A method and system for detecting short sequences of viral genes based on third-generation sequencing

Abstract

The application discloses a virus gene short sequence synthesis detection method and system based on third-generation sequencing, and relates to the technical field of nucleic acid detection. The method comprises the following steps: S1, synthesizing a target fragment based on a PCR method; S2, product purification and library construction; S3, sequencing the target fragment to obtain an original sequencing file; S4, merging the original sequencing file to generate a quality control report of a merged file; S5, quality controlling the merged file to obtain a quality-controlled sequencing file and generate a quality control report; S6, comparing the quality-controlled sequencing file to find a mutation site; S7, synthesizing a reference sequence according to the mutation site; S8, synthesizing a reference target sequence based on a short sequence synthesis model; and S9, performing base feature analysis to obtain a final target sequence. The method utilizes a third-generation sequencing platform to detect low-frequency mutations, and the coincidence degree of the synthesized sequence and the reference sequence is more than 99%, and thus the virus gene synthesis and mutation site detection are realized.

Description

Technical Field

[0001] This invention relates to the field of nucleic acid detection technology, and in particular to a method and system for detecting the synthesis of short viral gene sequences based on third-generation sequencing. Background Technology

[0002] In the field of viral gene detection and mutation analysis, the traditional Sanger sequencing method has long been the standard method for viral gene detection and mutation detection, with a high degree of standardization and strong result stability. However, this method has significant technical bottlenecks in practical applications: Limited detection sensitivity: It cannot effectively identify low-frequency mutant strains with a frequency of less than 1%-20%. Under low-frequency mutations, it cannot accurately detect viral genes, which can easily lead to an underestimation of viral mutation sites and affect the accuracy of clinical treatment plans.

[0003] Insufficient ability to analyze complex samples: When multiple subtypes or quasi-species coexist in a sample, overlapping peaks (hybrid peaks) often appear in the sequencing map, which seriously interferes with the detection of viral gene sequences and the accurate interpretation of mutation sites.

[0004] High-throughput testing is inefficient: it requires independent sequencing for multiple samples, making batch processing impossible and failing to meet the needs of large-scale screening or rapid clinical diagnosis.

[0005] In addition, although third-generation sequencing technology has the advantage of long read length, its existing data processing workflows, such as Flye and Canu, are generally designed with algorithms for long sequence splicing. They have significant defects in the analysis of short sequence data. When processing short PCR amplified sequences or viral sequences, the synthesis and detection of gene sequences are prone to failure due to insufficient algorithm adaptability, which limits the application expansion of third-generation sequencing technology in the fine analysis of viral genes. Summary of the Invention

[0006] The purpose of this invention is to propose a method and system for detecting the synthesis of short viral gene sequences based on third-generation sequencing, which solves the problems of low sensitivity, difficulty in interpreting results, low efficiency and limited data analysis in the existing technology.

[0007] To achieve the above objectives, this invention proposes a method for detecting short viral gene sequences based on third-generation sequencing, the steps of which are as follows: Step S1: Synthesize the target fragment of the virus using polymerase chain reaction (PCR). Step S2: Purify PCR products and construct libraries using the Nanopore third-generation sequencing platform; Step S3: Sequencing the target fragment using the Nanopore platform and obtaining the raw sequencing file; Step S4: Merge the original sequencing files and generate a quality control report for the merged original sequencing files; Step S5: Based on the quality control report of the merged original sequencing file, set the parameter thresholds for the sequencing fragments, perform quality control on the merged original sequencing file, obtain the quality-controlled sequencing file, and generate a quality control report. Step S6: Compare the sequence in the quality control sequencing file with the standard reference sequence of the virus to find mutation sites; Step S7: Based on the mutation site data, synthesize the virus's own reference sequence; Step S8: Establish a short sequence synthesis model based on the LightGBM tree model algorithm, and synthesize a reference target sequence according to the bam file generated in step S6; Step S9: Perform base feature analysis on the virus's own reference sequence and the reference target sequence to obtain the final target sequence.

