Rapid species identification system based on nanopore third-generation sequencing technology and application thereof

Abstract

The application belongs to the technical field of biotechnology, and particularly relates to a species rapid identification system and method based on Nanopore third-generation sequencing technology and application thereof. The species rapid identification system comprises: a quality control unit for performing quality control on the data downloaded from a machine and converting the data into a fasta file to obtain clean reads; a reference sequence acquisition unit for aligning the clean reads and selecting reference sequences; a sequence alignment unit for aligning the clean reads to the selected reference sequences; a variation detection unit for performing variation detection to generate consistent sequences; a species annotation unit for aligning the consistent sequences to obtain annotation results; and a report rendering unit for generating an analysis report. The application makes full use of the technical advantages of Nanopore third-generation sequencing technology and combines advanced bioinformatics analysis methods to internally build an entire analysis process from the data downloaded from a machine to an analysis report, thereby providing high-quality analysis services for researchers, saving analysis costs and improving the ease of use of identification.

Description

Technical Field

[0001] This application belongs to the field of biotechnology, specifically a rapid species identification system based on nanopore third-generation sequencing technology and its application. Background Technology

[0002] With the continuous development of science and technology, gene sequencing technology has made great breakthroughs. Among them, Nanopore third-generation sequencing technology has become one of the hot topics in current research and application fields due to its advantages such as portability, high throughput, long read length, and direct DNA sequencing. This technology achieves rapid sequencing of the entire genome by monitoring the changes in current of DNA molecules in nanoscale pores, providing a powerful tool for life sciences, medicine, and environmental research.

[0003] However, in practical applications, especially in fields such as environmental biology, microbiology, and ecology, rapidly and accurately identifying the species in sequencing samples remains a challenging problem, mainly manifested in the following ways: (1) For first-generation Sanger sequencing, this method is time-consuming and labor-intensive, and it is difficult to handle large-scale samples; for second-generation sequencing, although this method can handle large-scale samples, its read length is short and it is not suitable for diverse environments. (2) It requires high bioinformatics skills and programming ability. From sequencing data to analysis report, several analysis steps need to be performed. Each analysis step requires running several bioinformatics software programs. Finally, the results of these software programs need to be analyzed, the corresponding results need to be parsed and the report template needs to be filled in to form an analysis report. The whole process requires high bioinformatics skills and programming ability and can only be completed by professionals.

[0004] Currently, in the field of quarantine and inspection, metagenomic sequencing based on nanopore sequencing technology has been used in some preliminary studies on the detection of plant pathogenic microorganisms (Chalupowicz et al., 2019; Filloux et al., 2018; Naito et al., 2019; Chen et al., 2021; Lu et al., 2021). However, there are no research reports on the application of this technology to the detection and identification of harmful organisms based on amplicon sequencing. Moreover, in previous studies and applications, researchers usually send the amplified products to sequencing companies, and it takes at least a day for the company to return the sequencing results before species identification and analysis can be performed, which is very inefficient.

[0005] In conclusion, when applying Nanopore third-generation sequencing technology to species identification for quarantine and inspection purposes, it is still necessary to improve its data analysis efficiency, accuracy, ease of use, and report readability. Summary of the Invention

[0006] To address at least one technical problem existing in the prior art, this application provides a rapid species identification system, method, and application based on Nanopore third-generation sequencing technology.

[0007] In the first aspect, this application discloses a rapid species identification system based on Nanopore third-generation sequencing technology, comprising: The quality control unit is used to merge Nanopore third-generation sequencing data, perform quality control on the merged results, and finally convert the quality control results into FASTA format to obtain clean reads. The reference sequence acquisition unit is used to perform sequence alignment of the clean reads with all candidate sequences in a predetermined candidate alignment database, filter out the corresponding alignment results according to the set similarity and coverage thresholds, count the number of reads aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence. The sequence alignment unit is used to align the clean reads to the selected reference sequence, filter out alignment results with low alignment quality, and sort the alignment results, build an index, and calculate the depth of each site in the reference sequence. The mutation detection unit is used to perform mutation detection using Medaka based on the alignment results obtained by the sequence alignment unit to obtain a mutated vcf file, compress the mutated vcf file, and build an index to obtain a consistent sequence. Then, the depth of each site in the consistent sequence is counted, and all sites with a depth value less than the minimum depth value are marked as N. The species annotation unit is used to compare the consistent sequence with the species annotation database and annotate the species according to a set similarity threshold. The report rendering unit is used to generate an identification report based on the processing data or results of the aforementioned quality control unit, reference sequence acquisition unit, sequence alignment unit, variation detection unit, and species annotation unit. The report rendering unit includes: The variable acquisition module is used to obtain variable values ​​by parsing variable parameters. The variable parameters include: i. result, which is the result of the process operation of the quality control unit, reference sequence acquisition unit, sequence alignment unit, variation detection unit, and species annotation unit, i.e., variable data; ii. conf, which is the configuration parameters obtained during the process operation; iii. Env, environment variables; The image information acquisition module is used to retrieve image information from variable values; The paragraph retrieval module is used to retrieve paragraph information from variable values. A paragraph is a collection of all variable data within a paragraph in a report. A report generation module is used to generate reports, wherein a report is a collection of all paragraphs in the report, and the paragraphs include at least a quality control paragraph, a reference sequence paragraph, a sequence alignment paragraph, a variation detection paragraph, and a species annotation paragraph.

[0008] Optionally, the quality control unit includes: The fastq file merging module is used to merge offline data into a single fastq file, where offline data typically includes multiple fastq.gz files; The reads statistics module is used to use NanoPlot to count the number of reads, total base count, mean length, median length, standard deviation of length, mean quality, median quality, and length N50 index, and to plot read length density curves and scatter plots of read length versus sequencing quality. The reads filtering module is used to specify a sequencing quality threshold and a length threshold, and then uses Filtlong to filter reads based on these two thresholds. The fastq to fasta module is used to convert fastq files to fasta files.

[0009] Optionally, the reference sequence acquisition unit includes: The BLAST sequence alignment module is used to specify a sequence file and a predefined candidate alignment database, and to perform sequence alignment between the sequence file and all candidate sequences in the predefined candidate alignment database using BLAST. The reference sequence acquisition module is used to filter out alignment results with low similarity and low coverage, count the number of sequences aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence.

