A plasmid sequencing and sequence correction screening method based on nanopore sequencing and its application

Abstract

The present invention provides a plasmid sequencing and sequence correction screening method based on nanopore sequencing and its application, the method comprising: (1) designing barcoded vector primers, performing PCR amplification on plasmid target fragments, mixing PCR amplification products of different plasmid insert fragments into one tube, uniformly building a library for the mixed DNA and sequencing it on a machine, and harvesting a corresponding amount of original offline data according to the number of sequencing samples and the size of the plasmid; (2) implementing data quality control on the original sequencing data based on a sequence correction screening system for high-throughput sequencing, establishing a many-to-one alignment equation between the sequencing sequence and the template sequence, deducing conserved bases through position-specific base frequency statistics, and obtaining a consensus base sequence through sequence recombination; (3) developing a clone screening decision model, judging clones by setting a threshold for the alignment between the consensus sequence and the template sequence, and realizing automated hierarchical screening of clones.

Description

Technical Field

[0001] The present invention belongs to the field 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 clones, and more particularly to a plasmid sequencing and sequence correction screening method based on nanopore sequencing and its application. Background Art

[0002] Gene synthesis is the process of synthesizing a complete DNA molecule from a DNA sequence using chemical methods. Various functional components, including protein-coding sequences, RNA interference molecules, and gene expression regulators, can be designed and synthesized on demand. This technology has extensive applications in modern life sciences, medical research, biopharmaceuticals, and other fields. It plays a vital role in biological research and industrial applications, including antibody development, dual-luciferase reporter gene experiments, gene editing, protein labeling, and cloning and expression experiments, providing strong support for genetic engineering and genomics research. Verifying the correctness of synthesized genes is crucial for downstream use, and can be verified through DNA sequencing.

[0003] First-generation sequencing (Sanger sequencing) has the advantages of high accuracy, long read length, and applicability to complex sequences. Currently, most plasmid verification sequencing work is concentrated 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 it has the disadvantages of short read length (no more than 600 bp) that requires sequencing interruption when sequencing plasmids. The sequencing data requires complex bioinformatics analysis for data splicing and analysis in the later stage, and the sequencing time is long and inflexible.

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

[0006] However, the first-generation sequencing throughput of plasmid sequencing is low, and the time and reagent costs are high. The second-generation NGS sequencing cannot achieve long fragment sequencing, and the sequencing time is long, the cost is high, and the subsequent data analysis is complex. The third-generation sequencing throughput is low, and there are random errors and chain-specific biases in the raw data of single sequencing data, and the data has systematic deviations. To address the above key technical obstacles, the development of a high-accuracy and high-throughput sequencing solution has important application value. Summary of the Invention

[0007] In response to the shortcomings of the prior art, the present invention aims to provide a nanopore sequencing-based plasmid sequencing and sequence correction screening method and its application. This invention constructs a nanopore sequencing platform-based plasmid sequencing verification method for synthesized plasmid samples with known sequences. By adding tag sequences, high-throughput sequencing of plasmids is achieved. By integrating a multiple sequence alignment algorithm with a cluster analysis model, the ability to resolve low-frequency mutation signals in the raw data is significantly improved, achieving a screening throughput of over 10,000 plasmids per sequencing run. This significantly improves screening efficiency and throughput compared to traditional Sanger sequencing, significantly reduces screening costs, and enables high-throughput direct sequencing of long-fragment plasmids compared to second-generation sequencing. This method achieves efficient sequencing at a low cost, making it 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 deviation 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 eliminate 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 linker sequence position in the DNA sequence is first identified using the split_sequence_by_linker function; then the DNA sequence is split into multiple segments based on the linker sequence position, with each segment representing a functional region;

[0012] Sequence alignment analysis module: performs BLAST alignment of the DNA sequence processed by the barcode matching module with the reference sequence; extracts alignment information, which includes alignment position and similarity; based on the alignment information, calculates the base frequency at each position, merges the conserved bases at each position, and generates a consensus sequence.

[0013] The post-processing correction system of the present invention adopts a modular design, forming 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 alignment analysis module.

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

[0015] Preferably, the quality control and filtering steps include: setting the quality score threshold to 10, performing quality score filtering on the DNA sequence, 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.

