Plasmid sequencing and sequence correction screening method based on nanopore sequencing and application of plasmid sequencing and sequence correction screening method

Abstract

The invention provides a plasmid sequencing and sequence correction screening method based on nanopore sequencing and application thereof, and the method comprises the following steps: (1) designing a carrier primer with a bar code, carrying out PCR amplification on a plasmid target fragment, mixing PCR amplification products of different plasmid insertion fragments into one tube, uniformly building a database for the mixed DNA, and sequencing on a machine, according to the number of the sequencing samples and the size of the plasmids, original offline data of the corresponding data volume is harvested; (2) performing data quality control on the original sequencing data based on a sequence correction screening system of high-throughput sequencing, establishing a many-to-one comparison equation of a sequencing sequence and a template sequence, deducing conservative bases through position-specific base frequency statistics, and obtaining a consistent base sequence through sequence recombination; and (3) developing a clone screening decision model, and judging the clone by setting a comparison degree threshold value of the consistency sequence and the template sequence so as to realize automatic grading screening of the clone.

Description

Technical Field

[0001] The present invention belongs to the fields of high-throughput sequencing technology and synthetic biology, and specifically relates to the application technology of third-generation single-molecule real-time sequencing (TGS) in the verification of genetic engineering plasmid cloning. In particular, it relates to a plasmid sequencing and sequence correction and screening method based on nanopore sequencing and its application. Background Art

[0002] Gene synthesis is the process of chemically synthesizing a complete DNA molecule from a DNA sequence segment, and various functional elements such as protein coding sequences, RNA interference molecules, and gene expression regulation can be designed and synthesized according to requirements. This technology has a wide range of applications in modern life sciences, medical research, biopharmaceuticals, etc., and plays an important role in many aspects of biological research and industrial applications such as antibody formulation, dual-luciferase reporter gene experiments, gene editing experiments, protein labeling experiments, and clone expression experiments, providing strong support for genetic engineering and genomics research. The verification of the correctness of synthetic genes is crucial for downstream use, and the correctness of synthetic genes can be verified by DNA sequencing.

[0003] First-generation sequencing (Sanger sequencing) has the advantages of high accuracy, long read length, and suitability for complex sequences. Currently, most plasmid verification sequencing work focuses on the first-generation sequencing platform, but its disadvantages of low sequencing throughput, high sample preparation and sequencing costs, and long sequencing time limit its application in large-scale sequencing.

[0004] Second-generation sequencing (NGS high-throughput sequencing) has the advantages of high throughput and low cost, but when sequencing plasmids, it has the disadvantages of short read length (not exceeding 600 bp) and the need for fragmentation sequencing. The subsequent sequencing data requires complex bioinformatics analysis for data splicing and analysis, with long sequencing time and inflexibility.

[0005] Third-generation sequencing is suitable for long-fragment plasmid sequencing. Although it has significant technical advantages, the existing commercial kits for sequencing platforms are only suitable for simultaneous sequencing of a small number of samples, with disadvantages of low single-shot sequencing accuracy, low throughput, high cost, and no corresponding mature data analysis algorithm model.

[0006] However, in plasmid sequencing, the first-generation sequencing has low throughput, high time and reagent costs, the second-generation NGS sequencing cannot achieve long-fragment sequencing, with long sequencing time, high cost, and complex subsequent data analysis. The third-generation sequencing has low throughput, random errors and strand-specific biases in the raw data of single-shot sequencing data, and systematic deviation problems in the data. In view of the above key technical obstacles, developing a high-accuracy and high-throughput sequencing scheme has important application value. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a plasmid sequencing and sequence correction screening method based on nanopore sequencing and its application. The present invention constructs a plasmid sequencing verification method based on a nanopore sequencing platform for plasmid samples synthesized with known sequences, realizes high-throughput sequencing of plasmids by adding tag sequences, and significantly improves the analysis ability of low-frequency mutation signals in raw data by integrating multiple sequence alignment algorithms and cluster analysis models, achieving a screening throughput of more than 10,000 plasmids in a single sequencing. Compared with traditional Sanger sequencing, the screening efficiency and throughput are greatly improved, the screening cost is significantly reduced, and high-throughput direct sequencing of long-fragment plasmids is realized compared with second-generation sequencing, and efficient sequencing is realized at low cost, which is suitable for large-scale industrial plasmid screening and sequence discovery.

[0008] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a sequence correction screening system based on high-throughput sequencing, the system comprising:

[0010] Data preprocessing module: used to perform quality control and filtering on raw sequencing data and remove low-quality DNA sequences;

[0011] Barcode matching module: (A) For DNA sequences without linker sequences, multi-mode string matching is first performed; then the matching results are verified by BLAST; (B) For DNA sequences with linker sequences, the position of the linker sequence in the DNA sequence is first identified by the split_sequence_by_linker function; then the DNA sequence is split into multiple fragments according to the position of the linker sequence, and each fragment represents a functional region;

[0012] Sequence alignment analysis module: Perform BLAST alignment of the DNA sequence processed by the barcode matching module with the reference sequence; extract alignment information, which includes alignment position and similarity; based on the alignment information, calculate the base frequency of each position, merge the conserved bases at each position, and generate a consistent sequence.

[0013] The post-processing correction system of the present invention adopts a modular design, and forms a complete sequencing data processing pipeline through the organic combination of various functional modules. The system mainly includes a data preprocessing module, a barcode matching module, and a sequence comparison analysis module.

[0014] In the present invention, the quality control and filtering uses the seq_qc_filter function to process the input raw sequencing data.

[0015] Preferably, the steps of quality control and filtering include: setting the quality score threshold to 10, filtering the DNA sequences by quality score, and removing low-quality DNA sequences; setting the length range of the DNA sequences to ±15% of the standard sequence length, screening the lengths of the DNA sequences, setting the parameters of the minimum length to 400 and the maximum length to 3500, and removing DNA sequences that are too short or too long; and then converting the FASTQ format data of the original DNA sequences into FASTA format data.

[0016] Preferably, when the barcode matching module processes, a sequence front-end search optimization strategy is also adopted to search for the barcode in the base range of 1-150 bp at the beginning of the sequence.

[0017] In the present invention, since the barcode is designed at the very beginning of the sequence, the program only searches for the barcode in the 1-150 region, which greatly reduces the search space and improves the processing speed.

