculating the Hamming distance between adjacent read segments, and uses Bayesian statistical methods for probability correction.

4. A gene sequencing system for multiple gene repeat sequences according to claim 3, characterized in that: The central processing module comprises: A general computing unit is used to execute the algorithm part executed by the non-accelerator array unit, split the instructions of the dynamic programming algorithm, and distribute the split instruction information to the instruction parsing unit; An instruction parsing unit, used to parse instruction information, distribute the parsed instruction information to the accelerator array unit, and support the interaction between the general computing unit and the accelerator array unit; The accelerator array unit is used to perform accelerated calculations based on the results of the analysis; it supports computing tasks of different granularities and adjusts the allocation of computing resources; A storage unit is configured to store the computational data of the reference sequence, the read sequence, and the result sequence; provide the parameters required by the dynamic programming algorithm; and supply the required data to the general computing unit and the accelerator array unit.

5. A gene sequencing system for multiple gene repeat sequences according to claim 4, characterized in that: The in-memory computing module includes: A main memory unit, used to cache gene data in the gene sequencing process; The logic computing unit is used to call the gene data in the main memory module, call the preset in-memory sequencing operator, and execute the in-memory sequencing operator to process the called gene data.

6. A gene sequencing system for multiple gene repeat sequences according to claim 5, characterized in that: The in-memory computing module includes: An operating parameter monitoring module, used for real-time monitoring of the operating parameters of the main memory unit; wherein the operating parameters include main memory capacity, main memory capacity change rate, storage bandwidth and main memory access speed; A change rate comparison module, used for comparing the main memory capacity change rate with a preset main memory capacity change rate threshold; A first data retrieval module, configured to retrieve the current main memory capacity of the main memory unit when the main memory capacity change rate exceeds a preset main memory capacity change rate threshold; A capacity stability evaluation coefficient acquisition module, used to acquire a capacity stability evaluation coefficient by using the main memory capacity change rate and the current main memory capacity of the main memory unit; The capacity stability evaluation coefficient is obtained by the following formula: Where S represents the capacity stability evaluation coefficient; n represents the number of unit times of the main memory unit operation, and the unit time is 1s; C d Indicates the current main memory capacity of the main memory unit; C y Indicates the maximum remaining main memory capacity allowed for the normal operation of the main memory unit cache; C i represents the main memory capacity corresponding to the main memory unit in the i-th unit time; R i represents the rate of change of main memory capacity per unit time; s i represents the proportion of redundant data cached in the main memory unit corresponding to the i-th unit time; α represents the first adjustment coefficient, which is used to control the strength of the relationship between the main memory capacity of the main memory unit and the main memory capacity change rate, and the value range of the first adjustment coefficient is 2.31-4.68; β represents the second adjustment coefficient, which controls the negative impact of the main memory capacity change rate on the cache stability of the main memory unit, and the value range of the second adjustment coefficient is 0.18-0.87; R b R represents the standard deviation of the rate of change of main memory capacity corresponding to n unit time; fmax It represents the maximum value of the change rate of main memory capacity between two adjacent units of time in n units of time; A coefficient comparison module, used for comparing the capacity stability evaluation coefficient with a preset coefficient threshold; The abnormality determination module is used to determine whether the main memory unit has an operation abnormality by using the storage bandwidth and the main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold.

7. A gene sequencing system for multiple gene repeat sequences according to claim 6, characterized in that: The abnormality determination module comprises: A second data retrieval module, configured to retrieve storage bandwidth and main memory access speed when the capacity stability evaluation coefficient is lower than a preset coefficient threshold; An operation evaluation coefficient acquisition module, used to acquire the operation evaluation coefficient of the main memory unit by using the storage bandwidth and the main memory access speed; The operation evaluation coefficient of the main memory unit is obtained by the following formula: Wherein, G represents the operation evaluation coefficient of the main memory unit; n represents the number of unit time of the main memory unit operation, and the unit time is 1s; B i R represents the storage bandwidth corresponding to the main memory unit in the i-th unit time; i V represents the change rate of main memory capacity per unit time i; i represents the main memory access speed corresponding to the main memory unit in the i-th unit time; P Bi represents the storage bandwidth change rate corresponding to the main memory unit in the i-th unit time; P Vi represents the change rate of the main memory access speed corresponding to the main memory unit in the i-th unit time; A comprehensive operation evaluation coefficient acquisition module, used to integrate the operation evaluation coefficient of the main storage unit with the capacity stability evaluation coefficient to acquire the comprehensive operation evaluation coefficient of the main storage unit; The comprehensive operation evaluation coefficient is obtained by the following formula: Wherein, H represents the comprehensive operation evaluation coefficient; w 01 and w 02 Indicates the weight value corresponding to the capacity stability evaluation coefficient and the operation evaluation coefficient of the main memory unit; S represents the capacity stability evaluation coefficient; G represents the operation evaluation coefficient of the main memory unit; A comprehensive coefficient comparison module, used for comparing the comprehensive operation evaluation coefficient with a preset comprehensive coefficient threshold; The abnormality determination and alarm module is used to determine that the operation of the main memory unit is abnormal and issue an abnormality alarm when the comprehensive operation evaluation coefficient is lower than a preset comprehensive coefficient threshold.

8. A gene sequencing system for multiple gene repeat sequences according to claim 5, characterized in that: The objective function module is further used for: The sine wave and its frequency and phase in the residual curve are obtained by short-time Fourier transform; Obtain component estimation factors for each type of phase, obtain the optimal phase and a number of corresponding error sinusoidal waves, and form error components; Get the overall Gaussian kurtosis of the error component and construct the objective function based on the Gaussian kurtosis.

9. A gene sequencing system for multiple gene repeat sequences according to claim 8, characterized in that: The anomaly detection module comprises: An isolated forest tree construction unit is used to construct an isolated forest tree, identify abnormal amplification cycles, and recursively divide all fluorescence intensity amplitudes into left and right subsets until further division is impossible; An outlier calculation unit, used to calculate the number of paths of each node and use it as an outlier; The abnormal threshold setting unit is used to set the threshold of the abnormal score, identify the abnormal amplification cycle, preset an abnormal threshold, and perform linear normalization processing on the abnormal value provided by the abnormal value calculation unit to obtain the abnormal score. If the abnormal score of a certain amplification cycle is lower than the threshold, it is identified as an abnormal amplification cycle; A differential image analysis unit is used to analyze abnormal amplification cycles by using a frame difference method, using a real-time fluorescence PCR instrument to capture cumulative images in each amplification cycle, and using the frame difference method to differentiate the cumulative image corresponding to the abnormal amplification cycle from the cumulative image of the previous cycle to identify abnormally amplified DNA samples; The abnormal sample deletion unit is used to delete the abnormally amplified DNA sample and delete the sample according to the abnormally amplified sample information provided by the differential image analysis unit.

10. A gene sequencing method for multiple gene repetitive sequences, implemented based on a gene sequencing system for multiple gene repetitive sequences according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Use a high-throughput sequencing module to obtain raw sequencing data of the target DNA fragment; Step 2: amplify the target repetitive sequence using a nested multiplex PCR amplification module; Step 3: Analyze sequencing data using a machine learning-based data processing module to identify and correct sequencing errors; Step 4: Processing the gene data in parallel through the central processing unit, the in-memory computing unit and the storage computing unit; Step 5: Apply the objective function module to detect and correct abnormal amplification; Step 6: Use the anomaly detection module to obtain and delete abnormally amplified DNA samples; Step 7: Compare the corrected sequencing results with the known reference genome to determine the specific location and variation of the repeated sequences.

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