[0016] Preferably, the barcode matching module also adopts a sequence front-end search optimization strategy during processing, searching 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-mode string matching is performed based on an Aho-Corasick matching algorithm; the Aho-Corasick matching algorithm is implemented by a barcode_match_ac function.

[0019] Preferably, in the barcode matching module, the BLAST verification is implemented by a 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 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, and (3) the obtained sub-fragments are used for the next step of matching.

[0021] Preferably, 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 target DNA sequence in the sample information table as a reference sequence for comparison.

[0022] Preferably, 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.

[0023] Preferably, in the sequence alignment 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 conserved 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 true observation.

[0024] In the present invention, the data processed by the above-mentioned sequence deviation correction and screening system can be subjected to further analysis and processing, for example, sequence cluster analysis to identify subpopulations and variations in the sequence; or result evaluation and visualization.

[0025] In the present invention, sequence clustering analysis is implemented based on MMseqs2. First, the input sequence is preprocessed and formatted. Then, the MMseqs2 clustering command is executed according to the set parameters (including: minimum sequence similarity, coverage threshold, clustering mode or sensitivity, or a combination of at least two). Then, the clustering results are analyzed and statistical information is generated. Subpopulations in the sequence are identified using the run_clustering function.

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

[0027] (1) The system calculates the degree of match between the consensus sequence and the template sequence through the compare_with_template function, accurately locates and records the position and type of mismatch, and provides quantitative indicators for sequence quality assessment; and / or (2) detects special sequence features, including: doublet detection and / or polymer detection.

[0028] The double peak detection is to identify the position where the second high frequency base frequency is >8%.

[0029] The polymer detection is to identify a sequence region with more than 5 consecutive C / G sequences.

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

[0031] The data preprocessing module of the present invention is responsible for quality control and filtering of raw sequencing data, eliminating low-quality sequences and providing a high-quality data foundation for subsequent analysis. The barcode matching module uses a specific algorithm to identify and classify sequences of different samples, enabling accurate sample differentiation. The sequence alignment and analysis module uses the BLAST algorithm to perform sequence alignment and consistency analysis, and is the core processing unit of the system. The sequence clustering module performs sequence cluster analysis on specific samples and identifies subpopulations within the sequences. The result evaluation module is responsible for calculating and displaying various analysis results indicators, providing users with an intuitive presentation of the results. This modular design not only improves the system's maintainability and scalability, but also enables independent optimization of each processing step, thereby improving 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, the sequence is filtered based on the set quality threshold (the default is 10) to eliminate low-quality reads; second, the sequence of appropriate length is screened according to the preset length range (the 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 for subsequent analysis, simplifying the data structure and improving processing efficiency. In specific implementation, the present invention uses the seq_qc_filter function to process the input sequencing data. This function performs quality control and format conversion by calling the external tool seqkit (a sequence processing tool). This multi-level preprocessing strategy not only improves data quality, but also reduces the computational burden of subsequent analysis, laying the foundation for efficient and accurate sequence analysis. In addition, the 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 correction system of this invention proposes an efficient barcode matching method that can quickly and accurately identify sequences from different samples within large amounts of sequencing data. This method integrates multiple advanced algorithms, including multi-mode 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 matching efficiency, the invention also employs a sequence front-end search optimization strategy, which searches for barcodes only within the first N bases of the sequence (default 1-150 bp). This significantly reduces the search space and improves processing speed. In practical applications, the system first uses the Aho-Corasick matching algorithm for rapid initial screening, and then performs BLAST verification on matching results to ensure matching accuracy. This dual verification mechanism not only improves matching accuracy but also effectively addresses potential errors and variations in barcode sequences. Furthermore, the system supports custom settings for various barcode formats and matching parameters to accommodate diverse experimental designs.

[0034] The core of the post-processing error correction system of the present invention lies in its sequence alignment and consistency 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 sequences and target DNA sequences in the sample information table as a reference for alignment; then, a local BLAST database is created for each barcode sequence set to improve alignment efficiency; then, optimized BLAST parameters (such as evalue (effective match score), perc_identity (minimum similarity), word_size (word length)), etc.) are used for efficient alignment, balancing speed and accuracy; then, effective alignment information is extracted from the BLAST output, including key data such as alignment position and similarity; finally, based on the alignment results, the base frequency at each position is calculated to generate a consensus sequence.