[0018] Preferably, in the barcode matching module, the multi-pattern string matching is performed based on the Aho-Corasick matching algorithm; the Aho-Corasick matching algorithm is implemented through the barcode_match_ac function.

[0019] Preferably, in the barcode matching module, the BLAST verification is implemented through the barcode_match_blast function.

[0020] Preferably, in the barcode matching module, the split_sequence_by_linker function adopts a string search algorithm to locate multiple linker sequences; the split_sequence_by_linker function includes the following steps: (1) accurately locating the splitting position according to the provided linker; (2) the entire template sample will be split into n + 1 shorter subsequences according to the number n of linkers, and (3) the obtained sub-fragments are used for the next step of matching.

[0021] Preferably, in the sequence alignment and analysis module, the sources of the reference sequences include: the system constructs a complete template sequence based on the primer sequences and target DNA sequences in the sample information table as the reference sequence for alignment.

[0022] Preferably, in the sequence alignment analysis module, the parameters set for BLAST alignment include: gap open penalty gap_open = 3, gap extension penalty gap_extend = 1, word size word_size = 12, effective match score e_value = 1e - 5, and minimum similarity perc_identity = 0.95.

[0023] Preferably, in the sequence alignment analysis module, the method for calculating the base frequency includes: using a hidden Markov model to statistically analyze the base frequency at each site, and setting the judgment threshold to determine a conserved site when the main peak / secondary peak frequency ratio ≥ 4:1; introducing a Bayesian correction factor: , where P prior comes from a platform - specific error distribution database, and P corrected represents the probability predicted to be correct, and P observed represents the probability actually observed.

[0024] In the present invention, the data processed by the above - mentioned sequence correction and screening system can be further analyzed, for example, for sequence clustering analysis to identify sub - populations and variations in the sequence; or for result evaluation and visualization display.

[0025] In the present invention, sequence clustering analysis is implemented based on MMseqs2. First, the input sequences are pre - processed and format - converted, and then the MMseqs2 clustering command is executed according to the set parameters (including any one or at least two combinations of: minimum sequence similarity, coverage threshold, clustering mode, or sensitivity). Then, the clustering results are parsed and statistical information is generated; the sub - populations in the sequence are identified through the run_clustering function.

[0026] In the present invention, the result evaluation includes:

[0027] (1) The system calculates the matching degree between the consensus sequence and the template sequence through the compare_with_template function, accurately locates and records the positions and types of mismatches, providing a quantitative index for sequence quality evaluation; and / or (2) detection of special sequence features, including: bimodal detection and / or polymer detection.

[0028] The bimodal detection is to identify the positions where the second - highest base frequency > 8%.

[0029] The polymer detection is to identify the sequence regions with 5 or more consecutive C / Gs.

[0030] The visualization display includes: using the plotly library to generate an interactive base frequency chart, intuitively showing the base distribution at each position, and marking special sites and regions.

[0031] The data preprocessing module of the present invention is responsible for performing quality control and filtering on the original sequencing data, removing low-quality sequences, and providing a high-quality data basis for subsequent analysis; the barcode matching module identifies and classifies sequences of different samples based on a specific algorithm to achieve precise differentiation of samples; the sequence alignment analysis module performs sequence alignment and identity analysis through the BLAST algorithm and is the core processing unit of the system; the sequence clustering module performs sequence clustering analysis on specific samples to identify sub-populations in the sequences; the result evaluation module is responsible for calculating and presenting various indicators of the analysis results to provide users with an intuitive result presentation. This modular design not only improves the maintainability and scalability of the system but also enables each processing link to be independently optimized, thereby enhancing the overall performance.

[0032] The correction system of the present invention adopts a multi-level data preprocessing technology to ensure the data quality of subsequent analysis. First, based on a set quality threshold (default is 10), the sequence quality scores are filtered to remove low-quality reads; second, sequences of appropriate lengths are selected according to a preset length range (default is ±15% of the standard sequence length) to avoid interference of too short or too long sequences on the analysis results; finally, the original FASTQ format data is converted into FASTA format data convenient for subsequent analysis, simplifying the data structure and improving the processing efficiency. Specifically, the present invention uses the seq_qc_filter function to process the input sequencing data, and this function performs quality control and format conversion by calling an external tool seqkit (a sequence processing tool). This multi-level preprocessing strategy not only improves the data quality but also reduces the computational burden of subsequent analysis, laying a foundation for efficient and accurate sequence analysis. In addition, this system also supports users to adjust the quality threshold and length range parameters according to specific needs, providing flexible preprocessing options.

[0033] The post-processing deviation correction system of the present invention proposes an efficient barcode matching method, which can quickly and accurately identify the sequences of different samples in a large amount of sequencing data. This method comprehensively utilizes a variety of advanced algorithms, including multi-pattern string matching based on the Aho-Corasick matching algorithm (implemented by the barcode_match_ac function) and sequence similarity search based on BLAST (implemented by the barcode_match_blast function). To further improve the matching efficiency, the present invention also adopts a sequence front-end search optimization strategy, that is, only search for barcodes in the first N bases of the sequence (default 1-150 bp), which greatly reduces the search space and improves the processing speed. In practical applications, the system first uses the Aho-Corasick matching algorithm for rapid preliminary screening, and then verifies the matching results by BLAST to ensure the accuracy of the matching. This dual-verification mechanism not only improves the matching accuracy rate, but also can effectively handle the errors and variations that may exist in the barcode sequences. In addition, this system also supports the custom setting of various barcode formats and matching parameters to meet the needs of different experimental designs.

[0034] The core of the post-processing deviation correction system of the present invention lies in its sequence alignment and consensus analysis technology, which is the key link to achieve high-accuracy sequence error correction.

[0035] First, the system constructs a complete template sequence based on the primer sequence and the target DNA sequence in the sample information table as the reference for alignment; then, creates a local BLAST database for the sequence set of each barcode to improve the alignment efficiency; next, uses optimized BLAST parameters (such as evalue (effective match score), perc_identity (minimum similarity), word_size (word length), etc.) for efficient alignment, balancing speed and accuracy; subsequently, extracts effective alignment information from the BLAST output, including key data such as alignment positions and similarities; finally, based on the alignment results, calculates the base frequencies at each position to generate a consensus sequence.

[0036] In particular, the present invention implements two different sequence processing strategies in the process_single_barcode and process_single_barcode_linker functions, which are applicable to ordinary sequences and complex samples containing linker sequences respectively, greatly expanding the applicable range of the system. In addition, this system also adopts a variety of optimization strategies, such as parameter adaptive adjustment, result caching, etc., to further improve the efficiency and accuracy of the alignment analysis.