[0010] Optionally, the sequence alignment unit includes: The Minimap sequence alignment module is used to align clean reads to selected reference sequences using minimap2; The SAM filtering module is used to delete all alignments in a specified SAM file that are below the minimum alignment quality. The BAM sorting module is used to sort a specified BAM file. The BAM indexing module is used to create an index for a specified BAM file; The site depth calculation module is used to calculate the depth of each site in a reference sequence within a specified BAM file.

[0011] Optionally, the mutation detection unit includes: The Medaka consistency module is used to generate a consistent HDF file from a specified BAM file using Medaka. The Medaka mutation module is used to detect mutations in a specified consistent HDF file using Medaka and generate a mutated VCF file. The VCF compression module is used to compress a specified variant VCF file. The VCF indexing module is used to create an index for a specified compressed VCF file. The consistency sequence generation module is used to generate a consistency sequence using bcftools based on a specified compressed vcf file and a reference sequence file. The site labeling module is used to count the depth at each site and label all sites with a depth less than the minimum depth value as N. The ID conversion module is used to convert the old ID corresponding to a specified FASTA file into a new ID.

[0012] Optionally, the species annotation unit includes a BLAST sequence alignment module, which aligns the consistent sequence with the species annotation database to obtain the corresponding annotation results.

[0013] Secondly, this application requests the application of the aforementioned rapid species identification system based on Nanopore third-generation sequencing technology in species identification, including the following steps: Step S1: Perform Nanopore third-generation sequencing on the sample to be identified to obtain sequencing data; Step S2: Process the data obtained from the test using the rapid species identification system and output an identification report; Step S2 specifically includes the following steps: Quality control step S101: The Nanopore third-generation sequencing data are merged through the quality control unit, and the merged results are subjected to quality control processing. Finally, the quality control results are converted into FASTA format to obtain cleanreads files. Reference sequence acquisition step S102: The reference sequence acquisition unit performs sequence alignment between clean reads and all candidate sequences in the predetermined candidate alignment database. Based on the set similarity and coverage thresholds, the corresponding alignment results are filtered out, the number of reads aligned to the candidate sequences is counted, and the sequence with the most alignments is selected as the reference sequence. Sequence alignment step S103: The sequence alignment unit aligns clean reads to the selected reference sequence, filters out alignment results with low alignment quality, sorts the alignment results, builds an index, and calculates the depth of each site in the reference sequence; Variant detection step S104: Based on the alignment results, the variant detection unit uses Medaka to perform variant detection to obtain a variant vcf file, compresses the variant vcf file, and establishes an index to obtain a consistent sequence. The depth of each site in the consistent sequence is counted, and all sites with a depth value less than the minimum depth value are marked as N. Species annotation step S105: The consistent sequence is compared with the species annotation database through the species annotation unit, and species annotation is performed according to the set similarity threshold. Report rendering step S106: The report rendering unit generates an identification report based on the processing data or results of the quality control step, reference sequence acquisition step, sequence alignment step, variation detection step, and species annotation step.

[0014] Optionally, the Nanopore third-generation sequencing in step S1 further includes the following steps: Step S11: Extract DNA from the sample to be tested; Step S12: Perform PCR amplification of DNA using universal DNA barcoding primers; Step S13: Purify, quantify, and repair the ends of the PCR products, and then quantify them after repair. Step S14: Add the corresponding barcode to each sample; Step S15: Mix all the samples with added barcodes, and then purify them to obtain a mixed system; Step S16: Add sequencing adapters to the mixed system and ligate them; purify the ligation product to obtain the sequencing library. Step S17: Place the sequencing chip into the MinION nanopore sequencer and connect it to the computer. Perform quality checks on the MinION nanopore sequencer and the sequencing chip to ensure that the sequencing chip has more than 800 usable nanopores. Step S18: The constructed sequencing library is dripped into the sequencing chip for sequencing and the sequencing data is obtained.

[0015] Optionally, in step S18, the process of inoculating the constructed sequencing library into the sequencing chip for sequencing includes the following specific steps: Step S181: Remove 20-30 μl of buffer solution from the Priming well of the sequencing chip to remove air bubbles; Step S182: Take 30 μl of Flush Tether and add it to a tube of Flush Buffer and mix well to prepare Priming Mix. Add 800 μl of the mixed Priming Mix into the Priming well at a constant speed and let it stand for 5 min. Step S183: Prepare a 75 μl Loading Mix according to the following system: 37.5 μl Sequencing Buffer, 25.5 μl Loading Beads, and 12 μl Sequencing Library; Step S184: Then, push 200 μl of well-mixed Priming Mix into the Priming well at a constant speed. When large droplets of liquid flow upward from the Sample well of the sequencing chip, proceed to step S185. Step S185: Add 75 μl of the prepared loading mix of the library to be tested to the sample well, and control the dropping speed to ensure that the library fills the chip area; Step S186: Close the Priming and Sample wells and start sequencing using the MinION nanopore sequencer.

[0016] In the implementation scheme of this application, the rapid species identification system and method provided in this application were run and verified using fruit flies as an example.