[0036] Specifically, the present invention implements two distinct sequence processing strategies in the process_single_barcode and process_single_barcode_linker functions, respectively suitable for standard sequences and complex samples containing linker sequences, significantly expanding the system's applicability. Furthermore, the system employs multiple optimization strategies, such as adaptive parameter adjustment and result caching, to further enhance the efficiency and accuracy of alignment analysis.

[0037] The post-processing correction system of this invention proposes a specialized method for handling complex samples containing linker sequences, an area that is less addressed in the prior art. This method first identifies the location of linker sequences within a DNA sequence using the split_sequence_by_linker function. This function employs an efficient string search algorithm to accurately locate multiple linker sequences. The DNA sequence is then segmented into multiple fragments based on the location of the linker sequences, with each fragment representing a functional region. Each fragment is then individually aligned and analyzed to obtain fragment-level consensus sequences. Finally, the analysis results for each fragment are integrated in their original order to generate a complete consensus sequence. This segmented processing strategy not only improves alignment accuracy but also preserves the structural information of linker sequences, which is crucial for understanding complex DNA structures. Furthermore, the system implements an automatic detection and verification mechanism for linker sequences, enabling it to handle diverse linker types and quantities, adapting to the needs of a wide range of complex samples. This technological innovation enables the system to handle structurally complex DNA sequences, significantly expanding its application.

[0038] The post-processing and correction system of this invention provides a sequence clustering analysis function for specific samples (such as those marked "DB"), which is used to identify subpopulations and variants within the sequence. This function uses MMseqs2 to achieve efficient sequence clustering and supports clustering strategies with multiple adjustable parameters, including minimum sequence similarity, coverage threshold, clustering mode, and sensitivity. In practice, the system first preprocesses and converts the input sequence format. It then executes the MMseqs2 clustering command according to user-defined parameters. The clustering results are then analyzed and statistical information is generated. Finally, the clustering results are integrated into the final analysis report. Through the run_clustering function, this system can identify subpopulations within the sequence, providing deeper insights for the analysis of complex samples. This clustering analysis not only helps to discover heterogeneity within the sample but also identifies potential mutations and variants, which is of great value for disease diagnosis and biomarker discovery. In addition, this system provides a visual display of the clustering results, allowing researchers to intuitively understand the distribution and relationships of the sequences.

[0039] The post-processing and correction system of the present invention also includes comprehensive result evaluation and visualization technologies, providing users with intuitive and detailed analysis results. In terms of result evaluation, the system calculates the degree of match between the consensus sequence and the template sequence through the compare_with_template function, accurately locates and records the position and type of mismatches, and provides quantitative indicators for sequence quality assessment. In addition, the system also implements detection functions for various special sequence features, including double peak detection (identifying positions where the frequency of the second high-frequency base is >8%, which may indicate double clones) and polymer detection (identifying sequence regions with more than 5 consecutive C / G sequences, which may cause sequencing errors). In terms of visualization, the system uses the plotly library to generate interactive base frequency charts, intuitively displaying the base distribution at each position and annotating special sites and regions. These visualization tools not only provide a global view of the sequence data, but also support local zoom and detailed viewing, making it easier for researchers to discover and analyze special patterns and variations in the sequence. Through these evaluation and visualization technologies, the system can provide comprehensive and intuitive analysis results, greatly improving the efficiency and accuracy of data interpretation.

[0040] To improve processing efficiency, the bioinformatics-based post-processing and error correction process of this invention utilizes multiple parallel processing and performance optimization techniques, enabling the entire analysis process to efficiently process high-throughput sequencing data. Regarding parallel processing, the system enables multiple processes to process different barcodes in parallel and perform BLAST alignment tasks in parallel, fully leveraging the computing power of modern multi-core processors. Regarding performance optimization, the system utilizes a streaming strategy for large files, avoiding the need to load large amounts of data into memory all at once and reducing memory usage. It also implements an intelligent temporary file management mechanism that automatically creates and cleans temporary files, optimizing disk usage. Furthermore, the system employs multiple algorithm optimization strategies, such as index acceleration and caching, to further enhance processing speed. The combined application of these technologies enables the system to complete large-scale data analysis within a reasonable timeframe, significantly improving efficiency. Actual tests have shown that the system can complete comprehensive analyses of datasets containing hundreds of samples and millions of sequences in a few hours, whereas traditional methods may require days.