[0037] The post - processing deviation correction system of the present invention proposes a dedicated processing method for complex samples containing linker sequences, which is a field less involved in the prior art. This method first identifies the positions of linker sequences in the DNA sequence through the split_sequence_by_linker function. This function adopts an efficient string search algorithm and can accurately locate multiple linker sequences. Then, the DNA sequence is split into multiple fragments according to the positions of the linker sequences, and each fragment represents a functional region. Next, alignment analysis is performed on each fragment respectively to obtain the consensus sequence at the fragment level. Finally, the analysis results of each fragment are integrated in the original order to generate a complete consensus sequence. This segmented processing strategy not only improves the accuracy of alignment but also retains the structural information of the linker sequences, which is of great significance for understanding complex DNA structures. In addition, the system also implements an automatic detection and verification mechanism for linker sequences, which can handle different types and numbers of linker sequences and meet the requirements of various complex samples. This technological innovation enables the system to process DNA sequences with complex structures and significantly expands the application scope.

[0038] The post - processing deviation correction system of the present invention provides a sequence clustering analysis function for specific samples (such as samples labeled "DB") to identify sub - populations and variations in the sequences. This function realizes efficient sequence clustering based on MMseqs2 and supports various adjustable parameter clustering strategies, including minimum sequence similarity, coverage threshold, clustering mode, and sensitivity, etc. In practical applications, the system first pre - processes and converts the format of the input sequences, then executes the MMseqs2 clustering command according to the parameters set by the user, then parses the clustering results and generates statistical information, and finally integrates the clustering results into the final analysis report. Through the run_clustering function, the system can identify sub - populations in the sequences and provide more in - depth information for the analysis of complex samples. This clustering analysis not only helps to discover the heterogeneity in the samples but also identify potential mutations and variations, which is of great value for disease diagnosis and biomarker discovery. In addition, the system also provides a visual display of the clustering results, enabling researchers to intuitively understand the distribution and relationship of the sequences.

[0039] The post - processing error correction system of the present invention also includes comprehensive result evaluation and visualization techniques, providing users with intuitive and detailed analysis results. In terms of result evaluation, the system calculates the matching degree between the consensus sequence and the template sequence through the compare_with_template function, accurately locates and records the positions and types of mismatches, providing quantitative indicators for sequence quality evaluation. In addition, the system also implements the detection functions of various special sequence features, including bimodal detection (identifying positions where the frequency of the second - highest - frequency base > 8%, which may indicate monoclonal), polymer detection (identifying sequence regions with more than 5 consecutive C / Gs, which may lead to sequencing errors), etc. In terms of visualization, the system uses the plotly library to generate interactive base - frequency charts, intuitively showing the base distribution at each position and marking special sites and regions. These visualization tools not only provide a global view of the sequence data but also support local magnification and detailed viewing, facilitating researchers to discover and analyze special patterns and variations in the sequence. Through these evaluation and visualization techniques, this system can provide comprehensive and intuitive analysis results, greatly improving the efficiency and accuracy of data interpretation.

[0040] The post - processing error correction process method based on bioinformatics of the present invention adopts a variety of parallel processing and performance optimization techniques to improve processing efficiency, enabling the entire analysis process to efficiently process high - throughput sequencing data. In terms of parallel processing, the system can achieve multi - process parallel processing of different barcodes and execute BLAST alignment tasks in parallel, making full use of the computing power of modern multi - core processors. In terms of performance optimization, the system adopts the strategy of streaming large files, avoiding loading a large amount of data into memory at one time and reducing memory occupancy; it implements an intelligent temporary file management mechanism to automatically create and clean temporary files, optimizing disk usage; in addition, the system also adopts a variety of algorithm optimization strategies, such as index acceleration, caching mechanisms, etc., to further improve processing speed. The comprehensive application of these technologies enables this system to complete the analysis of large - scale data within a reasonable time, significantly improving work efficiency. Practical tests show that for a dataset containing hundreds of samples and millions of sequences, this system can complete a comprehensive analysis within a few hours, while traditional methods may take several days.

[0041] The post - processing correction process method based on bioinformatics of the present invention also designs a perfect error - handling and fault - tolerance mechanism to ensure that the analysis process can run stably and provide reliable results under various circumstances. In terms of input verification, the system comprehensively verifies the input files and parameters, including file format checking, parameter range verification, etc., and promptly discovers and reports potential problems. In terms of exception capture, the system implements a multi - level exception capture and handling mechanism in key processing steps, which can identify and record various types of exception situations and provide detailed error information. In terms of result verification, the system conducts consistency checks on the generated results to ensure the integrity and accuracy of the data. In terms of fault - tolerance processing, the system implements a variety of fault - tolerance strategies, such as allowing a certain degree of mismatch at the end of the sequence (e.g., the last 5 bp), which is very effective for dealing with the common problem of decreased quality at the end of sequencing data. In addition, the system also provides a detailed logging function to record the key steps and potential problems during the processing, facilitating subsequent analysis and debugging. The comprehensive application of these mechanisms ensures the stability of the system and the reliability of the results, and can provide valuable analysis results even when dealing with data of poor quality or complex structure.

[0042] The post - processing correction system of the present invention provides a flexible and extensible command - line interface, enabling users to configure system parameters according to specific requirements. This interface includes required parameters (sequence file path, sample information file path, and output file name) and a variety of optional parameters (parameters for each module including quality control, barcode matching, BLAST alignment, sequence clustering, etc.). To improve the user experience, the system organizes relevant parameters into logical groups, such as quality control parameter group, BLAST parameter group, clustering parameter group, etc., making the parameter structure clearer. Through the parse_arguments function, the system can parse and verify the parameters input by the user and configure the corresponding processing flow according to these parameters. In addition, the system also provides default parameter values and parameter range checks to reduce the possibility of user configuration errors. This flexible parameter configuration mechanism enables the system to adapt to different research needs and data characteristics, and can achieve appropriate parameter settings for both simple sequence alignment and complex multi - sample clustering analysis.