[0017] This application has at least the following beneficial technical effects: 1) In the application of the rapid species identification system and method based on Nanopore third-generation sequencing technology in this application, the technical advantages of Nanopore third-generation sequencing technology are fully utilized, advanced bioinformatics analysis methods are integrated, and an automatic operation unit module is built-in for the entire analysis process from data to analysis report. It can provide high-quality analysis services for researchers without the need for professional information processing or programming personnel, save analysis costs, and improve the operability of the identification procedure. 2) In the application of the rapid species identification system and method based on Nanopore third-generation sequencing technology in this application, a large-scale barcode database will be integrated, and Nanopore third-generation sequencing technology will be used to achieve accurate species identification of DNA fragments in sequencing samples. 3) In the application of the rapid species identification system and method based on Nanopore third-generation sequencing technology in this application, the identification equipment is portable, making it possible to perform gene sequencing in the field or in resource-limited locations, such as in the wild or in resource-scarce areas. 4) The application of the rapid species identification system and method based on Nanopore third-generation sequencing technology in this application will provide strong support for fields such as biodiversity monitoring, disease tracing, and environmental pollution detection, and promote the widespread application of Nanopore third-generation sequencing technology in species identification, especially in fields such as inspection and quarantine. Experimental examples demonstrate that the rapid species identification method based on Nanopore third-generation sequencing technology proposed in this application was used to perform one-time high-throughput sequencing on 14 fruit fly samples. The sequencing results showed that, after consistency sequence correction, all fruit fly samples yielded sequences completely consistent with the first-generation sequencing results. This indicates that, using the experimental procedures and data analysis methods of this application, nanopore sequencing technology can be fully applied to the quarantine identification of fruit fly pests based on DNA barcoding technology. Compared with first-generation sequencing, this application can obtain 181 Mb bases in just 44 minutes, with an average of 11,280 reads per sample, and the sequencing results are sufficient to guarantee sequence accuracy, greatly improving the time and efficiency required for gene sequencing. For massive amounts of data, no professional programming or data processing personnel are needed. The rapid species identification system developed in this invention directly provides a professional and readable species identification analysis report after processing, greatly improving the efficiency of quarantine identification based on sequence analysis. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the structure of the rapid species identification system based on Nanopore third-generation sequencing technology proposed in this application. Figure 2 This is a diagram illustrating the structure of the quality control unit in the rapid species identification system based on Nanopore third-generation sequencing technology of this application; Figure 3 This is a diagram illustrating the structure of the reference sequence acquisition unit in the rapid species identification system based on Nanopore third-generation sequencing technology described in this application. Figure 4 This is a diagram illustrating the composition of the sequence alignment unit in the rapid species identification system based on Nanopore third-generation sequencing technology described in this application. Figure 5 This is a diagram illustrating the structure of the variation detection unit in the rapid species identification system based on Nanopore third-generation sequencing technology described in this application. Figure 6 This is a diagram illustrating the structure of the report rendering unit in the rapid species identification system based on Nanopore third-generation sequencing technology of this application; Figure 7 This is a data structure diagram of the variables in the rapid species identification system based on Nanopore third-generation sequencing technology in this application; Figure 8This is a data structure diagram of the images in the rapid species identification system based on Nanopore third-generation sequencing technology in this application; Figure 9 This is a data structure diagram of a segment in the rapid species identification system based on Nanopore third-generation sequencing technology in this application; Figure 10 This is a data structure diagram reported in the rapid species identification system based on Nanopore third-generation sequencing technology of this application; Figure 11 This is a schematic diagram of the nanopore sequencing chip used in the rapid species identification method based on Nanopore third-generation sequencing technology in this application. Figure 12 This is a graph showing the electrophoretic detection results of PCR products in the application of the rapid species identification method based on Nanopore third-generation sequencing technology in this application; Lanes 1-14 are for South American fruit flies. Anastrepha fraterculus Mexican fruit fly A. ludens West Indian fruit fly A. obliqua Guava fruit fly Bactrocera correcta citrus fruit fly B. dorsalis Orange fruit fly B. minax , Satsuma fruit fly B. tsuneonis Queensland fruit fly B. tryoni peach fly B. zonata jujube fruit fly Carpomya vesuviana Mediterranean fruit fly Ceratitis capitata Cherry fruit fly Rhagoletis cerasi melon fly Zeugodacus cucurbitae South Asian fruit fly Z. tau ; Figure 13 This is a MinION nanopore sequencing sequence length distribution diagram in the application of the rapid species identification method based on Nanopore third-generation sequencing technology in this application; Figure 14 This is a statistical chart of the number of sample sequences in the application of the rapid species identification method based on Nanopore third-generation sequencing technology in this application; Figure 15A This is the first half of an exemplary report of the rapid species identification system running this application; Figure 15B This is the latter part of an exemplary report of the rapid species identification system running this application; Figure 16This is a comparison of nanopore sequencing and first-generation sequencing results in the application of the rapid species identification method based on Nanopore third-generation sequencing technology in this application (taking the jujube fly as an example). Among them, Sanger_Cv: jujube fly first-generation sequencing sequence; Nanosingle_Cv: jujube fly nanopore sequencing single sequence; Nanoconsensus_Cv: jujube fly nanopore sequencing consistency sequence. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings.

[0020] The purpose of this application is to design a system and method for rapid species identification using Nanopore third-generation sequencing technology combined with advanced bioinformatics analysis methods. This involves integrating a large-scale barcode database and using Nanopore third-generation sequencing technology to achieve accurate species identification of DNA fragments in fruit fly sequencing samples. At the same time, the system also takes into account practical applications, generating intuitive, comprehensive, and readable professional analysis reports to improve the usability of the identification system.

[0021] Reference Figures 1-10 As shown, in order to achieve the above objectives, this application provides a rapid species identification system based on Nanopore third-generation sequencing technology in the first aspect. The rapid species identification system can be divided into two subsystems: an analysis workflow subsystem and a report rendering subsystem.

[0022] I. Analysis Process Subsystem: First, an analysis module can perform a simple analysis that does not depend on any other external conditions and focuses on obtaining an analytical result from the user's input. An analysis process consists of multiple analysis modules, which organically combine user input parameters and the results of multiple analysis modules to ultimately obtain the analytical result.

[0023] The Nanopore third-generation sequencing technology-based rapid species identification system in this application involves 18 analysis modules, and the modules and their functions are as follows: 1) MergeFastQZipFile module: Downloaded data is often a zip compressed file containing multiple fastq.gz data. This module is responsible for merging this zip file into a single fastq file; 2) NanoPlot module: This module is responsible for statistical analysis of the sequencing data, including the number of reads, total base count, mean length, median length, standard deviation of length, mean quality, median quality, length N50, and other indicators. It also plots read length density curves and scatter plots of read length versus sequencing quality. 3) Filtlong module: This module is responsible for specifying a minimum sequencing quality value and a minimum length, and deleting reads smaller than these values; 4) FastqToFasta module: This module is responsible for converting fastq files to fasta files; 5) BLASTN module: This module specifies a sequence file, a database, and some algorithm parameters, and performs BLASTN nucleic acid sequence alignment between the sequence file and all sequences in the database; 6) PickRef module: This module specifies a sequence file and the result of a BLASTN module, along with some specific parameters, to obtain the reference sequence of the sequence file; 7) Minimap2 module: This module specifies a sequence file and a target sequence file, and performs minimap2 sequence alignment between the two; 8) FilterSAM module: This module specifies a SAM file and a minimum alignment quality, and is responsible for deleting all alignments below this quality value; 9) SortBAMFile module: This module specifies a BAM file and is responsible for sorting the file; 10) IndexBAMFile module: This module specifies a BAM file and is responsible for building the index; 11) BAMDepth module: This module specifies a bam file and is responsible for calculating the depth of each site in the reference sequence; 12) MedakaConsensus module: This module specifies a BAM file and is responsible for generating consistent HDF files; 13) MedakaVariant module: This module specifies a consistent HDF file, is responsible for detecting mutations, and generates a mutated VCF file; 14) CompressVCFFile module: This module specifies a modified VCF file and is responsible for compressing it; 15) IndexVCFFile module: This module specifies a compressed VCF file and is responsible for building the index; 16) BCFToolsConsensus module: This module specifies a compressed VCF file and a reference sequence file, and is responsible for generating a consistent sequence; 17) DepthMask module: This module specifies a reference sequence, a depth value for each site, and a minimum depth value, and is responsible for marking all sites with a depth value less than the minimum depth value as N; 18) RenameFasta module: This module specifies a mapping between a FASTA file and an ID, and is responsible for converting the corresponding old ID to the new ID.