[0041] The bioinformatics-based post-processing correction process of this invention also incorporates comprehensive error handling and fault tolerance mechanisms to ensure stable analysis and reliable results under all circumstances. Regarding input validation, the system comprehensively verifies input files and parameters, including file format checks and parameter range verification, promptly identifying and reporting potential issues. Regarding exception capture, the system implements a multi-layered exception capture and handling mechanism in key processing steps, identifying and recording various exceptions and providing detailed error information. Regarding result verification, the system performs consistency checks on generated results to ensure data integrity and accuracy. Regarding fault tolerance, the system implements multiple fault tolerance strategies, such as allowing a certain degree of mismatch at sequence ends (e.g., the last 5 bp), which is very effective in addressing the common problem of end-quality degradation in sequencing data. Furthermore, the system provides detailed logging to record key processing steps and potential issues, facilitating subsequent analysis and debugging. The integrated application of these mechanisms ensures system stability and reliable results, delivering valuable analytical results even when processing low-quality or complex data.

[0042] The post-processing correction system of the present invention provides a flexible and extensible command line interface, allowing users to configure system parameters according to specific needs. The interface includes required parameters (sequence file path, sample information file path and output file name) and a variety of optional parameters (including parameters of various modules such as quality control, barcode matching, BLAST alignment, sequence clustering, etc.). In order 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., to make the parameter structure clearer. Through the parse_arguments function, the system can parse and verify the parameters entered by the user, and configure the corresponding processing flow based on these parameters. In addition, the system also provides parameter default 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, from simple sequence alignment to complex multi-sample clustering analysis, all of which can be achieved through appropriate parameter settings.

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

[0044] (1) Design barcoded vector primers, perform PCR amplification on the plasmid target fragment, mix the PCR amplification products of different plasmid inserts 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 based on the number of sequencing samples and plasmid size;

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

[0046] (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.

[0047] Preferably, in step (1), the barcoded vector primers are designed based on the vector backbone and are used to amplify the target fragment. The primers are connected with tag sequences for distinguishing different plasmids.

[0048] Preferably, in step (3), the method for determining the clone 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%.

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

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, 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 a fifth aspect, the present invention provides a computer device comprising a memory and a processor, wherein a computer program that can be run on the processor is stored in 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 this invention:

[0053] BLAST, or Basic Local Alignment Search Tool, is abbreviated as BLAST. To run BLAST, you need a query sequence and a target sequence (also called a target sequence), or a sequence database containing multiple such sequences. BLAST searches the database for subsequences similar to the query sequence.

[0054] barcode_match_blast function: This function uses the blast algorithm to perform barcode matching. The goal is to extract the barcode-matching sequence from the entire offline database and store it separately for each barcode. The process is as follows: (1) Construct a library of barcodes to be matched; (2) Construct a library of offline sequences to be searched; (3) Match each barcode individually, using the following parameters: word_size = 16, effective match score e_value = 10, and minimum similarity perc_identity = 1.0; (4) Store the matching results for each barcode.

[0055] Aho-Corasick matching algorithm: For a detailed explanation of the algorithm, 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: This function uses the Aho-Corasick algorithm to perform barcode matching. Its purpose is to extract the corresponding barcode sequence from the entire offline database and store it separately for each barcode. The process is as follows: (1) Build a library of barcodes to be matched; (2) Build a library of offline sequences to be searched; (3) Match each barcode individually; (4) Store the matching results for each barcode.

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

[0058] The split_sequence_by_linker function: The name of this function means: the sequence is split according to the linker sequence (linker) as a separator. The purpose is to split the entire sequence into different subsequences according to the position of the linker in the sequence as an interval to facilitate subsequent comparison. This function is mainly adapted for plasmid sequencing corresponding to bispecific antibodies and scFv antibodies. The function includes the following steps: (1) accurately locate 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-fragments are used for the next step of matching.