[0008] Preferably, in step S4, the original sequencing files are merged to generate a quality control report for the merged original sequencing files. The specific steps are as follows: Step S41: Use shell language to read the raw sequencing files from the third-generation sequencing machine in fastq.gz format and merge them into a raw sequencing file merge.fastq.gz; Step S42: Use the visualization analysis tool NanoPlot to perform quality control on the merged original sequencing file merge.fastq.gz and generate a quality control report for the merged original sequencing file.

[0009] Preferably, in step S5, the sequencing data preprocessing software FastP is used to filter the original sequencing files that are smaller than a preset length threshold to obtain quality-controlled sequencing files and generate a quality control report.

[0010] Preferably, in step S6, the sequence in the quality control sequencing file is compared with the standard reference sequence of the virus to find mutation sites, as follows: Step S61: Use sequence alignment software bwa to generate an index for the standard reference sequence of the virus, align the sequence in the quality control sequencing file to the standard reference sequence, and generate a clean.sam file; Step S62: Use samtools, the core toolset for sequence alignment and variant detection, to sort the data and obtain the clean_sorted.bam file; Step S63: Use bcftools, the core toolset for mutation site and variant analysis, to perform variant detection on the clean_sorted.bam file, generating clean.snps.vcf file containing mutation sites and clean.indels.vcf file containing deletion sites; Step S64: Select mutation sites with a quality value (QUAL) > 20 and a sequencing depth (DP) > 150, and generate the selected clean.snps.vcf file; where the quality value (QUAL) represents the confidence level of the mutation site, and the higher the value, the greater the possibility of the mutation site having a mutation; the sequencing depth (DP) is the total number of original reads covering the mutation site.

[0011] Preferably, in step S7, the virus's own reference sequence is encoded and synthesized using the Python language based on the position, reference base, and mutant base information provided in the clean.snps.vcf file.

[0012] This invention also provides a viral gene short sequence synthesis detection system based on third-generation sequencing, comprising: The automated adjustment module is used to automatically identify the raw third-generation sequencing files and allocate them to the quality control analysis module, control the calling of the quality control analysis module and the sequence synthesis module, and transmit the results of the quality control analysis module to the sequence synthesis module. Quality control analysis module: used to acquire, merge, and quality control raw sequencing data from the third-generation sequencing platform, and analyze mutation site data; Sequence synthesis module: Used to synthesize and output the virus's own reference sequence using mutation site data.

[0013] Therefore, this invention proposes a method and system for detecting viral gene short sequence synthesis based on third-generation sequencing, with the following beneficial effects: (1) High sensitivity detection of low-frequency mutations: With the single-molecule detection capability of Oxford Nanopore third-generation sequencing, it can identify low-frequency mutations accounting for less than 1%, which is significantly more sensitive than traditional Sanger sequencing (detection limit of 20%) and second-generation sequencing (detection limit of 1%-5%).

[0014] (2) Complete coverage of viral genome: By utilizing the advantages of long reads in third-generation sequencing, the complete genome regions of viruses such as HBV can be directly covered, avoiding detection blind spots caused by PCR amplification failure.

[0015] (3) Accurate analysis of complex viral populations: It can fully analyze the complex situation of multiple subtypes or quasi-species coexisting in the same patient, avoiding the sequence mixing problem caused by short read length in traditional Sanger sequencing.

[0016] (4) Efficient processing of multiple sample sequencing: Through technologies such as barcode multiple labeling, parallel sequencing of multiple samples can be achieved, greatly improving detection efficiency. In actual operation, from the extraction and amplification of viral sequencing templates to the completion of Nanopore library construction and sequencing, and then to the synthesis of viral sequences based on third-generation sequencing, a set of efficient processes has been formed, which can meet the needs of large-scale detection.