[0043] In a second aspect, the present invention provides a plasmid sequencing and sequence correction and screening method based on nanopore sequencing, and the method includes:

[0044] (1) Design a vector primer with a barcode, perform PCR amplification on the plasmid target fragment, mix the PCR amplification products of different plasmid insert fragments into one tube, uniformly construct a library and perform on - machine sequencing for the mixed DNA, and harvest the original off - machine data with the corresponding data volume according to the number of sequencing samples and the plasmid size;

[0045] (2)Based on the high-throughput sequencing sequence correction and screening system described in the first aspect, perform data quality control on the original sequencing data, establish a many-to-one alignment equation between the sequencing sequence and the template sequence, deduce conserved bases through position-specific base frequency statistics, and obtain a consensus base sequence through sequence recombination;

[0046] (3)Develop a clone screening decision model, and judge the clones by setting the alignment degree threshold between the consensus sequence and the template sequence to achieve automated hierarchical screening of the clones.

[0047] Preferably, in step (1), the barcoded vector primer is designed based on the vector backbone and is used to amplify the target fragment, and a tag sequence is connected to the primer to distinguish different plasmids.

[0048] Preferably, in step (3), the method for judging the clones includes: using the Needleman-Wunsch global alignment algorithm to align the consensus sequence with the true template sequence, setting the mismatch penalty score to -5, the gap penalty score to -2, and the match score to +1, and a similarity of 100% is judged as a valid clone.

[0049] In the third aspect, the present invention provides an application of the plasmid sequencing and sequence correction and screening method based on nanopore sequencing described in the second aspect in high-throughput sequencing.

[0050] In the fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps in the sequence correction and screening system based on high-throughput sequencing described in the first aspect are implemented.

[0051] In the fifth aspect, the present invention provides a computer device, including a memory and a processor, and a computer program capable of running on the processor is stored on the memory, and when the processor executes the computer program, the steps in the sequence correction and screening system based on high-throughput sequencing described in the first aspect are implemented.

[0052] Explanation of professional terms involved in the present invention:

[0053] BLAST, Basic Local Alignment Search Tool, abbreviated as BLAST. When running the BLAST software, a query sequence to be searched and a sequence to be searched (also called the target sequence), or a sequence database containing multiple such sequences are required. BLAST will search for subsequences similar to the query sequence in the database.

[0054] barcode_match_blast function: The meaning of this function name is: to use the blast algorithm to achieve barcode matching, with the aim of extracting the sequences corresponding to the barcodes in the entire off-machine database and storing them separately according to different barcodes. Its process is as follows: (1) Construct the barcode library to be matched; (2) Construct the search library with the off-machine sequences; (3) Match each barcode one by one, with the parameters: word length word_size = 16, effective match score e_value = 10, minimum similarity perc_identity = 1.0; (4) Store the results matched by each barcode.

[0055] Aho-Corasick matching algorithm: For the specific explanation of the algorithm, please refer to the following reference link: https: / / oi-wiki.org / string / ac-automaton / ; In computer science, the Aho-Corasick matching algorithm is a string search algorithm invented by Alfred V. Aho and Margaret J. Corasick in 1975.

[0056] barcode_match_ac function: The meaning of this function name is: to use the Aho-Corasick algorithm to achieve barcode matching, with the aim of extracting the sequences corresponding to the barcodes in the entire off-machine database and storing them separately according to different barcodes. Its process is as follows: (1) Construct the barcode library to be matched; (2) Construct the search library with the off-machine sequences; (3) Match each barcode one by one; (4) Store the results matched by each barcode.

[0057] Needleman-Wunsch global alignment algorithm: Also known as the Needleman-Wunsch algorithm, the Needleman-Wunsch algorithm is an algorithm used in bioinformatics to align protein or nucleotide sequences. It is one of the first applications of dynamic programming in comparing biological sequences. Essentially, this algorithm decomposes a large problem (such as a complete sequence) into a series of smaller problems, and then uses the solutions of the smaller problems to find the optimal solution of the larger problem. It is sometimes also called the optimal matching algorithm and global alignment technique. The Needleman-Wunsch algorithm is still widely used for optimal global alignment, especially when the quality of the global alignment is crucial. This algorithm assigns a score to each possible alignment, and the purpose of this algorithm is to find all possible alignments with the highest scores.

[0058] split_sequence_by_linker function: The meaning of this function name is: split the sequence according to the linker (linker), with the aim of splitting the entire sequence into different subsequences at intervals based on the positions of the linker (linker) in the sequence, which is convenient for subsequent alignment. This function is mainly used to adapt to plasmid sequencing corresponding to bispecific antibodies and scFv antibodies. This function includes the following steps: (1) accurately locate the splitting positions according to the provided linker; (2) the entire template sample will be split into n + 1 shorter subsequences according to the number of linkers n; (3) the obtained sub-fragments are used for the next step of matching.

[0059] seq_qc_filter function: The meaning of this function name is: perform quality control and screening on the sequence. The main purpose of this function is to screen all the reads downloaded from the machine according to strict quality standards, so as to remove noise, improve the robustness of the entire process, and enhance the credibility of the final result. This function includes the following processes: (1) use the linux cat command to merge all the downloaded fastq format files to obtain the all.fastq file; (2) use the seqtk software to perform the operation of converting all.fastq to fasta to obtain the all.fasta file; (3) use the seqkit software to perform length and quality screening on all.fasta, with the parameter m (minimum length) being 400, M (maximum length) being 3500, and Q (quality) being 10, and finally obtain the Q10_lenthfilt.fasta file. The address of the seqtk software is: https: / / github.com / lh3 / seqtk; the address of the seqkit software is: https: / / github.com / shenwei356 / seqkit.

[0060] parse_arguments function: The meaning of this function name is to parse the input arguments. It is mainly used to parse command-line arguments. Its core function is to convert the arguments (such as options, flags, or argument values) entered by the user through the command line into structured data that can be recognized within the program. By defining argument rules (such as argument types, default values, help information, etc.), this function realizes the standardized processing of the input arguments, thereby enabling the program to dynamically adjust its running logic according to the arguments. The steps include: (1) Initialize the parser: Create an argument parser object and configure the global description information; (2) Define argument rules: Implement argument descriptions by adding argument definitions; (3) Parse the arguments: Call the parsing method. During this process, the legality of the arguments is automatically verified (such as type matching, whether required arguments exist); (4) Return and pass the arguments: Return the parsed argument object for other modules of the program to call, such as reading the input file path, setting the running mode, or enabling the debug flag.