[0059] seq_qc_filter function: The name of this function means: quality control screening of sequences. The main purpose of this function is to filter all reads (reads) according to strict quality standards, thereby removing noise, improving the robustness of the entire process, and enhancing the credibility of the final results. This function includes the following steps: (1) Use the Linux cat command to merge all fastq format files from the machine to obtain the all.fastq file; (2) Use the seqtk software to convert all.fastq to fasta to obtain the all.fasta file; (3) Use the seqkit software to filter the length and quality of all.fasta, with the parameters m (minimum length) of 400, M (maximum length) of 3500, and Q (quality) of 10, and finally obtain the Q10_lenthfilt.fasta file. The seqtk software address is: https: / / github.com / lh3 / seqtk; the seqkit software address is: https: / / github.com / shenwei356 / seqkit.

[0060] parse_arguments function: This function name means parsing input parameters. It is mainly used to parse command line parameters. Its core function is to convert the parameters (such as options, flags or parameter values) entered by the user through the command line into structured data that can be recognized within the program. This function implements the standardization of input parameters by defining parameter rules (such as parameter type, default value, help information, etc.), thereby supporting the program to dynamically adjust the operation logic according to the parameters. The steps include: (1) Initialize the parser: create a parameter parser object and configure global description information; (2) Define parameter rules: implement parameter description by adding parameter definitions; (3) Parse parameters: call the parsing method. In this process, the legality of the parameters is automatically verified (such as type matching, whether required parameters exist); (4) Return and pass parameters: return the parsed parameter object for other modules of the program to call, such as reading the input file path, setting the operation mode or enabling the debug flag.

[0061] MMseqs2 (Many-against-Many sequence searching) is a software for searching and clustering large sets of protein and nucleotide sequences. MMseqs2 is free and open-source software implemented in C++. The steps include (1) creating a database DB.fasta of plasmid sequences that may contain doublets to be clustered; (2) performing clustering using MMseqs2 with the parameters mmseqs easy-cluster examples / DB.fasta clusterRes tmp --min-seq-id 0.5-c 0.8 --cov-mode 1; and (3) submitting the clustering results to a professional for evaluation.

[0062] run_clustering function: The Chinese name of this function is: run clustering step. This function uses the above-mentioned MMSeqs2 software as support to cluster plasmid sequences that may have double peak confusion. The obtained clustering results can assist professionals in reviewing the results and further reduce errors. The steps include (1) building a database DB.fasta of plasmid sequences that may have double peaks to be clustered; (2) using mmseqs2 for clustering with the parameters mmseqs easy-clusterexamples / 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: compare with template sequence. The main purpose of this function is to compare the final consensus sequence with the real template sequence to ensure that the consensus sequence obtained by the process is real and usable, and to reduce errors. The steps of this function include: (1) preparing the consensus sequence; (2) preparing the template sequence; (3) using the Needleman-Wunch algorithm to perform a multiple sequence alignment on the two sequences; (4) judging based on the alignment score. If the match rate of the two sequences is 100%, they are considered usable; if it is less than 100%, they are considered unusable.

[0064] Plotly: Plotly's Python graphics library can produce 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 achieves simultaneous sequencing of a mixed library of more than 10,000 gene fragments inserted into different plasmids, overcoming the limitation of the number of samples that can be loaded onto a commercial kit at a time. It can also achieve simultaneous sequencing of plasmid inserts of different lengths, reducing library construction time and reagent costs.

[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 operational errors, and reduces library construction costs.

[0068] (3) The present invention optimizes the judgment algorithm of plasmid sequencing sequence, and can judge the plasmid sequence by achieving 100× sequencing depth. It can accurately judge the plasmid sequence with 1G of original offline data of 4000 samples, and can freely choose the amount of sequencing data according to the number of different samples. It can realize sequencing at any time on the 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, which greatly reduces the sequencing time and reagent cost.

[0069] (4) The present invention designs an algorithm for reading plasmid samples, which realizes automatic comparison of sequencing data to sample sequences and synthetic target sequences, and automatically determines positive plasmid clones. The reading time for 4000 samples is as short as 15 minutes, which greatly improves the efficiency of judging plasmid sequencing data.

[0070] (5) The post-processing correction system of the present invention adopts a modular design. Through the organic combination of various functional modules, 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 flow chart of the sequencing analysis of the present invention.

[0072] Figure 2 This is a summary analysis of the results of Application Example 1. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is further described below by way of specific embodiments. It should be understood by those skilled in the art that the embodiments are merely to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0074] If no specific techniques or conditions are specified in the examples, the experiments were carried out according to the techniques or conditions described in the literature in the field or according to the product instructions. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased through regular channels.