[0024] Furthermore, the analysis process subsystem of this application includes multiple functional implementation units composed of the above-mentioned modules, specifically including: a quality control unit 100, a reference sequence acquisition unit 200, a variation detection unit 400, and a species annotation unit 500.

[0025] (1) Quality control unit 100: The quality control unit 100 is used to merge Nanopore third-generation sequencing data, perform quality control on the merged results, and finally convert the quality control results into FASTA format to obtain clean reads.

[0026] Specifically, the quality control unit 100 may include the following analysis modules: FastQ file merging module 101 (i.e. the MergeFastQZipFile module mentioned above) is used to merge offline data into a single fastQ file, where offline data typically includes multiple fastq.gz files; The reads statistics module 102 (i.e. the NanoPlot module mentioned above) is used to use NanoPlot to count the number of reads, total base count, mean length, median length, standard deviation of length, mean quality, median quality, length N50 index, and to plot read length density curves and scatter plots of read length versus sequencing quality. The reads filtering module 103 (i.e. the Filtlong module mentioned above) is used to specify a sequencing quality threshold (minimum sequencing quality threshold) and a length threshold (minimum length threshold), and then use Filtlong to filter reads based on these two thresholds. The fastq to fasta module 104 (i.e. the FastqToFasta module mentioned above) is used to convert fastq files to fasta files.

[0027] (2) Reference sequence acquisition unit 200: The reference sequence acquisition unit 200 is used to perform sequence alignment of clean reads with all candidate sequences in a predetermined candidate alignment database, filter out the corresponding alignment results according to the set similarity and coverage thresholds, count the number of reads aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence.

[0028] Specifically, the reference sequence acquisition unit 200 may include the following analysis modules: BLAST sequence alignment module 201 (corresponding to the BLASTN module mentioned above) is used to specify a sequence file (i.e., clean reads) and a predetermined candidate alignment database, and to perform BLASTN nucleic acid sequence alignment between the FASTA file and all candidate sequences in the predetermined candidate alignment database using BLAST. The reference sequence acquisition module 202 (corresponding to the PickRef module mentioned above) is used to filter out alignment results with low similarity and low coverage, count the number of sequences aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence.

[0029] (3) Sequence alignment unit 300: The sequence alignment unit 300 is used to align clean reads to the selected reference sequence, filter out alignment results with low alignment quality, and sort, index, and calculate the depth of each site in the reference sequence.

[0030] Specifically, the sequence alignment unit 300 may include the following analysis modules: Minimap sequence alignment module 301 (corresponding to the Minimap2 module mentioned above) is used to align clean reads to selected reference sequences using minimap2. SAM filtering module 302 (corresponding to the FilterSAM module above) is used to delete all alignments in a specified SAM file that are below the minimum alignment quality. BAM sorting module 303 (corresponding to the SortBAMFile module mentioned above) is used to sort a specified BAM file; BAM index module 304 (corresponding to the IndexBAMFile module mentioned above) is used to create an index for a specified BAM file; The site depth calculation module 305 (corresponding to the BAMDepth module mentioned above) is used to calculate the depth of each site in a reference sequence in a specified BAM file.

[0031] (4) Mutation detection unit 400: The variant detection unit 400 uses Medaka to detect variants based on the alignment results obtained from the sequence alignment unit 300, generating variant VCF files. These variant VCF files are then compressed, indexed, and applied to obtain a consistent sequence. The depth at each site in the consistent sequence is then calculated, and all sites with a depth less than the minimum depth value are marked as N.

[0032] Specifically, the variant detection unit 400 may include the following analysis modules: Medaka Consistency Module 401 (corresponding to the MedakaConsensus module mentioned above) is used to generate a consistent HDF file from a specified BAM file using Medaka. Medaka mutation module 402 (corresponding to the MedakaVariant module mentioned above) is used to detect mutations in a specified consistent hdf file using Medaka and generate a mutated vcf file. VCF compression module 403 (corresponding to the CompressVCFFile module mentioned above) is used to compress a specified variant VCF file; VCF index module 404 (corresponding to the IndexVCFFile module mentioned above) is used to create an index for a specified compressed VCF file. Consistency sequence generation module 405 (corresponding to the BCFToolsConsensus module mentioned above) is used to generate a consistency sequence using bcftools based on a specified compressed vcf file and a reference sequence file. The site marking module 406 (corresponding to the DepthMask module mentioned above) is used to count the depth at each site and mark all sites with a depth value less than the minimum depth value as N. ID conversion module 407 (corresponding to the RenameFasta module mentioned above) is used to convert the old ID corresponding to a specified fasta file into a new ID.

[0033] (5) Species annotation unit 500: The species annotation unit 500 includes a sequence alignment module 201 (corresponding to the BLASTN module mentioned above), which is used to align the consistent sequence with the species annotation database and annotate the species according to a set similarity threshold.

[0034] II. Report Rendering Subsystem (corresponding to Report Rendering Unit 600): The report rendering unit 600 is used to generate an analysis report based on the process flow of the quality control unit 100, the reference sequence acquisition unit 200, the sequence alignment unit 300, the variation detection unit 400, and the species annotation unit 500, i.e., variable data.

[0035] Specifically, the report rendering unit 600 may include the following modules: Variable acquisition module 601 is used to generate variable values ​​by parsing the parameters result, conf, and env. Image information acquisition module 602, which is used to acquire image information from variable values; Paragraph acquisition module 603 is used to retrieve paragraph information from variable values, where a paragraph is a collection of all variable data within a paragraph in a report; The report generation module 604 is used to generate reports, wherein a report is a collection of all paragraphs in the report, and the paragraphs include at least the results of the operation of the quality control unit 100 (i.e., variable data): quality control paragraph, the results of the operation of the reference sequence acquisition unit 200 (i.e., variable data): reference sequence paragraph, the results of the operation of the sequence alignment unit 300 (sequence alignment paragraph), the results of the operation of the variation detection unit 400 (variation detection paragraph), and the results of the operation of the species annotation unit 500 (species annotation paragraph).