[0017] (5) Optimize third-generation sequencing data processing: Through self-developed algorithms and processes, the problem of errors that the third-generation sequencing platform data analysis software is prone to when dealing with short read data has been corrected to a certain extent, and the accuracy and reliability of sequence synthesis have been improved. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a method for detecting viral gene short sequences based on third-generation sequencing according to the present invention. Figure 2 This is a structural diagram of a viral gene short sequence synthesis and detection system based on third-generation sequencing according to the present invention; Figure 3 This refers to the pre-screening quality control data for hepatitis B virus sequencing sequences in Embodiment 2 of the present invention. Figure 4 This refers to the quality control data after screening the hepatitis B virus sequencing sequence in Embodiment 2 of the present invention. Figure 5 This refers to the data from Covid-19 sequencing sequences before screening after quality control in Example 3 of this invention; Figure 6 This refers to the data after quality control and screening of Covid-19 sequencing sequences in Embodiment 3 of the present invention; Figure 7 This is a schematic diagram showing the alignment of the target sequence with the hepatitis B virus sequence in Embodiment 2 of the present invention; Figure 8 This is a schematic diagram showing the alignment of the target sequence with the Covid-19 reference sequence in Embodiment 3 of the present invention. Detailed Implementation

[0019] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0020] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0021] Example 1 like Figure 1 The diagram shows a flowchart of a viral gene short sequence synthesis and detection method based on third-generation sequencing according to the present invention. The specific steps are as follows: S1. Synthesize the target fragment based on polymerase chain reaction (PCR) method; obtain the target viral fragment by PCR method, and extract and amplify it according to the instrument and kit requirements.

[0022] S2. Purification of PCR products and library construction based on the Nanopore third-generation sequencing platform; The PCR product purification steps are as follows: 1. Equilibrate the purified magnetic beads at room temperature for 30 minutes, and shake thoroughly before use. Mix the magnetic beads and sample product at a volume ratio of 1.5:1, and centrifuge briefly after gentle mixing. After standing at room temperature for 5 minutes, transfer to a magnetic rack and stand for 3 minutes. Once the supernatant becomes clear, gently discard the supernatant.

[0023] 2. Add 180 μl of 80% ethanol (prepared fresh for use), wash the magnetic beads twice on a magnetic rack without stirring, and discard the supernatant. After brief centrifugation, use a 10 μl pipette to remove the residual ethanol and let it stand for 2 minutes to evaporate the residual ethanol.

[0024] 3. After visually observing that the magnetic beads have become matte, add 50 μL of double-distilled water (ddH2O), suspend the magnetic beads by flicking the tube with your finger, let it stand at room temperature for 5 minutes, transfer it to a magnetic rack and let it stand until the solution becomes clear; aspirate the supernatant to an 8-tube strip, quantify using a quibit and record the value.

[0025] The steps for building a library are as follows: 1. Barcode sequencing adapter connection.

[0026] 1.1 Mix the PCR product with the fragmentation reaction solution (7.5 μl of PCR product and 2.5 μl of fragmentation reaction solution in a 400 ng system; 9 μl of PCR product and 1 μl of fragmentation reaction solution in a 50 ng system), with a total volume of 10 μl. Perform the PCR reaction at 30 °C for 2 min, followed by 80 °C for 2 min and brief cooling on ice.

[0027] 1.2 Mix all samples with added barcodes, with a total volume of 10*X μl; where X is the number of samples.

[0028] 1.3 Magnetic bead purification: When the number of samples is greater than 4 or concentration is required, use XP magnetic beads (10*Xμl), wash twice with 200μl of 80% ethanol, wash with 13μl of nuclease-free water, and take 11μl for subsequent experiments.

[0029] 1.4. Quantification using the Qubit nucleic acid quantification instrument: Take 1 μl of the mixed library for quantification, and take 400 ng of the mixed library for loading, depending on the concentration.