[0075] Example 1

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

[0077] 1. Library preprocessing

[0078] Universal primers were designed for each sample, and barcode sequence tags were added to the upstream primers to amplify the target gene fragments from different plasmids. The PCR products amplified with the different tag primers were initially quantified using a Nanodrop (micro-spectrophotometer) and the concentration difference was adjusted to within 2-fold. Using a 96-well pipetting station, equal volumes of DNA were pipetted into a single tube. The pooled DNA library was purified using AMPure XP magnetic beads. 1 μL of the purified product was quantified using the Qubit (Qubit dsDNA HS Assay Kit, Invitrogen, cat # Q32851) to ensure a minimum of 100–200 fmol of library for subsequent use. DNA fragment size analysis was performed using Qseq-1 plus.

[0079] 2. Library construction

[0080] The amplified DNA fragments were used to construct the library using the NANOPORE SQK-LSK114 library construction kit and T4 ligase.

[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 available number of 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 the sequencing sample to be loaded 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. Add the storage buffer and 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] Chimeras were detected based on k-mer spectrum analysis (k = 7) to ensure data quality.

[0094] 6. Barcode matching

[0095] Using the barcode corresponding to each clone, the entire offline database is searched and matched using regular expressions to find and split the READS (read segments) corresponding to each clone barcode, waiting for the next step of analysis.

[0096] 7. Sequence alignment

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

[0098] High-quality sequencing sequences were aligned many-to-one with template sequences to generate a multiple sequence alignment matrix (MSA).

[0099] 8. Base frequency statistics and conserved base deduction

[0100] The hidden Markov model (HMM) was used to count the base frequencies of each site. Due to the strong background noise of third-generation sequencing, the judgment threshold was set to a conserved site when the main peak / secondary peak frequency ratio was ≥4:1.

[0101] Introducing the 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 true observation.

[0102] 9. Consistent sequence generation

[0103] The conserved bases at each position are merged to generate a consensus base sequence.

[0104] 10. Clone Screening

[0105] The consensus sequence was aligned with the true template sequence using the Needleman-Wunsch global alignment algorithm, with a mismatch penalty of -5, a gap penalty of -2, and a match score of +1. A similarity of 100% was considered a valid clone. Valid clones were transferred to the production process, while invalid clones were deleted.

[0106] Example 2

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

[0108] 1. Library preprocessing

[0109] The steps of library pretreatment were as described in Example 1.

[0110] 2. Library construction

[0111] The steps for library construction were as described in Example 1.

[0112] 3. Sequencing

[0113] The steps for sequencing are as described in Example 1.

[0114] 4. Remove the sample from the machine and clean the chip for later use

[0115] This step refers to Example 1.

[0116] 5. Off-board data quality control

[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 a given linker sequence, the split_sequence_by_linker function is used to identify the position of the linker sequence in the DNA sequence. This function uses 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 of linker sequences n. For each shorter subsequence, the BLAST algorithm is used for multiple sequence alignment, with 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.

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

[0123] 8. Base frequency statistics and conserved base deduction

[0124] This step refers to Example 1.

[0125] 9. Consistent sequence generation

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

[0127] 10. Clone Screening

[0128] The consensus sequence was aligned with the true template sequence using the Needleman-Wunsch global alignment algorithm, with a mismatch penalty of -5, a gap penalty of -2, and a match score of +1. A similarity of 100% was considered a valid clone. Valid clones were transferred to the production process, while invalid clones were 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 follows: Figure 1 The data with the number 20250116-02 (data source: Shanghai Bio-Tech Co., Ltd.) is used to demonstrate and supplement the technical effects of the present invention. The number of clones in this batch of data is 4464.

[0131] This method achieves efficient and accurate plasmid sequencing and clone screening through optimized experimental procedures and computational analysis strategies. The specific technical indicators are as follows:

[0132] 1. Library preprocessing

[0133] Universal primers were designed for each sample, and barcode sequence tags were added to the upstream primers to amplify the target gene fragments from different plasmids. PCR products amplified with the different tag primers were initially quantified using a Nanodrop (micro-spectrophotometer) and adjusted to within a 2-fold difference. DNA was pipetted into equal volumes using a 96-well pipetting station and then mixed into a single tube. The pooled DNA library was purified using AMPure XP magnetic beads. 1 μL of the purified product was quantified using a Qubit (Qubit dsDNA HS Assay Kit, Invitrogen, cat #Q32851) to ensure a minimum of 100-200 fmol for subsequent use. DNA fragment size analysis was performed using Qseq-1 plus. The library pretreatment process took only 0.5 h for this batch of 4464 clones.