[0036] It should also be noted that the report rendering subsystem consists of four base classes: (1) Var, variable (e.g.) Figure 7 (As shown) A variable is a piece of variable data in a report. Variables come from three sources: Source 1, Results, refers to the results obtained from the process of running the quality control unit 100, reference sequence acquisition unit 200, sequence alignment unit 300, variation detection unit 400, and species annotation unit 500, with the corresponding parameter result; Source 2, configuration, which refers to the parameters used during the execution of the process, corresponding to the parameter conf; Source 3: Environment variables, corresponding to the parameter env; The parse function parses the above three parameters to generate variable values.

[0037] (2) Image, picture (e.g.) Figure 8 (As shown) Image is a subclass of Var, so you only need to implement the get_file function, whose parse function returns the base64 string of the image.

[0038] (3) Paragraph, a paragraph (e.g.) Figure 9 (As shown) A Paragraph is a collection of all variable data within a paragraph of a report. In this system, it is implemented using a collection of multiple Vars, with the same Var source.

[0039] (4) Report, such as Figure 10 (As shown) A report is a collection of all paragraphs in a report. In this system, multiple Paragraphs are used, which are the same as the Var source.

[0040] Furthermore, this system has 5 built-in paragraphs, namely: 1) QC, corresponding to the quality control section in the report; 2) pick_ref, which corresponds to the reference sequence paragraph in the report; 3) align, corresponding to the sequence alignment paragraph in the report; 4) variant_calling, corresponding to the variant detection section in the report; 5) assign, corresponding to the species annotation paragraph in the report.

[0041] Secondly, this application also discloses a method for rapid species identification based on Nanopore third-generation sequencing technology, comprising the following steps using any of the rapid species identification systems described in the first aspect above: Step S1: Perform Nanopore third-generation sequencing on the sample to be identified to obtain sequencing data; Step S2: Use the aforementioned rapid species identification system to process the data and output an identification report.

[0042] Step S2 specifically includes the following steps: Quality control step S101: Merge Nanopore third-generation sequencing data through quality control unit 100, perform quality control on the merged results, and finally convert the quality control results into FASTA format to obtain clean reads; Reference sequence acquisition step S102: The reference sequence acquisition unit 200 performs sequence alignment between clean reads and all candidate sequences in the predetermined candidate alignment database. Based on the set similarity and coverage thresholds, the corresponding alignment results are filtered out, the number of reads aligned to the candidate sequences is counted, and the sequence with the most alignments is selected as the reference sequence. Sequence alignment step S103: The clean reads are aligned to the selected reference sequence through the sequence alignment unit 300, the alignment results with low alignment quality are filtered out, and the alignment results are sorted, indexed, and the depth of each site in the reference sequence is calculated. Variance detection step S104: Based on the alignment results, the mutation detection unit 400 uses Medaka to perform mutation detection and obtain mutated VCF files. The mutated VCF files are then compressed, indexed, and these mutations are applied to obtain a consistent sequence. The depth at each site in the consistent sequence is calculated, and all sites with a depth less than the minimum depth value are marked as N. Species annotation step S105: The species annotation unit 500 performs sequence alignment with the species annotation database and annotates the species according to the set similarity threshold. Report rendering step S106: The report rendering unit 600 generates an analysis report based on the processing data or results of the above-mentioned quality control steps, reference sequence acquisition steps, sequence alignment steps, variation detection steps, and species annotation steps.

[0043] Furthermore, prior to the aforementioned quality control step S101, obtaining Nanopore third-generation sequencing data in step S1 includes the following steps: (1) Constructing sequencing libraries: Step S11: Extract DNA from the sample to be tested; Step S12: Perform PCR amplification of DNA using universal DNA barcoding primers; Step S13: Purify, quantify, and repair the ends of the PCR products, and then quantify them after repair. Step S14: Add the corresponding barcode to each sample; Step S15: Mix all the samples with added barcodes, and then purify them with magnetic beads to obtain a mixed system; Step S16: Add sequencing adapters to the mixed system and ligate them; purify the ligation product to obtain the sequencing library. (2) Nanosequencing: Step S17: Place the sequencing chip (its structure is shown in [reference]) Figure 11 Place the sequencer into the MinION nanopore sequencer and connect it to the computer. Perform quality checks on the MinION nanopore sequencer and sequencing chip to ensure that the sequencing chip has more than 800 usable nanopores. Step S18: Drop the constructed sequencing library into the sequencing chip for sequencing and obtain sequencing data.

[0044] Furthermore, in step S18, the constructed sequencing library is dripped into the sequencing chip for sequencing, which includes the following specific steps: Step S181: Remove 20-30 μl of buffer solution from the Priming well of the sequencing chip to remove air bubbles; Step S182: Take 30 μl of Flush Tether and add it to a tube of Flush Buffer and mix well to prepare Priming Mix. Add 800 μl of the mixed Priming Mix into the Priming well at a constant speed and let it stand for 5 min. Step S183: Prepare a 75 μl Loading Mix according to the following system: 37.5 μl Sequencing Buffer, 25.5 μl Loading Beads, and 12 μl Sequencing Library; Step S184: Then, push 200 μl of well-mixed Priming Mix into the Priming well at a constant speed. When large droplets of liquid flow upward from the Sample well of the sequencing chip, proceed to step S185. Step S185: Add 75 μl of the prepared loading mix of the library to be tested to the sample well, and control the dropping speed to ensure that the library fills the chip area; Step S186: Close the Priming and Sample wells and start sequencing using the MinION nanopore sequencer.

[0045] Exemplary embodiments The rapid species identification system and method based on Nanopore third-generation sequencing technology described in this application are used to identify fruit fly pests. I. Sequencing of the samples to be tested using Nanopore third-generation sequencing technology. 1.1 Test Sample The adult fruit fly samples used in this application include the South American fruit fly (Anopheles spp.). Anastrepha fraterculus Mexican fruit fly A. ludens West Indian fruit fly A. obliqua Guava fruit fly Bactrocera correcta citrus fruit fly B. dorsalis Orange fruit fly B. minax , Satsuma fruit fly B. tsuneonis Queensland fruit fly B. tryoni peach fly B. zonata jujube fruit fly Carpomya vesuviana Mediterranean fruit fly Ceratitis capitata Cherry fruit fly Rhagoletis cerasi melon fly Zeugodacus cucurbitae South Asian fruit fly Z. tau A total of 14 species were collected from the field, intercepted at ports, and donated by the International Atomic Energy Agency (IAEA). All samples were identified by morphology.