[0030] 1.5 Reagent preparation: Take the Rapid Adapter (RAP), Sequencing Buffer (SQB) and Loading Beads (LB) reagents from the Rapid Barcode Sequencing Kit RBK at -20℃, and take the Flush Buffer (FB) and Flush Tether (FLT) reagents from the Sequencing Chip Priming Kit FLP.

[0031] 2. Sequencing adapter connection.

[0032] Mix the mixed sample with the rapid sequencing library adapter, total volume 11 μl, and react at 20-25℃ (room temperature) for 5 min.

[0033] 3. Flow cell preparation and setup.

[0034] 3.1. Prepare the Priming Mix for starting the sequencing reaction: Add 30 μl of Flush Tether (FLT) protein to a tube of Flush Buffer (FB) used for cleaning sequencing chips or flow cells, and mix well.

[0035] 3.2. Open the pretreatment well P on the sequencing chip, adjust the volume of the pipette (1000 μl) to 800 μl, and then slowly rotate it to the larger volume (approximately 820 μl-830 μl) until a small amount of yellow liquid is drawn from the tip. Use the 1000 μl pipette to push 800 μl of the mixed Priming Mix into the well P at a constant speed, and wait for 5 minutes.

[0036] 3.3. Prepare the sequencing mixture: including 34 μl of sequencing buffer (SQB), 25.5 μl of sequencing magnetic beads (LB) (mix well by pipetting before use), 11 μl of mixed DNA library, and 4.5 μl of nuclease-free water.

[0037] 3.4. Flip the cap upwards to open the loading well S on the sequencing chip. Use a pipette with a volume of 1000 μl to push 200 μl of well-mixed Priming Mix into the loading well P at a constant speed.

[0038] 3.5. Using a pipette with a volume of 200 μl, add 75 μl of the well-mixed sequencing mixture to the S well, controlling the dropping speed to ensure that the library fills the chip area.

[0039] 3.6. Close the S and P wells, aspirate the waste liquid, and begin sequencing.

[0040] S3. Sequencing the target fragment using the Nanopore platform and obtaining the raw sequencing file; S4. Merge the original sequencing files and generate a quality control report for the merged original sequencing files. The specific steps are as follows: S41. Use shell language to read the raw sequencing files from the third-generation sequencing machine in fastq.gz format and merge them into a raw sequencing file merge.fastq.gz; S42. Use the visualization analysis tool NanoPlot to perform quality control on the merged original sequencing file merge.fastq.gz, set the initial parameter minlength=30, and generate a quality control report for the merged original sequencing file.

[0041] S5. Based on the quality control report of the merged original sequencing files, use the sequencing data preprocessing software FastP to filter the merged original sequencing files with a length of less than 300bp, obtain the quality-controlled sequencing file clean.fastq.gz, and generate a quality control report.

[0042] S6. Compare the sequences in the quality control sequencing files with the standard reference sequence of the virus to find mutation sites. The steps are as follows: S61. Using the sequence alignment software bwa, an index is generated for the reference sequence of the virus in the NCBI database of the National Center for Biotechnology Information. The sequence in the quality control sequencing file clean.fastq.gz is aligned to the standard reference sequence to generate the clean.sam file. S62. Use samtools, the core toolset for sequence alignment and variant detection, to sort the data and obtain the clean_sorted.bam file; S63. Using the core toolset bcftools for mutation site SNP and variant analysis, perform a call snps operation on the clean_sorted.bam file to detect variants, generating a clean.snps.vcf file containing mutation sites and a clean.indels.vcf file containing deletion sites; S64. Use bcftools software to screen out mutation sites with a quality value (QUAL) > 20 and a sequencing depth (DP) > 150, and generate the screened clean.snps.vcf file.

[0043] S7. Based on mutation site data, synthesize the virus's own reference sequence; Using Python, a viral reference sequence is synthesized based on the location, reference bases, and mutated bases information provided in the clean.snps.vcf file. The developed program reads and extracts information from the vcf file, and users can set the range of the reference sequence according to the target fragment for PCR. The program automatically synthesizes the target fragment sequence based on the set range of the reference sequence and the vcf information. Furthermore, when dealing with degenerate bases, the program defaults to selecting bases with sequencing advantage, which more closely reflects the actual situation of the target fragment. Researchers can also set the required mutation frequency to select degenerate bases to ensure comprehensive sequencing results.