[0061] MMseqs2: The full name is Many-against-Many sequence searching. It is a software used to search and cluster large sets of protein and nucleotide sequences. MMseqs2 is a free and open-source software implemented in C++. The steps include (1) Building a library of plasmid sequences that may have bimodality to be clustered into DB.fasta; (2) Using MMseqs2 for clustering with the parameter settings of mmseqs easy-cluster examples / DB.fasta clusterRes tmp --min-seq-id 0.5 -c 0.8 --cov-mode 1; (3) Sending the clustering results to professionals for judgment.

[0062] run_clustering function: The Chinese name of this function is: Run the clustering steps. This function uses the above-mentioned MMSeqs2 software as support to cluster plasmid sequences that may be confused by bimodality. The obtained clustering results can assist professionals in reviewing the results and further reducing errors. The steps include (1) Building a library of plasmid sequences that may have bimodality to be clustered into DB.fasta; (2) Using mmseqs2 for clustering with the parameters of mmseqs easy-cluster examples / DB.fasta clusterRes tmp --min-seq-id 0.5 -c 0.8 --cov-mode 1; (3) Sending the clustering results to professionals for judgment.

[0063] compare_with_template function: The Chinese name of this function is: Alignment with the template sequence. The main purpose of this function is to finally align the obtained consensus sequence with the real template sequence to ensure that the consensus sequence obtained by the process is truly usable and reduce errors. The steps of this function include: (1) Prepare the consensus sequence; (2) Prepare the template sequence; (3) Use the Needleman-wunch algorithm for multiple sequence alignment of the two sequences; (4) Make a judgment based on the alignment score. If the matching rate of the two sequences is 100%, it is considered usable; if it is less than 100%, it is judged as unusable.

[0064] plotly library: Plotly's Python graphics library can create publication-quality interactive graphics. Software link: https: / / github.com / plotly / plotly.py.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] (1) The present invention realizes the simultaneous library construction and sequencing of inserted gene fragments in more than ten thousand different plasmids, overcomes the limitation of the number of samples per run of commercial kits, and can realize the simultaneous sequencing of plasmid insertion fragments of different lengths, reducing the library construction time and reagent cost.

[0067] (2) The present invention uniformly constructs a library for mixed DNA, improves the efficiency of library construction, significantly reduces the workload, realizes automated operation, reduces operation errors, and reduces the library construction cost.

[0068] (3) The present invention optimizes the judgment algorithm for plasmid sequencing sequences, realizes the judgment of plasmid sequences with a sequencing depth of 100×, can accurately interpret plasmid sequences with 1G of raw off-machine data for 4000 samples, and can freely select the amount of sequencing data according to different sample numbers. It can perform off-machine sequencing at any time on a nanopore sequencer. The sequencing time required for 4000 samples is as short as 1.5 hours, the sequencing time is flexible, and the chip can be used multiple times, greatly reducing the sequencing time and reagent cost.

[0069] (4) The present invention designs a judgment algorithm for plasmid samples, realizes the automatic alignment of sequencing data to sample sequences and to the synthetic target sequence, automatically judges positive plasmid clones, and the judgment time for 4000 samples is as short as 15 minutes, greatly improving the judgment efficiency of plasmid sequencing data.

[0070] (5) The post-processing and correction system of the present invention adopts a modular design. Through the organic combination of each functional module, a complete sequencing data processing pipeline is formed; this modular design not only improves the maintainability and scalability of the system, but also enables each processing link to be independently optimized, thereby improving the overall performance. Brief Description of the Drawings

[0071] Figure 1 is a flowchart of the sequencing analysis of the present invention.

[0072] Figure 2 is the summary analysis of the results of Application Example 1. Detailed Embodiments

[0073] The technical solution of the present invention will be further described below through specific embodiments. Those skilled in the art should understand that the embodiments are only for helping to understand the present invention and should not be regarded as specific limitations on the present invention.

[0074] For those technical or conditions not specified in the embodiments, they shall be carried out according to the technologies or conditions described in the literature in the art or according to the product specifications. For the reagents or instruments whose manufacturers are not specified, they are all conventional products that can be purchased through regular channels.

[0075] Example 1

[0076] This example provides a plasmid sequencing and sequence correction and screening method based on nanopore sequencing, and the flowchart is as Figure 1 shown.

[0077] 1. Library Pretreatment

[0078] Design universal primers for vectors for different samples, and add barcode (Barcode) sequence tags to the upstream primers to amplify different plasmid target gene fragments respectively. The PCR products amplified by different tagged primers are preliminarily quantified by Nanodrop (micro-spectrophotometer) and the concentration difference is adjusted within 2 times. Use a 96-well pipetting workstation to aspirate DNA in equal volumes and mix them into one tube. Use AMPure XP magnetic beads to purify the mixed DNA library, take 1 μL of the purified product for qubit (Qubit dsDNA HS Assay Kit, Invitrogen, cat # Q32851) concentration quantification to ensure that the library has at least 100 - 200 fmol for subsequent use, and use Qseq-1 plus for DNA fragment size analysis.

[0079] 2. Library Construction

[0080] Use the NANOPORE SQK-LSK114 library construction kit and T4 ligase to construct the library for the amplified DNA fragments on the machine.

[0081] 1) End Repair

[0082] Thaw the DNA quality control product (DCS) at room temperature. Prepare the end repair system according to the reagent instructions of the NEBNext Ultra II End Repair / dA-tailing Module. Perform end repair on 100 - 200 fmol of amplified DNA and purify it using AMPure XP magnetic beads.

[0083] 2) Adapter ligation and purification

[0084] Prepare the adapter ligation system using T4 ligase (NEBNext Quick Ligation Module NEB, cat # E6056), and purify the DNA fragments using AMPure XP magnetic beads and SFB wash buffer. Elute the DNA and quantify it using a qubit (fluorescence quantifier) to ensure sufficient DNA amount for the library loaded onto the machine.

[0085] 3) Chip pretreatment and loading

[0086] Take out the sequencing chip (FLO-MIN114) in advance and equilibrate it at room temperature for 5 minutes before use. Complete the quality inspection of the flow cell on the GridION sequencer before loading the library to ensure that the number of available wells meets the sequencing requirements. Prepare and load the sequencing chip pretreatment solution mix, let it stand, prepare and load the library mix, and operate slowly to ensure that no bubbles are introduced into the chip during the whole operation process.

[0087] 3. Sequencing on the machine

[0088] Set the information for loading the sequencing sample onto the machine, calculate the sequencing data volume according to the number of samples, and terminate the sequencing, save the data and unload the sequencing data when the data volume is sufficient.