[0046] 1.2 DNA Extraction DNA was extracted from fruit fly samples using the "Blood / Cell / Tissue Genomic DNA Extraction Kit" (DP304) from Tiangen Biotech (Beijing) Co., Ltd. A single fruit fly leg was placed in a 1.5 mL centrifuge tube, liquid nitrogen was added, and the mixture was thoroughly ground. DNA extraction was then performed according to the kit instructions. The obtained DNA was stored at -20°C for later use.

[0047] 1.3 PCR Amplification Universal primers used in DNA barcoding LCO1490: 5'-GGTCAACAAATCATAAAGATATTGG-3'; HCO2198:5'-TAAACTTCAGGGTGACCAAAAAATCA-3' was used to amplify the sample DNA, targeting the 658 bp COI gene fragment in mitochondrial DNA. The total PCR reaction volume was 50 μl: 25 μl of 2×TaqPCR MasterMix (Tiangen Biotech (Beijing) Co., Ltd., KT201), 2 μl each of LCO1490 and HCO2198 (10 μM), 19 μl of ddH2O, and 2 μl of DNA template. PCR reaction conditions: 94℃ for 3 min; 94℃ for 30 min, 50℃ for 30 s, 72℃ for 30 s, for a total of 35 cycles; 72℃ for 5 min.

[0048] 5 μl of PCR product was electrophoresed on a 1.5% agarose gel, and 20 μl of the amplified product with the target band was sent to Beijing Qingke Biotechnology Co., Ltd. for first-generation bidirectional sequencing.

[0049] Electrophoresis results showed that all samples produced the target amplification band after PCR amplification (see...). Figure 12 The first-generation sequencing results of the PCR products, when compared with the BOLD and NCBI databases, also showed consistency with the morphological identification results. 1.4 Construction of Nanopore Sequencing Libraries Sequencing libraries were constructed using the ONT LSK109 ligation and EXP-NBD104 / 114 library construction kit, following the instructions of the Beijing Jinguoke Nanopore Sequencing DNA Multi-Sample Assisted Library Construction Kit (JC-EXP024).

[0050] First, the remaining PCR products from step 1.3 were purified using the magnetic bead method, and 1 μl of the purified product was quantified using a Qubit 2.0 fluorescence quantitative instrument (Life Qubit, USA) (quantification results are shown in Table 1).

[0051] Then, to ensure a relatively consistent number of reads for each sample, end repair was performed on each sample based on the quantification results of the purified PCR product and the recommended dosage of 300 fmol PCR product (Table 1): 54.5 μl of a mixture was prepared for each sample in a PCR tube for end repair. The reaction mixture consisted of 3.5 μl end repair buffer, 3 μl end repair enzyme, purified PCR product (volume details in Table 1), and nuclease-free water to bring the volume to 54.5 μl. The end repair reaction conditions were 20℃ for 10 min; 65℃ for 5 min; and 20℃ ∞. The reaction product was purified using magnetic beads and quantified using Qubit.

[0052] Next, a corresponding barcode was added to each sample and recorded. A 30 μl mixture was prepared in a PCR tube, consisting of 12.5 μl of end-repaired product, 2.5 μl of native barcode, and 15 μl of TA ligation premix. The reaction conditions were 25℃ for 15 min; 65℃ for 10 min; and 20℃ infinity. All barcode-added samples were then purified using magnetic beads and eluted with 50 μl of nuclease-free water.

[0053] Finally, a 71 μl mixture was prepared in a PCR tube. The reaction mixture consisted of 45 μl of mixed sample, 5 μl of sequencing adapter II (AMII), 14 μl of rapid ligation buffer, and 7 μl of T4 rapid ligase. The ligation reaction conditions were 20 °C for 15 min and 21 °C at ∞. The ligation product was purified by magnetic beads, eluted with 15 μl of EB, and 1 μl of the purified product was used for Qubit quantification. The remaining prepared library was used for sequencing. The final concentration of the library used for sequencing was 13.9 ng / μl.

[0054] Table 1 Results of each stage of nanopore sequencing library construction

[0055] 1.5 Nanopore Sequencing and Analysis The sequencing chip used was Flow Cell R9.4.1 (… Figure 11 First, place the sequencing chip into the MinION nanopore sequencer and connect it to a computer. Perform quality checks on the sequencer and the chip to ensure that the sequencing chip has more than 800 usable nanopores. Next, prepare to drop the constructed sequencing library into the chip for sequencing: Aspirate 20-30 μl of buffer from the Priming well to remove air bubbles; add 30 μl of Flush Tether to a tube of Flush Buffer and mix well to prepare Priming Mix; add 800 μl of the mixed Priming Mix to the Priming well at a constant speed and let stand for 5 min; prepare a 75 μl Loading Mix according to the following system: 37.5 μl Sequencing Buffer, 25.5 μl Loading Beads, and 12 μl sequencing library; then push 200 μl of the mixed Priming Mix into the Priming well at a constant speed, at which point large droplets should be seen flowing upwards from the Sample well; add the prepared 75 μl of the Loading Mix of the library to be tested to the Sample well, controlling the dropping speed to ensure that the library fills the chip area; close the Priming and Sample wells and begin sequencing.

[0056] The MinION nanopore sequencer ran for 44 minutes, producing 3.77 GB of data and 181 Mb bases of data.

[0057] Sequence length distribution as Figure 13 As shown, the sequence lengths are concentrated in the 700-800 bp range, which is consistent with the length of the target fragment. The reads generated for each sample are as follows: Figure 14 As shown, the number of reads for the 14 samples ranged from 8863 to 13758, with an average of 11280 reads per sample.