[0044] Step S8: Establish a short sequence synthesis model based on the LightGBM tree model algorithm, and synthesize a reference target sequence according to the bam file generated in step S6; 1. Set the relevant parameters for the short sequence synthesis model as follows: A_ratio: A base ratio, T_ratio: T base ratio, C_ratio: C base ratio, G_ratio: G base ratio, Depth: single base sequencing depth, AT_skew: combined AT base ratio, GC_skew: combined GC base ratio, max_min_diff: difference between the most and least bases, A_ratio_mean_win: moving average of A bases, T_ratio_std_win: moving standard deviation of T bases, depth_AT_ratio: combined AT base sequencing depth ratio.

[0045] 2. Limit the parameters of the LightGBM tree model, including: The number of decision trees is n_estimators=1000, the learning rate (step size) is learning_rate=0.1, the number of random seeds is random_state=42, the maximum depth of a single tree is max_depth=7, the minimum number of samples required for each child node to split is min_child_samples=2, and the maximum number of leaf nodes per tree is num_leaves=31.

[0046] 3. Input the clean_sorted.bam file obtained in step S62 into the short sequence synthesis model to obtain the reference target sequence.

[0047] Step S9: Perform base feature analysis on the virus's own reference sequence and the reference target sequence to obtain the final target sequence. Specifically, at degenerate base positions, parameters such as A base ratio (A_ratio), T base ratio (T_ratio), C base ratio (C_ratio), and G base ratio (G_ratio) are manually used to enhance the accuracy of individual sites. The final target sequence is then obtained.

[0048] Example 2 The invention will be further illustrated below with an example of hepatitis B virus gene synthesis. First, raw sequencing data of the hepatitis B virus were obtained through third-generation sequencing. All raw sequencing data files were merged to generate a merge.fastq.gz file, which was then subjected to quality control. A portion of the sequencing sequences in the merged quality control file were less than 300 bp in length (e.g., ...). Figure 3 As shown), by filtering, sequences shorter than 300bp are filtered out (e.g. Figure 4 As shown in the image, the clean.fastq.gz file is generated.

[0049] As you can see, the length of all the filtered sequences is greater than 300bp, which meets the requirements for subsequent processing.

[0050] The hepatitis B virus sequence (NC_075114.1, Hepatitis B virus strain IG29227, complete genome) from the NCBI GenBank database was used as a reference sequence for call SNPS operations. Finally, using QUAL>20 && DP>150 as screening criteria, the most accurate mutation site information was retained. The sequence synthesis module was used to generate clean.snps.vcf files containing mutation sites and clean.indels.vcf files containing deletion sites, ultimately generating the virus's own reference sequence.

[0051] Align clean.fastq.gz with the reference sequence to generate the clean_sorted.bam file. Then, use the LightGBM machine learning model to fit the clean_sorted.bam file to generate the reference alignment sequence.

[0052] The reference alignment sequence (as shown in SEQ ID NO.1), the virus's own reference sequence (as shown in SEQ ID NO.2), and the hepatitis B virus sequence in the NCBI database (as shown in SEQ ID NO.3) were compared, and the results are as follows: Figure 7As shown, the reference alignment sequence, the virus's own reference sequence, and the hepatitis B virus sequence have a alignment accuracy greater than 96%, except for a very few different sites, and can be considered as hepatitis B virus sequences.