[0089] 4. Unload the sample and clean the chip for future use

[0090] Clean the unloaded chip in time according to the instructions of the NANOPORE cleaning kit (EXP-WSH004). Prepare the chip cleaning buffer mix, stop or pause the sequencing experiment in MinKNOW, keep the sequencing chip in the sequencer, add the chip cleaning buffer and wait for 60 minutes, then aspirate the waste liquid. After adding the storage buffer, store the sequencing chip in a 4 - 8 °C refrigerator waiting for the next use.

[0091] 5. Quality control of the unloaded data

[0092] Use the sliding window method (window length 50 bp, step size 10 bp) to calculate the local Phred quality score, and filter out the reads with quality value < Q10 and length deviating from the template by ±15%.

[0093] Detect chimeras based on k-mer spectrum analysis (k = 7) to ensure data quality.

[0094] 6. Barcode Matching

[0095] Use the barcode corresponding to each clone to search and match the entire demultiplexed database using regular expressions, find the READS corresponding to each clone barcode and split them, waiting for the next step of analysis.

[0096] 7. Sequence Alignment

[0097] Perform multiple sequence alignment using the BLAST algorithm, set the gap open penalty gap_open = 3, the gap extension penalty gap_extend = 1, the word size word_size = 12, the effective match score e_value = 1e-5, and the minimum similarity perc_identity = 0.95.

[0098] Perform a many-to-one alignment of the high-quality sequencing sequences with the template sequence to generate a multiple sequence alignment matrix (MSA).

[0099] 8. Base Frequency Statistics and Conservative Base Deduction

[0100] Use the Hidden Markov Model (HMM) to statistically analyze the base frequencies at each position. Since the background noise of third-generation sequencing is too strong, the judgment threshold is set such that when the main peak / secondary peak frequency ratio ≥ 4:1, it is determined as a conservative site.

[0101] Introduce the Bayesian correction factor: , where P prior comes from the platform-specific error distribution database, and P corrected represents the probability predicted to be correct, and P observed represents the probability actually observed.

[0102] 9. Consensus Sequence Generation

[0103] Merge the conservative bases at each position to generate a consensus base sequence.

[0104] 10. Clone Screening

[0105] Use the Needleman-Wunsch global alignment algorithm to align the consensus sequence with the true template sequence, set the mismatch penalty -5, the gap penalty -2, the match score +1, and a similarity of 100% to determine a valid clone. Valid clones will be transferred to the production process, while invalid clones will be deleted.

[0106] Example 2

[0107] This embodiment provides a plasmid sequencing and sequence correction and screening method based on nanopore sequencing, and this embodiment provides a method for processing complex samples containing Linker sequences.

[0108] 1. Library pretreatment

[0109] The steps of library pretreatment refer to Example 1.

[0110] 2. Library construction

[0111] The steps of library construction refer to Example 1.

[0112] 3. Sequencing on the machine

[0113] The steps of sequencing on the machine refer to Example 1.

[0114] 4. The sample is taken off the machine and the chip is cleaned for later use

[0115] This step refers to Example 1.

[0116] 5. Quality control of the data taken off the machine

[0117] This step refers to Example 1.

[0118] 6. Barcode matching

[0119] This step refers to Example 1.

[0120] 7. Sequence alignment

[0121] First, for the given linker sequence, use the split_sequence_by_linker function to identify the positions of the linker sequences in the DNA sequence. This function adopts an efficient string search algorithm and can accurately locate multiple linker sequences; after this step, the entire template sample will be divided into n + 1 shorter subsequences according to the number n of linker sequences. For each shorter subsequence, use the BLAST algorithm for multiple sequence alignment, set the gap open penalty gap_open = 3, the gap extension penalty gap_extend = 1, the word length word_size = 12, the effective match score e_value = 1e-5, and the minimum similarity perc_identity = 0.95.

[0122] Perform a many-to-one alignment of the high-quality sequencing sequences with the subsequences of the template sequence to generate n + 1 multiple sequence alignment matrices (MSAs).

[0123] 8. Base frequency statistics and derivation of conserved bases

[0124] This step refers to Example 1.

[0125] 9. Generation of consensus sequences

[0126] Similar to step 7 above, the conserved bases of the n + 1 subsequences are merged respectively, and then the previously separated linker sequences are inserted into the middle of the subsequences to restore the length state of the original template sequence, thereby generating a consensus base sequence similar to that in Example 1 before.

[0127] 10. Clone Screening

[0128] Using the Needleman-Wunsch global alignment algorithm, the consensus sequence is aligned with the true template sequence. The mismatch penalty is set to -5, the gap penalty is set to -2, and the match score is +1. A similarity of 100% is determined as a valid clone. The valid clones will be transferred to the production process, while the invalid clones will be deleted.

[0129] Application Example 1

[0130] This application example provides a plasmid sequencing and sequence correction screening method based on nanopore sequencing. The flow chart is as Figure 1 shown. The data numbered 20250116 - 02 (the data is from Shanghai Bio Ying Biotechnology Co., Ltd.) is used as the display and supplement of the technical effect of the present invention. The number of clones in this batch of data is 4464.

[0131] Through the optimized experimental process and calculation analysis strategy, this method realizes efficient and accurate plasmid sequencing and clone screening. The specific technical indicators are as follows:

[0132] 1. Library Pretreatment

[0133] Universal primers for vectors are designed for different samples, and barcode sequence tags are added to the upstream primers to amplify different plasmid target gene fragments respectively. The PCR products amplified by different tagged primers are preliminarily quantified by Nanodrop (micro-spectrophotometer), and the concentration difference is adjusted within 2 times. Using a 96-well pipetting workstation, DNA is aspirated in equal volume and mixed into one tube. The mixed DNA library is purified using AMPure XP magnetic beads. 1 μL of the purified product is taken for qubit (fluorescence quantifier, Qubit dsDNA HS Assay Kit, Invitrogen, cat #Q32851) concentration quantification to ensure that the library has at least 100 - 200 fmol for subsequent use, and Qseq-1 plus is used for DNA fragment size analysis. The library pretreatment process only takes 0.5 h at the stage of 4464 clones in this batch.

[0134] 2. Library Construction

[0135] The DNA fragments obtained by amplification were used to construct a library for sequencing on the machine using the NANOPORE SQK-LSK114 library construction kit and T4 ligase. The total time taken for the library construction phase was 2 h.

