High-throughput sequencing data processing system

Abstract

The present invention discloses a high-throughput sequencing data processing system, device and electronic equipment, including a device and electronic equipment module, a data processing system module and a high-throughput sequencing module. The device and electronic equipment module are used to provide hardware support for gene sequencing, provide a high-speed and reliable data transmission interface and a user-system interaction interface. The data processing system module is used to collect gene data, store and manage the original data after pre-processing operations and generate a visual analysis report. The high-throughput sequencing module is used to construct a high-throughput sequencing library, perform biological gene sequencing and detect and correct errors generated in the sequencing process. The high-throughput sequencing data processing system, device and electronic equipment of the present invention propose a high-throughput sequencing algorithm based on a hybrid model to perform high-throughput sequencing on biological genes and process complex data structures, and propose a high-throughput sequencing error correction algorithm based on statistical analysis to detect and correct errors generated in high-throughput sequencing.

Description

Technical Field

[0001] The present invention relates to the fields of mixed models, statistical analysis and high-throughput sequencing, and in particular to a high-throughput sequencing data processing system. Background Art

[0002] Hybrid model technology is a combination of different probability distribution models, which aims to solve the heterogeneity and complexity of genomic data in the high-throughput sequencing process, effectively distinguish different genes and variant types, and improve the accuracy and reliability of sequencing results. Hybrid model technology uses statistical inference and computational methods to identify important biological features in large-scale high-throughput biological gene sequencing data, providing a reliable foundation for subsequent bioinformatics analysis.

[0003] Statistical analysis and high-throughput sequencing technology is a technology that uses statistical methods to analyze and process high-throughput sequencing data. It aims to solve errors generated in the sequencing process, including reading errors and sequencing deviations. By establishing mathematical models and statistical algorithms, statistical analysis and high-throughput sequencing technology can accurately identify and repair errors in sequencing data, improve data accuracy and reliability, and provide a reliable data foundation for subsequent bioinformatics analysis.

[0004] However, existing high-throughput sequencing data processing systems are limited in processing complex data models and are unable to effectively integrate sequencing data from different sources and properties, resulting in insufficient comprehensive understanding of biological genome variation. Secondly, existing high-throughput sequencing data processing systems have limited capabilities in detecting and correcting errors in sequencing data, which affects the accuracy and reliability of sequencing results. Summary of the invention

[0005] The purpose of the present invention is to provide a high-throughput sequencing data processing system to solve the problems that the existing high-throughput sequencing data processing systems mentioned in the above background technology are limited in processing complex data models, cannot effectively integrate sequencing data from different sources and properties, and thus have insufficient comprehensive understanding of biological genome variations, as well as limited ability to detect and correct errors in sequencing data, resulting in the accuracy and reliability of sequencing results being affected.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a high-throughput sequencing data processing system, comprising an apparatus and electronic equipment module, a data processing system module, and a high-throughput sequencing module, characterized in that: the apparatus and electronic equipment module comprises a sequencer equipment unit, a network equipment interface unit, and a user interface and control unit, the sequencer equipment unit is used to provide hardware equipment for high-throughput sequencing and perform large-scale biological gene sequencing, the network equipment interface unit is used to provide a high-speed and reliable network connection, support the transmission and remote access of sequencing data, including a high-speed Ethernet switch, a router, and optical fiber communication equipment, the user interface and control unit is used to provide an interface for user interaction with the system, monitor the sequencing progress and system status, including a graphical user interface and real-time data display and monitoring equipment, the data processing system module comprises a data acquisition and preprocessing unit, a data storage and management unit, and a report generation and visualization unit, the data acquisition and preprocessing unit is used to obtain raw data from the sequencer, and perform preliminary quality control and preprocessing, including The data storage and management unit is used to store and manage the processed and analyzed sequencing data to ensure the security and accessibility of the data, including a database management system and a data storage solution. The report generation and visualization unit is used to perform statistical analysis and bioinformatics analysis on the sequencing data analysis results, and generate charts, reports and visualization results. The high-throughput sequencing module includes a high-throughput sequencing execution unit, a sequencing analysis error correction unit and a sequencing library construction unit. The high-throughput sequencing execution unit proposes a high-throughput sequencing algorithm based on a hybrid model to perform high-throughput sequencing of biological genes and improve the accuracy of sequence alignment and variation detection. The sequencing analysis error correction unit proposes a high-throughput sequencing error correction algorithm based on statistical analysis to perform quality control and error correction on sequencing data to ensure the accuracy and reliability of the data. The sequencing library construction unit is used to convert the biological gene fragments prepared by the sample into a sequencing library for subsequent sequencing on a high-throughput sequencer, including library establishment, library amplification and library quality control.

[0007] Preferably, the device and electronic equipment module include a sequencer equipment unit, which ensures that the system can perform high-precision and high-throughput sequencing of a large number of biological gene samples by providing hardware equipment for high-throughput gene sequencing.

[0008] Preferably, the device and electronic equipment module include a network device interface unit, which ensures that the sequencing data can be quickly transmitted to the data processing system by providing a high-speed and reliable network connection and supports remote access and control.

[0009] Preferably, the device and electronic device module user interface and control unit ensure that the user can monitor and manage the entire sequencing process and view the system status and data progress in real time by providing an intuitive graphical user interface and remote detection and control functions.

[0010] Preferably, the data processing system module includes a data acquisition and preprocessing unit, which performs quality control on the original biological gene data by acquiring the original sequencing data from the sequencer device and performing preprocessing operations, including removing low-quality reads and removing adapter sequences, to ensure that the input data is of high quality and suitable for further analysis.