[0134] 2. Library construction

[0135] The amplified DNA fragments were used for library construction using the NANOPORE SQK-LSK114 library construction kit and T4 ligase. The total library construction time was 2 hours.

[0136] 1) End repair

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

[0138] 2) Adapter ligation and purification

[0139] T4 ligase (NEBNext Quick Ligation Module NEB, cat # E6056) was used to prepare the adapter ligation system, and the DNA fragments were purified using AMPure XP magnetic beads and SFB wash buffer. The DNA was eluted and quantified using a qubit (fluorimeter) to ensure sufficient library DNA was loaded onto the machine.

[0140] 3) Chip pretreatment and sample loading

[0141] Remove the sequencing chip (FLO-MIN114) in advance and equilibrate it at room temperature for 5 minutes before use. Before loading the library, perform flow cell quality control on the GridION sequencer to ensure that the number of available wells meets sequencing requirements. Prepare and load the sequencing chip pretreatment solution mix. Allow to stand, then prepare and load the library mix. Operate slowly to ensure that no bubbles are introduced into the chip during the entire process.

[0142] 3. Sequencing

[0143] Set up the sequencing sample loading information, calculate the sequencing data volume based on the number of samples, terminate sequencing once sufficient data is available, save the data, and then unload the sequencing machine. The entire sequencing phase takes only 1 hour and 40 minutes, generating 1 Gb of raw data at a sequencing depth of 300X.

[0144] 4. Remove the sample from the machine and clean the chip for later 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. Quality control of off-machine data

[0147] Calculate the local Phred quality score using the sliding window method (window length 50 bp, step size 10 bp), and filter the reads with quality value < Q10 and length deviation from the template ± 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 an average matching time per 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 memory), and the parameter optimization is as follows:

[0153] Set the gap open penalty gap_open = 3, the gap extend 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.

[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 (scale of ten thousand reads).

[0155] 8. Base frequency statistics and derivation of conserved bases

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

[0157] 9. Generation of consensus sequences

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

[0159] 10. Clone Screening

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

[0161] The invalid clone elimination rate is >99.5%, and the effective clone production conversion success rate is increased to 96.2%.

[0162] The results of this application example are summarized and analyzed as follows: Figure 2 shown.

[0163] This application example comprehensively verifies the effectiveness of the third-generation sequencing deviation correction method, and systematically demonstrates the excellent performance of this technology through four core indicators: accuracy, barcode matching recall rate, precision rate, and invalid clone elimination rate. Specifically, the application example results show that the accuracy of the deviation correction method is as high as 96.24%, indicating that its deviation correction processing of sequencing data almost completely eliminates errors, can almost perfectly restore the true sequence information, and provides extremely high data reliability for subsequent analysis. In terms of barcode matching recall rate, this method reaches 99.99%, which fully proves that it can effectively identify and match barcode sequences in samples, significantly reducing the risk of data loss due to barcode mismatch, and providing a strong guarantee for sample tracking and typing in high-throughput sequencing. The precision rate is 97.40%, which further illustrates that the method has an outstanding ability to suppress false positive results during the deviation correction process, can ensure the authenticity of the output results, and avoid the interference of misjudgment on the analysis conclusions. In addition, the invalid clone elimination rate was as high as 99.50%, highlighting the excellent efficiency of this technology in filtering low-quality or invalid sequencing data, effectively improving data utilization 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 correction method proposed in this invention shows significant advantages in data accuracy, integrity, and reliability, and its technical effect far exceeds that of existing conventional methods. In particular, under the synergistic effect of high accuracy and high invalid clone elimination rate, this method can not only significantly improve the quality of sequencing data, but also optimize the efficiency of data processing procedures, providing more accurate and efficient technical support for application scenarios such as genomic 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 over the traditional method are summarized in Table 1.