[0136] 1) End repair

[0137] Thaw the DNA quality control product (DCS) at room temperature. Prepare the end repair system according to the reagent instructions of the NEBNext Ultra II End Repair / dA-tailing Module. Perform end repair on 200 fmol of amplified DNA and purify it using AMPure XP magnetic beads.

[0138] 2) Adapter ligation and purification

[0139] Prepare the adapter ligation system using T4 ligase (NEBNext Quick Ligation Module NEB, cat # E6056). Purify the DNA fragments using AMPure XP magnetic beads and SFB wash buffer. Elute the DNA and quantify it using a qubit (fluorescence quantifier) to ensure there is enough DNA for the library to be sequenced on the machine.

[0140] 3) Chip pretreatment and loading

[0141] Take out the sequencing chip (FLO-MIN114) in advance and equilibrate it at room temperature for 5 min before use. Complete the quality inspection of the flow cell on the GridION sequencer before loading the library to ensure that the number of available wells meets the sequencing requirements. Prepare and load the sequencing chip pretreatment liquid mixture (mix), let it stand, prepare and load the library mixture (mix), and operate slowly to ensure that no bubbles are introduced into the chip during the whole operation process.

[0142] 3. Sequencing on the machine

[0143] Set the information for the samples to be sequenced on the machine. Calculate the amount of sequencing data based on the number of samples. When the amount of data is sufficient, terminate the sequencing, save the data, and remove the samples from the sequencer. The entire sequencing phase only takes 1 hour and 40 minutes. The original amount of data obtained by sequencing is 1 Gb, and the sequencing depth is 300X.

[0144] 4. Remove the samples from the sequencer and clean the chip for future use

[0145] Clean the off-machine chip in a timely manner according to the instructions of the NANOPORE Cleaning Kit (EXP-WSH004). Prepare the chip cleaning buffer mixture (mix), stop or pause the sequencing experiment in MinKNOW (a software used in sequencing), keep the sequencing chip in the sequencer, add the chip cleaning buffer and wait for 60 minutes, then aspirate the waste liquid. After adding the storage buffer, store the sequencing chip in a 4-8 °C refrigerator for the next use.

[0146] 5. Off-machine data quality control

[0147] Use the sliding window method (window length 50 bp, step size 10 bp) to calculate the local Phred quality score, and filter the reads with quality value < Q10 and length deviation from the template by ±15%.

[0148] Detect chimeras based on k-mer spectrum analysis (k = 7) to ensure data quality, and the quality control steps can achieve an accuracy of 99.9%.

[0149] 6. Barcode matching

[0150] Barcode matching based on regular expressions (recall rate 99.9%), with the average matching time for a single sample < 0.1 s (parallel processing with 32 cores and 64 threads).

[0151] 7. Sequence alignment

[0152] Adopt the parallelized BLAST algorithm (32 cores, 64 threads, 128 GB of memory), and the parameter optimization is as follows:

[0153] Set the gap open penalty gap_open = 3, the gap extension penalty gap_extend = 1, the word length word_size = 12, the effective match score e_value = 1e-5, and the minimum similarity perc_identity = 0.95.

[0154] The alignment speed reaches 2000 sequences / second, the recognition accuracy of the Linker sequence is 96.25%, and the MSA generation time < 10 min (for a scale of ten thousand reads).

[0155] 8. Base frequency statistics and conservative base derivation

[0156] The HMM model (training set accuracy > 99%) combined with Bayesian correction (error rate reduced by 40%), and the conservative site determination accuracy is 97.3% when the main peak / secondary peak threshold ≥ 4:1.

[0157] 9. Consensus sequence generation

[0158] Needleman-Wunsch global alignment (parameters set as: mismatch -5, gap -2, match +1), 96.8% accuracy rate for 100% similarity clone screening.

[0159] 10. Clone Screening

[0160] Use the Needleman-Wunsch global alignment algorithm to align the consensus sequence with the true template sequence. Set the mismatch penalty -5, gap penalty -2, and match score +1. A similarity of 100% is determined as a valid clone.

[0161] The rejection rate of invalid clones > 99.5%, and the production conversion success rate of valid clones is increased to 96.2%.

[0162] The result summary analysis of this application example is as Figure 2 shown.

[0163] This application example comprehensively verified the effect of the third-generation sequencing error correction method. Through four core indicators: accuracy rate, barcode matching recall rate, precision rate, and rejection rate of invalid clones, the excellent performance of this technology was systematically demonstrated. Specifically, the results of the application example showed that the accuracy rate of the error correction method was as high as 96.24%, indicating that its error correction processing of sequencing data almost completely eliminated errors and could almost perfectly restore the true sequence information, providing extremely high data reliability for subsequent analysis. In terms of the barcode matching recall rate, this method reached 99.99%, fully proving that it could effectively identify and match the barcode sequences in the samples, significantly reducing the risk of data loss caused by barcode mismatches, and providing strong guarantee for sample tracking and typing in high-throughput sequencing. The precision rate was 97.40%, further indicating that this method had outstanding ability to suppress false positive results during the error correction process, could ensure the authenticity of the output results, and avoid the interference of misjudgment on the analysis conclusion. In addition, the rejection rate of invalid clones was as high as 99.50%, highlighting the excellent efficiency of this technology in filtering low-quality or invalid sequencing data, effectively improving the data utilization rate and reducing the burden of invalid data on subsequent analysis.

[0164] Based on the above indicators, it can be seen that the third-generation sequencing error correction method proposed by the present invention shows significant advantages in terms of data accuracy, integrity, and reliability, and its technical effect far exceeds that of existing conventional methods. Especially under the synergistic effect of high accuracy rate and high rejection rate of invalid clones, this method can not only greatly improve the quality of sequencing data, but also optimize the efficiency of the data processing process, providing more accurate and efficient technical support for application scenarios such as genomics research and clinical diagnosis. In addition, the comprehensive performance of the four indicators also verifies the stability and adaptability of this method in complex sequencing environments, laying a solid foundation for its industrial application.

[0165] The technical advantages of the technical solution of the present invention compared with the traditional method are summarized in Table 1 as follows.

[0166] Table 1

[0167]

[0168] Note: All data are based on the standard test environment (GridION X5, Intel Xeon 6248R, 128 GB DDR4, Linux).