[0011] Preferably, the data processing system module comprises a data storage and management unit, which ensures that the processed data and the original data can be stored securely, accessed quickly, and can be easily managed and retrieved by providing a large-capacity and efficient data storage solution.

[0012] Preferably, the data processing system module includes a report generation and visualization unit, which analyzes the processed data and generates charts, reports and visualization results to ensure that users can intuitively understand and apply the sequencing results.

[0013] Preferably, the high-throughput sequencing module includes a high-throughput sequencing execution unit, which proposes a high-throughput sequencing algorithm based on a hybrid model, establishes an efficient sequencing calculation strategy by constructing a hybrid model, and performs high-throughput sequencing on biological gene samples, so that the system can process complex biological gene samples and a large amount of data, ensuring that the sequencing task of large-scale samples is completed in a shorter time, and maintaining data quality and sequencing accuracy.

[0014] Specifically, the high-throughput sequencing algorithm based on the hybrid model is as follows: First, the biological gene sequence data obtained by high-throughput sequencing technology is accurately mapped to identify and locate splicing sites. Let R be the set of read sequences, G be the reference genome sequence, and M(R, G) be the mapping function. The ratio of each read sequence mapped to the reference genome is calculated through an accurate mapping strategy. The specific formula is expressed as follows:

[0015]

[0016] Where P(R|G) is the probability of the read sequence R appearing under the condition of a given reference genome G, e is the base of the natural logarithm, λ is the rate of sequence generation on the reference genome, k is the actual number of read sequences mapped to the same position of the reference genome, and k! is the factorial of k. Fast and efficient sequence mapping is achieved by locating the short sequence reads obtained by sequencing. Then, the presence of splicing sites is determined by analyzing the read sequences spanning the exon-exon junction, thereby identifying splicing sites and quantitatively analyzing the frequency of splicing events. The specific formula is expressed as:

[0017]

[0018] Where F(S) represents the splice site recognition function, S represents the splice site set, s represents the single splice site index, and w s It is expressed as the weight of the splicing site s. By identifying the splicing sites and quantitatively analyzing the frequency of splicing events, the algorithm has sufficient basis for judging the complexity of gene expression regulation. Secondly, by constructing a hybrid model to connect the read sequences mapped to the reference genome, the transcript path is constructed. The hybrid model is used to evaluate the probability of the transcript path and select the optimal transcript structure. The specific formula is expressed as:

[0019]

[0020] Where A represents the scoring function of the optimal evaluation value of all transcripts in the transcript set T, It is represented as a maximum function, which is used to find the transcript with the highest probability among all existing transcripts. t represents a specific transcript in the transcript set T, T represents the set of all existing transcripts, and Π r∈t It is expressed as a continuous multiplication operation, which means multiplication operation is performed on all elements in the set. r represents a single read sequence in transcript t, and P(r) represents the probability of mapping a single read sequence r to transcript t. By comparing different transcript structures, a structure that can best represent the actual biological situation statistically is determined. Then, by comparing the transcript expression data under different conditions, the expression level of the transcript is quantitatively analyzed to identify differentially expressed transcripts. The specific formula is expressed as follows:

[0021]

[0022] Where D(E) represents the score of transcript differential expression analysis, which is used to measure the overall difference in the expression level changes of all transcripts in set E. It is represented as a summation operation, which means that all items that meet the conditions are accumulated. e1 and e2 are represented as two different expression level values ​​in the set E, representing the expression of the same transcript under different conditions. E is represented as a set of biological gene sequence expression levels, which includes the expression levels of all transcripts under different conditions. |·| is represented as an absolute value function. By constructing a quantitative indicator to measure the difference in gene expression, it is used to identify transcripts with significant changes in expression levels under different conditions. Finally, the splicing events are classified and annotated to identify the gene sequences and pathways affected by the splicing events. The specific formula is expressed as follows:

[0023]

[0024] Among them, C(N) represents the centrality measure of the entire functional annotation network, N represents the set of all known biological gene sequences and pathways, n represents a specific biological gene sequence and pathway in the set N, and S n It is represented as a set of splicing sites associated with a specific biological gene sequence and pathway n, s is represented as a specific splicing site associated with n, φ(s) is represented as a scoring function used to quantify the splicing site s in the biological gene sequence and pathway n. By evaluating the importance of each splicing site in the entire biological network and identifying the splicing events that play a key role in each pathway, the hybrid model containing fixed effects and random effects is constructed to estimate the effect size of the splicing event and evaluate the consistency of the splicing event under different samples and conditions. The specific formula of the constructed hybrid model is expressed as follows:

[0025] Y ij =μ+α i +u i +∈ ij

[0026] Among them, Y ij is represented as the observation function of the jth splicing event in the i-th biological gene sample, i is the number index of biological gene samples, j is the sample index of splicing events, μ is the overall mean of splicing events in all samples, and u is i The random effect of the i-th sample represented as the within-sample variability, ∈ ij is represented as an error term of random variation. Finally, after identifying and quantifying splicing events, the hybrid model can provide statistical significance information of splicing events and reveal the potential associations between different splicing events. By optimizing the weights of interactions between splicing events, the complexity of gene expression regulation can be accurately simulated. The specific formula is expressed as:

[0027]

[0028] Among them, W jkis represented as the interaction weight between splicing event j and splicing event k, where k represents a specific splicing event, β jk is the basic strength of the interaction between splicing event j and splicing event k, n is the total number of biological gene samples, γ lk It is represented as the random effect contribution of the lth condition to the splicing event k, l is represented as the condition index, and m is represented as the total number of conditions. The application of high-throughput sequencing algorithm based on hybrid model in high-throughput sequencing data processing system not only improves the efficiency and accuracy of data processing, but also provides powerful tools and methods for in-depth research on biological gene expression regulation, and provides important technical support for solving complex biological gene data processing and high-throughput sequencing research.