[0053] SEQ ID NO.1: NNTGCCTCTCNCACATCGTCAATCTTCTCGAGGACTGGGGACCCTGCACCGAACATGGAGAACACAACATCAGGATTCCTAGGACCCCTGCTCGTGTTACAGGCGGGGTTTTTCTTGTTGACAAGAATCCTCACAATACCACAGAGTCTAGACTCGTGGTGGACTTCTCTCAATTTTCTAGGGGGAGCACCCACGTGTCCTGGCCAAAATTCGCAGTCCCCAACCTCCAATCACTCACCAACCTCTTGTCCTCCAATTTGTCCTGGCTATCGCTGGATGTGTCTGCGGCGTTTTATCATATTCCTCTTCATCCTGCTGCTATGCCTCATCTGCTTGTTGGTTCTTCTGGACTACCAAGGTATGTTGCCCGTTTGTCCTCTACTTCCAGGAACATCAACTACCAGCACGGGACCATGCAAGACCTGCACAATTCCTGCTCAAGGAACCTCTATGTTTCCCTCTTGTTGCTGTACAAAACCTTCGGACGGAAACTGCACTTGTATTCCCATCCCATCATCCTGGGCTTTCGCAAGATTCCTATGGGAGTGGGCCTCAGTCCGTTTCTCCTGGCTCAGTTTACTAGTGCCATTTGTTCAGTGGTTCGTAGGGCTTTCCCCCACTGTTTGGCTTTCAGTTATATGGATGATGTGGTATTGGGGGCCAAGTCTGTACAACATCTTGAGTCCCTTTTTACCTCTATTACCAATTTTCTTTTGTCTTTGGGTATACATTTAAACCCCAATAAAACCAAACGTTGGGGCTACTCCNTTAACTTCATGGGATATGTAATTGGAAGTTGGGGTACTTTACCGCAGGAACATATTGTACAAAAACTCAAGCAATGTTTTCGAAAATTGCCTGTAAATAGGCCTATTGATTGGAAAGTATGTCAAAGAAT; SEQ ID NO.2: ACTGCCTCTCCCACATCGTCAATCTTCTCGAGGACTGGGGACCCTGCACCGAACATGGAGAACACAACATCAGGATTCCTAGGACCCCTGCTCGTGTTACAGGCGGGGTTTTTCTTGTTGACAAGAATCCTCACAATACCACAGAGTCTAGACTCGTGGTGGACTTCTCTCAATTTTCTAGGGGGAGCACCCACGTGTCCTGGCCAAAATTCGCAGTCCCCAACCTCCAATCACTCACCAACCTCTTGTCCTCCAATTTGTCCTGGCTATCGCTGGATGTGTCTGCGGCGTTTTATCATATTCCTCTTCATCCTGCTGCTATGCCTCATCTGCTTGTTGGTTCTTCTGGACTACCAAGGTATGTTGCCCGTTTGTCCTCTACTTCCAGGAACATCAACTACCAGCACGGGACCATGCAAGACCTGCACAATTCCTGCTCAAGGAACCTCTATGTTTCCCTCTTGTTGCTGTACAAAACCTTCGGACGGAAACTGCACTTGTATTCCCATCCCATCATCCTGGGCTTTCGCAAGATTCCTATGGGAGTGGGCCTCAGTCCGTTTCTCCTGGCTCAGTTTACTAGTGCCATTTGTTCAGTGGTTCGTAGGGCTTTCCCCCACTGTTTGGCTTTCAGTTATATGGATGATGTGGTATTGGGGGCCAAGTCTGTACAACATCTTGAGTCCCTTTTTACCTCTATTACCAATTTTCTTTTGTCTTTGGGTATACATTTAAACCCCAATAAAACCAAACGTTGGGGCTACTCCCTTAACTTCATGGGATATGTAATTGGAAGTTGGGGTACTTTGCCACAAGAACACATCACACAGAAAATTAAGCAATGTTTTCGGAAACTCCCTGTTAACAGGCCAATTGATTGGAAAGTCTGTCAACGAATAA; SEQ ID NO.3: ATTGCCTCTCACATCTCGTCAATCTTCTCCAGGATTGGGGACCCTGCACCGAACATGGAGAACATCACATCAGGATTCCTAGGACCCCTGCTCGTGTTACAGGCGGGGTTTTTCTTGTTGACAAGAATCCTCACAATACCGCAGAGTCTAGACTCGTGGTGGACTTCTCTCAATTTTCTAGGGGGAGTGCCCGTGTGTCCTGGCCTAAATTCGCAGTCCCCAACCTCCAATCACTCACCAATCTCCTGTCCTCCAACTTGTCCTGGCTATCGCTGGATGTGTCTGCGGCGTTTTATCATATTCCTCTTCATCCTGCTGCTATGCCTCATCTTCTTGTTGGTTCTTCTGGACTATCAAGGTATGTTGCCCGTTTGTCCTCTGATTCCAGGATCCTCGACCACCAGTACGGGACCCTGCAAAACCTGCACGACTCCTGCTCAAGGCAACTCTATGTATCCCTCATGTTGCTGTACAAAACCTTCGGACGGAAATTGCACCTGTATTCCCATCCCATCATCTTGGGCTTTCGCAAAATACCTATGGGAGTGGGCCTCAGTCCGTTTCTCTTGGCTCAGTTTACTAGTGCCATTTGTTCAGTGGTTCGTAGGGCTTTCCCCCACTGTCTGGCTTTCAGCTATATGGATGATGTGGTATTGGGGGCCAAATCTGTACAACATCTTGAGTCCCTTTATACCGCTGTTACCAATTTTCTTTTGTCTTTGGGTATACATCTAAACCCTAACAAAACAAAAAGATGGGGTTATTCCTTAAATTTTATGGGATATGTAATTGGAAGTTGGGGTACTTTGCCACAAGAACACATCACACAGAAAATTAAGCAATGTTTTCGGAAACTCCCTGTTAACAGGCCAATTGATTGGAAAGTCTGTCAACGAATAA.