[0169] In summary, the present invention provides a plasmid sequencing and sequence correction and screening method based on nanopore sequencing and its application. The method includes: (1) designing barcoded vector primers, respectively performing PCR amplification on plasmid target fragments, and constructing a sequencing library suitable for the nanopore sequencing platform for the amplified fragments; (2) performing on-machine sequencing, and harvesting the original off-machine data with corresponding data volume according to the number of sequencing samples and the plasmid size; (3) constructing a data analysis algorithm for the sequencing data, and establishing a deep correction algorithm based on multiple sequence alignment: performing data quality control on the original sequencing data, establishing a one-to-many comparison equation between the sequencing sequence and the template sequence, deriving conserved bases through position-specific base frequency statistics, and obtaining a consensus base sequence through sequence recombination; (4) developing a clone screening decision model, and realizing automatic hierarchical screening of clones by setting the alignment degree threshold between the consensus sequence and the template sequence. The innovation of the present invention lies in constructing a plasmid sequencing verification method based on the nanopore sequencing platform for plasmid samples synthesized from known sequences, realizing high-throughput sequencing of plasmids by adding tag sequences, significantly improving the analysis ability of low-frequency mutation signals in the original data by integrating multiple sequence alignment algorithms and clustering analysis models, realizing a screening throughput of more than 10,000 plasmids per single sequencing, greatly improving the screening efficiency and throughput compared with traditional Sanger sequencing, significantly reducing the screening cost, realizing high-throughput direct sequencing of long-fragment plasmids compared with second-generation sequencing, and achieving high-efficiency sequencing at low cost, which is applicable to large-scale industrial plasmid screening and sequence discovery.

[0170] The applicant declares that the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A sequence correction screening system based on high-throughput sequencing, characterized in that: The system comprises: Data preprocessing module: used to perform quality control and filtering on raw sequencing data and remove low-quality DNA sequences; Barcode matching module: (A) For DNA sequences without linker sequences, multi-mode string matching is first performed; then the matching results are verified by BLAST; (B) For DNA sequences with linker sequences, the position of the linker sequence in the DNA sequence is first identified by the split_sequence_by_linker function; then the DNA sequence is split into multiple fragments according to the position of the linker sequence, and each fragment represents a functional region; Sequence alignment analysis module: Perform BLAST alignment of the DNA sequence processed by the barcode matching module with the reference sequence; extract alignment information, which includes alignment position and similarity; based on the alignment information, calculate the base frequency of each position, merge the conserved bases at each position, and generate a consensus sequence.

2. The sequence correction screening system based on high-throughput sequencing according to claim 1, characterized in that: The quality control and filtering steps include: setting the quality score threshold to 10, filtering the DNA sequence by quality score, and eliminating low-quality DNA sequences; setting the length range of the DNA sequence to ±15% of the standard sequence length, screening the DNA sequence length, setting the minimum length parameter to 400 and the maximum length to 3500, and eliminating DNA sequences that are too short or too long; and then converting the original DNA sequence FASTQ format data into FASTA format data.

3. The sequence correction and screening system based on high-throughput sequencing according to claim 1, characterized in that: When the barcode matching module is processing, a sequence front-end search optimization strategy is also adopted to search for barcodes in the base range of 1-150 bp at the beginning of the sequence.

4. The sequence correction and screening system based on high-throughput sequencing according to claim 1, characterized in that: In the barcode matching module, the multi-mode string matching is performed based on the Aho-Corasick matching algorithm; the Aho-Corasick matching algorithm is implemented by the barcode_match_ac function; The BLAST verification is achieved by barcode_match_blast function; The split_sequence_by_linker function uses a string search algorithm to locate multiple linker sequences. The split_sequence_by_linker function includes the following steps: (1) accurately locating the split position according to the provided linker; (2) the entire template sample will be divided into n+1 shorter subsequences according to the number of linkers n; (3) the obtained sub-segments are used for the next step of matching; In the sequence comparison and analysis module, the source of the reference sequence includes: the system constructs a complete template sequence based on the primer sequence and the target DNA sequence in the sample information table as the reference sequence for comparison.

5. The sequence correction and screening system based on high-throughput sequencing according to claim 1, characterized in that: In the sequence alignment analysis module, the parameters of the BLAST alignment setting include: setting the gap open penalty gap_open=3, the extension penalty gap_extend=1, the word length word_size=12, the effective match score e_value=1e-5, and the minimum similarity perc_identity=0.95; In the sequence comparison analysis module, the base frequency calculation method includes: using a hidden Markov model to count the base frequency of each site, and setting the judgment threshold to determine that the site is conservative when the main peak / secondary peak frequency ratio is ≥4:1; introducing a Bayesian correction factor: , where P prior From the platform-specific error distribution database, P corrected represents the probability that the prediction is correct, P observed represents the probability of the actual observation.

6. A plasmid sequencing and sequence correction screening method based on nanopore sequencing, characterized in that: The method comprises: (1) Design barcoded vector primers, perform PCR amplification on the plasmid target fragment, mix the PCR amplification products of different plasmid insert fragments into one tube, build a unified library for the mixed DNA and sequence it on the machine, and obtain the corresponding amount of raw data off the machine based on the number of sequencing samples and plasmid size; (2) A sequence correction screening system for high-throughput sequencing according to any one of claims 1 to 5, performing data quality control on raw sequencing data, establishing a many-to-one comparison equation between sequencing sequences and template sequences, deducing conservative bases through position-specific base frequency statistics, and obtaining a consensus base sequence through sequence recombination; (3) Develop a clone screening decision model to determine clones by setting the alignment threshold between the consensus sequence and the template sequence, thereby achieving automated hierarchical screening of clones.

7. The method for plasmid sequencing and sequence correction screening based on nanopore sequencing according to claim 6, characterized in that: In step (1), the barcoded vector primer is designed based on the vector backbone and is used to amplify the target fragment. The primer is connected with a tag sequence to distinguish different plasmids; In step (3), the method for determining clones includes: using the Needleman-Wunsch global alignment algorithm to align the consensus sequence with the true template sequence, setting the mismatch penalty to -5, the gap penalty to -2, the match score to +1, and determining a valid clone when the similarity is 100%.

8. Use of the plasmid sequencing and sequence correction screening method based on nanopore sequencing according to claim 6 or 7 in high-throughput sequencing.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program, when executed by the processor, implements the steps in the sequence correction and screening system based on high-throughput sequencing according to any one of claims 1 to 5.

10. A computer device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the sequence correction and screening system based on high-throughput sequencing described in any one of claims 1 to 5 are implemented.

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