[0029] Preferably, the high-throughput sequencing module includes a sequencing analysis and error correction unit, which proposes a high-throughput sequencing error correction algorithm based on statistical analysis, and ensures the accuracy and reliability of the sequencing results by comparing the sequencing data with the reference genome, detecting and correcting sequencing errors.

[0030] Specifically, the high-throughput sequencing error correction algorithm based on statistical analysis is as follows: first, based on the characteristics of high-throughput sequencing data, the estimated size L of the genome and the average error rate P in the sequencing process are determined, and then the maximum window length W for witness matching is calculated. The maximum window length W is determined by minimizing the proportion of reads that cannot be corrected due to errors and the proportion of reads that are corrected by errors. The specific formula is expressed as follows:

[0031] W max =argmin W (U(W)+D(W))

[0032] Among them, W max Denote the maximum window length of witness matching obtained by minimizing U(W)+D(W), argmin W It is represented as the value of W that makes the given function achieve the minimum value, U(W) is represented as the proportion of reads that cannot be corrected when the window length is W, and D(W) is represented as the proportion of reads that are incorrectly corrected when the window length is W. Then, the read sequence string and the reverse complementary sequence are constructed. By forming a single string containing all read sequences and reverse complementary sequences, the basic data structure is provided for subsequent error location and correction. The specific formula is expressed as follows:

[0033]

[0034] Where R represents the new string formed by connecting all read sequences with the reverse complementary sequences. represents the set of all new strings, n represents the total number of read biological gene sequences, r iRepresented as the i-th read biological gene sequence, It is represented as the reverse complementary biological gene sequence of the i-th read biological gene sequence, and i is represented as the index of the number of read biological gene sequences. Secondly, through statistical analysis strategy, the two efficient data structures of suffix array and longest common prefix array are used to quickly locate and analyze the patterns in the read sequence, providing a basis for error correction. The suffix array SA is constructed for the string R to store the lexicographic order of all suffixes, and the longest common prefix array LCP is constructed to record the longest common prefix length between consecutive suffixes. Then, the error position is identified by calculating the support value of each base, providing a basis for subsequent error correction. The specific formula is expressed as follows:

[0035] supp(u,a)=|{k|uak∈R}|

[0036] T=min({k|Ec(k)>Ee(k)})+2

[0037] Among them, supp(u,a) represents the support value of the substring u followed by base a in the string R, which represents the frequency of a following u, u represents a substring used to detect existing errors, a represents a specific base, k represents a position in the string R, |·| represents the absolute value function, T represents the threshold for correcting errors, which is used to determine whether a base should be corrected, min represents the minimum value function, Ec(k) represents the expected number of correctly witnessed support for correct bases under given k, and Ee(k) represents the expected number of correctly witnessed support for incorrect bases under given k. Finally, base errors in the sequence are corrected according to the support value to improve the accuracy of high-throughput sequencing data. The specific formula is expressed as follows:

[0038]

[0039] Among them, Correct(a) represents the correction function, b represents the corrected base, and by traversing each position in R, if the support value supp(u,a) is less than the threshold T, the base a is corrected to the base b with the highest support value. Finally, the final corrected high-throughput sequencing data is output to provide accurate and reliable data for subsequent bioinformatics analysis. The specific formula is expressed as:

[0040] O={r'1,r'2,…,r' n}={r' i |i∈n}

[0041] Where O represents the corrected read sequence set output, r' i The corrected i-th read sequence, {r'1,r'2,…,r' n} is represented as the overall set of read sequences. The high-throughput sequencing error correction algorithm based on statistical analysis significantly improves the accuracy and reliability of high-throughput sequencing data through statistical analysis, optimizing window length, constructing a unified string, utilizing efficient data structures, accurately calculating support values ​​and thresholds, and efficiently correcting errors. It provides a more accurate and reliable data basis for subsequent bioinformatics analysis. The application of this algorithm in the high-throughput sequencing data processing system not only improves the data quality, but also optimizes the entire sequencing data processing process, and has significant application value and practical significance.