[0054] Example 3 The present invention is further illustrated below by taking the synthesis of Covid-19 nucleic acid gene as an example.

[0055] First, raw sequencing data for Covid-19 were obtained through third-generation sequencing. All raw sequencing data files were merged to generate a merge.fastq.gz file, which then underwent quality control. After quality control and screening, sequences smaller than 500bp were removed, resulting in a more accurate clean.fastq.gz file. Quality control data before and after screening are shown below. Figures 5-6 As shown.

[0056] Depend on Figure 6 It can be seen that the length of all filtered sequences is greater than 500bp, which meets the requirements for subsequent processing. Using the Covid-19 sequence with GenBank accession number NC_045512.2 in the NCBI database as the reference sequence, the sequence in the filtered clean.fastq.gz file is compared with the Covid-19 reference sequence, and a mutation detection callsnps operation is performed to generate a clean.snps.vcf file containing mutation sites and a clean.indels.vcf file containing deletion sites. Finally, the virus's own reference sequence is generated.

[0057] After clean.fastq.gz is generated, it is compared with the NC_045512.2 reference sequence to generate the clean_sorted.bam file. The LightGBM machine learning model is then applied to perform calculations to obtain the reference alignment sequence.

[0058] The DNAMAN software was used to align the reference alignment sequence (as shown in SEQ ID NO.4), the virus's own reference sequence (as shown in SEQ ID NO.5), and the Covid-19 reference sequence (as shown in SEQ ID NO.6) from the NCBI database. The alignment results are as follows: Figure 8 As shown. By Figure 8 It can be seen that, except for a very few different sites, the alignment accuracy of the reference alignment sequence, the virus's own reference sequence, and the Covid-19 reference sequence is greater than 99%, and it can be considered to be a Covid-19 sequence.