[0166] Table 1

[0167]

[0168] Note: All data is based on a 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 screening method based on nanopore sequencing and its application, the method comprising: (1) designing barcoded vector primers, performing PCR amplification on plasmid target fragments respectively, 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 of corresponding data volume according to the number of sequencing samples and the size of the plasmid; (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 many-to-one alignment equation between the sequencing sequence and the template sequence, deducing 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 automated hierarchical screening of clones by setting a threshold for the alignment degree between the consensus sequence and the template sequence. The innovation of the present invention lies in the construction of a plasmid sequencing verification method based on a nanopore sequencing platform for plasmid samples synthesized with known sequences. High-throughput sequencing of plasmids is achieved by adding tag sequences. By integrating multiple sequence alignment algorithms and clustering analysis models, the ability to analyze low-frequency mutation signals in the original data is significantly improved, and a screening throughput of more than 10,000 plasmids in a single sequencing is achieved. Compared with traditional Sanger sequencing, the screening efficiency and throughput are greatly improved, and the screening cost is significantly reduced. Compared with second-generation sequencing, high-throughput direct sequencing of long-fragment plasmids is achieved, and efficient sequencing is achieved at low cost. It is suitable for large-scale industrial plasmid screening and sequence discovery.

[0170] The applicant declares that the above is only a specific embodiment of the present invention, but the scope of protection 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 scope of protection and disclosure of the present invention.

Claims

1. A sequence correction and 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 eliminate low-quality DNA sequences; Barcode matching module: (A) For DNA sequences without junction sequences, multi-mode string matching is first performed; then the matching results are verified by BLAST; the multi-mode string matching is based on the Aho-Corasick matching algorithm; the Aho-Corasick matching algorithm is implemented by the barcode_match_ac function; the process of the barcode_match_ac function is as follows: (1) constructing a barcode library to be matched; (2) constructing a library to be searched based on the machine sequence; (3) matching each barcode one by one; (4) storing the matching results of each barcode; The BLAST verification is implemented by the barcode_match_blast function; the process of the barcode_match_blast function is as follows: (1) constructing a barcode library to be matched; (2) constructing a library to be searched by downloading the sequence; (3) matching each barcode one by one, with the following parameters: word length word_size=16, effective matching score e_value=10, minimum similarity perc_identity=1.0; (4) storing the results of each barcode match; (B) For a DNA sequence containing a linker sequence, the position of the linker sequence in the DNA sequence is first identified by the split_sequence_by_linker function; the DNA sequence is then split into multiple fragments according to the position of the linker sequence, each fragment representing a functional region; 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 split into n+1 shorter subsequences according to the number of linkers n, and (3) the obtained sub-fragments are used for the next step of matching; Sequence alignment analysis module: performs BLAST alignment of the DNA sequence processed by the barcode matching module with the reference sequence; extracts alignment information, which includes alignment position and similarity; based on the alignment information, calculates the base frequency at each position, merges the conserved bases at each position, and generates a consensus sequence.

2. The sequence deviation correction and 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, performing quality score filtering on the DNA sequence, 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 parameter minimum length 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 deviation correction and screening system based on high-throughput sequencing according to claim 1, characterized in that: The barcode matching module also uses a sequence front-end search optimization strategy during processing, searching for barcodes in the base range of 1-150 bp at the beginning of the sequence.

4. The sequence deviation correction and screening system based on high-throughput sequencing according to claim 1, characterized in that: 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 target DNA sequence in the sample information table as the reference sequence for comparison.

5. The sequence deviation 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 alignment analysis module, the base frequency calculation method includes: using a hidden Markov model to count the base frequency of each site, setting the judgment threshold to determine the site as a conserved site when the main peak / secondary peak frequency ratio is ≥4:1; introducing a Bayesian correction factor: ,in, 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 true 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 inserts 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 based on the number of sequencing samples and plasmid size; (2) A sequence deviation correction and screening system for high-throughput sequencing based on any one of claims 1 to 5, performing data quality control on the original sequencing data, establishing a many-to-one alignment equation between the sequencing sequence and the template sequence, deducing conserved 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 primers are designed based on the vector backbone and are used to amplify the target fragment. The primers are connected to tag sequences for distinguishing different plasmids. In step (3), the method for determining the clone 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 nanopore sequencing-based plasmid sequencing and sequence correction screening method 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 when the computer program is executed by the processor, the steps of the sequence deviation correction and screening system based on high-throughput sequencing are implemented as described in any one of claims 1 to 5.

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

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