[0042] Preferably, the high-throughput sequencing module includes a sequencing library construction unit, which ensures that the biological gene sample can be converted into a library format suitable for high-throughput sequencing by performing library construction on the sample, including library establishment, library amplification and library quality control, thereby ensuring the successful sequencing.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. High-throughput sequencing execution unit A high-throughput sequencing algorithm based on a hybrid model is proposed. First, the algorithm accurately maps the biological gene sequence data obtained by high-throughput sequencing technology to identify and locate splicing sites, thereby improving the accuracy and efficiency of sequence mapping. The algorithm uses a mapping function to calculate the proportion of each read sequence mapped to the reference genome. By evaluating the probability of occurrence of the read sequence under the reference genome condition, through this precise mapping strategy, the algorithm can quickly locate and analyze the read sequence spanning the exon-exon connection to identify the splicing site and quantitatively analyze the frequency of splicing events, thereby providing sufficient judgment basis for the complexity of gene expression regulation. Secondly, the algorithm connects the read sequence mapped to the reference genome, evaluates the probability of the transcript path and selects the best transcript structure. The scoring function is used to evaluate all transcripts in the transcript set, and the maximum value function is used to find the transcript with the highest probability among all existing transcripts, ensuring that the transcript structure that is most statistically representative of the real biological situation is selected. Then, the algorithm passes By comparing the transcript expression data under different conditions, the expression levels of transcripts are quantitatively analyzed to identify differentially expressed transcripts. This differential expression analysis enables the algorithm to effectively identify transcripts whose expression levels change significantly under different conditions. Secondly, the algorithm classifies and annotates splicing events and identifies the gene sequences and pathways affected by splicing events. Finally, after identifying and quantifying splicing events, the algorithm constructs a mixed model containing fixed effects and random effects to evaluate the effect size of splicing events and the consistency under different sample conditions. The constructed mixed model enables the algorithm to accurately evaluate the statistical significance of splicing events and reveal the potential associations between splicing events, thereby providing powerful tools and methods for detailed studies of gene expression regulation. In general, the application of high-throughput sequencing algorithms based on mixed models in high-throughput sequencing data processing systems not only improves the efficiency and accuracy of data processing, but also provides powerful tools and methods for in-depth studies of biological gene expression regulation, and provides important technical support for solving complex biological gene data processing and high-throughput sequencing research.2. Sequencing analysis and error correction unit A high-throughput sequencing error correction algorithm based on statistical analysis is proposed. First, by determining the estimated size of the genome and the average error rate in the sequencing process, the algorithm can calculate the maximum window length for witness matching. By minimizing the proportion of reads that cannot be corrected due to errors and the proportion of reads that are corrected by errors, the window length is optimized to ensure the efficiency and accuracy of the error correction process and to ensure that the selected window length can minimize the impact of errors. Secondly, through statistical analysis, the algorithm constructs the read sequence string and the reverse complementary sequence. By forming a single string containing all read sequences and reverse complementary sequences, a basic data structure is provided for error location and correction, ensuring that all data are processed under a unified framework, improving the efficiency and consistency of data processing. Next, through statistical analysis strategies, using two efficient data structures, suffix array and longest common prefix array, the algorithm can quickly locate and analyze patterns in the read sequence, providing a basis for error correction. The suffix array stores the lexicographic order of all suffixes and records the longest common prefix length between consecutive suffixes. Through these data structures, the algorithm can efficiently identify the error location and lay a foundation for subsequent error correction steps. The algorithm then identifies the error position by calculating the support value of each base, and determines the threshold for correcting errors by comparing the expected number of correct witnesses and incorrect witnesses, ensuring that the error is accurately located and corrected during the correction process, ensuring the accuracy and effectiveness of the error correction process. Finally, through the calculation based on the support value, the algorithm can correct the base errors in the sequence and significantly improve the accuracy of high-throughput sequencing data. By traversing each position in the string, it is determined whether the support value is lower than the threshold, and the bases below the threshold are corrected to the bases with the highest support value, thereby ensuring that the final output sequencing data is more accurate, and finally outputting the corrected read sequence set. In summary, the high-throughput sequencing error correction algorithm based on statistical analysis optimizes the window length, constructs a unified string, uses efficient data structures, accurately calculates support values ​​and thresholds, and efficiently corrects errors through statistical analysis, significantly improving the accuracy and reliability of high-throughput sequencing data, and providing a more accurate and reliable data basis for subsequent bioinformatics analysis. The application of this algorithm in the high-throughput sequencing data processing system not only improves the data quality, but also optimizes the entire sequencing data processing process, and has significant application value and practical significance. ; BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0046] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] See also Figure 1 The present invention provides a high-throughput sequencing data processing system, including an apparatus and electronic equipment module, a data processing system module, and a high-throughput sequencing module, characterized in that: the apparatus and electronic equipment module includes a sequencer device unit, a network device interface unit, and a user interface and control unit, the sequencer device unit is used to provide hardware equipment for high-throughput sequencing and perform large-scale biological gene sequencing, the network device interface unit is used to provide a high-speed and reliable network connection, support the transmission and remote access of sequencing data, including a high-speed Ethernet switch, a router, and optical fiber communication equipment, the user interface and control unit is used to provide an interface for user interaction with the system, monitor the sequencing progress and system status, including a graphical user interface and real-time data display and monitoring equipment, the data processing system module includes a data acquisition and preprocessing unit, a data storage and management unit, and a report generation and visualization unit, the data acquisition and preprocessing unit is used to obtain raw data from the sequencer, and perform preliminary quality control and preprocessing, including removing low-quality sequences and The data storage and management unit is used to store and manage the processed and analyzed sequencing data to ensure the security and accessibility of the data, including a database management system and a data storage solution. The report generation and visualization unit is used to perform statistical analysis and bioinformatics analysis on the sequencing data analysis results, and generate charts, reports and visualization results. The high-throughput sequencing module includes a high-throughput sequencing execution unit, a sequencing analysis error correction unit and a sequencing library construction unit. The high-throughput sequencing execution unit proposes a high-throughput sequencing algorithm based on a hybrid model to perform high-throughput sequencing of biological genes and improve the accuracy of sequence alignment and variation detection. The sequencing analysis error correction unit proposes a high-throughput sequencing error correction algorithm based on statistical analysis to perform quality control and error correction on sequencing data to ensure the accuracy and reliability of the data. The sequencing library construction unit is used to convert the biological gene fragments that have been prepared by the sample into a sequencing library for subsequent sequencing on a high-throughput sequencer, including library establishment, library amplification and library quality control.

[0049] See also Figure 1 Furthermore, the device and electronic equipment module include a sequencer equipment unit, which ensures that the system can perform high-precision and high-throughput sequencing of a large number of biological gene samples by providing hardware equipment for high-throughput gene sequencing.

[0050] See also Figure 1 Furthermore, the device and electronic equipment module include a network device interface unit, which ensures that the sequencing data can be quickly transmitted to the data processing system by providing a high-speed and reliable network connection, and supports remote access and control.

[0051] See also Figure 1 ,Furthermore, the device and electronic equipment module user interface and control unit, which ensure that users can monitor and manage the entire sequencing process and view the system status and data progress in real time by providing an intuitive graphical user interface and remote detection and control functions.