[0059] SEQ ID NO.4: SEQ ID NO.5: SEQ ID NO.6:

[0060] Example 4 like Figure 2 As shown, the present invention also provides a viral gene short sequence synthesis detection system based on third-generation sequencing, comprising: The automated adjustment module is used to automatically identify the raw third-generation sequencing files and allocate them to the quality control analysis module, control the calling of the quality control analysis module and the sequence synthesis module, and transmit the results of the quality control analysis module to the sequence synthesis module. Quality control analysis module: used to acquire, merge, and quality control raw sequencing data from the third-generation sequencing platform, and analyze mutation site data; Sequence synthesis module: Used to synthesize and output the virus's own reference sequence using mutation site data.

[0061] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0062] Therefore, this invention provides a method and system for detecting short viral gene sequences based on third-generation sequencing. Utilizing the Nanopore platform, it can detect low-frequency mutations of <1%, which is more sensitive than Sanger sequencing (≥20%) and second-generation sequencing (1%-5%). It can cover the entire viral genome and avoid detection blind spots. The third-generation sequencing data processing workflow is optimized, errors in short read analysis are corrected, and batch processing of samples can improve efficiency. The alignment accuracy between the synthesized sequence and the reference sequence exceeds 99%.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting the synthesis of short viral gene sequences based on third-generation sequencing, characterized in that, The steps are as follows: Step S1: Synthesize the target fragment of the virus using polymerase chain reaction (PCR). Step S2: Purify PCR products and construct libraries using the Nanopore third-generation sequencing platform; Step S3: Sequencing the target fragment using the Nanopore platform and obtaining the raw sequencing file; Step S4: Merge the original sequencing files and generate a quality control report for the merged original sequencing files; Step S5: Based on the quality control report of the merged original sequencing file, set the parameter thresholds for the sequencing fragments, perform quality control on the merged original sequencing file, obtain the quality-controlled sequencing file, and generate a quality control report. Step S6: Compare the sequence in the quality control sequencing file with the standard reference sequence of the virus to find mutation sites; Step S7: Based on the mutation site data, synthesize the virus's own reference sequence; Step S8: Establish a short sequence synthesis model based on the LightGBM tree model algorithm, and synthesize a reference target sequence according to the bam file generated in step S6; Step S9: Perform base feature analysis on the virus's own reference sequence and the reference target sequence to obtain the final target sequence; In step S4, the original sequencing files are merged to generate a quality control report for the merged original sequencing files. The specific steps are as follows: Step S41: Use shell language to read the raw sequencing files from the third-generation sequencing machine in fastq.gz format and merge them into a raw sequencing file merge.fastq.gz; Step S42: Use the visualization analysis tool NanoPlot to perform quality control on the merged original sequencing file merge.fastq.gz and generate a quality control report for the merged original sequencing file. In step S6, the sequences in the quality control sequencing files are compared with the standard reference sequence of the virus to find mutation sites. The steps are as follows: Step S61: Use sequence alignment software bwa to generate an index for the standard reference sequence of the virus, align the sequence in the quality control sequencing file to the standard reference sequence, and generate a clean.sam file; Step S62: Use samtools, the core toolset for sequence alignment and variant detection, to sort the data and obtain the clean_sorted.bam file; Step S63: Use bcftools, the core toolset for mutation site and variant analysis, to perform variant detection on the clean_sorted.bam file, generating clean.snps.vcf file containing mutation sites and clean.indels.vcf file containing deletion sites; Step S64: Select mutation sites with a quality value (QUAL) > 20 and a sequencing depth (DP) > 150, and generate the selected clean.snps.vcf file; The method is used for purposes other than disease diagnosis and treatment.

2. The method for detecting viral gene short sequence synthesis based on third-generation sequencing according to claim 1, characterized in that, In step S5, the sequencing data preprocessing software FastP is used to filter the raw sequencing files that are smaller than the preset length threshold to obtain the quality-controlled sequencing files and generate a quality control report.

3. The method for detecting viral gene short sequence synthesis based on third-generation sequencing according to claim 1, characterized in that, In step S7, the virus's own reference sequence is encoded and synthesized using Python based on the location, reference base, and mutated base information provided in the clean.snps.vcf file.

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