[0058] II. Data Analysis and Production Reporting The rapid species identification system provided by this invention is used to analyze and process the data obtained from the test. Quality control step S101: The MergeFastQZipFile module in the quality control unit merges the Nanopore third-generation sequencing data into a single fastq file, and the NanoPlot module and Filtlong module are used to perform quality control processing on the fastq file. Finally, the FastqToFasta module is used to convert the quality control processed fastq file into a fasta file to obtain clean reads. Reference sequence acquisition step S102: The BLASTN module of the reference sequence acquisition unit performs BLASTN nucleic acid sequence alignment between cleanreads and all candidate sequences in the predetermined candidate alignment database (in this experiment, gene sequences of fruit fly species in the BOLD database are used as candidate sequences); the PickRef module filters out alignment results with low similarity and low coverage, counts the number of reads aligned to the candidate sequences, and selects the sequence with the most alignments as the reference sequence; Sequence alignment step S103: The Minimap2 module in the sequence alignment unit aligns clean reads to the selected reference sequence, the FilterSAM module filters out sequences with alignment quality lower than 60 in the SAM file, the SortBAMFile module sorts the specified BAM file, the IndexBAMFile module builds an index for the specified BAM file, and the BAMDepth module calculates the depth of each site of the reference sequence in the BAM file. Variance detection step S104: The MedakaConsensus module in the mutation detection unit generates a consistent hdf file from the specified bam file; then the MedakaVariant module performs mutation detection on the consistent hdf file to generate a variant vcf file; subsequently, the CompressVCFFile module is called to compress the variant vcf file, and the IndexVCFFile module is used to build an index on the compressed vcf file. Then, the DepthMask module is used to count the depth at each site, and all sites with a depth value less than the minimum depth value (100 in this experiment) are marked as N. Finally, the BCFToolsConsensus module generates a consistent sequence. Species annotation step S105: The consistent sequence is aligned with the species annotation database (obtained by downloading sequencing data from the laboratory and public databases (mainly NCBI)) through the species annotation unit to obtain the corresponding annotation results; Report rendering step S106: The report rendering unit generates an analysis report based on the processing data or results of the quality control step, reference sequence acquisition step, sequence alignment step, variation detection step, and species annotation step. Specifically, The variable values ​​are obtained by parsing the variable parameters through the variable acquisition module 601. The variable parameters include i.result, which is the result obtained from the process of running the quality control unit 100, the reference sequence acquisition unit 200, the sequence alignment unit 300, the variation detection unit 400, and the species annotation unit 500; ii.conf, which is the configuration parameter obtained from the process; and iii.env, which is the environment variable. Image information is obtained from variable values ​​through the image information acquisition module 602; The paragraph acquisition module 603 retrieves paragraph information from variable values; The report is generated using report generation module 604, as shown in the figure. Figure 15A and 15B As shown ( Figure 15A and 15B These are two parts of a complete report, including a quality control section, a reference sequence section, a sequence alignment section, a variation detection section, and a species annotation section.

[0059] (3) Comparison of data accuracy Ten single sequences were randomly selected from the sequencing results of each sample. Comparative analysis showed that the single sequences from nanopore sequencing had a similarity of 92.10%-94.53% with the first-generation sequencing results of the same species.

[0060] After consistency sequence correction, the consistency sequence of each sample is completely consistent with the first-generation sequencing results.

[0061] Taking the jujube fruit fly, which produces the fewest reads in this application, as an example, the alignment results of a single sequence and the sequence after consistency correction are shown with the sequence obtained from first-generation sequencing (see [link]). Figure 16 Among them, Sanger_Cv: first-generation sequencing sequence of *F. jujube*; Nanosingle_Cv: single sequence from *F. jujube* nanopore sequencing; Nanoconsensus_Cv: consensus sequence from *F. jujube* nanopore sequencing.

[0062] In summary, the rapid species identification system and method based on Nanopore third-generation sequencing technology provided in this application, in an experiment involving one-time high-throughput sequencing of 14 fruit fly samples, showed that after consistency sequence correction, all fruit fly samples yielded sequences completely consistent with the first-generation sequencing results. This demonstrates that the Nanopore third-generation sequencing workflow and data analysis method of this invention can fully apply nanopore sequencing technology to the quarantine identification of fruit fly pests based on DNA barcoding technology.

[0063] Compared with first-generation sequencing, the Nanopore third-generation sequencing pipeline provided by this invention can obtain 181Mb bases in just 44 minutes, with an average of 11,280 reads per sample. The sequencing results are sufficient to ensure the accuracy of the sequence, greatly improving the time and efficiency required for gene sequencing. The rapid species identification system provided by this invention can efficiently and accurately process massive amounts of data and instantly output identification reports that are both professional and readable, greatly reducing the time required for quarantine identification based on sequence analysis.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A rapid species identification system based on Nanopore third-generation sequencing technology, characterized in that, include: The quality control unit (100) is used to merge Nanopore third-generation sequencing data, perform quality control on the merged results, and finally convert the quality control results into FASTA format to obtain clean reads. The reference sequence acquisition unit (200) is used to perform sequence alignment of the clean reads with all candidate sequences in a predetermined candidate alignment database, filter out the corresponding alignment results according to the set similarity and coverage thresholds, count the number of reads aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence. The sequence alignment unit (300) is used to align the clean reads to the selected reference sequence, filter out alignment results with low alignment quality, and sort the alignment results, build an index, and calculate the depth of each site in the reference sequence. The mutation detection unit (400) is used to perform mutation detection using Medaka based on the alignment results obtained by the sequence alignment unit (300) to obtain a mutated vcf file, compress the mutated vcf file, create an index, apply these mutations to obtain a consistent sequence, and then count the depth of each site in the consistent sequence, marking all sites with a depth value less than the minimum depth value as N. The species annotation unit (500) is used to perform sequence alignment between the consistent sequence and the species annotation database, and to annotate the species according to the set similarity threshold. The report rendering unit (600) is used to generate an identification report based on the processing data or results of the quality control unit (100), reference sequence acquisition unit (200), sequence alignment unit (300), variation detection unit (400) and species annotation unit (500). The report rendering unit (600) includes: The variable acquisition module (601) is used to obtain variable values ​​by parsing variable parameters, wherein the variable parameters include: i. result, which is derived from the process execution results of the quality control unit, reference sequence acquisition unit, sequence alignment unit, variation detection unit and species annotation unit, i.e., variable data; ii. conf, which is derived from the configuration parameters during the process execution; iii. Env, environment variables; The image information acquisition module (602) is used to obtain image information from variable values; The paragraph retrieval module (603) is used to retrieve paragraph information from variable values, where a paragraph is a collection of all variable data within a paragraph in a report; The report generation module (604) is used to generate a report, wherein a report is a collection of all paragraphs in the report, and the paragraphs include at least a quality control paragraph, a reference sequence paragraph, a sequence alignment paragraph, a variation detection paragraph, and a species annotation paragraph.