[0052] See also Figure 1 Furthermore, the data processing system module includes a data acquisition and preprocessing unit, which performs quality control on the original biological gene data by acquiring the original sequencing data from the sequencer device and performing preprocessing operations, including removing low-quality reads and removing adapter sequences, to ensure that the input data is of high quality and suitable for further analysis.

[0053] See also Figure 1 Furthermore, the data processing system module includes a data storage and management unit, which ensures that the processed data and the original data can be securely stored, quickly accessed, and easily managed and retrieved by providing a large-capacity and efficient data storage solution.

[0054] See also Figure 1 Furthermore, the data processing system module includes a report generation and visualization unit, which analyzes the processed data and generates charts, reports and visualization results to ensure that users can intuitively understand and apply the sequencing results.

[0055] See also Figure 1 Furthermore, the high-throughput sequencing module includes a high-throughput sequencing execution unit, which proposes a high-throughput sequencing algorithm based on a hybrid model, establishes an efficient sequencing calculation strategy by constructing a hybrid model, and performs high-throughput sequencing on biological gene samples, so that the system can process complex biological gene samples and a large amount of data, ensuring that the sequencing task of large-scale samples is completed in a shorter time, and maintaining data quality and sequencing accuracy.

[0056] See also Figure 1,Furthermore, the high-throughput sequencing algorithm based on the hybrid model is as follows: First, the biological gene sequence data obtained by high-throughput sequencing technology is accurately mapped to identify and locate splicing sites. Let R be the set of read sequences, G be the reference genome sequence, and M(R,G) be the mapping function. The proportion of each read sequence mapped to the reference genome is calculated through the accurate mapping strategy. The specific formula is expressed as:

[0057]

[0058] Where P(R|G) is the probability of the read sequence R appearing under the condition of a given reference genome G, e is the base of the natural logarithm, λ is the rate of sequence generation on the reference genome, k is the actual number of read sequences mapped to the same position of the reference genome, and k! is the factorial of k. Fast and efficient sequence mapping is achieved by locating the short sequence reads obtained by sequencing. Then, the presence of splicing sites is determined by analyzing the read sequences spanning the exon-exon junction, thereby identifying splicing sites and quantitatively analyzing the frequency of splicing events. The specific formula is expressed as:

[0059]

[0060] Where F(S) represents the splice site recognition function, S represents the splice site set, s represents the single splice site index, and w s It is expressed as the weight of the splicing site s. By identifying the splicing sites and quantitatively analyzing the frequency of splicing events, the algorithm has sufficient basis for judging the complexity of gene expression regulation. Secondly, by constructing a hybrid model to connect the read sequences mapped to the reference genome, the transcript path is constructed. The hybrid model is used to evaluate the probability of the transcript path and select the optimal transcript structure. The specific formula is expressed as:

[0061]

[0062] Where A represents the scoring function of the optimal evaluation value of all transcripts in the transcript set T, It is represented as a maximum function, which is used to find the transcript with the highest probability among all existing transcripts. t represents a specific transcript in the transcript set T, T represents the set of all existing transcripts, and Π r∈tIt is expressed as a continuous multiplication operation, which means multiplication operation is performed on all elements in the set. r represents a single read sequence in transcript t, and P(r) represents the probability of mapping a single read sequence r to transcript t. By comparing different transcript structures, a structure that can best represent the actual biological situation statistically is determined. Then, by comparing the transcript expression data under different conditions, the expression level of the transcript is quantitatively analyzed to identify differentially expressed transcripts. The specific formula is expressed as follows:

[0063]

[0064] Where D(E) represents the score of transcript differential expression analysis, which is used to measure the overall difference in the expression level changes of all transcripts in set E. It is represented as a summation operation, which means that all items that meet the conditions are accumulated. e1 and e2 are represented as two different expression level values ​​in the set E, representing the expression of the same transcript under different conditions. E is represented as a set of biological gene sequence expression levels, which includes the expression levels of all transcripts under different conditions. |·| is represented as an absolute value function. By constructing a quantitative indicator to measure the difference in gene expression, it is used to identify transcripts with significant changes in expression levels under different conditions. Finally, the splicing events are classified and annotated to identify the gene sequences and pathways affected by the splicing events. The specific formula is expressed as follows:

[0065]

[0066] Among them, C(N) represents the centrality measure of the entire functional annotation network, N represents the set of all known biological gene sequences and pathways, n represents a specific biological gene sequence and pathway in the set N, and S n It is represented as a set of splicing sites associated with a specific biological gene sequence and pathway n, s is represented as a specific splicing site associated with n, φ(s) is represented as a scoring function used to quantify the splicing site s in the biological gene sequence and pathway n. By evaluating the importance of each splicing site in the entire biological network and identifying the splicing events that play a key role in each pathway, the hybrid model containing fixed effects and random effects is constructed to estimate the effect size of the splicing event and evaluate the consistency of the splicing event under different samples and conditions. The specific formula of the constructed hybrid model is expressed as follows:

[0067] Y ij =μ+α i +u i +∈ ij

[0068] Among them, Y ijis represented as the observation function of the jth splicing event in the i-th biological gene sample, i is the number index of biological gene samples, j is the sample index of splicing events, μ is the overall mean of splicing events in all samples, and u is i The random effect of the i-th sample represented as the within-sample variability, ∈ ij is represented as an error term of random variation. Finally, after identifying and quantifying splicing events, the hybrid model can provide statistical significance information of splicing events and reveal the potential associations between different splicing events. By optimizing the weights of interactions between splicing events, the complexity of gene expression regulation can be accurately simulated. The specific formula is expressed as:

[0069]

[0070] Among them, W jk is represented as the interaction weight between splicing event j and splicing event k, where k represents a specific splicing event, β jk is the basic strength of the interaction between splicing event j and splicing event k, n is the total number of biological gene samples, γ lk It is represented as the random effect contribution of the lth condition to the splicing event k, l is represented as the condition index, and m is represented as the total number of conditions. The application of high-throughput sequencing algorithm based on hybrid model in high-throughput sequencing data processing system not only improves the efficiency and accuracy of data processing, but also provides powerful tools and methods for in-depth research on biological gene expression regulation, and provides important technical support for solving complex biological gene data processing and high-throughput sequencing research.