2. The rapid species identification system according to claim 1, characterized in that, The quality control unit (100) includes: The fastq file merging module (101) is used to merge the offline data into a single fastq file, wherein the offline data includes multiple fastq.gz files; The reads statistics module (102) is used to use NanoPlot to count the number of reads, total bases, mean length, median length, standard deviation of length, mean quality, median quality, length N50 index, and to plot read length density curves and scatter plots of read length versus sequencing quality. The reads filtering module (103) is used to specify a sequencing quality threshold and a length threshold, and to filter reads using Filtlong based on these two thresholds; The fastq to fasta module (104) is used to convert fastq files to fasta files.

3. The rapid species identification system according to claim 1, characterized in that, The reference sequence acquisition unit (200) includes: The BLAST sequence alignment module (201) is used to specify a sequence file and a predefined candidate alignment database, and to perform sequence alignment between the sequence file and all candidate sequences in the predefined candidate alignment database using BLAST. The reference sequence acquisition module (202) is used to filter out alignment results with low similarity and low coverage, count the number of sequences aligned to the candidate sequences, and select the sequence with the most alignments as the reference sequence.

4. The rapid species identification system according to claim 1, characterized in that, The sequence alignment unit (300) includes: The Minimap sequence alignment module (301) is used to align clean reads to selected reference sequences using minimap2; The SAM filtering module (302) is used to delete all alignments in a specified SAM file that are below the minimum alignment quality. The BAM sorting module (303) is used to sort a specified BAM file; The BAM index module (304) is used to create an index for a specified BAM file; The site depth calculation module (305) is used to calculate the depth of each site in a reference sequence in a specified bam file.

5. The rapid species identification system according to claim 1, characterized in that, The mutation detection unit (400) includes: The Medaka Consistency module (401) is used to generate a consistent HDF file from a specified BAM file using Medaka. The Medaka mutation module (402) is used to detect mutations in a specified consistent HDF file using Medaka and generate a mutated VCF file. The VCF compression module (403) is used to compress a specified variant VCF file; The VCF indexing module (404) is used to create an index for a specified compressed VCF file; The consistency sequence generation module (405) is used to generate a consistency sequence using bcftools based on a specified compressed vcf file and a reference sequence file. The site marking module (406) is used to count the depth at each site and mark all sites with a depth value less than the minimum depth value as N; The ID conversion module (407) is used to convert the old ID corresponding to a specified FASTA file into a new ID.

6. The rapid species identification system according to claim 1, characterized in that, The species annotation unit (500) includes a BLAST sequence alignment module (201), which aligns the consistent sequence with the species annotation database to obtain the corresponding annotation results.

7. The application of the rapid species identification system according to any one of claims 1 to 6, characterized in that, Includes the following steps: Step S1: Perform Nanopore third-generation sequencing on the sample to be identified to obtain sequencing data; Step S2: Process the data obtained from the test using the rapid species identification system and output an identification report; Step S2 specifically includes the following steps: Quality control step S101: The Nanopore third-generation sequencing data are merged through the quality control unit (100), and the merged results are subjected to quality control processing. Finally, the quality control results are converted into FASTA format to obtain cleanreads files. Reference sequence acquisition step S102: The reference sequence acquisition unit (200) performs sequence alignment between clean reads and all candidate sequences in the predetermined candidate alignment database. Based on the set similarity and coverage thresholds, the corresponding alignment results are filtered out, the number of reads aligned to the candidate sequences is counted, and the sequence with the most alignments is selected as the reference sequence. Sequence alignment step S103: The sequence alignment unit (300) aligns clean reads to the selected reference sequence, filters out alignment results with low alignment quality, sorts the alignment results, builds an index, and calculates the depth of each site in the reference sequence; Mutation detection step S104: Based on the alignment results, the mutation detection unit (400) uses Medaka to perform mutation detection to obtain a variant vcf file, compresses the variant vcf file, and establishes an index to obtain a consistent sequence. The depth of each site in the consistent sequence is counted, and all sites with a depth value less than the minimum depth value are marked as N. Species annotation step S105: The consistent sequence is compared with the species annotation database through the species annotation unit (500), and species annotation is performed according to the set similarity threshold. Report rendering step S106: The report rendering unit (600) generates an identification report based on the processing data or results of the quality control step, reference sequence acquisition step, sequence alignment step, variation detection step, and species annotation step.

8. The application according to claim 7, characterized in that, The Nanopore third-generation sequencing in step S1 includes the following steps: Step S11: Extract DNA from the sample to be tested; Step S12: Perform PCR amplification of DNA using universal DNA barcoding primers; Step S13: Purify, quantify, and repair the ends of the PCR products, and then quantify them after repair. Step S14: Add the corresponding barcode to each sample; Step S15: Mix all the samples with added barcodes, and then purify them to obtain a mixed system; Step S16: Add sequencing adapters to the mixed system and ligate them; purify the ligation product to obtain the sequencing library. Step S17: Place the sequencing chip into the MinION nanopore sequencer and connect it to the computer. Perform quality checks on the MinION nanopore sequencer and the sequencing chip to ensure that the sequencing chip has more than 800 usable nanopores. Step S18: The constructed sequencing library is dripped into the sequencing chip for sequencing and the sequencing data is obtained.

9. The application according to claim 8, characterized in that, In step S18, the constructed sequencing library is dripped into the sequencing chip for sequencing, which includes the following specific steps: Step S181: Remove 20-30 μl of buffer solution from the Priming well of the sequencing chip to remove air bubbles; Step S182: Take 30 μl of Flush Tether and add it to a tube of Flush Buffer to mix and prepare Priming Mix. Add 800 μl of the mixed Priming Mix into the Priming well at a constant speed and let it stand for 5 min. Step S183: Prepare a 75 μl Loading Mix according to the following system: 37.5 μl Sequencing Buffer, 25.5 μl Loading Beads, and 12 μl Sequencing Library; Step S184: Then, push 200 μl of well-mixed Priming Mix into the Priming well at a constant speed. When large droplets of liquid flow upward from the Sample well of the sequencing chip, proceed to step S185. Step S185: Add 75 μl of the prepared loading mix of the library to be tested to the sample well, and control the dropping speed to ensure that the library fills the chip area; Step S186: Close the Priming and Sample wells and start sequencing using the MinION nanopore sequencer.

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