[0071] Preferably, the high-throughput sequencing module includes a sequencing analysis and error correction unit, which proposes a high-throughput sequencing error correction algorithm based on statistical analysis, and ensures the accuracy and reliability of the sequencing results by comparing the sequencing data with the reference genome, detecting and correcting sequencing errors.

[0072] See also Figure 1 ,Furthermore, the high-throughput sequencing error correction algorithm based on statistical analysis is as follows: First, based on the characteristics of high-throughput sequencing data, the estimated size L of the genome and the average error rate P in the sequencing process are determined, and then the maximum window length W for witness matching is calculated. The maximum window length W is determined by minimizing the proportion of reads that cannot be corrected due to errors and the proportion of reads that are corrected by errors. The specific formula is expressed as:

[0073] W max =argmin W (U(W)+D(W))

[0074] Among them, W maxDenote the maximum window length of witness matching obtained by minimizing U(W)+D(W), argmin W It is represented as the value of W that makes the given function achieve the minimum value, U(W) is represented as the proportion of reads that cannot be corrected when the window length is W, and D(W) is represented as the proportion of reads that are incorrectly corrected when the window length is W. Then, the read sequence string and the reverse complementary sequence are constructed. By forming a single string containing all read sequences and reverse complementary sequences, the basic data structure is provided for subsequent error location and correction. The specific formula is expressed as follows:

[0075]

[0076] Where R represents the new string formed by connecting all read sequences with the reverse complementary sequences. represents the set of all new strings, n represents the total number of read biological gene sequences, r i Represented as the i-th read biological gene sequence, It is represented as the reverse complementary biological gene sequence of the i-th read biological gene sequence, and i is represented as the index of the number of read biological gene sequences. Secondly, through statistical analysis strategy, the two efficient data structures of suffix array and longest common prefix array are used to quickly locate and analyze the patterns in the read sequence, providing a basis for error correction. The suffix array SA is constructed for the string R to store the lexicographic order of all suffixes, and the longest common prefix array LCP is constructed to record the longest common prefix length between consecutive suffixes. Then, the error position is identified by calculating the support value of each base, providing a basis for subsequent error correction. The specific formula is expressed as follows:

[0077] supp(u,a)=|{k|uak∈R}|

[0078] T=min({k|Ec(k)>Ee(k)})+2

[0079] Among them, supp(u,a) represents the support value of the substring u followed by base a in the string R, which represents the frequency of a following u, u represents a substring used to detect existing errors, a represents a specific base, k represents a position in the string R, |·| represents the absolute value function, T represents the threshold for correcting errors, which is used to determine whether a base should be corrected, min represents the minimum value function, Ec(k) represents the expected number of correctly witnessed support for correct bases under given k, and Ee(k) represents the expected number of correctly witnessed support for incorrect bases under given k. Finally, base errors in the sequence are corrected according to the support value to improve the accuracy of high-throughput sequencing data. The specific formula is expressed as follows:

[0080]

[0081] Among them, Correct(a) represents the correction function, b represents the corrected base, and by traversing each position in R, if the support value supp(u,a) is less than the threshold T, the base a is corrected to the base b with the highest support value. Finally, the final corrected high-throughput sequencing data is output to provide accurate and reliable data for subsequent bioinformatics analysis. The specific formula is expressed as:

[0082] O={r'1,r'2,…,r' n}={r' i |i∈n}

[0083] Where O represents the corrected read sequence set output, r' i The corrected i-th read sequence, {r'1,r'2,…,r' n} is represented as the overall set of read sequences. The high-throughput sequencing error correction algorithm based on statistical analysis significantly improves the accuracy and reliability of high-throughput sequencing data through statistical analysis, optimizing window length, constructing a unified string, utilizing efficient data structures, accurately calculating support values ​​and thresholds, and efficiently correcting errors. It provides a more accurate and reliable data basis for subsequent bioinformatics analysis. The application of this algorithm in the high-throughput sequencing data processing system not only improves the data quality, but also optimizes the entire sequencing data processing process, and has significant application value and practical significance.

[0084] Preferably, the high-throughput sequencing module includes a sequencing library construction unit, which ensures that the biological gene sample can be converted into a library format suitable for high-throughput sequencing by performing library construction on the sample, including library establishment, library amplification and library quality control, thereby ensuring the successful sequencing.

[0085] During specific use, first, the device and electronic equipment module includes a sequencer equipment unit, a network equipment interface unit and a user interface and control unit. The sequencer equipment unit is used to provide hardware equipment for high-throughput sequencing and perform large-scale biological gene sequencing. The network equipment interface unit is used to provide high-speed and reliable network connections to support the transmission and remote access of sequencing data, including high-speed Ethernet switches, routers and fiber-optic communication equipment. The user interface and control unit is used to provide an interface for user interaction with the system and monitor the sequencing progress and system status, including a graphical user interface and real-time data display and monitoring equipment. Then, the data processing system module includes a data acquisition and preprocessing unit, a data storage and management unit and a report generation and visualization unit. The data acquisition and preprocessing unit is used to obtain raw data from the sequencer and perform preliminary quality control and preprocessing, including removing low-quality sequences and connector sequences. The data storage and management unit is used to store and manage processing. and analyzed sequencing data to ensure data security and accessibility, including database management system and data storage solution. The report generation and visualization unit is used to perform statistical analysis and bioinformatics analysis on the sequencing data analysis results, and generate charts, reports and visualization results. Finally, the high-throughput sequencing module includes a high-throughput sequencing execution unit, a sequencing analysis error correction unit and a sequencing library construction unit. The high-throughput sequencing execution unit proposes a high-throughput sequencing algorithm based on a hybrid model to perform high-throughput sequencing of biological genes and improve the accuracy of sequence alignment and variation detection. The sequencing analysis error correction unit proposes a high-throughput sequencing error correction algorithm based on statistical analysis to perform quality control and error correction on sequencing data to ensure data accuracy and reliability. The sequencing library construction unit is used to convert the biological gene fragments that have been prepared by samples into sequencing libraries for subsequent sequencing on a high-throughput sequencer, including library establishment, library amplification and library quality control.

[0086] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-throughput sequencing data processing system, characterized in that: The system comprises an electronic device module, a data processing system module and a high-throughput sequencing module. The electronic device module comprises a sequencer device unit, a network device interface unit and a user interface and control unit. The sequencer device unit is used to provide hardware equipment for high-throughput sequencing and perform large-scale biological gene sequencing. The network device interface unit is used to provide high-speed and reliable network connection and support the transmission and remote access of sequencing data, including high-speed Ethernet switches, routers and optical fiber communication equipment. The user interface and control unit are used to provide an interface for user interaction with the system and monitor the sequencing progress and system status, including a graphical user interface and real-time data display and monitoring equipment. The data processing system module comprises a data acquisition and preprocessing unit, a data storage and management unit and a report generation and visualization unit. The data acquisition and preprocessing unit is used to obtain raw data from the sequencer and perform preliminary quality control and preprocessing, including removing low-quality sequences and connector sequences. The data storage and management unit is used to store and manage the processed and analyzed sequencing data to ensure the security and accessibility of the data, including a database management system and a data storage solution. The report generation and visualization unit is used to perform statistical analysis and bioinformatics analysis on the sequencing data analysis results, and Generate charts, reports and visualized results. The high-throughput sequencing module includes a high-throughput sequencing execution unit, a sequencing analysis and error correction unit and a sequencing library construction unit. The high-throughput sequencing execution unit proposes a high-throughput sequencing algorithm based on a hybrid model to perform high-throughput sequencing of biological genes and improve the accuracy of sequence alignment and variation detection. The sequencing analysis and error correction unit proposes a high-throughput sequencing error correction algorithm based on statistical analysis to perform quality control and error correction on sequencing data to ensure the accuracy and reliability of the data. The sequencing library construction unit is used to convert the biological gene fragments prepared by the sample into a sequencing library for subsequent sequencing on a high-throughput sequencer. Sequencing is performed, including library establishment, library amplification and library quality control; the high-throughput sequencing algorithm based on the hybrid model is as follows: first, the biological gene sequence data obtained by high-throughput sequencing technology is accurately mapped to identify and locate splicing sites; then, the splicing sites are identified and the frequency of splicing events is quantitatively analyzed; secondly, the best transcript structure is selected by constructing a transcript pathway; then, the expression level of the transcript is quantitatively analyzed by comparing the transcript expression data under different conditions to identify differentially expressed transcripts; then, the splicing events are classified and annotated to identify the gene sequences and pathways affected by the splicing events; Secondly, a mixed model containing fixed effects and random effects was constructed to estimate the effect size of splicing events and evaluate the consistency of splicing events in different samples and conditions; finally, after identifying and quantifying splicing events, the mixed model can provide statistical significance information of splicing events and reveal potential associations between different splicing events, by optimizing the weights of interactions between splicing events to accurately simulate the complexity of gene expression regulation.

2. The high-throughput sequencing data processing system according to claim 1, characterized in that: The high-throughput sequencing execution unit proposes a high-throughput sequencing algorithm based on a hybrid model. By constructing a hybrid model to establish an efficient sequencing calculation strategy, high-throughput sequencing is performed on biological gene samples, so that the system can process complex biological gene samples and a large amount of data, ensuring that the sequencing task of large-scale samples is completed in a relatively short time, and maintaining data quality and sequencing accuracy. The detailed process is as follows: First, the biological gene sequence data obtained by high-throughput sequencing technology is accurately mapped to identify and locate splicing sites. Let R be the read sequence set and G be the reference genome sequence. The specific formula is: Where P(R|G) represents the probability of the read sequence R appearing under the condition of a given reference genome G, e represents the base of the natural logarithm, and λ represents the rate of sequence generation on the reference genome. It is expressed as the actual number of reads that map to the same position in the reference genome. Expressed as Then, the splicing sites are identified and the frequency of splicing events is quantitatively analyzed. The specific formula is expressed as: Where F(S) represents the splice site recognition function, S represents the splice site set, s represents the single splice site index, and w s It is expressed as the weight of the splicing site s. Secondly, the best transcript structure is selected by constructing the transcript path. The specific formula is expressed as: Where A represents the scoring function of the optimal evaluation value of all transcripts in the transcript set T, It is represented as a maximum function, t represents a specific transcript in the transcript set T, T represents the set of all existing transcripts, It is represented as a continuous multiplication operation, which means multiplication operation is performed on all elements in the set. Represented as a single read sequence in transcript t, Represented as a single read sequence The probability of mapping to transcript t, then, the expression level of the transcript is quantitatively analyzed by comparing the transcript expression data under different conditions to identify differentially expressed transcripts. The specific formula is expressed as: Wherein, D(E) represents the score of transcript differential expression analysis. It is represented as a summation operation, e1 and e2 represent two different expression level values ​​in the set E, E represents the set of expression levels of biological gene sequences, and |·| represents the absolute value function. Then, the splicing events are classified and annotated to identify the gene sequences and pathways affected by the splicing events. The specific formula is expressed as follows: Among them, C(N) represents the centrality measure of the entire functional annotation network, N represents the set of all known biological gene sequences and pathways, n represents a specific biological gene sequence and pathway in the set N, and S n It is represented as a set of splice sites associated with a specific biological gene sequence and pathway n. Represents a specific splice site associated with n, Expressed as for quantification of splice sites The scoring function in the biological gene sequence and pathway n, secondly, the constructed mixed model containing fixed effects and random effects estimates the effect size of splicing events and evaluates the consistency of splicing events under different samples and conditions. The specific formula of the constructed mixed model is expressed as: Y ij =μ+a i +u i +∈ ij Among them, Y ij Expressed as the observation function of the jth splicing event in the i-th biological gene sample, α i represents the conditional effect of the i-th biological gene sample, i represents the number index of biological gene samples, j represents the splicing event sample index, μ represents the overall mean of splicing events in all samples, and u i The random effect of the i-th sample represented as the within-sample variability, ∈ ij is represented as an error term of random variation. Finally, after identifying and quantifying splicing events, the hybrid model can provide statistical significance information of splicing events and reveal the potential associations between different splicing events. By optimizing the weights of interactions between splicing events, the complexity of gene expression regulation can be accurately simulated. The specific formula is expressed as: Among them, W jk is represented as the interaction weight between splicing event j and splicing event k, where k represents a specific splicing event, β jk is represented by the basic strength of the interaction between splicing event j and splicing event k, I is represented by the total number of biological gene samples, and γ lk It is represented as the random effect contribution of the lth condition to the splicing event k, l is represented as the condition index, and m is represented as the total number of conditions. The application of high-throughput sequencing algorithm based on hybrid model in high-throughput sequencing data processing system not only improves the efficiency and accuracy of data processing, but also provides powerful tools and methods for in-depth research on biological gene expression regulation, and provides important technical support for solving complex biological gene data processing and high-throughput sequencing research.

3. The high-throughput sequencing data processing system according to claim 1, characterized in that: The sequencing analysis error correction unit proposes a high-throughput sequencing error correction algorithm based on statistical analysis, which ensures the accuracy and reliability of the sequencing results by comparing the sequencing data with the reference genome, detecting and correcting sequencing errors. The detailed process is as follows: First, based on the characteristics of high-throughput sequencing data, the estimated size L of the genome and the average error rate P in the sequencing process are determined, and then the maximum window length W for witness matching is calculated. The maximum window length W is determined by minimizing the proportion of reads that cannot be corrected due to errors and the proportion of reads that are corrected by errors. The specific formula is expressed as follows: W max =argmin W (U(W)+D(W)) Among them, W max Denote the maximum window length of witness matching obtained by minimizing U(W)+D(W), argmin W It is represented as the value of W that makes the given function achieve the minimum value, U(W) is represented as the proportion of reads that cannot be corrected when the window length is W, and D(W) is represented as the proportion of reads that are incorrectly corrected when the window length is W. Then, the read sequence string and the reverse complementary sequence are constructed. The specific formula is expressed as follows: Where R represents the new string formed by connecting all read sequences with the reverse complementary sequences. represents the set of all new strings, S represents the total number of read biological gene sequences, r i Represented as the i-th read biological gene sequence, It is represented as the reverse complementary biological gene sequence of the i-th read biological gene sequence, and i is represented as the index of the number of read biological gene sequences. Secondly, through statistical analysis strategy, the two efficient data structures of suffix array and longest common prefix array are used to quickly locate and analyze the patterns in the read sequence, providing a basis for error correction. The suffix array SA is constructed for the string R to store the lexicographic order of all suffixes, and the longest common prefix array LCP is constructed to record the longest common prefix length between consecutive suffixes. Then, the error position is identified by calculating the support value of each base, providing a basis for subsequent error correction. The specific formula is expressed as follows: supp(u,a)=|{q|uaq∈R}| T=min({q|Ec(q)>Ee(q)})+2 Among them, supp(u,a) represents the support value of the substring u followed by base a in the string R, which represents the frequency of a following u, u represents a substring used to detect existing errors, a represents a specific base, q represents a position in the string R, |·| represents the absolute value function, T represents the threshold for correcting errors, which is used to determine whether a base should be corrected, min represents the minimum value function, Ec(q) represents the expected number of correctly witnessed support for correct bases under given q, and Ee(q) represents the expected number of correctly witnessed support for incorrect bases under given q. Finally, base errors in the sequence are corrected according to the support value to improve the accuracy of high-throughput sequencing data. The specific formula is expressed as follows: Among them, Correct(a) represents the correction function, b represents the corrected base, and by traversing each position in R, if the support value supp(u,b) is less than the threshold T, the base a is corrected to the base b with the highest support value. Finally, the final corrected high-throughput sequencing data is output to provide accurate and reliable data for subsequent bioinformatics analysis. The specific formula is expressed as follows: O={r'1,r'2,…,r' n }={r' i |i∈n} Where O represents the corrected read sequence set output, r' i Represented as the corrected i-th read sequence, {r'1, r'2, ..., r' n } is represented as the overall set of read sequences. The high-throughput sequencing error correction algorithm based on statistical analysis significantly improves the accuracy and reliability of high-throughput sequencing data through statistical analysis, optimizing window length, constructing a unified string, utilizing efficient data structures, accurately calculating support values ​​and thresholds, and efficiently correcting errors, providing a more accurate and reliable data foundation for subsequent bioinformatics analysis.

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